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Abstract As cardiovascular disorders are prevalent, there is a growing demand for reliable and precise diagnostic methods within this domain. Audio signal-based heart disease detection is a promising area of research that leverages sound signals generated by the heart to identify and diagnose cardiovascular disorders. Machine learning (ML) and deep learning (DL) techniques are pivotal in classifying and identifying heart disease from audio signals. This study investigates ML and DL techniques to detect heart disease by analyzing noisy sound signals. This study employed two subsets of datasets from the PASCAL CHALLENGE having real heart audios. The research process and visually depict signals using spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs). We employ data augmentation to improve the model’s performance by introducing synthetic noise to the heart sound signals. In addition, a feature ensembler is developed to integrate various audio feature extraction techniques. Several machine learning and deep learning classifiers are utilized for heart disease detection. Among the numerous models studied and previous study findings, the multilayer perceptron model performed best, with an accuracy rate of 95.65%. This study demonstrates the potential of this methodology in accurately detecting heart disease from sound signals. These findings present promising opportunities for enhancing medical diagnosis and patient care.

Abstract In recent years, theoretical and phenomenological studies with effective field theories have become a trending and prolific line of research in the field of high-energy physics. In order to discuss present and future prospects concerning automated tools in this field, the SMEFT-Tools 2022 workshop was held at the University of Zurich from 14th–16th September 2022. The current document collects and summarizes the content of this workshop.

Adversarial attacks involve manipulating data to trick Artificial Intelligence (AI) models, making false predictions or classifications or even disrupting the normal functions of the smart grid. This can be done by providing the wrong information to the models, hence producing wrong predictions and therefore leading to instabilities, power imbalances and overall operational failure. Thus, employing an AI model is vital for controlling energy usage, identifying and even predicting equipment failures, and accurately determining power availability. Thus, their input dependency does not enable them to withstand cyber-attacks, which compromises the stability of the grid. These attacks affect energy control, cause losses and intrude into the critical infrastructure part of the smart grid, hence the call for enhanced smart grid protection. This study first generates a novel adversarial attack dataset on smart grids with three attacks: adversarial perturbation, backdoor injection, DOS attacks, and one benign class. Next, it provides a fine-tuned Deep Neural Network (DNN) model to significantly improve resistance against adversarial attacks on smart grids. Results from the various Machine Learning (ML) and DNN algorithms showed accuracy varying between 29.10% to 73.9%, with DNN recording the highest levels of accuracy. This shows how the approach can be utilised in the prediction of such attacks and how the grid can be protected and secured.

Darknet refers to a significant portion of the internet that is hidden and not indexed by traditional search engines. It is often associated with illicit activities such as the trafficking of illicit goods, such as drugs, weapons, and stolen data. To keep our online cyber spaces safe in this era of rapid technological advancement and global connectivity, we should analyse and recognise darknet traffic. Beyond cybersecurity, this attention to detail includes safeguarding intellectual property, stopping illegal activity, and following the law. In order to improve accuracy and precision in identifying illicit activities, this study presents a novel approach named Active-Darknet that uses an active learning-based machine learning model for detecting darknet traffic. In order to guarantee high-quality analysis, our methodology includes extensive data preprocessing, such as numerically encoding categorical labels and improving the representation of minority classes using data balancing. In addition to machine learning models, we also use Deep Neural Networks (DNN), Bidirectional Long Short-Term Memory (BI-LSTM) and Flattened-DNN for experimentation. The majority of models exhibited encouraging outcomes; however, the models that utilised active learning, specifically the Random Forest (RF) and Decision Tree (DT) models, attained promising accuracy levels of 87%, rendering them the most efficient in detecting darknet traffic. Large traffic analysis is greatly enhanced by this method, which also increases the detection process’s robustness and effectiveness.

