Perfil epidemiológico de casos de abuso sexual em crianças e adolescentes no estado de rondônia, brasil, no período de 2010 a 2021: um olhar da psicologia forense.
Tesis Materias > Psicología Universidad Europea del Atlántico > Docencia > Trabajos finales de Máster Cerrado Portugués A violência sexual em crianças e adolescentes é um dos maus tratos mais frequentes no Brasil e implica em complicações físicas e psicológicas em todas as fases do desenvolvimento humano. É essencial que crianças e adolescentes tenham seus direitos garantidos, além de uma política de prevenção pelo estado e pela sociedade. Neste sentido, a psicologia criminal, com ênfase na psicologia forense contribui positivamente via análises psicológicas e identificação de um possível comportamento criminoso para validar o crime e o agressor. Entretanto, ainda existes lacunas na literatura sobre esta temática, principalmente estudos que abordam os fatores associados ao crime em regiões pouco desenvolvidas, como o norte do Brasil. Deste modo, o objetivo deste estudo foi analisar o perfil epidemiológico das notificações de casos de violência sexual em crianças e adolescentes residentes no estado de Rondônia, na região norte do Brasil, no período de 2010 a 2021. Trata-se de um estudo ecológico, que analisou banco de dados oficiais e de livre acesso que contém informações sobre casos de abusos em crianças e adolescentes que ocorreram em Rôndonia. Os resultados foram analisados pelo teste de Qui-Quadrado, considerando uma diferença significativa quando p<0,05. Foi encontrado 1.637 crianças e adolescentes vítimas de abusos sexual, predominantemente adolescentes (69,76%) entre 10 a 14 anos (50,27%), do sexo feminino (92,24%) e de etnia parda (64,57%). Os crimes foram acometidos principalmente por amigos ou conhecidos (25,65%). Os casos de repetição foram mais elevados em adolescentes (50,09%). Entre os abusos que ocorreram na escola, as crianças foram as maiores vítimas (4,44%) em relação aos adolescentes (0,70%). Entretanto, o cenário foi inverso para os que ocorreram em bares ou similares e em vias públicas. Conclui-se que, no estado de Rondônia, esforços são necessários para aumentar as notificações de casos de abusos sexuais em crianças e adolescentes e reduzir os impactos físicos e psicológicos para as vítimas e seus familiares. Neste sentido a psicologia criminal, especialmente a psicologia forense, representam uma área de ampla expansão com potencial direto de beneficiar estes indivíduos vulneráveis, via atendimento especializado, minimizando traumas e auxiliando em processos jurídicos para confirmação do crime e reconhecimento dos criminosos. metadata Martins Pereira, Jucelia mail jucelyamarthins@gmail.com (2022) Perfil epidemiológico de casos de abuso sexual em crianças e adolescentes no estado de rondônia, brasil, no período de 2010 a 2021: um olhar da psicologia forense. Masters thesis, SIN ESPECIFICAR.
Texto completo no disponible.Resumen
A violência sexual em crianças e adolescentes é um dos maus tratos mais frequentes no Brasil e implica em complicações físicas e psicológicas em todas as fases do desenvolvimento humano. É essencial que crianças e adolescentes tenham seus direitos garantidos, além de uma política de prevenção pelo estado e pela sociedade. Neste sentido, a psicologia criminal, com ênfase na psicologia forense contribui positivamente via análises psicológicas e identificação de um possível comportamento criminoso para validar o crime e o agressor. Entretanto, ainda existes lacunas na literatura sobre esta temática, principalmente estudos que abordam os fatores associados ao crime em regiões pouco desenvolvidas, como o norte do Brasil. Deste modo, o objetivo deste estudo foi analisar o perfil epidemiológico das notificações de casos de violência sexual em crianças e adolescentes residentes no estado de Rondônia, na região norte do Brasil, no período de 2010 a 2021. Trata-se de um estudo ecológico, que analisou banco de dados oficiais e de livre acesso que contém informações sobre casos de abusos em crianças e adolescentes que ocorreram em Rôndonia. Os resultados foram analisados pelo teste de Qui-Quadrado, considerando uma diferença significativa quando p<0,05. Foi encontrado 1.637 crianças e adolescentes vítimas de abusos sexual, predominantemente adolescentes (69,76%) entre 10 a 14 anos (50,27%), do sexo feminino (92,24%) e de etnia parda (64,57%). Os crimes foram acometidos principalmente por amigos ou conhecidos (25,65%). Os casos de repetição foram mais elevados em adolescentes (50,09%). Entre os abusos que ocorreram na escola, as crianças foram as maiores vítimas (4,44%) em relação aos adolescentes (0,70%). Entretanto, o cenário foi inverso para os que ocorreram em bares ou similares e em vias públicas. Conclui-se que, no estado de Rondônia, esforços são necessários para aumentar as notificações de casos de abusos sexuais em crianças e adolescentes e reduzir os impactos físicos e psicológicos para as vítimas e seus familiares. Neste sentido a psicologia criminal, especialmente a psicologia forense, representam uma área de ampla expansão com potencial direto de beneficiar estes indivíduos vulneráveis, via atendimento especializado, minimizando traumas e auxiliando em processos jurídicos para confirmação do crime e reconhecimento dos criminosos.
