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Número de registros en este nivel: 374.

A

Advancing fake news combating using machine learning: a hybrid model approach.

An Analytical Framework for Innovation Determinants and Their Impact on Business Performance.

Behavioral economics and artificial intelligence (AI) have been two rapidly growing fields of research over the past few years. While behavioral economics aims to combine concepts from psychology, sociology, and neuroscience with classical economic thoughts to understand human decision-making processes in the complex economic environment, AI on the other hand, focuses on creating intelligent machines that can mimic human cognitive abilities such as learning, problem-solving, decision-making, and language understanding. The intersection of these two fields has led to thrilling research theories and practical applications. This study provides a bibliometric analysis of the literature on AI and behavioral economics to gain insight into research trends in this field. We conducted this bibliometric analysis using the Web of Science database on articles published between 2012 and 2022 that were related to AI and behavioral economics. VOSviewer and Bibliometrix R package were utilized to identify influential authors, journals, institutions, and countries in the field. Network analysis was also performed to identify the main research themes and their interrelationships. The analysis revealed that the number of publications on AI and behavioral economics has been increasing steadily over the past decade. We found that most studies focused on customer and consumer behavior, including topics such as decision-making under uncertainty, neuroeconomics, and behavioral game theory, combined mainly with machine learning and deep learning techniques. We also identified several emerging themes, including the use of AI in nudging and prospect theory in behavioral finance, as well as undeveloped themes such as AI-driven behavioral macroeconomics. The findings suggests that there is a need for more interdisciplinary collaboration between researchers in behavioral economics and AI. We also suggest that future research on AI and behavioral economics further consider the ethical implications of using AI and behavioral insights in decision-making. This study can serve as a valuable resource for researchers interested in AI and behavioral economics.

Artificial Intelligence and Behavioral Economics: A Bibliographic Analysis of Research Field.

Betalains: The main bioactive compounds of Opuntia spp and their possible health benefits in the Mediterranean diet.

Blockchain based transparent and reliable framework for wheat crop supply chain.

Botnet detection in internet of things using stacked ensemble learning model.

Competitive Coevolution-Based Improved Phasor Particle Swarm Optimization Algorithm for Solving Continuous Problems.

A Comprehensive Review on Techno-Economic Analysis and Optimal Sizing of Hybrid Renewable Energy Sources with Energy Storage Systems.

Correction: Enhancing fault detection in new energy vehicles via novel ensemble approach.

Detecting Pragmatic Ambiguity in Requirement Specification Using Novel Concept Maximum Matching Approach Based on Graph Network.

DiabSense: early diagnosis of non-insulin-dependent diabetes mellitus using smartphone-based human activity recognition and diabetic retinopathy analysis with Graph Neural Network.

Edge of Things Inspired Robust Intrusion Detection Framework for Scalable and Decentralized Applications.

Humans can carry various diseases, some of which are poorly understood and lack comprehensive solutions. Such a disease can exists in human eye that can affect one or both eyes is diabetic retinopathy (DR) which can impair function, vision, and eventually result in permanent blindness. It is one of those complex complexities. Therefore, early detection of DR can significantly reduce the risk of vision impairment by appropriate treatment and necessary precautions. The primary aim of this study is to leverage cutting-edge models trained on diverse image datasets and propose a CNN model that demonstrates comparable performance. Specifically, we employ transfer learning models such as DenseNet121, Xception, Resnet50, VGG16, VGG19, and InceptionV3, and machine learning models such as SVM, and neural network models like (RNN) for binary and multi-class classification. It has been shown that the proposed approach of multi-label classification with softmax functions and categorical cross-entropy works more effectively, yielding perfect accuracy, precision, and recall values. In particular, Xception achieved an impressive 82% accuracy among all the transfer learning models, setting a new benchmark for the dataset used. However, our proposed CNN model shows superior performance, achieving an accuracy of 95.27% on this dataset, surpassing the state-of-the-art Xception model. Moreover, for single-label (binary classifications), our proposed model achieved perfect accuracy as well. Through exploration of these advances, our objective is to provide a comprehensive overview of the leading methods for the early detection of DR. The aim is to discuss the challenges associated with these methods and highlight potential enhancements. In essence, this paper provides a high-level perspective on the integration of deep learning techniques and machine learning models, coupled with explainable artificial intelligence (XAI) and gradient-weighted class activation mapping (Grad-CAM). We prese...

Enhancing Early Detection of Diabetic Retinopathy Through the Integration of Deep Learning Models and Explainable Artificial Intelligence.

Enhancing fault detection in new energy vehicles via novel ensemble approach.

Ensemble stacked model for enhanced identification of sentiments from IMDB reviews.

A Fuzzy-Genetic-Based Integration of Renewable Energy Sources and E-Vehicles.

Hybrid Model with Wavelet Decomposition and EfficientNet for Accurate Skin Cancer Classification.

Impact of Innovation-Oriented Human Resource on Small and Medium Enterprises’ Performance.

Incorporating soil information with machine learning for crop recommendation to improve agricultural output.

Influence of E-learning training on the acquisition of competences in basketball coaches in Cantabria.

IoTTPS: Ensemble RKSVM Model-Based Internet of Things Threat Protection System.

Lightweight Hybrid Deep Learning Architecture and Model for Security in IIOT.

Background: The increasing complexity of basketball and the need for optimal decision-making in order to maximize competitive performance highlight the necessity of specialized training for basketball coaches. This systematic review aims to compile, synthesize, and integrate international research published in specialized journals on the training of basketball coaches and students, examining their characteristics and needs. Specifically, it analyzes the content, technical-tactical actions, and methodologies used in practice and education programs to determine which essential parameters for their technical and tactical development. Methods: A structured search was carried out following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA®) guidelines and the PICOS® model until January 30, 2025, in the MEDLINE/PubMed, Web of Science (WOS), ScienceDirect, Cochrane Library, SciELO, EMBASE, SPORTDiscus, and Scopus databases. The risk of bias was assessed and the PEDro scale was used to analyze methodological quality. Results: A total of 14,090 articles were obtained in the initial search. After inclusion and exclusion criteria, the final sample was 23 articles. These studies maintained a high standard of quality. This revealed data on the technical-tactical actions addressed in different categories; the profiles, characteristics, and influence of coaches on player development; and the approaches, teaching methods, and evaluation methodologies used in acquiring knowledge and competencies for the professional development of basketball coaches. Conclusions: Adequate theoretical and practical training for basketball coaches is essential for player development. Therefore, training programs for basketball coaches must integrate technical-tactical, physical, and psychological knowledge with the acquisition of skills and competencies that are refined through practice. This training should be continuous, more specialized, and comprehensive, focusing on understanding and constructing knowledge that supports the professional growth of basketballers. Additionally, training should incorporate digital tools and informal learning opportunities, with blended learning emerging as the most effective methodology for this purpose.

Methodology and content for the design of basketball coach education programs: a systematic review.

Multipath Routing in Wireless Body Area Sensor Network for Healthcare Monitoring.

A Novel Framework for Fog-Assisted Smart Healthcare System with Workload Optimization.

Novel model to authenticate role-based medical users for blockchain-based IoMT devices.

Novel transfer learning approach for hand drawn mathematical geometric shapes classification.

Novel transfer learning based bone fracture detection using radiographic images.

Nutritional Value and Preventive Role of Nigella sativa L. and Its Main Component Thymoquinone in Cancer: An Evidenced-Based Review of Preclinical and Clinical Studies.

Optimal Modeling and Feasibility Analysis of Grid-Interfaced Solar PV/Wind/Pumped Hydro Energy Storage Based Hybrid System.

Optimal Sizing and Power System Control of Hybrid Solar PV-Biogas Generator with Energy Storage System Power Plant.

Pneumonia Detection Using Chest Radiographs With Novel EfficientNetV2L Model.

Polyphenol-rich strawberry extract (PRSE) shows in vitro and in vivo biological activity against invasive breast cancer cells.

Prickly pear fruits from "Opuntia ficus-indica" varieties as a source of potential bioactive compounds in the Mediterranean diet.

Privacy preserved collaborative transfer learning model with heterogeneous distributed data for brain tumor classification.

Real Word Spelling Error Detection and Correction for Urdu Language.

Roman urdu hate speech detection using hybrid machine learning models and hyperparameter optimization.

A Smart Cloud and IoVT-Based Kernel Adaptive Filtering Framework for Parking Prediction.

Plant stress reduction research has advanced significantly with the use of Artificial Intelligence (AI) techniques, such as machine learning and deep learning. This is a significant step toward sustainable agriculture. Innovative insights into the physiological responses of plants mostly crops to drought stress have been revealed through the use of complex algorithms like gradient boosting, support vector machines (SVM), recurrent neural network (RNN), and long short-term memory (LSTM), combined with a thorough examination of the TYRKC and RBR-E3 domains in stress-associated signaling proteins across a range of crop species. Modern resources were used in this study, including the UniProt protein database for crop physiochemical properties associated with specific signaling domains and the SMART database for signaling protein domains. These insights were then applied to deep learning and machine learning techniques after careful data processing. The rigorous metric evaluations and ablation analysis that typified the study’s approach highlighted the algorithms’ effectiveness and dependability in recognizing and classifying stress events. Notably, the accuracy of SVM was 82%, while gradient boosting and RNN showed 96%, and 94%, respectively and LSTM obtained an astounding 97% accuracy. The study observed these successes but also highlights the ongoing obstacles to AI adoption in agriculture, emphasizing the need for creative thinking and interdisciplinary cooperation. In addition to its scholarly value, the collected data has significant implications for improving resource efficiency, directing precision agricultural methods, and supporting global food security programs. Notably, the gradient boosting and LSTM algorithm outperformed the others with an exceptional accuracy of 96% and 97%, demonstrating their potential for accurate stress categorization. This work highlights the revolutionary potential of AI to completely disrupt the agricultural industry while simultaneously advancing our understanding of plant stress responses.

Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops.

A Systematic Literature Review on Integrated Deep Learning and Multi-Agent Vision-Language Frameworks for Pathology Image Analysis and Report Generation.

Unleashing the Potential of Blockchain and Machine Learning: Insights and Emerging Trends From Bibliometric Analysis.

A ferramenta SWOT na gestão escolar.

A lightweight deep learning approach for COVID-19 detection using X-ray images with edge federation.

A novel approach for disease and pests detection in potato production system based on deep learning.

B

Behavioral and Performance Analysis of a Real-Time Case Study Event Log: A Process Mining Approach.

Histopathological evaluation is necessary for the diagnosis and grading of prostate cancer, which is still one of the most common cancers in men globally. Traditional evaluation is time-consuming, prone to inter-observer variability, and challenging to scale. The clinical usefulness of current AI systems is limited by the need for comprehensive pixel-level annotations. The objective of this research is to develop and evaluate a large-scale benchmarking study on a weakly supervised deep learning framework that minimizes the need for annotation and ensures interpretability for automated prostate cancer diagnosis and International Society of Urological Pathology (ISUP) grading using whole slide images (WSIs). This study rigorously tested six cutting-edge multiple instance learning (MIL) architectures (CLAM-MB, CLAM-SB, ILRA-MIL, AC-MIL, AMD-MIL, WiKG-MIL), three feature encoders (ResNet50, CTransPath, UNI2), and four patch extraction techniques (varying sizes and overlap) using the PANDA dataset (10,616 WSIs), yielding 72 experimental configurations. The methodology used distributed cloud computing to process over 31 million tissue patches, implementing advanced attention mechanisms to ensure clinical interpretability through Grad-CAM visualizations. The optimum configuration (UNI2 encoder with ILRA-MIL, 256 256 patches, 50% overlap) achieved 78.75% accuracy and 90.12% quadratic weighted kappa (QWK), outperforming traditional methods and approaching expert pathologist-level diagnostic capability. Overlapping smaller patches offered the best balance of spatial resolution and contextual information, while domain-specific foundation models performed noticeably better than generic encoders. This work is the first large-scale, comprehensive comparison of weekly supervised MIL methods for prostate cancer diagnosis and grading. The proposed approach has excellent clinical diagnostic performance, scalability, practical feasibility through cloud computing, and interpretability using visualization tools.

Benchmarking multiple instance learning architectures from patches to pathology for prostate cancer detection and grading using attention-based weak supervision.

Detecting Cyberattacks to Federated Learning on Software-Defined Networks.

FMDNet: An Efficient System for Face Mask Detection Based on Lightweight Model during COVID-19 Pandemic in Public Areas.

Formal modeling and analysis of security schemes of RPL protocol using colored Petri nets.

Imperative Role of Integrating Digitalization in the Firms Finance: A Technological Perspective.

Infrared thermography to assess fatigue, injury risk factors and recovery in soccer: a systematic review of original studies.

Lipophilic antioxidants prevent lipopolysaccharide-induced mitochondrial dysfunction through mitochondrial biogenesis improvement.

Natural Language Processing-Based Software Testing: A Systematic Literature Review.

Natural Products in Alzheimer’s Disease: A Systematic Review of Clinical Trials and Underlying Molecular Mechanisms.

On the correlation between Google Play Store application icons and downloads.

SARSMutOnto: An Ontology for SARS-CoV-2 Lineages and Mutations.

Use of Fuzzy Approach Methodology and Consensus in Creating a Hierarchy of Satisfaction for Measurement Criteria: Application to Online Training Actions Directed at Classification by Key Competency Profiles in Sales Supervision (SPV) within the Automotive.

C

Adherence to the Mediterranean Diet Is Inversely Associated with the Prevalence of Metabolic Syndrome in Older People from the North of Spain.

Antimicrobial Resistance Determinants in Genomes and Plasmids from Acinetobacter baumannii Clinical Isolates.

