Search Results (45)

Objective: This study explores the prevalence of violence and forced displacement as indicators of inequity among Bogotá’s elderly population, with a particular focus on how these factors affect their health and access to healthcare services. Methods: This is a subsidiary analysis of the SABE-Bogotá survey. The design was a probabilistic cluster sample of 2000 people aged 60 and over. The study was carried out by the Pontificia Universidad Javeriana’s Institute on Aging and cosponsored by Colciencias. The variables of interest were displacement and experiences of violence, assessed through self-reporting. A descriptive analysis of all variables was performed, calculating simple frequency distributions. Subsequently, dependency and association analyses were performed using Chi-square, T-tests, and multivariate logistic regressions, depending on each case. Results: 43.32% of the subjects were victims of some type of violence in the last year, among which offensive language was one of the most frequent. Individuals with severe depression (OR 2.10 [1.21–3.65]) and those who had been victims of displacement (OR 2.55, CI 95% [1.65–3.95]) had the highest risk of violence. The results reveal a direct correlation between these experiences and pre-existing health conditions. For instance, severe depression and a history of displacement were associated with a higher risk of experiencing violence, while the risk of displacement was higher among individuals with diabetes, severe depression, and, crucially, those who lacked access to health insurance. Conclusion: A high percentage of the elderly population in the city of Bogotá has been victims of different types of violence, including ones related to armed conflict and forced displacement, which is a particular and exclusive form of violence suffered by this group of people. These findings suggest that violence and displacement are social determinants of health that exacerbate inequities, underscoring the need for more inclusive health policies and improved access to medical care for this vulnerable population. Full article

The Global Trade Alert (GTA) website, managed by the United Nations, releases a large number of industrial policy (IP) announcements daily. Recently, leading nations including the United States and China have increasingly turned to IPs to protect and promote their domestic corporate interests. They use both offensive and defensive tools such as tariffs, trade barriers, investment restrictions, and financial support measures. To evaluate how these policy announcements may affect national interests, many countries have implemented logistic regression models to automatically classify them as either IP or non-IP. This study proposes ensemble models—widely recognized for their superior performance in binary classification—as a more effective alternative. The random forest model (a bagging technique) and boosting methods (gradient boosting, XGBoost, and LightGBM) are proposed, and their performance is compared with that of logistic regression. For evaluation, a dataset of 2000 randomly selected policy documents was compiled and labeled by domain experts. Following data preprocessing, hyperparameter optimization was performed using the Optuna library in Python 3.10. To enhance model robustness, cross-validation was applied, and performance was evaluated using key metrics such as accuracy, precision, and recall. The analytical results demonstrate that ensemble models consistently outperform logistic regression in both baseline (default hyperparameters) and optimized configurations. Compared to logistic regression, LightGBM and random forest showed baseline accuracy improvements of 3.5% and 3.8%, respectively, with hyperparameter optimization yielding additional performance gains of 2.4–3.3% across ensemble methods. In particular, the analysis based on alternative performance indicators confirmed that the LightGBM and random forest models yielded the most reliable predictions. Full article

Social media empowers freedom of expression but is often misused for abuse and hate. The detection of such content is crucial, especially in under-resourced languages like Urdu. To address this challenge, this paper designed a comprehensive multilabel dataset, the Urdu toxicity corpus (UTC). Second, the Urdu toxicity detection model is developed, which detects toxic content from an Urdu dataset presented in Nastaliq Font. The proposed framework initially processed the gathered data and then applied feature engineering using term frequency-inverse document frequency, bag-of-words, and N-gram techniques. Subsequently, the synthetic minority over-sampling technique is used to address the data imbalance problem, and manual data annotation is performed to ensure label accuracy. Four machine learning models, namely logistic regression, support vector machine, random forest, and gradient boosting, are applied to preprocessed data. The results indicate that the RF outperformed all evaluation metrics. Deep learning algorithms, including long short-term memory (LSTM), Bidirectional LSTM, and gated recurrent unit, have also been applied to UTC for classification purposes. Random forest outperforms the other models, achieving a precision, recall, F1-score, and accuracy of 0.97, 0.99, 0.98, and 0.99, respectively. The proposed model demonstrates a strong potential to detect rude, offensive, abusive, and hate speech content from user comments in Urdu Nastaliq. Full article