Tipo de Documento: | Tesis (Masters) |
---|---|
Palabras Clave: | Abuso sexual de crianças e adolescentes, Psicologia criminal, Psicologia Forense. |
Clasificación temática: | Materias > Psicología |
Divisiones: | Universidad Europea del Atlántico > Docencia > Trabajos finales de Máster |
Depositado: | 16 Nov 2023 23:30 |
Ultima Modificación: | 16 Nov 2023 23:30 |
URI: | https://repositorio.uneatlantico.es/id/eprint/2107 |
Acciones (logins necesarios)
![]() |
Ver Objeto |
en
close
Enzymatic treatment shapes in vitro digestion pattern of phenolic compounds in mulberry juice
The health benefits of mulberry fruit are closely associated with its phenolic compounds. However, the effects of enzymatic treatments on the digestion patterns of these compounds in mulberry juice remain largely unknown. This study investigated the impact of pectinase (PE), pectin lyase (PL), and cellulase (CE) on the release of phenolic compounds in whole mulberry juice. The digestion patterns were further evaluated using an in vitro simulated digestion model. The results revealed that PE significantly increased chlorogenic acid content by 77.8 %, PL enhanced cyanidin-3-O-glucoside by 20.5 %, and CE boosted quercetin by 44.5 %. Following in vitro digestion, the phenolic compound levels decreased differently depending on the treatment, while cyanidin-3-O-rutinoside content increased across all groups. In conclusion, the selected enzymes effectively promoted the release of phenolic compounds in mulberry juice. However, during gastrointestinal digestion, the degradation of phenolic compounds surpassed their enhanced release, with effects varying based on the compound's structure.
Peihuan Luo mail , Jian Ai mail , Qiongyao Wang mail , Yihang Lou mail , Zhiwei Liao mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Maurizio Battino mail maurizio.battino@uneatlantico.es, Elwira Sieniawska mail , Weibin Bai mail , Lingmin Tian mail ,
Luo
en
close
A novel machine learning-based proposal for early prediction of endometriosis disease
Background Endometriosis is one of the causes of female infertility, with some studies estimating its prevalence at around 10 % of reproductive-age women worldwide and between 30 and 50 % in symptomatic women. However, its diagnosis is complex and often delayed, highlighting the need for more accessible and accurate diagnostic methods. The difficulty lies in its diverse etiology and the variability of symptoms among those affected. Methods This study proposes a predictive model based on supervised machine learning for the early identification of endometriosis, providing support for decision-making by healthcare professionals. For this purpose, an anonymised dataset of 5,143 female patients diagnosed with endometriosis at the private fertility clinic Inebir was used. The model integrates clinical records and genetic analysis through supervised machine learning algorithms, focusing on clinical variables and pathogenic and potentially pathogenic genetic variants. Results The developed predictive model achieves high accuracy in identifying the presence of endometriosis, highlighting the importance of combining clinical and genetic data in diagnosis. The integration of this data into the DELFOS platform, a clinical decision support system, demonstrates the utility of machine learning in improving the diagnosis of endometriosis. Conclusions The findings underscore the potential of clinical and genetic factors in the early diagnosis of endometriosis using supervised machine learning algorithms. This study contributes to the classification of clinical variables that influence endometriosis, offering a valuable tool for clinicians in making therapeutic and management decisions for their female patients.