Blockchain Enabled Transparent and Anti-Counterfeiting Supply of COVID-19 Vaccine Vials.

Building Heating and Cooling Load Prediction Using Ensemble Machine Learning Model.

Can alpha‐linolenic acid be a modulator of “cytokine storm,” oxidative stress and immune response in SARS‐CoV‐2 infection?

Can the phenolic compounds of Manuka honey chemosensitize colon cancer stem cells? A deep insight into the effect on chemoresistance and self-renewal.

Daily Intake of Two or More Servings of Vegetables Is Associated with a Lower Prevalence of Metabolic Syndrome in Older People.

Genetic Resistance Determinants in Clinical Acinetobacter pittii Genomes.

Human‐based new approach methodologies to accelerate advances in nutrition research.

An Image Retrieval Framework Design Analysis Using Saliency Structure and Color Difference Histogram.

Importancia de la seguridad de la información en las empresas de tecnología de información corporativa.

Angola, as with many countries on the African continent, has great inequalities or asymmetries between its provinces. At the economic, financial, and technological level, there is a great disparity between them, where it is observed that the province of Luanda is the largest financial business center to the detriment of others, such as Moxico, Zaire, and Cabinda. In the latter, despite the advantages of high oil production, from a regional point of view, they remain almost stagnant in time, in a social dysfunction where the population lives on extractivism and artisanal fishing. This article analyzes the most important events in contemporary regional history, the Portuguese occupation that was the Portuguese colonial rule over Angola (1890–1930) and the civil war that was a struggle between Angolans for control of the country (1975–2002), in the consolidation of the asymmetries between provinces. For this work, a theoretical-reflective study was conducted based on the reading of books, articles, and previous investigations on the phenomenon studied. Considering the interpretation and analysis of the theoretical content obtained through the bibliographic research conducted, this theoretical construction approaches the qualitative approach. We conclude that the deep inequalities between regions and within them, between the provinces studied, originated historically in the form of exploitation of the regions and from the consequences of the war. The asymmetries, observed through the variables studied show that the provinces historically explored and considered object regions present a lower growth compared to those that were considered subject regions in which the applied geopolitical strategy, as they are centers of primary production flows, was different. We also observe that, due to the conflicts of the civil war in the less developed regions, the inequalities have deepened, contributing seriously to a higher level of poverty and a lower development of the provinces where these conflicts took place.

Inequalities and Asymmetries in the Development of Angola’s Provinces: The Impact of Colonialism and Civil War.

A Lightweight Energy-Efficient Routing Scheme for Real-Time WSN-VANET-Based Applications.

The Molecular Basis of Different Approaches for the Study of Cancer Stem Cells and the Advantages and Disadvantages of a Three-Dimensional Culture.

COVID-19 is an infectious disease caused by the deadly virus SARS-CoV-2 that affects the lung of the patient. Different symptoms, including fever, muscle pain and respiratory syndrome, can be identified in COVID-19-affected patients. The disease needs to be diagnosed in a timely manner, otherwise the lung infection can turn into a severe form and the patient’s life may be in danger. In this work, an ensemble deep learning-based technique is proposed for COVID-19 detection that can classify the disease with high accuracy, efficiency, and reliability. A weighted average ensemble (WAE) prediction was performed by combining three CNN models, namely Xception, VGG19 and ResNet50V2, where 97.25% and 94.10% accuracy was achieved for binary and multiclass classification, respectively. To accurately detect the disease, different test methods have been proposed and developed, some of which are even being used in real-time situations. RT-PCR is one of the most successful COVID-19 detection methods, and is being used worldwide with high accuracy and sensitivity. However, complexity and time-consuming manual processes are limitations of this method. To make the detection process automated, researchers across the world have started to use deep learning to detect COVID-19 applied on medical imaging. Although most of the existing systems offer high accuracy, different limitations, including high variance, overfitting and generalization errors, can be found that can degrade the system performance. Some of the reasons behind those limitations are a lack of reliable data resources, missing preprocessing techniques, a lack of proper model selection, etc., which eventually create reliability issues. Reliability is an important factor for any healthcare system. Here, transfer learning with better preprocessing techniques applied on two benchmark datasets makes the work more reliable. The weighted average ensemble technique with hyperparameter tuning ensures better accuracy than using a randomly selected single CNN model.

A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling.

Organizational Culture Assessment Based on a Values-Based Coaching Program for Strategic Level Employees: The Case of GEDEME, Cuba.

Possible role of nutrition in the prevention of Inflammatory Bowel Disease-related colorectal cancer: a focus on human studies.

PyDEMATEL: A Python-based tool implementing DEMATEL and fuzzy DEMATEL methods for improved decision making.

The Regular Consumption of Nuts Is Associated with a Lower Prevalence of Abdominal Obesity and Metabolic Syndrome in Older People from the North of Spain.

Single-cell omics for nutrition research: an emerging opportunity for human-centric investigations.

Strawberry as a health promoter: an evidence-based review. Where are we 10 years later?

Data mining is an analytical approach that contributes to achieving a solution to many problems by extracting previously unknown, fascinating, nontrivial, and potentially valuable information from massive datasets. Clustering in data mining is used for splitting or segmenting data items/points into meaningful groups and clusters by grouping the items that are near to each other based on certain statistics. This paper covers various elements of clustering, such as algorithmic methodologies, applications, clustering assessment measurement, and researcher-proposed enhancements with their impact on data mining thorough grasp of clustering algorithms, its applications, and the advances achieved in the existing literature. This study includes a literature search for papers published between 1995 and 2023, including conference and journal publications. The study begins by outlining fundamental clustering techniques along with algorithm improvements and emphasizing their advantages and limitations in comparison to other clustering algorithms. It investigates the evolution measures for clustering algorithms with an emphasis on metrics used to gauge clustering quality, such as the F-measure and the Rand Index. This study includes a variety of clustering-related topics, such as algorithmic approaches, practical applications, metrics for clustering evaluation, and researcher-proposed improvements. It addresses numerous methodologies offered to increase the convergence speed, resilience, and accuracy of clustering, such as initialization procedures, distance measures, and optimization strategies. The work concludes by emphasizing clustering as an active research area driven by the need to identify significant patterns and structures in data, enhance knowledge acquisition, and improve decision making across different domains. This study aims to contribute to the broader knowledge base of data mining practitioners and researchers, facilitating informed decision making and fostering advancements in the field through a thorough analysis of algorithmic enhancements, clustering assessment metrics, and optimization strategies.

A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective.

Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques.

Underwater Wireless Sensor Networks: Enabling Technologies for Node Deployment and Data Collection Challenges.

Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease driven by persistent inflammation and oxidative stress. Ilex paraguariensis (yerba mate) contains bioactive compounds—particularly chlorogenic acids, quercetin, and rutin—with documented antioxidant and anti-inflammatory properties. Objectives: To systematically review the mechanistic and clinical evidence on Ilex paraguariensis and its main constituents in RA-relevant inflammatory, oxidative, and bone metabolic pathways. Methods: Following PRISMA 2020, PubMed/MEDLINE, LILACS, and SciELO were searched up to September 2025. Eligible studies included yerba mate preparations (last 10 years) or isolated compounds (last 5 years) assessing RA-relevant clinical, inflammatory, oxidative, or bone metabolic outcomes. Non-original studies were excluded. Owing to heterogeneity, findings were narratively synthesized, and risk of bias was evaluated using RoB 2, ROBINS-I, OHAT, and SYRCLE. Results: Twenty-three studies met inclusion criteria: 11 human (clinical or observational), 7 human-based in vitro, and 5 animal studies. Interventions with yerba mate infusions or standardized extracts suggest reductions in inflammatory markers (e.g., C-reactive protein, interleukin-6) and indicate improvements in glutathione-related oxidative balance. Evidence from isolated compounds, particularly quercetin and rutin, suggests comparable anti-inflammatory and antioxidant effects. Preclinical studies appear to indicate modulation of inflammatory and redox pathways relevant to RA. Conclusions: Yerba mate and its constituents show preliminary indications of anti-inflammatory and antioxidant effects with potential relevance to RA pathophysiology. However, in the absence of clinical trials in RA patients, conclusions remain tentative, constrained by small sample sizes, methodological heterogeneity, species differences, and internal validity concerns. Future research should include rigorously designed randomized trials and mechanistic studies using advanced human-relevant platforms, such as organoids and organ-on-chip systems.

Yerba Mate (Ilex paraguariensis) and Rheumatoid Arthritis: A Systematic Review of Mechanistic and Clinical Evidence.

Elite performance and sporting success are often the result of optimal integration and synergy of all components of sports preparedness (i.e., health, technical and tactical skills, bioenergetic and neuromuscular abilities and capacities, anthropometric characteristics, cognition, emotions, creativity, or personality), which evolve because of systematic long-term sports preparation. However, the relative importance of these characteristics varies between individual and team sports. While some individual sports require a high standard of bioenergetic and neuromuscular abilities and capacities, team sports performance is closely related to technical and tactical skills, which may compensate for weakness within the fitness level (1). Nonetheless, successful team sport performances seem to be much more dependent on the interaction among a wide range of factors than on the maximum development of one or two factors in isolation. In team sports, elite performance emerges from the interaction among the individual parts (2) to overcome the opponent during competition. Sports may be categorized according to the degree of predictability of the environment that they are played in (3). Team sports occur in highly unpredictable environments due to the interactions with both teammates and opponents, with performance dealing with this unpredictability. Thus, it is important to have a clear understanding of the integrative systems and the principles that rule their interactions with the environment, keeping in mind the main aim of the process: developing the diversity/unpredictability potential of athletes/teams (4) to afford the emergence of rich patterns of behavior from players to adapt quickly and effectively in dynamically changing and unpredictable environments (5). Performance in team sports is affected by several factors that affect the organization of training and competitions. These include, for example, COVID-19 cases (6), PCR tests (7), air flights and their effects prior to competition (8), injuries (9), or match-congested schedules (10). The interaction among these factors may also influence player availability. The concept of player availability is a common one in elite team sports. Available players can be considered the ones who are injury-free and ready to compete whether the head coach chooses to put them on the lineup. Thus, an available state would be when a player is fit and recovered enough to compete. On the other hand, player unavailability would be considered a state which includes injury, sanction or suspension, or other reasons that would keep a player out of match. However, this topic needs to be explored more in elite team sport environments. Considering previous enriching work, it remains important to further progress and provide academic knowledge in order to support coaches/managers, strength and conditioning coaches, sport scientists, and medical team members (e.g., doctors, physicians, and physiotherapists) in their working environments. While widely-advocated scientific groundwork is considered throughout this manuscript, the main aim of this opinion article is to provide a review of factors related to player availability and its influence on performance in elite team sports (Figure 1). Finally, some practical suggestions and recommendations are provided to deal with constant alterations in player's availability and performance fluctuations.

A commentary of factors related to player availability and its influence on performance in elite team sports.

An enhanced opportunistic rank-based parent node selection for sustainable & smart IoT networks.

A scalable and secure federated learning authentication scheme for IoT.

D

Deep learning model for detection of brown spot rice leaf disease with smart agriculture.

Enhancing Urban Traffic Management Through Real-Time Anomaly Detection and Load Balancing.

Intelligent Approach for Clustering Mutations’ Nature of COVID-19 Genome.

NLP-Based Application for Analyzing Private and Public Banks Stocks Reaction to News Events in the Indian Stock Exchange.

A Novel Deep Learning Based Healthcare Model for COVID-19 Pandemic Stress Analysis.

Performance of the 4C and SEIMC scoring systems in predicting mortality from onset to current COVID-19 pandemic in emergency departments.

População infantil e adolescente nas ruas e estudantes estrangeiros: impactos interculturais no desenvolvimento e no acesso às escolas.

Prehospital acute life-threatening cardiovascular disease in elderly: an observational, prospective, multicentre, ambulance-based cohort study.

E

Adherence to the pyramid of the Mediterranean diet (2010), non-communicable diseases and lifestyle in online postgraduate Spanish students in the food area.

Advancing Nutritional Science: Contemporary Perspectives on Diet’s Role in Metabolic Health and Disease Prevention.

With the growing academic pressure and competitive educational environment, students often face mental stress, which can affect their academic performance and mental health. Its accurate and timely detection and prevention is important. Traditionally, mental stress has been reported by self-assessment, which is highly subjective and can be erroneous. With advances in neuroscience, electroencephalogram (EEG) signals have been used to study brain states more objectively. EEG-based features, including time-domain, frequency-domain, and various types of connectivity features, have been used to effectively classify stress signals. However, these individual features are only able to present one aspect of the brain under stress. Several studies have combined a distinct set of features extracted from EEG signals, including time and frequency domain features, with other peripheral signals. Stress is a complex mechanism which leads to alternation in brain dynamics, its connectivity patterns and information flow. This study proposed a feature-fusion model that can effectively combine spatial features, i.e. Microstates (MS), connectivity features like Transfer Entropy (TE) and Granger Causality (GC), which provided a new neuromarker for stress classification. These features are combined with attention fusion, which enhances the discriminant features and mitigates the individual limitations within each modality. We also extracted microstates for stress-based signals. It provided a new set of microstate topomaps to study brain networks when under stress, which was not explored previously. The proposed Attention-fusion based multi-feature set is classified using Support Vector Machine, Linear Discriminant Analysis (LDA) and Multilayer Perceptron (MLP) and gave a reliable accuracy of 95.47%, 98.91%, and 83.49%, respectively. To validate the proposed method, the classification results were compared with individual and binary fusion of MS, TE and GC features, which further confirmed the robustness of the framework. This proposed feature fusion provides a more robust stress classification neuromarker, which can effectively cover the brain dynamics for accurate reporting of the underlying mental state.

