Estudo de currículo e formação de agentes de segurança pública, na perspectiva dos desafios contra as novas formas de criminalidade em Moçambique (caso de Formação Superior da Polícia da República de Moçambique, 2010 a 2021)

Tesis Materias > Educación Universidad Internacional Iberoamericana México > Investigación > Tesis Doctorales Cerrado Portugués RESUMO Currículo e Formação de Agentes de Segurança Pública, na Perspectiva dos Desafios contra as novas Formas de Criminalidade em Moçambique foi o tema concebido no período entre 2010 e 2019, que informes anuais da Procuradoria- Geral da República apresentados à Assembleia da República relatavam o incremento de crimes como rapto, sequestro de empresários e seus familiares, onde para o resgate os criminosos exigiam altas somas monetárias, em dólar norte-americano. Também, ocorriam crimes violentos contra cidadãos com insuficiências da pigmentação da pele, vulgo albinos, extracção e venda de tecidos e órgãos humanos, cujo móbil teve interpretações supersticiosas. Paralelamente aos factos se notou a espoliação de recursos florestais, faunísticos e minerais, na sua maioria protegidos e vulneráveis à extinção na natureza. A globalização e a emergência das tecnologias de informação e comunicação constituem determinados factores principais na eclosão dessas infracções criminosas não acostumadas pelo povo moçambicano. Analisar as distintas formas de currículo e formação para identificação, definição de estratégias e modelos curriculares, que intensificam melhorias na constituição de competências profissionais para agentes de segurança pública, na perspectiva dos desafios contra as novas formas de criminalidade na sociedade humana, foi o objectivo geral do estudo. A metodología teve a abordagem qualitativa e quantitativa, onde a revisão da literatura, análise documental, entrevista padronizada e aprofundada, a observação participante, questionário, escala social de Likert e roteiros, foram técnicas e instrumentos de recolha de dados. A população e amostra do estudo foram constituidos por investigadores científicos, docentes, estudantes dos cursos de licenciatura e mestrado em ciências policiais, funcionarios entre membros da PRM e Corpo tecnico administrativo da ACIPOL. A adopção de estratégias curriculares modernas para formação de recursos humanos de qualidade dentro dos padrões internacionais foi um dos maiores desafios desta instituição de ensino e aprendizagem. metadata Caetano, José mail jose.j.caetano@gmail.com (2024) Estudo de currículo e formação de agentes de segurança pública, na perspectiva dos desafios contra as novas formas de criminalidade em Moçambique (caso de Formação Superior da Polícia da República de Moçambique, 2010 a 2021). Doctoral thesis, Universidad Internacional Iberoamericana México.

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RESUMO Currículo e Formação de Agentes de Segurança Pública, na Perspectiva dos Desafios contra as novas Formas de Criminalidade em Moçambique foi o tema concebido no período entre 2010 e 2019, que informes anuais da Procuradoria- Geral da República apresentados à Assembleia da República relatavam o incremento de crimes como rapto, sequestro de empresários e seus familiares, onde para o resgate os criminosos exigiam altas somas monetárias, em dólar norte-americano. Também, ocorriam crimes violentos contra cidadãos com insuficiências da pigmentação da pele, vulgo albinos, extracção e venda de tecidos e órgãos humanos, cujo móbil teve interpretações supersticiosas. Paralelamente aos factos se notou a espoliação de recursos florestais, faunísticos e minerais, na sua maioria protegidos e vulneráveis à extinção na natureza. A globalização e a emergência das tecnologias de informação e comunicação constituem determinados factores principais na eclosão dessas infracções criminosas não acostumadas pelo povo moçambicano. Analisar as distintas formas de currículo e formação para identificação, definição de estratégias e modelos curriculares, que intensificam melhorias na constituição de competências profissionais para agentes de segurança pública, na perspectiva dos desafios contra as novas formas de criminalidade na sociedade humana, foi o objectivo geral do estudo. A metodología teve a abordagem qualitativa e quantitativa, onde a revisão da literatura, análise documental, entrevista padronizada e aprofundada, a observação participante, questionário, escala social de Likert e roteiros, foram técnicas e instrumentos de recolha de dados. A população e amostra do estudo foram constituidos por investigadores científicos, docentes, estudantes dos cursos de licenciatura e mestrado em ciências policiais, funcionarios entre membros da PRM e Corpo tecnico administrativo da ACIPOL. A adopção de estratégias curriculares modernas para formação de recursos humanos de qualidade dentro dos padrões internacionais foi um dos maiores desafios desta instituição de ensino e aprendizagem.

Tipo de Documento: Tesis (Doctoral)
Palabras Clave: Currículo, formação, segurança pública, estratégias e competências.
Clasificación temática: Materias > Educación
Divisiones: Universidad Internacional Iberoamericana México > Investigación > Tesis Doctorales
Depositado: 26 Sep 2023 23:30
Ultima Modificación: 25 Oct 2024 23:30
URI: https://repositorio.unini.edu.mx/id/eprint/3313

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

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.