The widespread use of smartphones has brought convenience and connectivity to the fingertips of the masses. As a result, this has paved the way for potential security vulnerabilities concerning sensitive data, particularly by exploiting side-channel attacks. When typing on a smartphone’s keyboard, its vibrations can be misused to discern the entered characters, thus facilitating side-channel attacks. These smartphone hardware sensors can capture such information while users input sensitive data like personal details, names, email addresses, age, bank details and passwords. This study presents a novel Graph Neural Network (GNN) approach to predict side-channel attacks on smartphone keyboards; different GNN architectures were used, including GNN, DeepGraphNet, Gradient Boosting (GB)+DeepGraphNet, Extreme Gradient Boosting (XGB)+DeepGraphNet and K-Nearest Neighbor (KNN)+DeepGraphNet. The proposed approach detects the side channel attack using vibrations produced while typing on the smartphone soft keyboard. The data was collected from three smartphone sensors, an accelerometer, gyroscope, and magnetometer, and evaluated this data using common evaluation measures such as accuracy, precision, recall, F1-score, ROC curves, confusion matrix and accuracy and loss curves. This study demonstrated that GNN architectures can effectively capture complex relationships in data, making them well-suited for analyzing patterns in smartphone sensor data. Likewise, this research aims to fill a crucial gap by enhancing data privacy in the information entered through smartphone keyboards, shielding it from side-channel attacks by providing an accuracy of 98.26%. Subsequently, the primary objective of this study is to assess the effectiveness of GNN architectures in this precise context. Similarly, the GNN model exhibits compelling performance, achieving accuracy, precision, recall, and f1 score metrics that showcase the model’s effectiveness, with the highest values of 0.98, 0.98, 0.98, and 0.98, respectively. Significantly, the metrics mentioned in the study outperform those documented in the previous literature. Overall, the study contributes to the detection of side-channel smartphone attacks, which advances secure data practices.INDEX TERMS Graph neural networks (GNN), keystroke inference, motion sensors, machine learning, smartphone security, side-channel attacks.

In a smart home environment, multiple users can access a single smart device simultaneously. Moreover, these multiple users may have conflicting demands at a time; that is, one user’s demands differ from another for the same device based on the role of users and environmental factors. Therefore, existing single-user access control systems cannot handle such conflicting and dynamically changing demands, considering both roles and environmental factors in the multi-user smart home environment. Considering this issue, we proposed a Smart Access Control and Authorization framework (SAAC). It is a multi-user access control solution that has four modules, namely, a user interaction module, a backend server module, a policy manager module, and a policy execution module. The user interaction module collects user data and resource policies, which are processed by the backend server and forwarded to the policy manager. The policy manager resolves conflicts and generates final policies, which are stored in the backend server for enforcement by the policy execution module. The finalized policies are shared with the backend server module and saved there till needed for execution by the policy execution module to enforce the access control decision. We have implemented a proof of concept of the proposed framework on VS Code using the Casbin library. The performance evaluation results show our framework’s effectiveness and efficiency with lower computational complexity requirements than existing methods. Finally, we performed a security analysis of the proposed model based on the STRIDE model that confirms its robustness against access control attacks.

Suspicious Human activity recognition (SHAR) is crucial for improving surveillance and security systems by recognizing and reducing possible hazards in different situations. This study focuses on the task of precisely identifying potentially suspicious human behaviour by utilizing an innovative approach that harnesses advanced deep learning methods. Despite the abundance of research on the subject of SHAR, current methods frequently need to be revised with restricted levels of precision and efficiency. This research aims to address these constraints by presenting a thorough methodology for detecting and recognizing suspicious human activities. By rigorously collecting and preparing data, as well as training models, we aim to solve the issue of inaccurate and inefficient activity recognition in surveillance systems. By utilizing Convolutional Neural Networks (CNNs) and deep learning structures, such as the proposed time-distributed CNN model and Conv3D model, we attain notably enhanced accuracy rates of 90.14% and 88.23%, respectively, surpassing current research approaches. Moreover, the efficacy of our approach is illustrated by conducting prediction experiments on previously unreported test data and YouTube videos. Through the process of evaluating the trained models on unseen test data, we ascertain their accuracy and ability to apply learned knowledge to new situations. Moreover, the algorithms are utilized to predict dubious human conduct in a YouTube video, demonstrating their practical usefulness in real-life surveillance situations. The results of this study have important consequences for improving surveillance and security systems, allowing for better identification and reduction of possible dangers in various settings. Our methodology enhances the precision and effectiveness of SHAR, advancing the construction of more resilient and dependable surveillance systems and ultimately strengthening public safety and security.