Attention-based multi-feature fusion neuromarker for EEG-driven stress classification in learners.

A Comparison of the Clinical Characteristics of Short-, Mid-, and Long-Term Mortality in Patients Attended by the Emergency Medical Services: An Observational Study.

Emotional Management in Journalism and Communication Studies.

Exploring the Potential of Microservices in Internet of Things: A Systematic Review of Security and Prospects.

Fundus image classification using feature concatenation for early diagnosis of retinal disease.

Background/Objectives: The growing integration of Artificial Intelligence (AI) and chatbots in health professional education offers innovative methods to enhance learning and clinical preparedness. This study aimed to evaluate the educational impact and perceptions in university students of Human Nutrition and Dietetics, regarding the utility, usability, and design of the E+DIEting_Lab chatbot platform when implemented in clinical nutrition training. Methods: The platform was piloted from December 2023 to April 2025 involving 475 students from multiple European universities. While all 475 students completed the initial survey, 305 finished the follow-up evaluation, representing a 36% attrition rate. Participants completed surveys before and after interacting with the chatbots, assessing prior experience, knowledge, skills, and attitudes. Data were analyzed using descriptive statistics and independent samples t-tests to compare pre- and post-intervention perceptions. Results: A total of 475 university students completed the initial survey and 305 the final evaluation. Most university students were females (75.4%), with representation from six languages and diverse institutions. Students reported clear perceived learning gains: 79.7% reported updated practical skills in clinical dietetics and communication were updated, 90% felt that new digital tools improved classroom practice, and 73.9% reported enhanced interpersonal skills. Self-rated competence in using chatbots as learning tools increased significantly, with mean knowledge scores rising from 2.32 to 2.66 and skills from 2.39 to 2.79 on a 0–5 Likert scale (p < 0.001 for both). Perceived effectiveness and usefulness of chatbots as self-learning tools remained positive but showed a small decline after use (effectiveness from 3.63 to 3.42; usefulness from 3.63 to 3.45), suggesting that hands-on experience refined, but did not diminish, students’ overall favorable views of the platform. Conclusions: The implementation and pilot evaluation of the E+DIEting_Lab self-learning virtual patient chatbot platform demonstrate that structured digital simulation tools can significantly improve perceived clinical nutrition competences. These findings support chatbot adoption in dietetics curricula and inform future digital education innovations.

Innovative Application of Chatbots in Clinical Nutrition Education: The E+DIEting_Lab Experience in University Students.

Risk Factors for Eating Disorders in University Students: The RUNEAT Study.

The Scope of Technostress and Care of The Self on Journalists During the Pandemic.

Triple-Band Notched Ultra-Wideband Microstrip MIMO Antenna with Bluetooth Band.

A systematic review of deep learning methods for community detection in social networks.

F

Advancements in Dentistry with Artificial Intelligence: Current Clinical Applications and Future Perspectives.

In the rapidly advanced and evolving information technology industry, adequate client engagement plays a critical role as it is very important to understand the client’s concerns, and requirements, have the records, authorizations, and go-ahead of previously agreed requirements, and provide the feasible solution accordingly. Previously multiple solutions have been proposed to enhance the efficiency of client engagement, but they lack traceability, trust, transparency, and conflict in agreements of previous contracts. Due to the lack of these shortcomings, the client requirement is getting delayed which is causing client escalations, integrity issues, project failure, and penalties. In this study, we proposed the UniferCollab framework to overcome the issues of collaboration between various teams, transparency, the record of client authorizations, and the go-ahead on previous developments by implementing blockchain technology. We store the data on the permissible network in the proposed approach. It allows us to compile all the requirements and information shared by clients on permissible blockchain to secure a large amount of data which enhances the traceability of all the requirements. All the authorizations from the client generate push notifications for any changes in their current system executed through smart contracts. It removes the ambiguity between various development teams if the client has only shared the requirement with one team. The data is stored in the decentralized network from where information is gathered which resolves the traceability, transparency, and trust issues. Lastly, evaluations involved a total of 800 hypertext transfer protocol (HTTP) requests tested using Postman with blockchain block sizes ranging from 0.568 KB to 550 KB and an average size increase of 280 KB was observed as new blocks were added. The longest chain in the network was observed during 800 repetitions of blockchain operations. Latency analysis revealed that delays in processing HTTP requests were influenced by decentralized node processing, local machine response times, and internet bandwidth through various experiments. Results show that the proposed framework resolves all client engagement issues in implementation between all stakeholders which enhances trust, and transparency improves client experience and helps us manage disputes effectively.

Client engagement solution for post implementation issues in software industry using blockchain.

Consortium Framework Using Blockchain for Asthma Healthcare in Pandemics.

Creciendo en la adversidad: la resiliencia del estudiantado con discapacidad en la Universidad Nacional, Costa Rica.

Deep Learning-Based Multiclass Instance Segmentation for Dental Lesion Detection.

Detection of cotton crops diseases using customized deep learning model.

DrunkChain: Blockchain-Based IoT System for Preventing Drunk Driving-Related Traffic Accidents.

Harnessing AI forward and backward chaining with telemetry data for enhanced diagnostics and prognostics of smart devices.

The Healthy Effects of Strawberry Polyphenols: Which Strategy behind Antioxidant Capacity?

Image Watermarking Using Least Significant Bit and Canny Edge Detection.

Information technology-based revolution in music education using AHP and TOPSIS.

IoT Enabled Intelligent Stick for Visually Impaired People for Obstacle Recognition.

Lipid Accumulation in HepG2 Cells Is Attenuated by Strawberry Extract through AMPK Activation.

Resilience Optimization of Post-Quantum Cryptography Key Encapsulation Algorithms.

Strawberry (cv. Romina) Methanolic Extract and Anthocyanin-Enriched Fraction Improve Lipid Profile and Antioxidant Status in HepG2 Cells.

Sustentabilidade em sistemas de segurança do trabalho na construção civil.

Systematic Review of Machine Learning applied to the Prediction of Obesity and Overweight.

An enhanced approach for predicting air pollution using quantum support vector machine.

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Adherence to the Mediterranean-Style Eating Pattern and Macular Degeneration: A Systematic Review of Observational Studies.

An Anchor-Based Localization in Underwater Wireless Sensor Networks for Industrial Oil Pipeline Monitoring.

Anthocyanin metabolites from gut microbiota and cognitive health.

Anthocyanins: what do we know until now?

Anti-inflammatory effect of strawberry extract against LPS-induced stress in RAW 264.7 macrophages.

Approach to a Project Framework in the Environment of Sustainability and Corporate Social Responsibility (CSR): Case Study of a Training Proposal to a Group of Students in a Higher Education Institution.

Are by-products from beeswax recycling process a new promising source of bioactive compounds with biomedical properties?

Autophagy in Human Health and Disease: Novel Therapeutic Opportunities.

Chronotype and Cancer: Emerging Relation Between Chrononutrition and Oncology from Human Studies.

Development Agencies and Local Governments—Coexistence within the Same Territory.

Diet, Eating Habits, and Lifestyle Factors Associated with Adequate Sleep Duration in Children and Adolescents Living in 5 Mediterranean Countries: The DELICIOUS Project.

Energy-Aware Live VM Migration Using Ballooning in Cloud Data Center.

Enhanced FPGA-based smart power grid simulation using Heun and Piecewise analytic method.

Evaluation of the Sustainable Development Goals in the Diagnosis and Prediction of the Sustainability of Projects Aimed at Local Communities in Latin America and the Caribbean.

Exercise Addiction and Perfectionism, Joint in the Same Path? A Systematic Review.

Flavan-3-ols and Vascular Health: Clinical Evidence and Mechanisms of Action.

Gestão escolar democrática e participativa: um olhar para as habilidades, competências, perspectivas e desafios dos directores escolares do município da Caála.

Imperative Role of Automation and Wireless Technologies in Aquaponics Farming.

Instrumentalization of a Model for the Evaluation of the Level of Satisfaction of Graduates under an E-Learning Methodology: A Case Analysis Oriented to Postgraduate Studies in the Environmental Field.

Integration of Sustainable Criteria in the Development of a Proposal for an Online Postgraduate Program in the Projects Area.

Machine Learning Models to Predict Readmission Risk of Patients with Schizophrenia in a Spanish Region.

Mediterranean Diet and Quality of Life in Adults: A Systematic Review.

Mediterranean Diet and Sleep Features: A Systematic Review of Current Evidence.

Modelo de comunicación efectiva para la difusión de los programas y proyectos de inversión pública del Departamento de Loreto, Perú.

Modelo de comunicación efectiva para la difusión de los programas y proyectos de inversión pública del departamento de Loreto (Perú).

A Modified Marx Generator Circuit with Enhanced Tradeoff between Voltage and Pulse Width for Electroporation Applications.

Polyphenols are naturally occurring compounds that can be found in plant-based foods, including fruits, vegetables, nuts, seeds, herbs, spices, and beverages, the use of which has been linked to enhanced brain health and cognitive function. These natural molecules are broadly classified into two main groups: flavonoids and non-flavonoid polyphenols, the latter including phenolic acids, stilbenes, and tannins. Flavonoids are primarily known for their potent antioxidant properties, which help neutralize harmful reactive oxygen species (ROS) in the brain, thereby reducing oxidative stress, a key contributor to neurodegenerative diseases. In addition to their antioxidant effects, flavonoids have been shown to modulate inflammation, enhance neuronal survival, and support neurogenesis, all of which are critical for maintaining cognitive function. Phenolic acids possess strong antioxidant properties and are believed to protect brain cells from oxidative damage. Neuroprotective effects of these molecules can also depend on their ability to modulate signaling pathways associated with inflammation and neuronal apoptosis. Among polyphenols, hydroxycinnamic acids such as caffeic acid have been shown to enhance blood-brain barrier permeability, which may increase the delivery of other protective compounds to the brain. Another compound of interest is represented by resveratrol, a stilbene extensively studied for its potential neuroprotective properties related to its ability to activate the sirtuin pathway, a molecular signaling pathway involved in cellular stress response and aging. Lignans, on the other hand, have shown promise in reducing neuroinflammation and oxidative stress, which could help slow the progression of neurodegenerative diseases and cognitive decline. Polyphenols belonging to different subclasses, such as flavonoids, phenolic acids, stilbenes, and lignans, exert neuroprotective effects by regulating microglial activation, suppressing pro-inflammatory cytokines, and mitigating oxidative stress. These compounds act through multiple signaling pathways, including NF-κB, MAPK, and Nrf2, and they may also influence genetic regulation of inflammation and immune responses at brain level. Despite their potential for brain health and cognitive function, polyphenols are often characterized by low bioavailability, something that deserves attention when considering their therapeutic potential. Future translational studies are needed to better understand the right dosage, the overall diet, the correct target population, as well as ideal formulations allowing to overcome bioavailability limitations.

Molecular mechanisms underlying the neuroprotective effects of polyphenols: implications for cognitive function.

Nut Consumption Is Associated with Cognitive Status in Southern Italian Adults.

“Oh, My God! My Season Is Over!” COVID-19 and Regulation of the Psychological Response in Spanish High-Performance Athletes.

Polyphenol-Rich Strawberry Extract Protects Human Dermal Fibroblasts against Hydrogen Peroxide Oxidative Damage and Improves Mitochondrial Functionality.

Protective Effect of Strawberry Extract against Inflammatory Stress Induced in Human Dermal Fibroblasts.

Real Time Monitoring of Muscle Fatigue with IoT and Wearable Devices.

Cardiovascular diseases are among the leading causes of mortality worldwide, with dietary factors being the main risk contributors. Diets rich in bioactive compounds, such as (poly)phenols, have been shown to potentially exert positive effects on vascular health. Among them, resveratrol has gained particular attention due to its potential antioxidant and anti-inflammatory action. Nevertheless, the results in humans are conflicting possibly due to interindividual different responses. The gut microbiota, a complex microbial community that inhabits the gastrointestinal tract, has been called out as potentially responsible for modulating the biological activities of phenolic metabolites in humans. The present review aims to summarize the main findings from clinical trials on the effects of resveratrol interventions on endothelial and vascular outcomes and review potential mechanisms interesting the role of gut microbiota on the metabolism of this molecule and its cardioprotective metabolites. The findings from randomized controlled trials show contrasting results on the effects of resveratrol supplementation and vascular biomarkers without dose-dependent effect. In particular, studies in which resveratrol was integrated using food sources, i.e., red wine, reported significant effects although the resveratrol content was, on average, much lower compared to tablet supplementation, while other studies with often extreme resveratrol supplementation resulted in null findings. The results from experimental studies suggest that resveratrol exerts cardioprotective effects through the modulation of various antioxidant, anti-inflammatory, and anti-hypertensive pathways, and microbiota composition. Recent studies on resveratrol-derived metabolites, such as piceatannol, have demonstrated its effects on biomarkers of vascular health. Moreover, resveratrol itself has been shown to improve the gut microbiota composition toward an anti-inflammatory profile. Considering the contrasting findings from clinical studies, future research exploring the bidirectional link between resveratrol metabolism and gut microbiota as well as the mediating effect of gut microbiota in resveratrol effect on cardiovascular health is warranted.

Resveratrol and vascular health: evidence from clinical studies and mechanisms of actions related to its metabolites produced by gut microbiota.

