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.

Legionnaires' disease became widely known following an outbreak of pneumonia in the United States in 1976. It is often caused by infection from artificial water sources such as cooling towers, water supply and heating systems, and recirculating hot tubs. To effectively implement infection prevention measures for Legionnaires' disease, collaboration among healthcare workers, water supply and heating system managers, building hygiene personnel, and other relevant parties is essential. It is important to note that outbreaks of Legionnaires' disease continue to occur frequently both domestically and internationally. While the number of reported cases of Legionnaires' disease in Japan has increased, the mortality rate has decreased but has stabilized at a lower level. Caution is also required as reports have been made in association with disasters and travel, in addition to artificial environmental water.

Background: Adolescent girls living in disaster-prone coastal regions of Bangladesh face heightened nutritional vulnerability due to limited food access, poor dietary diversity, and environmental stressors. Despite growing concerns about adolescent malnutrition, few studies have examined the combined influence of food security, dietary diversity, and age on nutritional outcomes in these settings. Objectives: This study aimed to assess the association between dietary diversity, food security, and age with the nutritional status of adolescent girls in coastal Bangladesh. Methods: A cross-sectional survey was conducted among 345 adolescent girls aged 10–19 in Chattogram and Cox’s Bazar. Data on dietary intake were collected using a 24-hour dietary recall and a food frequency questionnaire. Household food security was assessed using a validated scale. Nutritional status was determined using BMI-for-age classifications. Bivariate and multivariate analyses explored associations between dietary diversity, food security, age, and nutritional status. Results: Among participants, 10.14% were underweight, and 29.85% were either overweight or obese. While 17.39% demonstrated high dietary diversity (≥7 food groups), the majority had moderate diversity (5 or 6 food groups) (59.42%). Food-insecure households were significantly more likely to have overweight or obese adolescents (p < 0.05). Although dietary diversity was associated with BMI in bivariate analysis, it was not a significant predictor in the multivariate model. Age showed a significant relationship with both dietary diversity and nutritional status. Conclusion: The findings emphasize the importance of addressing household food security and age-related nutritional vulnerabilities in coastal areas. Interventions should prioritize age-sensitive, culturally appropriate strategies to improve dietary quality and prevent the double burden of malnutrition among adolescent girls.

The Mambaka watershed is extends between latitudes 1 3°45'E and 14°15'E and longitudes 7°16'N and 6°45'N. The geology, various tectonic and structural events that have affected the Adamawa Plateau in Cameroon make it rich in multi-substance mining. The objective of this study is to map rare earth (REE) geochemical anomalies in the sediments of the watershed streams west of Mambaka, and to trace their origins and geochemical processes. Predictive maps from inverse distance interpolations (IDW), factor analysis (F1) or principal component analysis (PCA) and hierarchical bottom-up classification maps provided a better understanding of the central tendency, distribution and dispersion of REE in the samples and in the study area, based on standard deviation and variance values that generated two factors F1 (Ho-Tm-Er-Yb-Lu-Dy-Tb-Gd-Eu-Sm) and F2 (Pr-Nd-Ce-La-Sm) representing 92.44% of the total cumulative variance. The ratios Ce/Ce* > 0.78 and Eu/Eu* > 1 demonstrate positive anomalies in Ce and Eu, and clear differentiation. The normalized concentrations used to calculate fractionation ratios show that the values for LaN/YbN (0.58 to 1.34), LaN/SmN (0.61 to 0.88) and LaN/LuN (0.62 to 1.43) suggest higher fractionation in SS09 and lower fractionation in SS01. Similarly, the ratios La/Lu (61.71 to 143.46), La/Yb (9.00 to 20.72), La/Sm (4.02 to 5.83) and La/ Lu (61.71 to 143.46) confirm these higher ratios in SS09 and lower in SS01. The REE in the study area comes from hydrothermal processes based on high lineament densities at sampling points in igneous rocks with a mean ∑REE value of between 174-219 ppm.