In the present study, 96 teams’ performances across 48 matches in the group stage of the FIFA Club World Cup 2025 were analyzed. Teams scoring first won 62.5% of matches (p < 0.05), while goals were evenly distributed between halves (p > 0.05) and showed marginal variation across six 15 min intervals, peaking near the 30–45 and 75–90 min marks. Parametric analyses revealed a significant effect of match outcome on possession, with winning teams exhibiting higher average possession (53.3%) compared to losing and drawing teams. Non-parametric analyses identified significant differences between match outcomes for goals scored, attempts at goal, total and completed passes, pass completion rate, defensive line breaks, receptions in the final third, ball progressions, defensive pressures, and total distance covered. Winning teams scored more goals and registered more attempts on target than losing teams, although some metrics showed no significant difference between wins and draws. Logistic regression analysis identified attempts at goal on target, defensive pressures, total completed passes, total distance covered, and receptions in the final third as significant predictors of match success (AUC = 0.85), correctly classifying 80.2% of match outcomes. These results emphasized the crucial role of offensive accuracy and possession dominance in achieving success in elite football. Full article

Data science has become an essential component in professional sports, particularly for predicting team performance and outcomes. This study aims to develop and evaluate machine learning models that accurately predict game outcomes in the Chinese Professional Baseball League (CPBL). Method: A total of 859 games from the 2021 to 2023 regular seasons were analyzed, using both traditional baseball statistics and advanced sabermetric indicators such as the Weighted Runs Created Plus (wRC+), Weighted Runs Above Average (wRAA), and Percentage of Leadoff Batters on Base (PLOB%). Five machine learning models—decision tree, logistic regression, Neural Network, Random Forest, and XGBoost—were constructed and assessed through a five-fold cross-validation. Evaluation metrics included accuracy, F1 scores, sensitivity, specificity, and the AUC-ROC. Results: Among the models, logistic regression and XGBoost achieved the highest performance, with an accuracy ranging from 0.89 to 0.93 and an AUC-ROC from 0.97 to 0.98. The feature importance and SHapley Additive exPlanations (SHAP) analysis revealed that the wRC+ and PLOB% were the most influential predictors, reflecting the offensive efficiency and pitching control. Conclusion: The results suggest that combining interpretable machine learning with sabermetrics provides valuable insights for coaches and analysts in professional baseball. Furthermore, incorporating performance weighting based on game context may further enhance model accuracy. This research demonstrates the potential of data-driven strategies in sports analytics and decision-making. Full article

Background/Objectives: The high sensitivity of paediatric nurses directly influences the quality of nursing care provided to patients. The purpose of this study is to present the most frequent issues faced by paediatric nurses in their everyday work and their responses to difficult situations at work, define the actions applied when a difficult situation occurs, and assess the level of stress and other factors influencing the level of stress experienced by paediatric nurses. Methods: This study was conducted using an original survey form and a standardised psychological questionnaire based on the Perceived Stress Scale (PSS-10) for paediatric nurses. Results: The study involved 416 paediatric nurses and indicated a medium level of stress among the nurses. The median stress level, calculated as the sum of answers to questions based on the PSS-10, was 18 (16.0 ÷ 20.0), and the mean was 17.9 (min–max = 1–30). The median Sten score was 6 (5.0 ÷ 7.0), and the mean Sten score was 5.94 (min–max = 2–9). Nurses aged 21–30 years, who live in a city, have a Bachelor of Science in Nursing or a Master of Science in Nursing, and work ≥ 61 h a week and 161–250 h a month experience a higher level of stress. Factors such as choosing which child to help first, spending a great deal of time filling out medical documentation, obtaining a sick child’s consent to perform nursing procedures which the child does not understand, involving the minor in decision-making, impolite or offensive behaviour from a sick child or their parents, ineffective nursing and treatment methods, providing care against the opinion/views of a sick child or their parents, difficulties in or a lack of understanding of the situation of a sick child and their family, performing nursing procedures that can cause the child pain, and the inability to fulfil a sick child’s request increase the level of stress experienced by paediatric nurses. When a difficult situation occurs at work occurs, the nurses usually meet and talk about the situation with someone close (72.4%) or engage in other activities to avoid thinking about the situation (66.6%). They consult a psychologist/psychotherapist very rarely (9.6%) and a psychiatrist extremely rarely (4.6%). Conclusions: Polish paediatric nurses were found to experience a medium level of stress. Since paediatric nurses are exposed to stress, providing them with psychological care is important. The level of perceived stress is dependent on the nurse’s age, place of residence, and education, as well as weekly and monthly working durations. Paediatric nurses experience many difficult situations in their everyday work that influence their stress levels. Management should pay special attention to difficult workplace situations faced by paediatric nurses and implement regular actions to reduce the levels of stress experienced. Full article