Background Anterior shoulder instability is a common condition, especially among young and active individuals, often associated with both osseous and soft tissue injuries. Recent innovations have introduced various surgical options for managing critical and subcritical instability. Therefore, the primary objective of this systematic review was to collect, synthesize, and integrate international research published across multiple scientific databases on shoulder ligamentoplasty, arthroscopic Latarjet, dynamic anterior stabilization (DAS), and arthroscopic Trillat techniques used in the treatment of shoulder instability. Method A structured search was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the PICOS model, up to January 30, 2025, in the MEDLINE/PubMed, Web of Science (WOS), ScienceDirect, Cochrane Library, SciELO, EMBASE, SPORTDiscus, and Scopus databases. The risk of bias was evaluated, and the PEDro scale was used to assess methodological quality. Results The initial search yielded a total of 964 articles. After applying the inclusion and exclusion criteria, the final sample consisted of 25 articles. These studies demonstrated a high standard of methodological quality. The review summarized the effects of ligamentoplasty, arthroscopic Latarjet, dynamic anterior stabilization, and arthroscopic Trillat techniques in treating shoulder instability, detailing the sample population, immobilization period, frequency of instability episodes—including recurrent dislocations and subluxations—surgical methods, study designs, assessed variables, main findings, and reported outcomes. Conclusions Arthroscopic ligamentoplasty is advantageous in preserving the patient’s native anatomy, maintaining joint integrity, and allowing for alternative interventions in case of failure. The arthroscopic Trillat technique offers a minimally invasive solution for anterior instability without significant bone loss. The DAS technique utilizes the biceps tendon to provide dynamic stabilization, aiming to generate a sling effect over the subscapularis muscle. The Latarjet procedure remains the gold standard for managing anterior glenoid bone loss greater than 20%. Each surgical option for anterior shoulder instability carries specific implications, and treatment decisions should be tailored based on bone loss severity, capsuloligamentous quality, and the patient’s functional needs.

Shoulder ligamentoplasty, arthroscopic Latarjet, dynamic anterior stabilization, and arthroscopic trillat for the treatment of shoulder instability: a systematic review of original studies on surgical techniques.

Strawberry consumption alleviates doxorubicin-induced toxicity by suppressing oxidative stress.

Strawberry consumption improves aging-associated impairments, mitochondrial biogenesis and functionality through the AMP-activated protein kinase signaling cascade.

Strawberry extracts efficiently counteract inflammatory stress induced by the endotoxin lipopolysaccharide in Human Dermal Fibroblast.

Strawberry-Based Cosmetic Formulations Protect Human Dermal Fibroblasts against UVA-Induced Damage.

Towards Security Mechanism in D2D Wireless Communication: A 5G Network Approach.

A Transfer Learning-Based Artificial Intelligence Model for Leaf Disease Assessment.

Transforming Urban Sanitation: Enhancing Sustainability through Machine Learning-Driven Waste Processing.

Over the last decades, the Mediterranean diet gained enormous scientific, social, and commercial attention due to proven positive effects on health and undeniable taste that facilitated a widespread popularity. Researchers have investigated the role of Mediterranean-type dietary patterns on human health all around the world, reporting consistent findings concerning its benefits. However, what does truly define the Mediterranean diet? The myriad of dietary scores synthesizes the nutritional content of a Mediterranean-type diet, but a variety of aspects are generally unexplored when studying the adherence to this dietary pattern. Among dietary factors, the main characteristics of the Mediterranean diet, such as consumption of fruit and vegetables, olive oil, and cereals should be accompanied by other underrated features, such as the following: (i) specific reference to whole-grain consumption; (ii) considering the consumption of legumes, nuts, seeds, herbs and spices often untested when exploring the adherence to the Mediterranean diet; (iii) consumption of eggs and dairy products as common foods consumed in the Mediterranean region (irrespectively of the modern demonization of dietary fat intake). Another main feature of the Mediterranean diet includes (red) wine consumption, but more general patterns of alcohol intake are generally unmeasured, lacking specificity concerning the drinking occasion and intensity (i.e., alcohol drinking during meals). Among other underrated aspects, cooking methods are rather simple and yet extremely varied. Several underrated aspects are related to the quality of food consumed when the Mediterranean diet was first investigated: foods are locally produced, minimally processed, and preserved with more natural methods (i.e., fermentation), strongly connected with the territory with limited and controlled impact on the environment. Dietary habits are also associated with lifestyle behaviors, such as sleeping patterns, and social and cultural values, favoring commensality and frugality. In conclusion, it is rather reductive to consider the Mediterranean diet as just a pattern of food groups to be consumed decontextualized from the social and geographical background of Mediterranean culture. While the methodologies to study the Mediterranean diet have demonstrated to be useful up to date, a more holistic approach should be considered in future studies by considering the aforementioned underrated features and values to be potentially applied globally through the concept of a “Planeterranean” diet.

Underrated aspects of a true Mediterranean diet: understanding traditional features for worldwide application of a “Planeterranean” diet.

WPAN and IoT Enabled Automation to Authenticate Ignition of Vehicle in Perspective of Smart Cities.

Background/Objectives: The diet quality of younger individuals is decreasing globally, with alarming trends also in the Mediterranean region. The aim of this study was to assess diet quality and adequacy in relation to country-specific dietary recommendations for children and adolescents living in the Mediterranean area. Methods: A cross-sectional survey was conducted of 2011 parents of the target population participating in the DELICIOUS EU-PRIMA project. Dietary data and cross-references with food-based recommendations and the application of the youth healthy eating index (YHEI) was assessed through 24 h recalls and food frequency questionnaires. Results: Adherence to recommendations on plant-based foods was low (less than ∼20%), including fruit and vegetables adequacy in all countries, legume adequacy in all countries except for Italy, and cereal adequacy in all countries except for Portugal. For animal products and dietary fats, the adequacy in relation to the national food-based dietary recommendations was slightly better (∼40% on average) in most countries, although the Eastern countries reported worse rates. Higher scores on the YHEI predicted adequacy in relation to vegetables (except Egypt), fruit (except Lebanon), cereals (except Spain), and legumes (except Spain) in most countries. Younger children (p < 0.005) reporting having 8–10 h adequate sleep duration (p < 0.001), <2 h/day screen time (p < 0.001), and a medium/high physical activity level (p < 0.001) displayed a better diet quality. Moreover, older respondents (p < 0.001) with a medium/high educational level (p = 0.001) and living with a partner (p = 0.003) reported that their children had a better diet quality. Conclusions: Plant-based food groups, including fruit, vegetables, legumes, and even (whole-grain) cereals are underrepresented in the diets of Mediterranean children and adolescents. Moreover, the adequate consumption of other important dietary components, such as milk and dairy products, is rather disregarded, leading to substantially suboptimal diets and poor adequacy in relation to dietary guidelines.

Youth Healthy Eating Index (YHEI) and Diet Adequacy in Relation to Country-Specific National Dietary Recommendations in Children and Adolescents in Five Mediterranean Countries from the DELICIOUS Project.

The healthy effects of strawberry bioactive compounds on molecular pathways related to chronic diseases.

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Background: To address the current pandemic, multiple studies have focused on the development of new mHealth applications to help curb the number of infections, these applications aim to accelerate the identification and self-isolation of people exposed to SARS-CoV- 2, the coronavirus known to cause COVID-19, by being in close contact with infected individuals. Objective: The main objectives of this paper are: 1)To analyze the current status of COVID-19 apps available the main virtual stores: Google Play Store and App Store, and 2)To propose a novel mobile application based on the limitations of the analyzed apps. Methods: The search for apps in this research was carried out in the main virtual stores: Google Play Store and App Store, until May 2021. After the analysis of the selected apps, a novel app is proposed whose main function will be the multiple transmission of information about the patient's symptoms from the application, without the need for phone calls or chat in real time. For its development, the flowchart shown in this session is followed. Results: The search yielded a total of 50 apps, of which 24 were relevant to this study. It is important to note that 23 of the apps analyzed are free. Of the total number of apps, 54% are available for Android and iOS operating systems. 50% of the apps have more than 5 thousand downloads. This means that Covid-19 related apps are in high demand among mobile device users today. The developed app is called COVINFO and its name comes from the union of the words COVID-19 and information, inserted in such a way that the user can get an idea of the app's functionality just by listening or reading the resulting name. The application has been created for mobile devices with Android operating system, being compatible with Android 4.4 and higher. Conclusions: Of the apps found, 37.5% only offer information about the virus and the necessary measures to avoid infection. During the analysis it was detected that 12.5% of the apps are focused on locating outbreaks and that none of them have been successful for the following reasons: not being interconnected to share data; and the request for access to the user's geolocation, generating distrust on the part of the user who, consequently, rejects them. This work addresses the development of an application for the transmission of the user's symptoms to his regular doctor, based on the fact that only 16.6% of the existing applications have this functionality. The COVINFO app offers a service that no other application on the market has: doctor-patient interaction without the need for calls or chat in real time for constant monitoring by the doctor of the patient's condition and evolution.

Analysis of mobile apps for information, prevention and monitoring of covid-19 and proposal of an innovative app in this field.

Contextual Urdu Lemmatization Using Recurrent Neural Network Models.

To address the current pandemic, multiple studies have focused on the development of new mHealth apps to help in curbing the number of infections, these applications aim to accelerate the identification and self-isolation of people exposed to SARS-CoV-2, the coronavirus known to cause COVID-19, by being in close contact with infected individuals. The main objectives of this paper are: (1) Analyze the current status of COVID-19 apps available on the main virtual stores: Google Play Store and App Store for Spain, and (2) Propose a novel mobile application that allows interaction and doctor-patient follow-up without the need for real-time consultations (face-to-face or telephone). In this research, a search for eHealth and telemedicine apps related to Covid-19 was performed in the main online stores: Google Play Store and App Store, until May 2021. Keywords were entered into the search engines of the online stores and relevant apps were selected for study using a PRISMA methodology. For the design and implementation of the proposed app named COVINFO, the main weaknesses of the apps studied were taken into account in order to propose a novel and useful app for healthcare systems. The search yielded a total of 50 apps, of which 24 were relevant to this study, of which 23 are free and 54% are available for Android and iOS operating systems (OS). The proposed app has been developed for mobile devices with Android OS being compatible with Android 4.4 and higher. This app enables doctor-patient interaction and constant monitoring of the patient's progress without the need for calls, chats or face-to-face consultation in real time. This work addresses design and development of an application for the transmission of the user's symptoms to his regular doctor, based on the fact that only 16.6% of existing applications have this functionality. The COVINFO app offers a novel service: asynchronous doctor-patient communication, as well as constant monitoring of the patient’s condition and evolution. This app makes it possible to better manage the time of healthcare personnel and avoid overcrowding in hospitals, with the aim of preventing the collapse of healthcare systems and the spread of the coronavirus.

Descriptive Analysis of Mobile Apps for Management of COVID-19 in Spain and Development of an Innovate App in that field.

Do people drive as they live together? Associations between aggressive behaviour on the road and intimate relationships.

Internet of Things in Pregnancy Care Coordination and Management: A Systematic Review.

PRUS: Product Recommender System Based on User Specifications and Customers Reviews.

Smart Physiotherapy: Advancing Arm-Based Exercise Classification with PoseNet and Ensemble Models.

Objective: The objective of this paper is to review and analyze the current state of telemedicine and ehealth in the field of vascular surgery. Methods: This paper collects the relevant information obtained after reviewing the articles related to telemedicine in vascular surgery, published from 2012 to 2022 contained in scientific databases. In addition, the results obtained are statistically studied based on various factors, such as the year of publication or the search engine. In this way, we obtain a complete vision of the current state of telemedicine in the field of vascular surgery. Results: After performing this search and applying selection criteria, 29 articles were obtained for subsequent study and discussion, of which 20 were published in the second half of the decade, representing 70% of the results. In the analysis carried out according to the search criteria used, it can be seen that using the word telemedicine we obtained 69% of the articles while with the criteria mHealth and eHealth we only obtained 22% and 9% of the results, respectively. It can be seen that the filter with the most potential content articles was “vascular surgery AND telemedicine”. In the analysis performed according to the search engine, it was observed that the Google Scholar database contains 93% of the articles found in the massive search and the relevant articles contained therein represent 52% of the total. Conclusion: An upward trend has been observed in recent years, with a clear increase in the number of publications and much lower figures in the first years. One aspect to highlight is that 47.8% of the articles analyzed focus only on postoperative treatment, which may be due to the help provided by telemedicine in detecting surgical site infections by sending images and videos, this being one of the most common postoperative complications. The analyzed works show the importance of telemedicine in vascular surgery and identify possible future lines of research. In the analysis carried out on the origin of the selected relevant papers, an important interest of the US in this topic is demonstrated since more than 50% of the research contains authors from this country, it is also observed that there is no research from Spain, so this research would be an initial step to determine the weaknesses of telemedicine in this field of medicine and a good opportunity to open a research gap in this branch.

Systematic Review of Telemedicine and eHealth Systems Applied to Vascular Surgery.

Validation of a video-based tool to assess risk perception both objectively and subjectively in pedestrians.

Which drivers drive as they live and who are transformed while driving? Analysis of moderators in the relationship between general anger and driving anger.

The role of subjective risk perception and self-esteem in the relationship between sensation seeking and aberrant behaviors on the road: A moderated-moderation model.

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Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction.

Design and development of patient health tracking, monitoring and big data storage using Internet of Things and real time cloud computing.

An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer.

Investigation of structural frustration in symmetric diblock copolymers confined in polar discs through cell dynamic simulation.

Obstacle Detection and Warning System for Visually Impaired Using IoT Sensors.

Virtual histopathology methods in medical imaging - a systematic review.

A deep learning approach to optimize remaining useful life prediction for Li-ion batteries.

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Deep Learning Approaches for Image Captioning: Opportunities, Challenges and Future Potential.

Ensemble Partition Sampling (EPS) for Improved Multi-Class Classification.

A Lesion-Based Diabetic Retinopathy Detection Through Hybrid Deep Learning Model.