Artificial Intelligence (AI) has made tremendous progress in anomaly detection. However, AI models work as a black-box, making it challenging to provide reasoning behind their judgments in a Log Anomaly Detection (LAD). To the rescue, Explainable Artificial Intelligence (XAI) improves system log analysis. It follows a white-box model for transparency, understandability, trustworthiness, and dependability of Machine Learning (ML) and Deep Learning (DL) Models. In addition, Shapely Additive Explanation (SHAP), added to system dynamics, makes informed judgments and adoptable proactive methods to optimize system functionality and reliability. Therefore, this paper proposed the Shapely eXplainable Anomaly Detection (SXAD) framework to identify different events (features) that impact the models’ interpretability, trustworthiness, and explainability. The framework utilizes the Kernel SHAP approach, which is based on Shapley values principle, providing an innovative approach to event selection and identifying specific events causing abnormal behavior. This study addresses the LAD by transforming it from a black-box model into a white-box one, leveraging XAI to make it transparent, interpretable, explainable, and dependable. It utilizes benchmark data from the Hadoop Distributed File System (HDFS), organized using a Drain parser, and employs several ML models, such as Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB). These models achieve impressive accuracy rates of 99.99%, 99.85%, and 99.99%, respectively. Our contribution are novel because no earlier work has been done in the area of Log Anomaly Detection (LAD) with integration of XAI-SHAP.

The increasing occurrence of Hypertension highlights the need for advanced predictive tools in healthcare. This research proposes a novel approach that combines machine and deep learning for new feature generation and hypertension prediction. We explore machine learning-based models: Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), XGBoost (XGB), and Gradient Boosting (GB) for new Feature Prediction (FP) and integrated these predictions with the original dataset for training deep Long short-term memory (LSTM) model. To evaluate the efficiency of the proposed approach, we compare all models, predicting new features, with those in the existing study. The results demonstrate that the GB-based FP + LSTM are standout performers. The GB-based FP + LSTM combination demonstrates the highest accuracy at 98.48%. On the contrary, the LR-based FP + LSTM combination exhibits a lower accuracy of 89.39%. The remaining combinations, including RF-based FP + LSTM, XGB-based FP + LSTM, and DT-based FP + LSTM, showcase accuracies ranging from 95.45% to 97.97%. In practical terms, the high F1-score of 98.48% is achieved by the combination of GB-based FP + LSTM, which implies a reliable tool for clinicians to aid in early hypertension detection. These findings hold deep practical implications, offering healthcare practitioners and policymakers a pathway to deploy accurate and timely hypertension identification tools.

Abstract Background Although dementia has emerged as an important risk factor for severe SARS-CoV-2 infection, results on COVID-19-related complications and mortality are not consistent. We examined the clinical presentations and outcomes of COVID-19 in a multicentre cohort of in-hospital patients, comparing those with and without dementia. Methods This retrospective observational study comprises COVID-19 laboratory-confirmed patients aged ≥ 60 years admitted to 38 hospitals from 19 cities in Brazil. Data were obtained from electronic hospital records. A propensity score analysis was used to match patients with and without dementia (up to 3:1) according to age, sex, comorbidities, year, and hospital of admission. Our primary outcome was in-hospital mortality. We also assessed admission to the intensive care unit (ICU), invasive mechanical ventilation (IMV), kidney replacement therapy (KRT), sepsis, nosocomial infection, and thromboembolic events. Results Among 1,556 patients included in the study, 405 (4.5%) had a diagnosis of dementia and 1,151 were matched controls. When compared to matched controls, patients with dementia had a lower frequency of dyspnoea, cough, myalgia, headache, ageusia, and anosmia; and higher frequency of fever and delirium. They also had a lower frequency of ICU admission (32.7% vs. 47.1%, p

This paper introduces PureFed, an innovative Federated Learning (FL) framework designed for efficiency, collaboration, and trustworthiness. In the background of FL research, it was observed that previous frameworks often neglected participant privacy, a critical aspect not aligned with the core FL concept. Additionally, there was room for improving the efficiency of existing frameworks. PureFed addresses these shortcomings by offering participants the flexibility to initiate FL tasks or join existing ones without sharing any private data and removing unnecessary actions that led to an inefficient system. Leveraging blockchain technology, it employs smart contracts to ensure traceability and immutability, enhancing the security of the framework. Additionally, PureFed employs symmetric key encryption and dual digital signature mechanisms using ECDSA to guarantee the confidentiality and integrity of shared models. To expedite model convergence, PureFed incorporates a dynamic aggregation scheme, selecting the most suitable model from three distinct techniques: FedAvg, accuracy-based, and loss-based. Furthermore, the framework introduces a dynamic incentive and punishment mechanism to incentivize collaboration and maintain trust. Extensive performance evaluations reveal PureFed’s significant advantages. It outperforms its counterparts by 63.39% and 67.72% in terms of smart contract deployment and interaction gas costs, respectively. Lastly, scalability analyses indicate PureFed’s ability to adapt efficiently, achieving target accuracy in fewer rounds.