Producción Científica

Naveed Anwer Butt mail , Dilawaiz Sarwat mail , Irene Delgado Noya mail irene.delgado@uneatlantico.es, Kilian Tutusaus mail kilian.tutusaus@uneatlantico.es, Nagwan Abdel Samee mail , Imran Ashraf mail ,

Butt

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A scalable and secure federated learning authentication scheme for IoT

Secure and scalable authentication remains a fundamental challenge in Internet of Things (IoT) networks due to constrained device resources, dynamic topology, and the absence of centralized trust infrastructures. Conventional password-based and certificate-driven authentication schemes incur high computation, storage, and communication overhead, limiting their suitability for large-scale deployments. To address these limitations, this paper proposes ScLBS, a federated learning (FL)–based self-certified authentication scheme for distributed and sustainable IoT environments. ScLBS integrates self-certified public key cryptography with FL-driven trust adaptation, enabling decentralized public key derivation without reliance on third-party certificate authorities or exposure of private credentials. A zero-knowledge mechanism combined with location-aware authentication strengthens resistance to impersonation, Sybil, and replay attacks. Hierarchical key management supported by a -tree enables efficient group rekeying and preserves forward and backward secrecy under dynamic membership. Formal security verification is conducted under the Dolev–Yao adversary model using ProVerif, confirming secrecy of private and session keys (SKs) and correctness of authentication. Extensive NS-3 simulations and ablation analysis demonstrate that ScLBS achieves lower authentication delay, reduced message overhead, improved network utilization, and decreased energy consumption compared to representative IoT authentication schemes, while maintaining bounded FL overhead. These results indicate that ScLBS provides a balanced trade-off between security strength, scalability, and resource efficiency for constrained IoT networks.

Producción Científica

Premkumar Chithaluru mail , B. Veera Jyothi mail , Fahd S. Alharithi mail , Wojciech Ksiazek mail , M. Ramchander mail , Aman Singh mail aman.singh@uneatlantico.es, Ravi Kumar Rachavaram mail ,

Chithaluru

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

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.

Producción Científica

Iñaki Elío Pascual mail inaki.elio@uneatlantico.es, Kilian Tutusaus mail kilian.tutusaus@uneatlantico.es, Imanol Eguren García mail imanol.eguren@uneatlantico.es, Álvaro Lasarte García mail , Arturo Ortega-Mansilla mail arturo.ortega@uneatlantico.es, Thomas Prola mail thomas.prola@uneatlantico.es, Sandra Sumalla Cano mail sandra.sumalla@uneatlantico.es,

Elío Pascual

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

Identifying the emotional state of individuals has useful applications, particularly to reduce the risk of suicide. Users’ thoughts on social media platforms can be used to find cues on the emotional state of individuals. Clinical approaches to suicide ideation detection primarily rely on evaluation by psychologists, medical experts, etc., which is time-consuming and requires medical expertise. Machine learning approaches have shown potential in automating suicide detection. In this regard, this study presents a soft voting ensemble model (SVEM) by leveraging random forest, logistic regression, and stochastic gradient descent classifiers using soft voting. In addition, for the robust training of SVEM, a hybrid feature engineering approach is proposed that combines term frequency-inverse document frequency and the bag of words. For experimental evaluation, “Suicide Watch” and “Depression” subreddits on the Reddit platform are used. Results indicate that the proposed SVEM model achieves an accuracy of 94%, better than existing approaches. The model also shows robust performance concerning precision, recall, and F1, each with a 0.93 score. ERT and deep learning models are also used, and performance comparison with these models indicates better performance of the SVEM model. Gated recurrent unit, long short-term memory, and recurrent neural network have an accuracy of 92% while the convolutional neural network obtains an accuracy of 91%. SVEM’s computational complexity is also low compared to deep learning models. Further, this study highlights the importance of explainability in healthcare applications such as suicidal ideation detection, where the use of LIME provides valuable insights into the contribution of different features. In addition, k-fold cross-validation further validates the performance of the proposed approach.

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Erol KINA mail , Jin-Ghoo Choi mail , Abid Ishaq mail , Rahman Shafique mail , Mónica Gracia Villar mail monica.gracia@uneatlantico.es, Eduardo René Silva Alvarado mail eduardo.silva@funiber.org, Isabel de la Torre Diez mail , Imran Ashraf mail ,

KINA

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

Human metapneumovirus (hMPV) is one of the potential pandemic pathogens, and it is a concern for elderly subjects and immunocompromised patients. There is no vaccine or specific antiviral available for hMPV. We conducted an in-silico study to predict initial antiviral candidates against human metapneumovirus. Our methodology included protein modeling, stability assessment, molecular docking, molecular simulation, analysis of non-covalent interactions, bioavailability, carcinogenicity, and pharmacokinetic profiling. We pinpointed four plant-derived bio-compounds as antiviral candidates. Among the compounds, apigenin showed the highest binding affinity, with values of − 8.0 kcal/mol for the hMPV-F protein and − 7.6 kcal/mol for the hMPV-N protein. Molecular dynamic simulations and further analyses confirmed that the protein-ligand docked complexes exhibited acceptable stability compared to two standard antiviral drugs. Additionally, these four compounds yielded satisfactory outcomes in bioavailability, drug-likeness, and ADME-Tox (absorption, distribution, metabolism, excretion, and toxicity) and STopTox analyses. This study highlights the potential of apigenin and xanthoangelol E as an initial antiviral candidate, underscoring the necessity for wet-lab evaluation, preclinical and clinical trials against human metapneumovirus infection.

Producción Científica

Hasan Huzayfa Rahaman mail , Afsana Khan mail , Nadim Sharif mail , Wasifuddin Ahmed mail , Nazmul Sharif mail , Rista Majumder mail , Silvia Aparicio Obregón mail silvia.aparicio@uneatlantico.es, Rubén Calderón Iglesias mail ruben.calderon@uneatlantico.es, Isabel De la Torre Díez mail , Shuvra Kanti Dey mail ,

Rahaman