A Novel Approach to Integrating Uncertainty into a Push Re-Label Network Flow Algorithm for Pit Optimization.

A Novel Large-Scale Stochastic Pushback Design Merged with a Minimum Cut Algorithm for Open Pit Mine Production Scheduling.

An Optimized Open Pit Mine Application for Limestone Quarry Production Scheduling to Maximize Net Present Value.

In the world of remote sensing, hyperspectral imaging has emerged as a powerful tool that captures incredibly detailed information about our environment. These images contain hundreds of spectral bands that reveal what the human eye cannot see, making them invaluable for applications ranging from precision agriculture to environmental monitoring. However, extracting insights from complex data requires sophisticated analytical approaches. Our research dives into the performance comparison of two popular machine learning approaches: the support vector machine (SVM) and the more recent deep learning-based stacked autoencoder (SAE). We wanted to understand which approach works better under different real-world conditions that researchers and practitioners face. Through extensive experiments across five diverse public hyperspectral datasets, we discovered that the choice between these models is not straightforward, it depends significantly on your specific circumstances. When labeled data are scarce, which is a common challenge in remote sensing, SVM proves more reliable and efficient. Conversely, when abundant training data are available, SAE demonstrates impressive capabilities in learning complex patterns. One interesting finding was how active learning as a technique that intelligently selects the most informative samples for labeling, improved SAE’s performance on medium-sized datasets, potentially offering a practical solution to the data scarcity problem. The proposed approaches showed vulnerability to noise, highlighting the importance of preprocessing steps in real-world applications. Although SVM generally requires less computational resources, SAE’s potential to handle large and complex datasets makes it an attractive option when the appropriate computing infrastructure is available. The model training also achieved high accuracy, compared to other models published in the literature. The results achieved provide a practical path for researchers and practitione...

Performance Evaluation of Support Vector Machine and Stacked Autoencoder for Hyperspectral Image Analysis.

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Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization Variants.

Advanced Line-of-Sight (LOS) model for communicating devices in modern indoor environment.

Big Data Analysis Framework for Water Quality Indicators with Assimilation of IoT and ML.

Diagnosing epileptic seizures using combined features from independent components and prediction probability from EEG data.

Efficient CNN architecture with image sensing and algorithmic channeling for dataset harmonization.

Enhancing detection of epileptic seizures using transfer learning and EEG brain activity signals.

Evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting: A systematic review.

A high penetration of renewable energy (RE) in utility grids creates the problems of power system flexibility, high transmission losses, and voltage variations. These problems can be solved using a hybrid combination of transmission network restructuring and optimal placement of distributed energy generator (DEG) units. Hence, this work investigated a technologically and economically feasible solution for improving the flexibility of power networks and reducing losses in a practical transmission utility network by implementing a restructuring of the network and optimal deployment of the distributed energy generators (DEGs). Two solutions for this network restructuring were proposed. Furthermore, a grid-oriented genetic algorithm (GOGA) was designed by combining the conventional genetic algorithm (GA) and mathematical solutions to identify optimal DEG placement. A power system restructuring and GOGA flexibility index (PSRGFI) was formulated for the assessment of network flexibility. A cost–benefit assessment was also performed to estimate the payback period for the investment required for restructuring of the network and DEG placement. The least-square approximation technique was applied for load projection for the year 2031 considering the base year 2021. It was established that minimization of transmission losses, reduction in voltage deviations, and improvement of network flexibility were achieved through hybrid application of network restructuring and DEG placement using GOGA. A network loss saving of 61.19 MW was achieved via optimal restructuring and GOGA. For the projected year 2031, the PSRGFI increased from 30.94 to 132.78 after the placement of DEGs using GOGA and optimal restructuring, indicating that network flexibility increased significantly. The payback period for the investment was very small, equal to 0.985 years. The performance of the designed method was superior to the GA-based method, simulated annealing technique, and bee colony algorithm (BCA) used for placement of DEG units in the test network. The study was completed using MATLAB software, considering data from a practical transmission network owned by Rajasthan Rajya Vidyut Prasaran Nigam Ltd. (RVPN), India.

Hybrid Combination of Network Restructuring and Optimal Placement of Distributed Generators to Reduce Transmission Loss and Improve Flexibility.

A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images.

An Intelligent Dual-Axis Solar Tracking System for Remote Weather Monitoring in the Agricultural Field.

IoT Fog-Enabled Multi-Node Centralized Ecosystem for Real Time Screening and Monitoring of Health Information.

Model Driven Approach for Efficient Flood Disaster Management with Meta Model Support.

Monitoring Ambient Parameters in the IoT Precision Agriculture Scenario: An Approach to Sensor Selection and Hydroponic Saffron Cultivation.

Optimal Sizing and Deployment of Renewable Energy Generators in Practical Transmission Network Using Grid-Oriented Multiobjective Harmony Search Algorithm for Loss Reduction and Voltage Profile Improvements.

An Optimized Intelligent Computational Security Model for Interconnected Blockchain-IoT System & Cities.

Prediction of leukemia peptides using convolutional neural network and protein compositions.

Revolutionary Strategies Analysis and Proposed System for Future Infrastructure in Internet of Things.

A Secure and Efficient Signature Scheme for IoT in Healthcare.

Suicide Ideation Detection Using Social Media Data and Ensemble Machine Learning Model.

Disaster management is a critical area that requires efficient methods and techniques to address various challenges. This comprehensive assessment offers an in-depth overview of disaster management systems, methods, obstacles, and potential future paths. Specifically, it focuses on flood control, a significant and recurrent category of natural disasters. The analysis begins by exploring various types of natural catastrophes, including earthquakes, wildfires, and floods. It then delves into the different domains that collectively contribute to effective flood management. These domains encompass cutting-edge technologies such as big data analysis and cloud computing, providing scalable and reliable infrastructure for data storage, processing, and analysis. The study investigates the potential of the Internet of Things and sensor networks to gather real-time data from flood-prone areas, enhancing situational awareness and enabling prompt actions. Model-driven engineering is examined for its utility in developing and modeling flood scenarios, aiding in preparation and response planning. This study includes the Google Earth engine (GEE) and examines previous studies involving GEE. Moreover, we discuss remote sensing; remote sensing is undoubtedly a valuable tool for disaster management, and offers geographical data in various situations. We explore the application of Geographical Information System (GIS) and Spatial Data Management for visualizing and analyzing spatial data and facilitating informed decision-making and resource allocation during floods. In the final section, the focus shifts to the utilization of machine learning and data analytics in flood management. These methodologies offer predictive models and data-driven insights, enhancing early warning systems, risk assessment, and mitigation strategies. Through this in-depth analysis, the significance of incorporating these spheres into flood control procedures is highlighted, with the aim of improving disaster management techniques and enhancing resilience in flood-prone regions. The paper addresses existing challenges and provides future research directions, ultimately striving for a clearer and more coherent representation of disaster management techniques.

A Systematic Review of Disaster Management Systems: Approaches, Challenges, and Future Directions.

A Systematic Survey of AI Models in Financial Market Forecasting for Profitability Analysis.

The probability of medical staff to get affected from COVID19 is much higher due to their working environment which is more exposed to infectious diseases. So, as a preventive measure the body temperature monitoring of medical staff at regular intervals is highly recommended. Infrared temperature sensing guns have proved its effectiveness and therefore such devices are used to monitor the body temperature. These devices are either used on hands or forehead. As a result, there are many issues in monitoring the temperature of frontline healthcare professionals. Firstly, these healthcare professionals keep wearing PPE (Personal Protective Equipment) kits during working hours and as a result it would be very difficult to monitor their body temperature. Secondly, these healthcare professionals also wear face shields and in such cases monitoring temperature by exposing forehead needs removal of face shield. Doing so after regular intervals is surely uncomfortable for healthcare professionals. To avoid such issues, this paper is disclosing a technologically advanced face shield equipped with sensors capable of monitoring body temperature instantly without the hassle of removing the face shield. This face shield is integrated with a built-in infrared temperature sensor. A total of 10 such face shields were printed and assembled within the university lab and then handed over to a group of ten members including faculty and students of nursing and health science department. This sequence was repeated four times and as a result 40 healthcare workers participated in the study. Thereafter, feedback analysis was conducted on questionnaire data and found a significant overall mean score of 4.59 out of 5 which indicates that the product is effective and worthy in every facet. Stress analysis is also performed in the simulated environment and found that the device can easily withstand the typically applied forces. The limitations of this product are difficulty in cleaning the product and comparatively high cost due to the deployment of electronic equipment

Technologically Advanced Reusable 3D Face Shield for Health Workers Confronting COVID19.

Transmission Network Loss Reduction and Voltage Profile Improvement Using Network Restructuring and Optimal DG Placement.

Underwater Thermal Energy Harvesting: Frameworks, Challenges, Applications, and Future Investigation.

A deep learning approach for Named Entity Recognition in Urdu language.

A real-time air-writing model to recognize Bengali characters.

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Association between blood cortisol levels and numerical rating scale in prehospital pain assessment.

Clinical phenotypes and short-term outcomes based on prehospital point-of-care testing and on-scene vital signs.

FairHealth: Long-Term Proportional Fairness-Driven 5G Edge Healthcare in Internet of Medical Things.

Isoflavones Effects on Vascular and Endothelial Outcomes: How Is the Gut Microbiota Involved?

Measurement of chest muscle mass in COVID-19 patients on mechanical ventilation using tomography.

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Analysis and Implementation of Human Mobility Behavior Using Similarity Analysis Based on Co-Occurrence Matrix.

Antifragile and Resilient Geographical Information System Service Delivery in Fog Computing.

Correction to: Systematic Review of Telemedicine and eHealth Systems Applied to Vascular Surgery.

Mango is one of the most beloved fruits and plays an indispensable role in the agricultural economies of many tropical countries like Pakistan, India, and other Southeast Asian countries. Similar to other fruits, mango cultivation is also threatened by various diseases, including Anthracnose and Red Rust. Although farmers try to mitigate such situations on time, early and accurate detection of mango diseases remains challenging due to multiple factors, such as limited understanding of disease diversity, similarity in symptoms, and frequent misclassification. To avoid such instances, this study proposes a multimodal deep learning framework that leverages both leaf and fruit images to improve classification performance and generalization. Individual CNN-based pre-trained models, including ResNet-50, MobileNetV2, EfficientNet-B0, and ConvNeXt, were trained separately on curated datasets of mango leaf and fruit diseases. A novel Modality Attention Fusion (MAF) mechanism was introduced to dynamically weight and combine predictions from both modalities based on their discriminative strength, as some diseases are more prominent on leaves than on fruits, and vice versa. To address overfitting and improve generalization, a class-aware augmentation pipeline was integrated, which performs augmentation according to the specific characteristics of each class. The proposed attention-based fusion strategy significantly outperformed individual models and static fusion approaches, achieving a test accuracy of 99.08%, an F1 score of 99.03%, and a perfect ROC-AUC of 99.96% using EfficientNet-B0 as the base. To evaluate the model’s real-world applicability, an interactive web application was developed using the Django framework and evaluated through out-of-distribution (OOD) testing on diverse mango samples collected from public sources. These findings underline the importance of combining visual cues from multiple organs of plants and adapting model attention to contextual features for real-world agricultural diagnostics.

Dual-modality fusion for mango disease classification using dynamic attention based ensemble of leaf & fruit images.

Efectos del juego terapéutico Creciendo en Mindfulness (CEM) sobre los problemas emocionales y de conducta de los menores en conflicto con la Ley Penal del CPI “Jalteva” en Honduras.

Efficient deep learning-based approach for malaria detection using red blood cell smears.

Intelligent Load-Balancing Framework for Fog-Enabled Communication in Healthcare.

Mitigating 5G security challenges for next-gen industry using quantum computing.

Nivel de conocimiento sobre el tiburón toro (Carcharhinus leucas) como base para su conservación y gestión medioambiental en la Reserva de la Biósfera Caribe Mexicano.

Parentalidad Positiva y Prevención de la Población infanto-juvenil con relación a su Salud Mental: una Revisión Actualizada.

Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network.

Prehospital qSOFA, mSOFA, and NEWS2 performance for sepsis prediction: A prospective, multi-center, cohort study.

A Prospective Observational Study of Frailty in Geriatric Revitalization Aimed at Community-Dwelling Elderly.

Pupilometer efficacy in monitoring anxiety in undergraduate medical students during high-fidelity clinical simulation.

Threatening URDU Language Detection from Tweets Using Machine Learning.

Utilization of Stockwell Transform, Support Vector Machine and D-STATCOM for the Identification, Classification and Mitigation of Power Quality Problems.

The genetic aspects of berries: from field to health.

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Amyloid β-but not Tau-induced neurotoxicity is suppressed by Manuka honey via HSP-16.2 and SKN-1/Nrf2 pathways in an in vivo model of Alzheimer's disease.

Effects of a Garlic Hydrophilic Extract Rich in Sulfur Compounds on Redox Biology and Alzheimer's Disease Markers in Caenorhabditis Elegans.

In Vitro and In Vivo Insights into a Broccoli Byproduct as a Healthy Ingredient for the Management of Alzheimer’s Disease and Aging through Redox Biology.

Novel hybrid transfer neural network for wheat crop growth stages recognition using field images.

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Associations between Sleep, Cortisol Regulation, and Diet: Possible Implications for the Risk of Alzheimer Disease.

Can the Functional Physical Fitness of Older People with Overweight or Obesity Be Improved through a Multicomponent Physical Exercise Program? A Chilean Population Study.

Conocimientos, actitudes y prácticas de familias de adolescentes con discapacidad cognitiva en sexualidad y afectividad.

Cytotoxicity and Antimicrobial Resistance of Aeromonas Strains Isolated from Fresh Produce and Irrigation Water.