(1) Background: Psychological flexibility, the ability to adapt to changing demands while staying true to personal values, is linked to better health behaviors. However, its relationship with cardiovascular health behaviors, particularly in Filipino populations, remains underexplored. (2) Aim: This study aimed to examine the relationship between psychological flexibility and cardiovascular health behaviors among outpatients in Rizal, Philippines, focusing on BMI, smoking, physical activity, and dietary habits. (3) Methods: A cross-sectional, descriptive-correlational design was used with 100 outpatient participants from selected hospitals. Psychological flexibility was measured using the Acceptance and Action Questionnaire-II (AAQ-II), and cardiovascular health behaviors were assessed using a modified version of the American Heart Association’s Life Simple 7 tool. Descriptive statistics and Pearson’s correlation were applied. (4) Results: The findings showed that respondents had neutral levels of psychological flexibility. While most exhibited intermediate BMI and physical activity, dietary habits varied. A significant positive correlation was found between psychological flexibility and whole grain consumption. However, no significant associations were found with BMI, smoking, or physical activity. (5) Conclusions: Psychological flexibility appears to influence specific health behaviors, such as dietary choices, but does not strongly correlate with other cardiovascular health behaviors. Further research is needed to explore its broader role in cardiovascular disease prevention.

COVID-19 vaccines have demonstrated efficacy in reducing the prevalence of serious illnesses. The relative risk of hospitalization and mortality for patients who get breakthrough infections after immunization versus those who develop reinfections after a prior spontaneous infection is examined in this correspondence. Based on a study on U.S. Veterans who were not vaccinated and experienced reinfections had a much higher risk of experiencing severe illness outcomes compared to those who had received immunizations and experienced breakthrough infections, even if the rates of reinfection and breakthrough infection were similar. Our findings highlight the value of immunization in reducing severe COVID-19 outcomes, even in the presence of reinfections.

Background: Type 2 Diabetes Mellitus (T2DM) and Alzheimer’s Disease (AD) are increasingly linked through shared pathophysiological mechanisms, giving rise to the concept of Type 3 Diabetes Mellitus (T3DM). Brain insulin resistance, oxidative stress, and neuroinflammation are central to both conditions, contributing to cognitive decline and AD progression. Aim: This review aims to explore this emerging relationship and its implications for prevention and management. Methods: Using an integrative review, 21 studies were systematically analyzed. The review focused on identifying demographic, genetic, and lifestyle factors contributing to T2DM and AD and examined shared molecular pathways such as insulin dysregulation and amyloid-beta accumulation. Results: The findings reveal that T3DM shares key features with T2DM and AD, including insulin resistance and chronic inflammation. Lifestyle interventions, such as diet and exercise, alongside routine cognitive and metabolic screenings, are critical in mitigating progression. Conclusions: Further research into diagnostic biomarkers and targeted therapies is essential to manage T3DM and its impact on AD. The role of nursing professionals in early detection, education, and holistic management is emphasized as vital in addressing this dual disease burden. This review offers actionable insights into integrated strategies for addressing these interconnected conditions.

Background: Food security and dietary diversity are essential determinants of maternal health, particularly among pregnant women in refugee populations who face heightened vulnerabilities due to displacement and inadequate living conditions. This study examines the impact of food security on dietary diversity and nutritional intake among pregnant Rohingya women residing in the makeshift camps of Ukhiya, Cox’s Bazar. Methods: A descriptive cross-sectional study was conducted among 96 pregnant Rohingya women from June to September 2022. Data were collected using structured questionnaires assessing socio-demographic characteristics, food security, and dietary diversity. Food security was evaluated using the Household Food Insecurity Access Scale (HFIAS), while dietary diversity was assessed through a 24-hour dietary recall and a 7-day food frequency questionnaire. Data were analyzed using SPSS (Version 26) and Stata (Version 13), employing descriptive statistics and chi-square tests to examine associations. Results: Most participants (57.3%) were food secure, and 85.4% demonstrated high dietary diversity, consuming seven or more food groups. However, 21.9% of households experienced severe food insecurity, highlighting ongoing challenges in food access. The highest consumption was observed for starch, flesh foods, dark green leafy vegetables, and vitamin A-rich fruits and vegetables (99.0%), while dairy products (69.8%) and organ meat (34.4%) were consumed less frequently. Despite high dietary diversity, severe food insecurity persists, indicating gaps in food assistance programs. Conclusions: While food support programs appear to contribute to high dietary diversity among pregnant Rohingya women, severe food insecurity remains a significant concern. Strengthening food security interventions, improving access to diverse nutrient-rich foods, and integrating sustainable food assistance models are essential to addressing these challenges. Future research should explore long-term strategies to enhance food security and assess the impact of targeted nutritional interventions on maternal health outcomes in refugee settings.