This research delves into applying active and machine learning techniques to predict student anxiety. This research explores how these technologies can be explored to understand and predict student anxiety levels. This study utilizes active learning strategies to increase the effectiveness of machine learning models in predicting anxiety levels among students. Additionally, this adaptation emphasizes the usefulness of active learning methodologies in enhancing the precision of machine learning models for student anxiety prediction. This study uses two datasets containing information on student behavior and leverages machine learning methods to construct predictive models for student anxiety. This study uses various machine learning models: K-Nearest Neighbors (KNN), Logistic Regression (LR), XGBoost (XGB), Naive Bayes (NB), and Random Forest (RF). Experiments revealed that active learning-based LR yielded a score of 0.61, and RF performed well with an average accuracy of 0.60 on the first dataset. Similarly, for the second dataset, RF is the most effective model, achieving an accuracy of 0.83. These results provide valuable insights into the models’ performance across key metrics. Further, this research highlights the potential of employing machine learning techniques and active learning methodologies to predict and manage student anxiety.

This research explores the potential of technologies in human activity recognition among the elderly population. More precisely, using sensor data and implementing Active Learning (AL), Machine Learning (ML), and Deep learning (DL) techniques for elderly activity recognition. Moreover, the study leverages the HAR70+ dataset, providing insight into the daily activities of older individuals and AL-based ML and DL techniques to construct predictive models for these activities. The findings have implications for proactive and personalized elderly care, representing an approach to improving prediction performance in this domain. The research experiments are presented systematically, summarizing the outcomes of various machine-learning models across three iterative experiments. This research explored a diverse array of ML algorithms, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGB) and DL methods such as Deep Neural Networks (DNN) and Long Short-Term Memory networks (LSTM) for experimentation. This research trained models on 7 activities: walking, shuffling, climbing stairs (up and down), standing, sitting, and lying down, and 4 activities separately: standing, sitting, walking, and lying down, using the same classifiers. Results reveal that LSTM achieved the best accuracy of 0.95% for 7 activities and 0.96% using RF on 4 actives, showing the potential of DL and ML techniques, particularly when integrated with AL, to enhance activity recognition rate, patient care, optimize medication strategies and improve the well-being of elderly individuals. Hence, the findings presented in this study have showcased the potential to enhance the quality of life for seniors using the blend of ML, DL and AL.

Abstract Temperature is a fundamental driver of species distribution and ecosystem functioning. Yet, our knowledge of the microclimatic conditions experienced by organisms inside tropical forests remains limited. This is because ecological studies often rely on coarse-gridded temperature estimates representing the conditions at 2 m height in an open-air environment (i.e., macroclimate). In this study, we present a high-resolution pantropical estimate of near-ground (15 cm above the surface) temperatures inside forests. We quantify diurnal and seasonal variability, thus revealing both spatial and temporal microclimate patterns. We find that on average, understory near-ground temperatures are 1.6 °C cooler than the open-air temperatures. The diurnal temperature range is on average 1.7 °C lower inside the forests, in comparison to open-air conditions. More importantly, we demonstrate a substantial spatial variability in the microclimate characteristics of tropical forests. This variability is regulated by a combination of large-scale climate conditions, vegetation structure and topography, and hence could not be captured by existing macroclimate grids. Our results thus contribute to quantifying the actual thermal ranges experienced by organisms inside tropical forests and provide new insights into how these limits may be affected by climate change and ecosystem disturbances.

Smartwatches with cutting-edge sensors are becoming commonplace in our daily lives. Despite their widespread use, it can be challenging to interpret accelerometer and gyroscope data efficiently for Human Activity Recognition (HAR). This study explores active learning integrated with machine learning, intending to maximize the use of smartwatch technology across a range of applications. The previous research on the HAR lacks promising performance, which could make it difficult to make highly accurate recognition. This paper proposes a novel approach to predict human activity from the Heterogeneity Human Activity Recognition (HHAR) dataset that integrates active learning with machine learning models: Random Forest (RF), Extreme Gradient Boosting (XGBoost), K-nearest Neighbors (KNN), Decision Tree (DT), Gradient Boosting (GB) and Light Gradient Boosting Machine (LGBM) classifier to predict heterogeneous activities accurately. We evaluated our approach to these models on the HHAR dataset that was generated using an accelerometer and gyroscope of smartwatches. The experiments are evaluated on 3 iterations where the results demonstrated that the proposed approach predicts human activities with the highest F1-Score of 99.99%. The results indicate that this approach is the most accurate and effective compared to the conventional approaches and baseline.