Effect of Sulfonic Groups Concentration on IEC Properties in New Fluorinated Copolyamides.

Effects of caloric restriction on immunosurveillance, microbiota and cancer cell phenotype: Possible implications for cancer treatment.

Effects of phytochemicals on thyroid function and their possible role in thyroid disease.

Kinetics of the invasion of a non-phagocytic fish cell line, RTG-2 by Yersinia ruckeri serotype O1 biotype 1.

Metaheuristic-based optimal energy assessment of hybrid multi-effect evaporator with synergy of solar and wind energy sources.

Nutritional patterns associated with the maintenance of neurocognitive functions and the risk of dementia and Alzheimer’s disease: A focus on human studies.

Plant-Based and Plant-Rich Diet Patterns during Gestation: Beneficial Effects and Possible Shortcomings.

Role of gut microbiota and nutrients in amyloid formation and pathogenesis of Alzheimer disease.

Smart Fault Monitoring and Normalizing of a Power Distribution System Using IoT.

Targeting Glioblastoma with the Use of Phytocompounds and Nanoparticles.

Towards Design and Feasibility Analysis of DePaaS: AI Based Global Unified Software Defect Prediction Framework.

The Use of Neuroimaging to Assess Associations Among Diet, Nutrients, Metabolic Syndrome, and Alzheimer’s Disease.

Wastewater Treatment with Technical Intervention Inclination towards Smart Cities.

The use of natural compounds for the targeting and chemoprevention of ovarian cancer.

The use of plant-derived bioactive compounds to target cancer stem cells and modulate tumor microenvironment.

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Analyzing preventive precautions to limit spread of COVID-19.

Emotion Detection Using Facial Expression Involving Occlusions and Tilt.

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Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson’s disease behavioral analysis.

Advancement in medical report generation: current practices, challenges, and future directions.

Analyzing Sentiments Regarding ChatGPT Using Novel BERT: A Machine Learning Approach.

Análisis y mejores prácticas proyectuales de una obra civil hidroeléctrica de Honduras.

An Artificial Neural Network Model for Water Quality and Water Consumption Prediction.

Attitudes and Skills in Basic Life Support after Two Types of Training: Traditional vs. Gamification, of Compulsory Secondary Education Students: A Simulation Study.

Background: Physical activity in children and adolescents represents one of the most important lifestyle factors to determine current and future health. Aim: The aim of the study is to assess the lifestyle and dietary factors linked to physical activity in younger populations across five countries in the Mediterranean region. Design: A total of 2,011 parents of children and adolescents (age range 6–17 years) participating to a preliminary survey of the DELICIOUS project were investigated to determine children's adequate physical activity level (identified using the short form of the international physical activity questionnaire) as well as diet quality parameters [measured as Youth-Healthy Eating Index (Y-HEI)] and eating and lifestyle factors (i.e., meal habits, sleep duration, screen time, etc.). Logistic regression analyses were performed to assess the odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between variables of interest. Results: Younger children of younger parents currently working had higher rates and probability to have adequate physical activity. Multivariate analysis showed that children and adolescents who had breakfast (OR = 1.88, 95% CI: 1.38, 2.56) and often ate with their family (OR = 1.80, 95% CI: 0.90, 3.61) were more likely to have an adequate level of physical activity. Children and adolescents who reported a sleep duration (8–10 h) closest to the recommended one were significantly more likely to achieve adequate levels of physical activity (OR = 1.88, 95% CI: 1.38, 2.56). Conversely, those with more than 4 h of daily screen time were less likely to engage in adequate physical activity (OR = 0.77, 95% CI: 0.54, 1.10). Furthermore, children and adolescents in the highest tertile of YEHI scores showed a 60% greater likelihood of engaging in adequate physical activity (OR = 1.60, 95% CI: 1.27, 2.01). Conclusion: These results emphasize the importance of promoting healthy diet and lifestyle habits, including structured and high quality shared meals, sufficient sleep, and screen time moderation, as key strategies to support active behaviors in younger populations. Future interventions should focus on reinforcing these behaviors through parental guidance and community-based initiatives to foster lifelong healthy habits.

Children's and adolescents' lifestyle factors associated with physical activity in five Mediterranean countries: the DELICIOUS project.

A Cost-Optimized Data Parallel Task Scheduling with Deadline Constraints in Cloud.

Predicting depression intensity from microblogs and social media posts has numerous benefits and applications, including predicting early psychological disorders and stress in individuals or the general public. A major challenge in predicting depression using social media posts is that the existing studies do not focus on predicting the intensity of depression in social media texts but rather only perform the binary classification of depression and moreover noisy data makes it difficult to predict the true depression in the social media text. This study intends to begin by collecting relevant Tweets and generating a corpus of 210000 public tweets using Twitter public application programming interfaces (APIs). A strategy is devised to filter out only depression-related tweets by creating a list of relevant hashtags to reduce noise in the corpus. Furthermore, an algorithm is developed to annotate the data into three depression classes: ‘Mild,’ ‘Moderate,’ and ‘Severe,’ based on International Classification of Diseases-10 (ICD-10) depression diagnostic criteria. Different baseline classifiers are applied to the annotated dataset to get a preliminary idea of classification performance on the corpus. Further FastText-based model is applied and fine-tuned with different preprocessing techniques and hyperparameter tuning to produce the tuned model, which significantly increases the depression classification performance to an 84% F1 score and 90% accuracy compared to baselines. Finally, a FastText-based weighted soft voting ensemble (WSVE) is proposed to boost the model’s performance by combining several other classifiers and assigning weights to individual models according to their individual performances. The proposed WSVE outperformed all baselines as well as FastText alone, with an F1 of 89%, 5% higher than FastText alone, and an accuracy of 93%, 3% higher than FastText alone. The proposed model better captures the contextual features of the relatively small sample class and aids in the detection of early depression intensity prediction from tweets with impactful performances.

Depression Intensity Classification from Tweets Using FastText Based Weighted Soft Voting Ensemble.

A Detectability Analysis of Retinitis Pigmetosa Using Novel SE-ResNet Based Deep Learning Model and Color Fundus Images.

Detecting hate in diversity: a survey of multilingual code-mixed image and video analysis.

Distributed Denial of Service Attack Detection in Network Traffic Using Deep Learning Algorithm.

Editorial: Host-bacteria interactions in fish pathogens.

Effect of olive leaf phytochemicals on the anti-acetylcholinesterase, anti-cyclooxygenase-2 and ferric reducing antioxidant capacity.

Enhanced detection of diabetes mellitus using novel ensemble feature engineering approach and machine learning model.

Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models.

Forecasting of Post-Graduate Students’ Late Dropout Based on the Optimal Probability Threshold Adjustment Technique for Imbalanced Data.

From by-products to new application opportunities: the enhancement of the leaves deriving from the fruit plants for new potential healthy products.

Harvesting Scenedesmus obliquus via Flocculation of Moringa oleifera Seed Extract from Urban Wastewater: Proposal for the Integrated Use of Oil and Flocculant.

Human activity recognition (HAR) is essential in many applications, such as smart homes, assisted living, healthcare monitoring, rehabilitation, physiotherapy, and geriatric care. Conventional methods of HAR use wearable sensors, e.g., acceleration sensors and gyroscopes. However, they are limited by issues such as sensitivity to position, user inconvenience, and potential health risks with long-term use. Optical camera systems that are vision-based provide an alternative that is not intrusive; however, they are susceptible to variations in lighting, intrusions, and privacy issues. The paper uses an optical method of recognizing human domestic activities based on pose estimation and deep learning ensemble models. The skeletal keypoint features proposed in the current methodology are extracted from video data using PoseNet to generate a privacy-preserving representation that captures key motion dynamics without being sensitive to changes in appearance. A total of 30 subjects (15 male and 15 female) were sampled across 2734 activity samples, including nine daily domestic activities. There were six deep learning architectures, namely, the Transformer (Transformer), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Multilayer Perceptron (MLP), One-Dimensional Convolutional Neural Network (1D CNN), and a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture. The results on the hold-out test set show that the CNN–LSTM architecture achieves an accuracy of 98.78% within our experimental setting. Leave-One-Subject-Out cross-validation further confirms robust generalization across unseen individuals, with CNN–LSTM achieving a mean accuracy of 97.21% ± 1.84% across 30 subjects. The results demonstrate that vision-based pose estimation with deep learning is a useful, precise, and non-intrusive approach to HAR in smart healthcare and home automation systems.

Human Activity Recognition in Domestic Settings Based on Optical Techniques and Ensemble Models.

Background/Objectives: Estimating energy and macronutrients from food images is clinically relevant yet challenging, and rigorous evaluation requires transparent accuracy metrics with uncertainty and clear acknowledgement of reference data limitations across heterogeneous sources. This study assessed ChatGPT-5, a general-purpose vision-language model, across four scenarios differing in the amount and type of contextual information provided, using a composite dataset to quantify accuracy for calories and macronutrients. Methods: A total of 195 dishes were evaluated, sourced from Allrecipes.com, the SNAPMe dataset, and Home-prepared, weighed meals. Each dish was evaluated under Case 1 (image only), Case 2 (image plus standardized non-visual descriptors), Case 3 (image plus ingredient lists with amounts), and Case 4 (replicates Case 3 but excluding the image). The primary endpoint was kcal Mean Absolute Error (MAE); secondary endpoints included Median Absolute Error (MedAE) and Root Mean Square Error (RMSE) for kcal and macronutrients (protein, carbohydrates, and lipids), all reported with 95% Confidence Intervals (CIs) via dish-level bootstrap resampling and accompanied by absolute differences (Δ) between scenarios. Inference settings were standardized to support reproducibility and variance estimation. Source stratified analyses and quartile summaries were conducted to examine heterogeneity by curation level and nutrient ranges, with additional robustness checks for error complexity relationships. Results and Discussion: Accuracy improved from Case 1 to Case 2 and further in Case 3 for energy and all macronutrients when summarized by MAE, MedAE, and RMSE with 95% CIs, with absolute reductions (Δ) indicating material gains as contextual information increased. In contrast to Case 3, estimation accuracy declined in Case 4, underscoring the contribution of visual cues. Gains were largest in the Home-prepared dietitian-weighed subset and smaller yet consistent for Allrecipes.com and SNAPMe, reflecting differences in reference curation and measurement fidelity across sources. Scenario-level trends were concordant across sources, and stratified and quartile analyses showed coherent patterns of decreasing absolute errors with the provision of structured non-visual information and detailed ingredient data. Conclusions: ChatGPT-5 can deliver practically useful calorie and macronutrient estimates from food images, particularly when augmented with standardized nonvisual descriptors and detailed ingredients, as evidenced by reductions in MAE, MedAE, and RMSE with 95% CIs across scenarios. The decline in accuracy observed when the image was omitted, despite providing detailed ingredient information, indicates that visual cues contribute meaningfully to estimation performance and that improvements are not solely attributable to arithmetic from ingredient lists. Finally, to promote generalizability, it is recommended that future studies include repeated evaluations across diverse datasets, ensure public availability of prompts and outputs, and incorporate systematic comparisons with non-artificial-intelligence baselines.

Image-Based Dietary Energy and Macronutrients Estimation with ChatGPT-5: Cross-Source Evaluation Across Escalating Context Scenarios.

Improvement of energy conservation using blockchain-enabled cognitive wireless networks for smart cities.

In silico prediction, molecular docking and simulation of natural flavonoid apigenin and xanthoangelol E against human metapneumovirus.

Introduction: Artificial Intelligence (AI) is transforming multiple sectors within our society, including education. In this context, emotions play a fundamental role in the teaching-learning process given that they influence academic performance, motivation, information retention, and student well-being. Thus, the integration of AI in emotional assessment within educational environments offers several advantages that can transform how we understand and address the socio-emotional development of students. However, there remains a lack of comprehensive approach that systematizes advancements, challenges, and opportunities in this field. Aim: This systematic literature review aims to explore how artificial intelligence (AI) is used to evaluate emotions within educational settings. We provide a comprehensive overview of the current state of research, focusing on advancements, challenges, and opportunities in the domain of AI-driven emotional assessment within educational settings. Method: The review involved a search across the following academic databases: Pubmed, Web of Science, PsycINFO and Scopus. Forty-one articles were selected that meet the established inclusion criteria. These articles were analyzed to extract key insights related to the integration of AI and emotional assessment within educational environments. Results: The findings reveal a variety of AI-driven approaches that were developed to capture and analyze students’ emotional states during learning activities. The findings are summarized in four fundamental topics: (1) emotion recognition in education, (2) technology integration and learning outcomes, (3) special education and assistive technology, (4) affective computing. Among the key AI techniques employed are machine learning and facial recognition, which are used to assess emotions. These approaches demonstrate promising potential in enhancing pedagogical strategies and creating adaptive learning environments that cater to individual emotional needs. The review identified emerging factors that, while important, require further investigation to understand their relationships and implications fully. These elements could significantly enhance the use of AI in assessing emotions within educational settings. Specifically, we are referring to: (1) federated learning, (2) convolutional neural network (CNN), (3) recurrent neural network (RNN), (4) facial expression databases, and (5) ethics in the development of intelligent systems. Conclusion: This systematic literature review showcases the significance of AI in revolutionizing educational practices through emotion assessment. While advancements are evident, challenges related to accuracy, privacy, and cross-cultural validity were also identified. The synthesis of existing research highlights the need for further research into refining AI models for emotion recognition and emphasizes the importance of ethical considerations in implementing AI technologies within educational contexts.

Integrating artificial intelligence to assess emotions in learning environments: a systematic literature review.

