Produção Científica e Didática
Produção científica recente
Framework to Detect Adulterated Honey Using Hyperspectral Imaging and Machine Learning
Honey is a natural product renowned for its nutritional and medicinal properties. However, the increasing market demand in Pakistan has led to frequent instances of adulterated and fraudulent honey. Conventional detection methods are widely used but are often ineffective in accurately identifying adulteration. This study proposes a novel approach to detecting adulteration in honey, specifically the mixing of sugar, using machine learning (ML) techniques in conjunction with hyperspectral imaging (HSI). By leveraging HSI, we capture distinct spectral features of both pure and adulterated honey. These spectral features are processed and analyzed using ML algorithms trained on a dataset comprising pure and mixed honey samples. Experiments involve binary, as well as, multiclass (5 classes) honey samples with different levels of adulteration. In addition, data balancing is also carried out using synthetic minority oversampling technique (SMOTE). The results prove the proposed approach to be efficient in detecting adulteration across various honey varieties, with linear regression achieved 99.9% while support vector machine and multilayer perceptron obtaining a 98.7% accuracy for binary class. For the multiple classes of honey, the multilayer perceptron shows a 96.67% accuracy outperforming the remaining ML and deep learning models. Among the deep learning models, the convolutional neural network (CNN) achieved the best performance with an accuracy of 94.67% after applying the SMOTE × 3 (three times of over samples of original samples of each class) data balancing strategy, whereas recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU) models showed comparatively lower performance.
Parental Decision-Related Factors Are Associated with Discretionary Ultra-Processed Food Consumption Among Children and Adolescents Living in the Mediterranean Area
Background/Objectives: Nutrition during childhood and adolescence is a key determinant of long-term health, influencing metabolic homeostasis, neurocognitive development, and immune system maturation. Globalization and technological advances have reshaped food production and consumption, increasing the availability of ultra-processed foods (UPF) of low nutritional quality. This study aimed to investigate the relationship between parental factors, namely food literacy, perceived barriers and enablers, dietary attitudes, and healthy eating behaviors, and the consumption of discretionary UPF among children and adolescents living in 5 Mediterranean countries. Methods: This cross-sectional study was based on a survey completed by 2011 parents of children and adolescents aged 6–17 years from 5 Mediterranean countries, who reported on their children’s dietary and lifestyle habits. Adherence to the Mediterranean diet was assessed through the KIDMED index. Parental food literacy was measured using the Short Food Literacy Questionnaire (SFLQ). Perceived barriers and enablers were assessed based on the Theory of Planned Behavior, and parents’ attitudes toward their child’s diet were evaluated using the Healthy-Eating Attitudes Questionnaire (HEAQ). Finally, the Theory of Internet Use Related to Health (TIUH) questionnaire was used to assess parents’ tendencies related to health information use online. Results: Higher perceived barriers and enablers were significantly associated with lower discretionary UPF consumption across all models. Parental food literacy (SFLQ) showed a positive association with discretionary UPF consumption, remaining significant in the fully adjusted model, although with reduced magnitude. Healthy-eating attitudes (HEAQ) were initially positively associated with discretionary UPF intake but lost statistical significance after full adjustment. Regarding health-related internet use (TIUH), the Health Information dimension showed a strong positive association with discretionary UPF consumption, while other dimensions (Consciousness and Beliefs) showed inconsistent and non-significant associations in the fully adjusted model. Conclusions: Children’s consumption of discretionary UPF is shaped by several interrelated factors, such as family environment, eating patterns, and parents’ perceptions, rather than solely by knowledge or attitudes.
Artificial intelligence-based clinical decision support systems in neurophysiology: conceptual framework, design criteria, and ethical considerations
Diagnosis in neurophysiology using visual evoked potentials (VEP) for multiple sclerosis and optic neuropathies remains specialist-dependent and subject to variability. Artificial intelligence (AI) offers transformative potential, but its responsible implementation is hindered by algorithmic metrics. This paper develops a conceptual framework for AI-based clinical decision support systems (CDSS) in neurophysiology, structured around three integrated dimensions: CDSS typology and foundations, responsible design criteria, and ethical-regulatory considerations. Six architectural principles are proposed: transparency, clinical anchoring in validated criteria (ISCEV 2023), preservation of physician judgment, false-alarm minimization, reproducibility, and interoperability, applicable to any system in the field. The framework is illustrated through an EEG‑VEP system on the VEPCON dataset, integrating automated classification, clinical hierarchy over statistical thresholds, and a dashboard with an explicit ethical disclaimer. The responsible development of CDSS in neurophysiology demands a systemic approach that integrates clinical evidence, operational design, and medical device regulation, ensuring their role as support rather than as a substitute for clinical judgment.
Algorithmic human resource management and employee well-being: currentevidence from a systematic literature review
This study examines the relationship between artificial intelligence (AI), algorithmic human resource management, and employee well-being through a systematic review of the scientific literature published between 2020 and 2026. The review addresses the growing need to understand how algorithm-supported decision-making influences employee experience, psychological well-being, organizational trust, and perceptions of fairness in contemporary workplaces. The study followed the PRISMA 2020 guidelines and included peer-reviewed articles retrieved from Scopus, Web of Science, ScienceDirect, and SpringerLink. Following the identification, screening, and eligibility stages, 82 studies were selected for qualitative thematic analysis and descriptive synthesis. The findings indicate that artificial intelligence enhances recruitment and selection, performance evaluation, and human resource analytics while simultaneously introducing challenges related to digital surveillance, algorithmic opacity, technological anxiety, and reduced employee autonomy. Overall, the evidence suggests that algorithmic human resource management can promote employee well-being when implemented within transparent governance frameworks characterized by human oversight, algorithmic fairness, ethical data governance, and active employee participation. The review contributes by synthesizing current evidence, identifying major research trends and knowledge gaps, and highlighting future research priorities, particularly for Latin American organizational contexts, where empirical evidence remains limited.
Rethinking legal protection against gender-based and domestic violence in Angola: human rights and governance
Gender-based and domestic violence remain among the most significant challenges to the protection of women's human rights in Angola. This study aimed to analyse the conceptual evolution of gender-based violence, examine the applicable national and international legal framework, and assess the institutional challenges affecting the effectiveness of the protection system from a socio-legal perspective. A qualitative research design was adopted through a legal-documentary review of scientific literature, national legislation, international human rights instruments, and institutional documents published mainly between 2020 and 2026. The findings reveal important legal advances through the Constitution of Angola, Law No. 25/11 on Domestic Violence, and international human rights instruments that strengthen the legal protection of women. However, significant challenges remain regarding effective access to justice, institutional coordination, prevention strategies, and the implementation of public policies. As its main contribution, the study proposes a socio-legal model structured around five strategic dimensions—prevention, comprehensive protection, access to justice, institutional coordination, and comprehensive reparation—to strengthen the State's response and improve the effective protection of women's rights in Angola.