Social media platforms like X (formerly Twitter) generate vast volumes of user-generated content that provide real-time insights into public sentiment. Despite the widespread use of traditional machine learning methods, their limitations in capturing contextual nuances in noisy social media text remain a challenge. This study leverages the Sentiment140 dataset, comprising 1.6 million labeled tweets, and develops predictive models for binary sentiment classification using Naive Bayes, Logistic Regression, and the transformer-based BERT model. Experiments were conducted on a balanced subset of 12,000 tweets after comprehensive NLP preprocessing. Evaluation using accuracy, F1-score, and confusion matrices revealed that BERT significantly outperforms traditional models, achieving an accuracy of 89.5% and an F1-score of 0.89 by effectively modeling contextual and semantic nuances. In contrast, Naive Bayes and Logistic Regression demonstrated reasonable but consistently lower performance. To support practical deployment, we introduce SentiFeel, an interactive tool enabling real-time sentiment analysis. While resource constraints limited the dataset size and training epochs, future work will explore full corpus utilization and the inclusion of neutral sentiment classes. These findings underscore the potential of transformer models for enhanced public opinion monitoring, marketing analytics, and policy forecasting.

The article describes the use of publicly available materials of radar mapping from spacecraft as a way to increase the reliability, as well as to reduce the cost and time of work to select the site of linear construction projects situated in remote underdeveloped areas. Based on the results of theoretical study and practical application of radar mapping of the Earth's surface from spacecrafts the conclusion is made about the availability of these materials, their reliability (relevance) and accuracy in order to select the site of linear construction projects at the concept design stage.

This article presents a mathematical model designed to simulate the impact of targeted interventions aimed at preventing HIV transmission among female sex workers (FSWs) and their clients, while also analyzing their effects on the health of the general population. The compartmental model distinguishes between high-risk populations (FSWs and their clients) and low-risk populations (sexually active men and women in the general population), and links prevention efforts in high-risk groups to the evolution of the epidemic in the low-risk population. The fundamental properties of the model, such as the positivity of solutions and the boundedness of the system, have been verified, and the basic reproduction number R₀ has been calculated. Finally, the stability of the model was studied using Varga’s theorem and the Lyapunov method. Simulation results show that targeted prevention among FSWs and their clients reduces HIV incidence in the general population. This framework provides a valuable tool for guiding policymakers in the design of effective strategies to combat the epidemic, especially relevant in the context of suspension of USAID funding.

Objective: To identify and evaluate the main functional assessment tools applied in the postoperative monitoring of patients undergoing total knee arthroplasty (TKA), and to synthesize the functional outcomes reported through these instruments in the current scientific literature. Methodology: A structured review was conducted following PRISMA 2020 guidelines. Thirty-one peer-reviewed studies were selected through a targeted manual search based on predefined eligibility criteria. Included studies evaluated functional recovery following TKA using validated outcome measures such as the WOMAC, KSS, KOOS, IKDC, SF-36, and SANE. Data extraction focused on the instruments used, patient population characteristics, and reported outcomes. A descriptive synthesis was compiled in Table 1. Additionally, 15 studies with quantitative data were analyzed using a forest plot to illustrate risk ratios (RR) and 95% confidence intervals (CI) for functional improvement. Risk of bias was assessed qualitatively based on methodological rigor, clarity of reporting, and validation of the outcome tools. Results: All included studies reported improvements in functional status following TKA. Most risk ratios ranged from 0.66 to 0.85, indicating a consistent reduction in the risk of postoperative functional limitation. High-quality studies demonstrated more precise effect estimates and greater internal validity. The SANE scale emerged as a valid and practical tool with high responsiveness, including in its culturally adapted Brazilian version. Despite heterogeneity in study design, the direction of effect remained consistent across all included studies. Conclusion: Validated functional assessment tools are essential for monitoring recovery after total knee arthroplasty. Instruments such as WOMAC and SANE demonstrate strong clinical utility and psychometric validity. Their systematic use enhances outcome comparability, supports individualized rehabilitation planning, and improves decision-making in orthopedic care.