Lifestyle Factors Associated with Children’s and Adolescents’ Adherence to the Mediterranean Diet Living in Mediterranean Countries: The DELICIOUS Project.

Molecular Interactions between Dietary Lipids and Bone Tissue during Aging.

Molecular Mechanisms of the Protective Effects of Olive Leaf Polyphenols against Alzheimer’s Disease.

The Molecular Weaponry Produced by the Bacterium Hafnia alvei in Foods.

Olive Leaf Extracts With High, Medium, or Low Bioactive Compounds Content Differentially Modulate Alzheimer's Disease via Redox Biology.

Phage Therapy as a Focused Management Strategy in Aquaculture.

Phages and Enzybiotics in Food Biopreservation.

Prediction β-Thalassemia carriers using complete blood count features.

Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data.

Routing Protocol for MANET Based on QoS-Aware Service Composition with Dynamic Secured Broker Selection.

Software Cost and Effort Estimation: Current Approaches and Future Trends.

Ventilator pressure prediction employing voting regressor with time series data of patient breaths.

White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images.

An improved deep convolutional neural network-based YouTube video classification using textual features.

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Antimicrobial resistant enteric bacteria are widely distributed among environmental water sources in Dhaka, Bangladesh.

The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment.

Chronic obstructive pulmonary disease (COPD) is a severe and chronic ailment that is currently ranked as the third most common cause of mortality across the globe. COPD patients often experience debilitating symptoms such as chronic coughing, shortness of breath, and fatigue. Sadly, the disease frequently goes undiagnosed until it is too late, leaving patients without the care they desperately need. So, COPD detection at an early stage is crucial to prevent further damage to the lungs and improve quality of life. Traditional COPD detection methods often rely on physical examinations and tests such as spirometry, chest radiography, blood gas tests, and genetic tests. However, these methods may not always be accurate or accessible. One of the key vital signs for detecting COPD is the patient’s respiration rate. However, it is crucial to consider a patient’s medical and demographic characteristics simultaneously for better detection results. To address this issue, this study aims to detect COPD patients using artificial intelligence techniques. To achieve this goal, a novel framework is proposed that utilizes ultra-wideband (UWB) radar-based temporal and spectral features to build machine learning and deep learning models. This new set of temporal and spectral features is extracted from respiration data collected non-invasively from 1.5 m distance using UWB radar. Different machine learning and deep learning models are trained and tested on the collected dataset. The findings are promising, with a high accuracy score of 100% for COPD detection. This means that the proposed framework could potentially save lives by identifying COPD patients at an early stage. The k-fold cross-validation technique and performance comparison with the state-of-the-art studies are applied to validate its performance, ensuring that the results are robust and reliable. The high accuracy score achieved in the study implies that the proposed framework has the potential for the efficient detection of COPD at an early stage.

An Approach to Detect Chronic Obstructive Pulmonary Disease Using UWB Radar-Based Temporal and Spectral Features.

An Artificial Neural Network-Based Approach for Real-Time Hybrid Wind–Solar Resource Assessment and Power Estimation.

Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning.

A CAD System for Alzheimer’s Disease Classification Using Neuroimaging MRI 2D Slices.

Carotenoids Intake and Cardiovascular Prevention: A Systematic Review.

Changes in the Lifestyle of the Spanish University Population during Confinement for COVID-19.

Clinical epidemiology of dengue and COVID-19 co-infection among the residents in Dhaka, Bangladesh, 2021-2023: A cross-sectional study.

A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health.

Deep Learning-Based Real Time Defect Detection for Optimization of Aircraft Manufacturing and Control Performance.

Deep transfer learning-based bird species classification using mel spectrogram images.

Patient care and convenience remain the concern of medical professionals and caregivers alike. An unconscious patient confined to a bed may develop fluid accumulation and pressure sores due to inactivity and deficiency of oxygen flow. Moreover, weight monitoring is crucial for an effective treatment plan, which is difficult to measure for bedridden patients. This paper presents the design and development of a smart and cost-effective independent system for lateral rotation, movement, weight measurement, and transporting immobile patients. Optimal dimensions and practical design specifications are determined by a survey across various hospitals. Subsequently, the proposed hoist-based weighing and turning mechanism is CAD-modeled and simulated. Later, the structural analysis is carried out to select suitable metallurgy for various sub-assemblies to ensure design reliability. After fabrication, optimization, integration, and testing procedures, the base frame is designed to mount a hydraulic motor for the actuator, a DC power source for self-sustenance, and lockable wheels for portability. The installation of a weighing scale and a hydraulic actuator is ensured to lift the patient for weight measuring up to 600 pounds or lateral turning of 80 degrees both ways. The developed system offers simple operating characteristics, allows for keeping patient weight records, and assists nurses in changing patients’ lateral positions both ways, comfortably massage patients’ backs, and transport them from one bed to another. Additionally, being lightweight offers reduced contact with the patient to increase the healthcare staff’s safety in pandemics; it is also height adjustable and portable, allowing for use with multiple-sized beds and easy transportation across the medical facility. The feedback from paramedics is encouraging regarding reducing labor-intensive nursing tasks, alleviating the discomfort of long-term bed-ridden patients, and allowing medical practitioners to suggest better treatment plans

Design and Development of Smart Weight Measurement, Lateral Turning and Transfer Bedding for Unconscious Patients in Pandemics.

Diagnosing Training Needs in European Tourism SMEs: The TC-NAV Project for Managing and Overcoming Virulent Crises.

Digital competencies: perceptions of primary school teachers pursuing master’s degrees from eight African countries.

Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection.

Dual adaption based evolutionary algorithm for optimized the smart healthcare communication service of the Internet of Things in smart city.

Edge-Based Autonomous Fire and Smoke Detection Using MobileNetV2.

An Effective Method for Lung Cancer Diagnosis from CT Scan Using Deep Learning-Based Support Vector Network.

Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence.

Enhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning.

Evolving epidemiology, clinical features, and genotyping of dengue outbreaks in Bangladesh, 2000–2024: a systematic review.

In contemporary society, depression has emerged as a prominent mental disorder that exhibits exponential growth and exerts a substantial influence on premature mortality. Although numerous research applied machine learning methods to forecast signs of depression. Nevertheless, only a limited number of research have taken into account the severity level as a multiclass variable. Besides, maintaining the equality of data distribution among all the classes rarely happens in practical communities. So, the inevitable class imbalance for multiple variables is considered a substantial challenge in this domain. Furthermore, this research emphasizes the significance of addressing class imbalance issues in the context of multiple classes. We introduced a new approach Feature group partitioning (FGP) in the data preprocessing phase which effectively reduces the dimensionality of features to a minimum. This study utilized synthetic oversampling techniques, specifically Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic (ADASYN), for class balancing. The dataset used in this research was collected from university students by administering the Burn Depression Checklist (BDC). For methodological modifications, we implemented heterogeneous ensemble learning stacking, homogeneous ensemble bagging, and five distinct supervised machine learning algorithms. The issue of overfitting was mitigated by evaluating the accuracy of the training, validation, and testing datasets. To justify the effectiveness of the prediction models, balanced accuracy, sensitivity, specificity, precision, and f1-score indices are used. Overall, comprehensive analysis demonstrates the discrimination between the Conventional Depression Screening (CDS) and FGP approach. In summary, the results show that the stacking classifier for FGP with SMOTE approach yields the highest balanced accuracy, with a rate of 92.81%. The empirical evidence has demonstrated that the FGP approach, when combined with the SMOTE, able to produce better performance in predicting the severity of depression. Most importantly the optimization of the training time of the FGP approach for all of the classifiers is a significant achievement of this research.

Feature group partitioning: an approach for depression severity prediction with class balancing using machine learning algorithms.

Hierarchical Attention Module-Based Hotspot Detection in Wafer Fabrication Using Convolutional Neural Network Model.

La comunidad docente y las competencias digitales: la formación a lo largo de la vida.

Mitigating Low-Frequency Oscillations and Enhancing the Dynamic Stability of Power System Using Optimal Coordination of Power System Stabilizer and Unified Power Flow Controller.

Molecular epidemiology, transmission and clinical features of 2022‐mpox outbreak: A systematic review.

Multidrug resistance pattern and molecular epidemiology of pathogens among children with diarrhea in Bangladesh, 2019–2021.

Nerve Root Compression Analysis to Find Lumbar Spine Stenosis on MRI Using CNN.

This paper introduces a power quality (PQ) detection and categorization algorithm actuated by multiple signal processing techniques and rule-based decision tree (RBDT). This is aimed to recognize PQ events of simple nature and higher order multiplicity with less computational time using hybridization of the signal processing techniques. A voltage waveform with a PQ event (PQE) is processed using the Stockwell transform (ST) to compute the Stockwell PQ detection index (SPDI). The voltage waveform is also processed using the Hilbert transform (HT) to compute the Hilbert PQ detection index (HPDI). A voltage waveform is also decomposed using the Discrete Wavelet transform (DWT) to compute the classification feature index (CFI) [CFI1 to CFI4]. A combined PQ detection index (CPDI) is computed by multiplication of the SPDI, the HPDI and CFI1 to CFI4. Incidence of a PQE on a voltage signal is located with the help of a location PQ disturbance index (LPDI) which is computed by differentiating the CPDI with respect to time. CFI5, CFI6 and CFI7 are computed from the SPDI, the HPDI and the CPDI, respectively. Categorization of PQ events is performed using CFI1 to CFI7 by the rule-based decision tree (RBDT) with the help of simple decision rules. We conclude that the proposed algorithm is effective to identify the PQE with an accuracy of 98.58% in a noise-free environment and 97.62% in the presence of 20 dB SNR (signal-to-noise ratio) noise. Ten simple nature PQEs and eight combined PQ events (CPQEs) with multiplicity of two, three and four are effectively detected and categorized using the algorithm. The algorithm is also tested to detect a sag PQ event due to a line-to-ground (LG) fault incident on a practical distribution utility network. The performance of the investigated method is compared with a DWT-based technique in terms of accuracy of classification with and without noise, maximum computational time of PQ detection and multiplicity of PQE which can be effectively detected. A simulation is performed using the MATLAB software. MATLAB codes are used for modelling the PQE disturbances and the proposed algorithm using mathematical formulations.

Power Quality Detection and Categorization Algorithm Actuated by Multiple Signal Processing Techniques and Rule-Based Decision Tree.

Introduction: Rotavirus infection is a major cause of mortality among children under 5 years in Bangladesh. There is lack of integrated studies on rotavirus prevalence and genetic diversity during 1973 to 2023 in Bangladesh. Methods: This meta-analysis was conducted to determine the prevalence, genotypic diversity and seasonal distribution of rotavirus during pre-vaccination period in Bangladesh. This study included published articles on rotavirus A, rotavirus B and rotavirus C. We used Medline, Scopus and Google Scholar for published articles. Selected literatures were published between 1973 to 2023. Results: This study detected 12431 research articles published on rotavirus. Based on the inclusion criteria, 29 of 75 (30.2%) studies were selected. Molecular epidemiological data was taken from 29 articles, prevalence data from 29 articles, and clinical symptoms from 19 articles. The pooled prevalence of rotavirus was 30.1% (95% CI: 22%-45%, p = 0.005). Rotavirus G1 (27.1%, 2228 of 8219) was the most prevalent followed by G2 (21.09%, 1733 of 8219), G4 (11.58%, 952 of 8219), G9 (9.37%, 770 of 8219), G12 (8.48%, 697 of 8219), and G3 (2.79%, 229 of 8219), respectively. Genotype P[8] (40.6%, 2548 of 6274) was the most prevalent followed by P[4] (12.4%, 777 of 6274) and P[6] (6.4%, 400 of 6274), respectively. Rotavirus G1P[8] (19%) was the most frequent followed by G2P [4] (9.4%), G12P[8] (7.2%), and G9P[8], respectively. Rotavirus infection had higher odds of occurrence during December and February (aOR: 2.86, 95% CI: 2.43-3.6, p = 0.001). Discussion: This is the first meta-analysis including all the studies on prevalence, molecular epidemiology, and genetic diversity of rotavirus from 1973 to 2023, pre-vaccination period in Bangladesh. This study will provide overall scenario of rotavirus genetic diversity and seasonality during pre-vaccination period and aids in policy making for rotavirus vaccination program in Bangladesh. This work will add valuable knowledge for vaccination against rotavirus and compare the data after starting vaccination in Bangladesh.

Prevalence and genetic diversity of rotavirus in Bangladesh during pre-vaccination period, 1973-2023: a meta-analysis.

Prevalence and impact of long COVID-19 among patients with diabetes and cardiovascular diseases in Bangladesh.

Deep learning is used to address a wide range of challenging issues including large data analysis, image processing, object detection, and autonomous control. In the same way, deep learning techniques are also used to develop software and techniques that pose a danger to privacy, democracy, and national security. Fake content in the form of images and videos using digital manipulation with artificial intelligence (AI) approaches has become widespread during the past few years. Deepfakes, in the form of audio, images, and videos, have become a major concern during the past few years. Complemented by artificial intelligence, deepfakes swap the face of one person with the other and generate hyper-realistic videos. Accompanying the speed of social media, deepfakes can immediately reach millions of people and can be very dangerous to make fake news, hoaxes, and fraud. Besides the well-known movie stars, politicians have been victims of deepfakes in the past, especially US presidents Barak Obama and Donald Trump, however, the public at large can be the target of deepfakes. To overcome the challenge of deepfake identification and mitigate its impact, large efforts have been carried out to devise novel methods to detect face manipulation. This study also discusses how to counter the threats from deepfake technology and alleviate its impact. The outcomes recommend that despite a serious threat to society, business, and political institutions, they can be combated through appropriate policies, regulation, individual actions, training, and education. In addition, the evolution of technology is desired for deepfake identification, content authentication, and deepfake prevention. Different studies have performed deepfake detection using machine learning and deep learning techniques such as support vector machine, random forest, multilayer perceptron, k-nearest neighbors, convolutional neural networks with and without long short-term memory, and other similar models. This study aims to highlight the recent research in deepfake images and video detection, such as deepfake creation, various detection algorithms on self-made datasets, and existing benchmark datasets.