Despite the growing popularity of women’s basketball in recent years, scientific literature on the subject remains significantly less extensive compared to its male counterpart. The main objective of this research was to analyze successful offensive actions and patterns during critical moments in the Women’s EuroLeague. The sample consisted of 377 technical–tactical actions corresponding to plays with score differences of three points or less (one-possession games) in the final minute and overtime periods of the Women’s EuroLeague during the 2021/22 and 2022/23 seasons. This study was based on an observational design, utilizing the LINCE PLUS software together with a customized observation tool. Descriptive statistics and chi-square (χ²) tests were carried out using SPSS version 25, while T-Pattern detection was performed through Theme 5 software. A threshold for statistical significance was established at p < 0.05. The findings indicated that home teams achieved a higher percentage of successful plays compared to visiting teams. Most successful patterns occurred during the final phase of possession (8”–0”), regardless of game location or team result. Additionally, layups, plays involving shots after on-ball screen, and actions following personal fouls demonstrated the highest success rates. The practical implications discussed in this research provide valuable insights for coaches to optimize offensive strategies during high-pressure moments in elite women’s basketball. Full article

Traditional command and control (C2) frameworks struggle with evasion, automation, and resilience against modern detection techniques. This paper introduces covert C2 (C3), a novel C2 framework designed to enhance operational security and minimize detection. C3 employs a decentralized architecture, enabling independent victim communication with the C2 server for covert persistence. Its adaptable design supports diverse post-exploitation and lateral movement techniques for optimized results across various environments. Through optimized performance and the use of the native messaging API, C3 agents achieve a demonstrably low detection rate against prevalent Endpoint Detection and Response (EDR) solutions. A publicly available proof-of-concept implementation demonstrates C3’s effectiveness in real-world adversarial simulations, specifically in direct code execution for privilege escalation and lateral movement. Our findings indicate that integrating novel techniques, such as the native messaging API, and a decentralized architecture significantly improves the stealth, efficiency, and reliability of offensive operations. The paper further analyzes C3’s post-exploitation behavior, explores relevant defense strategies, and compares it with existing C2 solutions, offering practical insights for enhancing network security. Full article

The present study aimed to verify the (1) differences between players’ roles in relation to technical and tactical and time–motion indicators, and the (2) relationships between individual time–motion and technical and tactical indicators for each role in a men’s Italian football Serie A team. A total of 227 performances were analyzed (28 players: 8 forwards, FWs; 11 midfielders, MDs; 9 defenders, DFs). Technical and tactical indicators, such as ball possession (played balls, successful passes, successful playing patterns, lost balls, ball possession time), offensive play (total and successful dribbles, crosses, assists), and shooting (total shots, shots on target) were obtained by means of Panini Digital (DigitalSoccer Project S.r.l). In addition, a time–motion analysis included the total distance, distances covered at intensities of 16.0–19.8 km/h, 19.8–25.2 km/h, and over 25.2 km/h, the average recovery time between metabolic power peaks, and burst occurrence, the latter of which was performed by means of a 18 Hz GPS device (GPexe Pro2 system tool) worn by the players. Results showed role-specific differences: MDs covered more distance, while DFs had better ball possession. MDs and DFs had more successful playing patterns, and MDs and FWs performed more dribbles and shots. Strong correlations (p < 0.01, ρ > 0.8) were found between bursts and assists for FWs, high-intensity running and ball possession for MDs, and distance, dribbling, and shots for DFs. These findings highlight the importance of individual and tailored training programs to optimize role-specific performance demands. Full article

This study aimed to identify differences in match running performance (MRP) on artificial turf (AT) and natural grass (NG) among female football players. The players’ MRPs (AT; 96 observations, NG; 80 observations) were obtained from all matches (n = 22) of the First Slovenian women’s football league in the season 2023/24 using a global positioning system. Data were categorized into four subsets according to the players’ tactical roles: central defensive player (CD), wide defensive player (FB), midfield player (CM), and offensive player (OF). The variables included total distance (TD), high-intensity running (HIR), high-intensity accelerations (HIA), and decelerations (HID). Results indicated that (i) CDs (Cohen’s d (d) = 0.93) and CMs (d = 1.07) covered significantly greater TD on AT compared to NG, with (ii) no significant differences in TD among FBs and OFs. Additionally, (iii) similar HIR, HIA, and HID values were found for players on all playing positions, irrespective of the pitch surface. These findings suggest that overall match intensity remains consistent between surfaces, but that AT may impose a higher physical demand regarding match volume for CDs and CMs. Therefore, the physical condition of these players should be a major consideration when playing on AT. Full article