Abstract The particle spectrum of all Majorana neutrino mass models with spontaneous violation of global lepton number include a Goldstone boson, the so-called majoron. The presence of this massless pseudoscalar changes the phenomenology dramatically. In this work we derive general analytical expressions for the 1-loop coupling of the majoron to charged leptons. These can be applied to any model featuring a majoron that have a clear hierarchy of energy scales, required for an expansion in powers of the low-energy scale to be valid. We show how to use our general results by applying them to some example models, finding full agreement with previous results in several popular scenarios and deriving novel ones in other setups.

Resumo A produção do Pinus no Espírito Santo tornou-se viável devido ao desenvolvimento da tecnologia de utilização de sua madeira nas indústrias de laminados e serrarias. Uma vez que as mudanças climáticas globais podem alterar o cenário silvicultural brasileiro, o objetivo do trabalho foi avaliar seus possíveis impactos sobre o zoneamento climático das espécies de Pinus no Estado. Utilizaram-se dados climáticos atuais de 110 estações e dados climáticos futuros obtidos com base no modelo HadCM3 para a realização do balanço hídrico atual e futuro, necessário ao zoneamento. Verificou-se a existência de 100% de áreas inaptas na década de 2050 para duas espécies e para todas as espécies na década de 2080.

Resumo Este estudo foi realizado para avaliar a contribuição de Neoregelia cruenta na fertilidade do solo em uma duna na Restinga Marambaia (RJ). Foram coletadas amostras de terra nas profundidades de 0-5 e 5-15 cm debaixo de cobertura de bromélias e em área adjacente (5m de distância). Foram observados valores mais elevados de P e Ca sob a vegetação de bromélia, porém pequena influência desta no conteúdo de carbono orgânico total. Os resultados demonstram a importância da cobertura herbácea na fertilidade do solo em ambientes costeiros.

Energy providers and the power grid are severely harmed by electricity theft, which also causes economic and non-technical losses. Energy theft causes a decline in power quality and overall profitability. Smart grids may address the problem of power theft by merging data and energy flow. The analysis of smart grid data helps to find power theft. The prior methods, however, could have done a better job of identifying energy theft. In this research, we presented an active learning-based machine learning model for energy theft identification and classification of a smart grid. The suggested approach is based on the following steps. We use a dataset from the Open Energy Data Initiative (OEDI), an energy research database that gets information from numerous OEDI offices and labs. Next, we pre-process the data and employ machine learning methods like Active Learning (AL) based Random Forests (RFAL), eXtreme Gradient Boosting (XGboostAL), Decision Tree (DTAL), Gradient Boosting (GBAL), K-Nearest Neighbors (KNNAL), Categorical Boosting (CatboostAL) and Light Gradient Boosting Machine (LGBMAL) classifier. Using the smart grid-based energy theft detection dataset, the proposed RFAL model outperforms competing models and obtains an accuracy of 70.61%. The principles of smart grid tasks streamline decisions and enhance interaction between humans and machines by combining AL with machine learning. The application of this technology in this area has the potential to enhance the accuracy of energy theft detection and electricity-related problems and consequences.

Objetivo: Identificar os fatores explicativos da condição financeira dos pequenos municípios de Minas Gerais. Metodologia: Sob a ótica da Teoria da Condição Financeira, a pesquisa foi realizada em duas etapas: na primeira calculou-se a condição financeira dos municípios por meio do modelo de Brown (1993); na sequência, identificaram-se as variáveis financeiras capazes de influenciar a condição financeira dos municípios da amostra. Para isso, foram coletadas informações contábeis no período de 2017 a 2020 de 229 municípios do estado de Minas Gerais que apresentaram população inferior a 5 mil habitantes no ano de 2020. Optou-se pela utilização de um modelo econométrico com dados em painel para análise das relações. Resultados: Constatou-se que as variáveis Gasto com Pessoal (GP) e Grau de Endividamento (GE) foram significativas a um nível de significância de 1%, sendo que GP se relaciona positivamente e GE negativamente com a condição financeira governamental. Assim, os resultados revelam a existência de fatores financeiros relacionados à solvência orçamentária e à solvência de longo prazo que impactam na condição financeira de pequenos municípios. Contribuições do Estudo: A pesquisa contribui para a literatura sobre os fatores explicativos da condição financeira ao analisar pequenos municípios, utilizando um modelo consolidado na literatura internacional, especialmente em meio à discussão da PEC 188/2019 sobre a fusão destes municípios. Além disso, pode auxiliar os gestores públicos na tomada de decisões ao apontar os fatores explicativos significativos para a condição financeira municipal.