A Review of Image Processing Techniques for Deepfakes.

Scalable Comprehensive Automatic Inspection, Cleaning, and Evaluation Mechanism for Large‐Diameter Pipes.

Siamese Convolutional Neural Network-Based Twin Structure Model for Independent Offline Signature Verification.

Assessment of side effects associated with COVID-19 vaccination is required to monitor safety issues and acceptance of vaccines in the long term. We found a significant knowledge gap in the safety profile of COVID-19 vaccines in Bangladesh. We enrolled 1805 vaccine recipients from May 5, 2021, to April 4, 2023. Kruskal-Wallis test and χ2 test were performed. Multivariable logistic regression was also performed. First, second and third doses were administered among 1805, 1341, and 923 participants, respectively. Oxford–AstraZeneca (2946 doses) was the highest administered followed by Sinopharm BIBP (551 doses), Sinovac (214 doses), Pfizer-BioNTech (198 doses), and Moderna (160 doses), respectively. Pain at the injection site (80-90%, 3200–3600), swelling (85%, 3458), redness (78%, 3168), and heaviness in hand (65%, 2645) were the most common local effects, and fever (85%, 3458), headache (82%, 3336), myalgia (70%, 2848), chills (67%, 2726), muscle pain (60%, 2441) were the most prevalent systemic side effects reported within 48 h of vaccination. Thrombosis was only reported among the Oxford–AstraZeneca recipients (3.5-5.7%). Both local and systemic effects were significantly associated with the Oxford–AstraZeneca (p-value < 0.05), Pfizer–BioNTech (p-value < 0.05), and Moderna (p-value < 0.05) vaccination. Chronic urticaria and psoriasis were reported by 55-60% of the recipients after six months or later. The highest percentage of local and systemic effects after 2nd and 3rd dose were found among recipients of Moderna followed by Pfizer-BioNTech and Oxford–AstraZeneca. Homogenous doses of Oxford–AstraZeneca and heterogenous doses of Moderna and Pfizer-BioNTech were significantly associated with elevated adverse effects. Females, aged above 60 years with preexisting health conditions had higher risks. Vaccination with Pfizer-BioNTech (OR 4.34, 95% CI 3.95–4.58) had the highest odds of severe and long-term effects followed by Moderna (OR 4.15, 95% CI 3.92–4.69) and Oxford–AstraZeneca (OR 3.89, 95% CI 3.45–4.06), respectively. This study will provide an integrated insight into the safety profile of COVID-19 vaccines.

Side effects associated with homogenous and heterogenous doses of Oxford–AstraZeneca vaccine among adults in Bangladesh: an observational study.

Socio-economic status, food security and adherence to the Mediterranean diet in five Mediterranean countries: the DELICIOUS project.

Spare Parts Forecasting and Lumpiness Classification Using Neural Network Model and Its Impact on Aviation Safety.

Stress Detector Supported Galvanic Skin Response System with IoT and LabVIEW GUI.

A Systematic Review and Meta-Analysis of Premenstrual Syndrome with Special Emphasis on Herbal Medicine and Nutritional Supplements.

Technologies Empowered Environmental, Social, and Governance (ESG): An Industry 4.0 Landscape.

Teeth Lesion Detection Using Deep Learning and the Internet of Things Post-COVID-19.

Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence.

Ultra-Wide Band Radar Empowered Driver Drowsiness Detection with Convolutional Spatial Feature Engineering and Artificial Intelligence.

Visualization of Customized Convolutional Neural Network for Natural Language Recognition.

Voxel Extraction and Multiclass Classification of Identified Brain Regions across Various Stages of Alzheimer’s Disease Using Machine Learning Approaches.

Wireless Body Area Routing Protocols Impact Analysis on Entity Mobility Models with Static Sink Node.

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Introduction: Jackfruit cultivation is highly affected by leaf diseases that reduce yield, fruit quality, and farmer income. Early diagnosis remains challenging due to the limitations of manual inspection and the lack of automated and scalable disease detection systems. Existing deep-learning approaches often suffer from limited generalization and high computational cost, restricting real-time field deployment. Methods: This study proposes CNNAttLSTM, a hybrid deep-learning architecture integrating Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) units, and an attention mechanism for multi-class classification of algal leaf spot, black spot, and healthy jackfruit leaves. Each image is divided into ordered 56×56 spatial patches, treated as pseudo-temporal sequences to enable the LSTM to capture contextual dependencies across different leaf regions. Spatial features are extracted via Conv2D, MaxPooling, and GlobalAveragePooling layers; temporal modeling is performed by LSTM units; and an attention mechanism assigns adaptive weights to emphasize disease-relevant regions. Experiments were conducted on a publicly available Kaggle dataset comprising 38,019 images, using predefined training, validation, and testing splits. Results: The proposed CNNAttLSTM model achieved 99% classification accuracy, outperforming the baseline CNN (86%) and CNN–LSTM (98%) models. It required only 3.7 million parameters, trained in 45 minutes on an NVIDIA Tesla T4 GPU, and achieved an inference time of 22 milliseconds per image, demonstrating high computational efficiency. The patch-based pseudo-temporal approach improved spatial–temporal feature representation, enabling the model to distinguish subtle differences between visually similar disease classes. Discussion: Results show that combining spatial feature extraction with temporal modeling and attention significantly enhances robustness and classification performance in plant disease detection. The lightweight design enables real-time and edge-device deployment, addressing a major limitation of existing deep-learning techniques. The findings highlight the potential of CNNAttLSTM for scalable, efficient, and accurate agricultural disease monitoring and broader precision agriculture applications.

CNNAttLSTM: an attention-enhanced CNN–LSTM architecture for high-precision jackfruit leaf disease classification.

Current- and Voltage-Actuated Transmission Line Protection Scheme Using a Hybrid Combination of Signal Processing Techniques.

Exploring factors influencing the severity of pregnancy anemia in India: a study using proportional odds model.

Novel Transfer Learning Approach for Detecting Infected and Healthy Maize Crop Using Leaf Images.

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Public concern regarding health systems has experienced a rapid surge during the last two years due to the COVID-19 outbreak. Accordingly, medical professionals and health-related institutions reach out to patients and seek feedback to analyze, monitor, and uplift medical services. Such views and perceptions are often shared on social media platforms like Facebook, Instagram, Twitter, etc. Twitter is the most popular and commonly used by the researcher as an online platform for instant access to real-time news, opinions, and discussion. Its trending hashtags (#) and viral content make it an ideal hub for monitoring public opinion on a variety of topics. The tweets are extracted using three hashtags #healthcare, #healthcare services, and #medical facilities. Also, location and tweet sentiment analysis are considered in this study. Several recent studies deployed Twitter datasets using ML and DL models, but the results show lower accuracy. In addition, the studies did not perform extensive comparative analysis and lack validation. This study addresses two research questions: first, what are the sentiments of people toward medical services worldwide? and second, how effective are the machine learning and deep learning approaches for the classification of sentiment on healthcare tweets? Experiments are performed using several well-known machine learning models including support vector machine, logistic regression, Gaussian naive Bayes, extra tree classifier, k nearest neighbor, random forest, decision tree, and AdaBoost. In addition, this study proposes a transfer learning-based LSTM-ETC model that effectively predicts the customer’s satisfaction level from the healthcare dataset. Results indicate that despite the best performance by the ETC model with an 0.88 accuracy score, the proposed model outperforms with a 0.95 accuracy score. Predominantly, the people are happy about the provided medical services as the ratio of the positive sentiments is substantially higher than the negative sentiments. The sentiments, either positive or negative, play a crucial role in making important decisions through customer feedback and enhancing quality.

Analyzing patients satisfaction level for medical services using twitter data.

End-to-end emergency response protocol for tunnel accidents augmentation with reinforcement learning.

Fault Pattern Diagnosis and Classification in Sensor Nodes Using Fall Curve.

O presente artigo reflete sobre a realidade das mulheres em tempos de pandemia. A problemática que envolve o presente trabalho parte da pergunta: como tem sido a experiência das mulheres na pandemia do coronavírus (Covid-19), devido ao acúmulo dos cuidados como tarefa feminina e o grito uterino que vem dessa situação? Para responder a essa questão, buscamos referências da teologia feminista, que parte do princípio da experiência das mulheres para a análise da realidade e a reflexão teológica e que coloca a vida mesma em sua amplitude como critério hermenêutico. A metodologia utilizada é bibliográfica, a partir de artigos de revistas, entrevistas e livros. Além do mais, somos três mulheres, profissionais, afetadas também pelo home office que se mistura com o trabalho da casa e a necessidade de uma nova organização. O processo de ensino aprendizagem da pandemia tem sido cruel e tem afetado, especialmente, a vida das mulheres. A casa, que deveria ser um lugar seguro, apresenta-se para muitas como um lugar de perigo constante. Muitos trabalhos de cuidado remunerados ou não são realizados pelas mulheres. Historicamente o cuidado tem sido delegado às mulheres, sendo, por um lado, exaltado como parte do ser/fazer feminino (mãe e dona da casa) e, por outro lado, é um trabalho não remunerado ou mal remunerado (enfermeiras, assistentes sociais). Apresenta-se o artigo em três partes: a experiência das mulheres, a necessidade de reinventar a economia do cuidado e o grito uterino que ecoa com justa indignação. Evidencia-se que a pandemia visibilizou questões preexistentes: o aumento do cuidado sob os ombros das mulheres seja em casa ou nas diferentes profissões em que as mulheres estão na linha de frente, a violência contra as mulheres. A pandemia acentua a desigualdade social, racial e de gênero da sociedade brasileira, sendo que as mais atingidas são mulheres pobres, negras, pardas, idosas e com deficiência. O grito que nasce do feminismo clama por uma reinvenção do mundo que habitamos

Mulheres em tempos de pandemia: a cotidianidade, a economia do cuidado e o grito uterino!

Perseguindo o inédito viável: a pedagogia freiriana, a necesssidade da linguagem inclusiva e a denúncia à neolíngua do generismo queer.

StackIL10: A stacking ensemble model for the improved prediction of IL-10 inducing peptides.

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Aplicación de contra-inteligencia empresarial: análisis sobre la protección contra el espionaje cibernético en las organizaciones.

Application of the Gaussian Model for Monitoring Scenarios and Estimation of SO2 Atmospheric Emissions in the Salamanca Area, Bajío, Mexico.

Curcumina en niños con trastorno del espectro autista: inflamación sistémica, regulación sensorial y trastornos de la interacción intestino-cerebro. Ensayo clínico aleatorizado, doble ciego y controlado con placebo (CURATEA).

Detection of Upper Limb Asymmetries in Athletes According to the Stage of the Season—A Longitudinal Study.

Efecto de un entrenamiento propioceptivo para prevenir el riesgo de caída en adultos mayores.

IoT-Inspired Reliable Irregularity-Detection Framework for Education 4.0 and Industry 4.0.

Background: Scientific research should be carried out to prevent sports injuries. For this purpose, new assessment technologies must be used to analyze and identify the risk factors for injury. The main objective of this systematic review was to compile, synthesize and integrate international research published in different scientific databases on Countermovement Jump (CMJ), Functional Movement Screen (FMS) and Tensiomyography (TMG) tests and technologies for the assessment of injury risk in sport. This way, this review determines the current state of the knowledge about this topic and allows a better understanding of the existing problems, making easier the development of future lines of research. Methodology: A structured search was carried out following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines and the PICOS model until November 30, 2024, in the MEDLINE/PubMed, Web of Science (WOS), ScienceDirect, Cochrane Library, SciELO, EMBASE, SPORTDiscus and Scopus databases. The risk of bias was assessed and the PEDro scale was used to analyze methodological quality. Results: A total of 510 articles were obtained in the initial search. After inclusion and exclusion criteria, the final sample was 40 articles. These studies maintained a high standard of quality. This revealed the effects of the CMJ, FMS and TMG methods for sports injury assessment, indicating the sample population, sport modality, assessment methods, type of research design, study variables, main findings and intervention effects. Conclusions: The CMJ vertical jump allows us to evaluate the power capacity of the lower extremities, both unilaterally and bilaterally, detect neuromuscular asymmetries and evaluate fatigue. Likewise, FMS could be used to assess an athlete's basic movement patterns, mobility and postural stability. Finally, TMG is a non-invasive method to assess the contractile properties of superficial muscles, monitor the effects of training, detect muscle asymmetries, symmetries, provide information on muscle tone and evaluate fatigue. Therefore, they should be considered as assessment tests and technologies to individualize training programs and identify injury risk factors.

Tensiomyography, functional movement screen and counter movement jump for the assessment of injury risk in sport: a systematic review of original studies of diagnostic tests.

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An Enhanced Feed-Forward Back Propagation Levenberg–Marquardt Algorithm for Suspended Sediment Yield Modeling.

Hybridizing Artificial Intelligence Algorithms for Forecasting of Sediment Load with Multi-Objective Optimization.

Suspended Sediment Yield Forecasting with Single and Multi-Objective Optimization Using Hybrid Artificial Intelligence Models.

An attention-based deep learning model for early detection of polyphagous shot hole borer infestations in plants.

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Exploring body composition and somatotype profiles among youth professional soccer players.

Secure Data Management Life Cycle for Government Big-Data Ecosystem: Design and Development Perspective.