To deal with the offense-defense confrontation problem of multi-missile cooperative intercepting a high-speed and large-maneuvering target, a differential game-based cooperative interception guidance law with collision avoidance is proposed, in which the offense-defense parties are the incoming target and the interceptors, respectively. Given that both offense-defense parties have uniformly decreasing speeds and first-order biproper dynamics, the relative motion models among the offense-defense parties are established, and the performance indices of the target and the interceptors are proposed. After that, the cooperative interception guidance law with collision avoidance is derived based on a differential game. The guidance law considers the effects of speed variations and rudder layouts on the motions of both offense-defense parties, ensuring excellent algorithmic real-time property and interception accuracy while introducing inter-missile collision avoidance constraints. In addition, the parameters of the target performance index are set according to the target acceleration information estimated by the interceptors. The simulation results verify the effectiveness of the guidance law designed in this paper, under various three-to-one scenarios, the interceptors could achieve collision-free interceptions with the interception accuracy of less than 5 m and the interception time difference of less than 0.1 s. Full article

This study presents the analysis of the UEFA Euro 2024 in Germany, focusing on the impact of the first goal on match outcomes, goal distribution between halves and quarters, and the relationship between offensive, defensive, and goalkeeping metrics and match outcomes. Moreover, a regression model is developed to identify the key factors that significantly contribute to teams’ success. The analysis of the 36 group stage matches revealed that scoring the first goal significantly increased the likelihood of a positive match outcome. There were no significant differences between goals scored in the first and second halves or per 15 min of the game. Kruskal–Wallis tests highlighted that winning teams had more assists, attempts on target and runs into the penalty area. Defensive metrics showed that winning teams recovered more balls, while goalkeeping metrics revealed that winning teams had more clean sheets. The logistic regression model identified “Attempts on Target” and “Passes into Attacking Third” as significant positive predictors of winning, while “Attempts on Target Outside Area” and “Crosses Attempted” were negative predictors. These findings offer valuable insights for coaching staff to develop strategies focusing on key performance indicators that enhance the likelihood of winning. Full article

The pervasiveness of offensive language on social media emphasizes the necessity of automated systems for identifying and categorizing content. To ensure a more secure online environment and improve communication, effective identification and categorization of this content is essential. However, existing research encounters challenges such as limited datasets and biased model performance, hindering progress in this domain. To address these challenges, this research presents a comprehensive framework that simplifies the utilization of support vector machines (SVM), random forest (RF) and artificial neural networks (ANN). The proposed methodology yields notable gains in offensive language detection, automatic categorization of offensiveness, and offense target identification tasks by utilizing the Offensive Language Identification Dataset (OLID). The simulation results indicate that SVM performs exceptionally well, exhibiting excellent accuracy scores (77%, 88%, and 68%), precision scores (76%, 87%, and 67%), F1 scores (57%, 88%, and 68%), and recall rates (45%, 88%, and 68%), proving to be practically successful in identifying and moderating offensive content on social media. By applying sophisticated preprocessing and meticulous hyperparameter tuning, our model outperforms some earlier research in detecting and categorizing offensive language tasks. Full article

The current investigation scrutinized the strategic approaches employed by the top four teams in the Brazilian Men’s Volleyball Superliga, according to the match’s opponent. The study encompasses the analysis of 22 matches, involving teams ranked first through fourth, competing against each of the 12 teams participating in the 21–22 season of the Brazilian Men’s Volleyball Superliga, including one home and one away match for each team. Social network analysis facilitated the identification of the interconnections and particularities among all variables, offering a comprehensive perspective. The findings unveiled that during the offensive phase, the second-, third-, and fourth-ranked teams consistently exhibited higher eigenvector values, irrespective of the opposing team, notably when the middle-blocker positioned themselves in front and near the setter. Conversely, the championship-winning team displayed variations in offensive tactics. The team securing the second position demonstrated alterations in setting placement, whereas the other teams executed settings aimed at zones 3 and 4. Additionally, the initial setter’s position at the commencement of a rally displayed varying eigenvector values based on the opponent, indicating team rotation as a performance determinant. Thus, barring the finalist team, the performance of the remaining teams is intricately intertwined with the individual characteristics of players. Full article

Sports teams aim to understand the tactical behaviour of their opposition to gain a competitive advantage. Prior research of tactical behaviour in team sports has predominantly focused on the relationship between key performance indicators and match outcomes. However, key performance indicators fail to capture the patterns of ball movement deployed by teams, which provide deeper insight into a team’s playing style. The purpose of this study was to quantify existing ball movement strategies in Australian-rules Football (AF) using detailed descriptions of possession types from 396 matches of the 2019 season. Ball movement patterns were measured by social network analysis for each team during offensive phases of play. K-means clustering identified four unique offensive strategies. The most successful offensive strategy, defined by the number of matches won (83/396), achieved a win/loss ratio of 1.69 and was characterised by low ball movement predictability, low reliance on well-connected athletes, and a high number of passes. This study’s insights into offensive strategy are instructional to AF coaches and high-performance support staff. The outcomes of this study can be used to support the design of tactical training and inform match-day decisions surrounding optimal offensive strategies. Full article