Introducción. Las diferentes formas de vivir la sexualidad, alternativas a la dominante, han recibido, históricamente, respuestas en forma de violencia y rechazo social e institucional. Sin embargo, no son abundantes los estudios que las analizan a través de experiencias personales vividas en entornos educativos. Objetivo. Con esta investigación se ha pretendido acceder a las distintas formas a través de las cuales se puede manifestar la normatividad sexual y de género en centros escolares (e.g., las relaciones interpersonales, los conocimientos del profesorado, las prácticas docentes, etc.) y sus posibles efectos en el alumnado transgénero. Metodología. La técnica empleada para lograr estos objetivos ha sido la entrevista semiestructurada, la cual fue validada por un gabinete de personas expertas. Este instrumento pudo aplicarse a dos participantes: a la abuela y cuidadora de una niña transgénero de seis años de edad y a la maestra de esta. Resultados. Se observó que dicha normatividad aparecía en tres niveles: institucional (nivel macro), escolar (nivel meso) y personal (nivel micro). Los resultados ponen de relieve la necesidad de políticas específicas y la fuerte influencia mediática en el primer nivel, el desconocimiento y resistencias del centro educativo, en el segundo, y las prácticas estereotipadas, la violencia puntual y su impacto, en el tercero. Conclusión. La falta de políticas específicas, la escasa formación en género y sexualidad y los prejuicios interiorizados pueden suponer un obstáculo a la libre expresión de la diversidad en las aulas.

Purpose Morgan’s iceberg profile of mood states (low scores in tension, depression, anger, fatigue and confusion, and high scores in vigour) may influence skilled athletes’ performance. Aims: The present study aimed to examine in 30 professional goalball athletes whether: (1) there is an ‘iceberg mood profile’, (2) the profile of mood states predicts the motor performance, (3) the experience level (international or national) and the condition (training or competition) modulate the relationship between the profile of mood states and the motor performance. Methods We used Brunel Mood Scale (BRUMS) to evaluate the profile of mood states and successful actions, errors and goals to assess motor performance. Measurements were obtained during official competition and pre-competition training with similar game rules. Results Athletes demonstrated the ‘iceberg mood profile’ regardless of the experience level and contextual condition. Additionally, tension seems to be associated with motor performance during training, whereas vigour predicts successful action in competition. Conclusions Iceberg mood profile characterises the profile of mood states of professional goalball athletes independent of experience level (international and national) and contextual conditions (pre-competition training and competition).

Este artigo tem como objetivo analisar a relação entre os conceitos de necropolítica, proposto por Achille Mbembe, e de unheimlich, desenvolvido por Sigmund Freud. Por meio dessa possibilidade de intersecção, coloca-se em análise a situação enfrentada pelos Yanomami no norte do Brasil, na qual diversos indígenas tiveram suas terras invadidas e seus direitos violados, levando-os a uma grave crise sanitária. A presença de uma lógica de gestão necropolítica, na qual corpos que não interessam ao Estado são deixados à margem, coloca em risco populações que não são incluídas no discurso hegemônico neoliberal. No encontro deste discurso com o estranho, pode-se inferir que é produzida uma experiência unheimlich, um deparar-se com o que há de mais incômodo e ao mesmo tempo familiar à sociedade. Reconhecer-se na diferença que há no outro cria um escape pela via da eliminação, faz-se desse corpo o inimigo e, assim, cria-se o respaldo para tirá-lo de cena. Desse modo, esta investigação destaca a necessidade de repensarmos as vias existentes para lidar com a diferença, isto porque, construir uma sociedade em detrimento de determinados grupos que a compõem é destruir também a si mesma.