Elena Enamorado-Díaz mail , Leticia Morales-Trujillo mail , Julián-Alberto García-García mail , Ana Teresa Marcos Rodríguez mail anateresa.marcos@uneatlantico.es, José Manuel Navarro-Pando mail jose.navarro@uneatlantico.es, María-José Escalona-Cuaresma mail ,
Enamorado-Díaz
<a href="/17788/1/s40537-025-01167-w.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
Detecting hate in diversity: a survey of multilingual code-mixed image and video analysis
The proliferation of damaging content on social media in today’s digital environment has increased the need for efficient hate speech identification systems. A thorough examination of hate speech detection methods in a variety of settings, such as code-mixed, multilingual, visual, audio, and textual scenarios, is presented in this paper. Unlike previous research focusing on single modalities, our study thoroughly examines hate speech identification across multiple forms. We classify the numerous types of hate speech, showing how it appears on different platforms and emphasizing the unique difficulties in multi-modal and multilingual settings. We fill research gaps by assessing a variety of methods, including deep learning, machine learning, and natural language processing, especially for complicated data like code-mixed and cross-lingual text. Additionally, we offer key technique comparisons, suggesting future research avenues that prioritize multi-modal analysis and ethical data handling, while acknowledging its benefits and drawbacks. This study attempts to promote scholarly research and real-world applications on social media platforms by acting as an essential resource for improving hate speech identification across various data sources.
Hafiz Muhammad Raza Ur Rehman mail , Mahpara Saleem mail , Muhammad Zeeshan Jhandir mail , Eduardo René Silva Alvarado mail eduardo.silva@funiber.org, Helena Garay mail helena.garay@uneatlantico.es, Imran Ashraf mail ,
Raza Ur Rehman
<a class="ep_document_link" href="/17794/1/s41598-025-95836-8.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
Accurate solar and photovoltaic (PV) power forecasting is essential for optimizing grid integration, managing energy storage, and maximizing the efficiency of solar power systems. Deep learning (DL) models have shown promise in this area due to their ability to learn complex, non-linear relationships within large datasets. This study presents a systematic literature review (SLR) of deep learning applications for solar PV forecasting, addressing a gap in the existing literature, which often focuses on traditional ML or broader renewable energy applications. This review specifically aims to identify the DL architectures employed, preprocessing and feature engineering techniques used, the input features leveraged, evaluation metrics applied, and the persistent challenges in this field. Through a rigorous analysis of 26 selected papers from an initial set of 155 articles retrieved from the Web of Science database, we found that Long Short-Term Memory (LSTM) networks were the most frequently used algorithm (appearing in 32.69% of the papers), closely followed by Convolutional Neural Networks (CNNs) at 28.85%. Furthermore, Wavelet Transform (WT) was found to be the most prominent data decomposition technique, while Pearson Correlation was the most used for feature selection. We also found that ambient temperature, pressure, and humidity are the most common input features. Our systematic evaluation provides critical insights into state-of-the-art DL-based solar forecasting and identifies key areas for upcoming research. Future research should prioritize the development of more robust and interpretable models, as well as explore the integration of multi-source data to further enhance forecasting accuracy. Such advancements are crucial for the effective integration of solar energy into future power grids.
Oussama Khouili mail , Mohamed Hanine mail , Mohamed Louzazni mail , Miguel Ángel López Flores mail miguelangel.lopez@uneatlantico.es, Eduardo García Villena mail eduardo.garcia@uneatlantico.es, Imran Ashraf mail ,
Khouili
<a href="/17796/1/1-s2.0-S2773032825000070-main.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
Despite the global increase of women in business, there is still a very small proportion of female business leaders, although the distribution varies greatly by region and sector. Considering innovation, in its many forms, as well as female entrepreneurship, both considered as a path towards sustainability, the question arises as to whether this drive for sustainability leads to a greater presence of female CEOs. Current studies predominantly examine the impact of women's presence on a company's economic and financial performance, as well as any potential effects on its innovation strategy. However, the examination of factors that help understand the economic and business context influencing the presence of women in leadership roles is often overlooked. This empirical study fills this gap by exploring the micro and macro context influencing the presence of female CEOs in innovative firms worldwide stressing the influence of female owners. The sample comprises 107,026 companies from manufacturing and service industries in 118 countries, from 2007 to 2023, data obtained from the World Bank Enterprise Surveys. The econometric model applied is logistic regression with clustered standard errors. The study contains six estimations generating strong evidence supporting most of the formulated hypotheses. Findings suggest women CEOs are likely to lead women-owned firms which promote (sustainable) innovation through developing new products for new markets, allocating less investment in R&D, product innovation and business processes, although with some nuances. Other important factors to consider are productivity, sales strategy, firm size, sector, and socio-economic context with a gender focus.
Inna Alexeeva-Alexeev mail , Pilar Guaita-Fernandez mail , Cristina Mazas Pérez-Oleaga mail cristina.mazas@uneatlantico.es,
Alexeeva-Alexeev