In light of the already acknowledged underrepresentation of women in Computing, the ongoing project “Latin American Open Data for Gender Equality Policies Focusing on Leadership in STEM” aims to support the implementation of institutional policies to promote gender equality in STEM. Activity 4 of this project involves mapping 1the factors, actors and policies that influence the career development and leadership of women in STEM, as well as collecting and analyzing this data. To explore these factors, both a systematic mapping study and a grey literature mapping were conducted. The results encompass 8 types of contextual factors and 196 sub-factors. Some competency questions were also raised, providing valuable data for future steps.

Abstract Background Computer Vision Syndrome (CVS) is a complex of eye and visual problems that arise while using a computer or other Video Display Terminal (DVT). With the advent of the COVID-19 pandemic, the use of these DVTs has become indispensable in the lives of students and teachers. This study aims to identify the prevalence of CVS and associated factors in students and teachers at Lúrio University, in Nampula, during the pandemic period. Methods This is a cross-sectional study, carried out between November 2020 and March 2021. The validated CVS questionnaire (CVS-Q) and another semi-structured questionnaire on ergonomic risk factors were applied. Odds ratios (OR) and adjusted odds ratios (aOR) were calculated to measure the association between CVS and computer use conditions. Results The prevalence of CVS was 76.6%, and the female gender, age ≤ 20 years, levels I, II, III of course, lack of knowledge about ergonomics, use the computer to study, use more than 6 hours daily, absence of anti-reflex treatment, use of other devices and sitting in an inappropriate chair were risk factors for the occurrence of CVS, while being a teacher was a protective factor. Conclusion The prevalence of CVS found in this study was high, due to several factors, especially not using ergonomic principles when using computers and other DVTs. There is a need to adopt intervention strategies focused on the most vulnerable groups such as women, age group ≤20 years and students, especially at the first year level, right after entering the University.

Antimicrobial resistance is an increasing societal burden worldwide, with ESKAPEE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter species and Escherichia coli) pathogens overwhelming the healthcare sectors and more recently becoming predominantly a concern for their persistence in food and food industries, including agricultural settings and animal husbandry environments. The aim of this review is to explore the mechanisms by which the ESKAPEE group gained its multidrug resistance profiles, to analyse their occurrence in different foods and other related reservoirs, including water, and to address the current challenges due to their spread within the food production chain. Moreover, the repertoire of surveillance programmes available focused on monitoring their occurrence, common reservoirs and the spread of antimicrobial resistance are described in this review paper. Evidence from the literature suggests that restricting our scope in relation to multidrug resistance in ESKAPEE pathogens to healthcare and healthcare-associated facilities might actually impede unveiling the actual issues these pathogens can exhibit, for example, in food and food-related reservoirs. Furthermore, this review addresses the need for increasing public campaigns aimed at addressing this challenge, which must be considered in our fight against antimicrobial resistance shown by the ESKAPEE group in food and food-related sectors.

Nasal polyps (NPs) are usually part of chronic rhinosinusitis with nasal polyposis (CRSwNP). However, the exact etiology of CRSwNP is still unknown. In addition, the suggested etiological causes are infection, allergy, and immunological disorders, among others, such as genetic predisposition. Moreover, it is also suggested that oxygen-free radicals play a vital role in the pathogenesis of nasal polyposis, and inflammatory cells produce free radicals during phagocytosis, which is the primary source of ROS, controlled by the glutathione S-transferase (GST) system. Although, vascular endothelial growth factor (VEGF) plays an important role in angiogenesis, it is closely interwoven with the mobilization of inflammatory cells. This pilot study evaluated the association between genetic variant VEGF-A (rs28357093) and GSTM1/GSTT1 deletion polymorphism in susceptibility to CRSwNP. A case–control study was conducted with 61 individuals diagnosed with CRSwNP and 100 healthy subjects. VEGF-A (rs28357093) and GSTM1/GSTT1 deletion polymorphisms were genotyped by RFLP-PCR and SYBR Green real-time PCR, respectively. Individuals with allergic rhinitis carriers with AC genotype (rs28357093) presented a 4-fold increased risk to CRSwNP (OR = 4.20, 95% CI = 1.31 to 13.50; p = 0.015). This evidence shows that the increased vascular permeability probably causes an inflamed nasal area leading to extensive edema and polyp growth. On the other hand, no association was verified for each genetic variant by inheritance models. Interestingly, the GSTT1 present genotype showed a protective effect on CRSwNP. In conclusion, additional studies that have larger groups in different geographic localizations may be useful to verify and assess the association between genetic variants and CRSwNP.