Background: Scapulohumeral periarthritis commonly afflicts individuals in their middle age. Its etiology is multifaceted, and treatment presents a challenge with a high risk of recurrence. The main symptoms include shoulder pain and limited joint mobility, seriously affect the quality of life of the patients. Recent research indicate that acupuncture combined with bloodletting cupping can significantly improve the function of activity of shoulder joint and the pain in individuals with scapulohumeral periarthritis. However, these studies have typically been limited in scope, therefore additional research to substantiate the efficacy and safety of these intervention. Methods: To evaluate the efficacy of acupuncture combined with bloodletting cupping for treating patients with scapulohumeral periarthritis. We conducted an online search of databases in both Chinese and English, including PubMed, the Cochrane Library, Embase, Web of Science, CNKI, Wangfang Data, China Science and Technology Journal Database (VIP) and Chinese BioMedical Literature Database (CBM), to collect randomized controlled trials (RCTs) concerning the use of acupuncture combined with bloodletting cupping in scapulohumeral periarthritis patients. We also examined the references within the identified literature. Search utilised subject headings and free-text terms in both languages, without racial restrictions, for records up to April 3, 2024. Two researchers independently screened the literature, extracted data, and evaluated their qualities. RevMan 5.3 software was used for meta-analysis of the included studies. The protocol of this review was recorded in the International Platform of Registered Systematic Review and Meta-analysis Protocols (PROSPERO). Its registration number is CRD42023454614. Results: This review incorporated 22 RCTs involving a total of 1,774 patients. The results of meta-analysis showed that the clinical effective rate (RR=1.25, 95%CI [1.20, 1.30], P＜0.00001) of treating scapulohumeral periarthritis with acupuncture combined with bloodletting cupping was higher in the experimental group than in the control group. The all of Visual Analogue Scale (VAS) score (MD=-1.70, 95% CI [-2.17, -1.22], P＜0.00001). Melle score (SMD=-2.45, 95% CI [-2.55, -2.34], P=0.007]) and recurrence rate (RR=0.23, 95% CI [0.07, 0.77], P=0.02) were lower in the experimental group than in the control group with statistical significance (P<0.05). Conclusion: The acupuncture combined with bloodletting cupping for the treatment of shoulder impingement syndrome demonstrates definite efficacy and safety, with superior clinical effectiveness, pain relief, improvement in shoulder joint mobility, and reduction in recurrence compared to acupuncture alone. Therefore, it is worthy of being promoted and applied clinically.

Pembrolizumab, an immune checkpoint inhibitor targeting the programmed cell death 1 (PD-1) protein, is widely used for renal cell carcinoma but rarely causes central nervous system adverse events such as myelitis. A 58-year-old woman with stage IV renal clear cell carcinoma developed radiating hip pain, paresthesia, hypoesthesia (T10 and below), constipation, urinary retention, and sudden right-eye blurred vision one month after her sixth cycle of pembrolizumab and lenvatinib. Neurologic examination revealed asymmetrical inferior paraparesis, upper motor neuron signs, and right eye papilledema. MRI demonstrated patchy hyperintensity on C2-C6 and T2-T5, supportive of myelitis. Intravenous methylprednisolone was initiated, leading to pain relief and motor improvement. This is the first reported case of pembrolizumab-induced myelitis in Indonesia, emphasizing the importance of early recognition and corticosteroid therapy for optimal recovery.

Background: Proper umbilical cord care prevents neonatal infections and reduces neonatal mortality. Despite global recommendations for evidence-based cord care practices, traditional beliefs, and inadequate maternal knowledge often lead to unsafe practices, particularly in low-resource settings like Bangladesh. This study aimed to assess the understanding of umbilical cord care among mothers of neonates in Sherpur District, Bangladesh, and identify factors associated with knowledge levels. Methods: A descriptive cross-sectional study was conducted from July to October 2020 at Sherpur Sadar Hospital. A total of 193 mothers of neonates were recruited using a non-randomized purposive sampling method. Data was collected through a pre-tested, semi-structured, interviewer-administered questionnaire. Knowledge levels were categorized as "Good" (>6) or "Poor" (≤6) based on responses to 10 structured questions. Statistical analyses, including chi-square tests and crude odds ratios (COR), were performed to identify socio-demographic factors associated with knowledge levels. Results: Of the 193 participants, 48.7% demonstrated "Good" knowledge, while 51.3% had "Poor" knowledge. Education level (p = 0.01), occupation (p = 0.02), family type (p < 0.001), and family size (p = 0.04) were significantly associated with knowledge levels. Mothers with higher education and those from joint families exhibited better knowledge. However, 28.5% of respondents were unaware of the typical umbilical cord-shedding timeframe, and 44% could not identify signs of infection. Unsafe practices, such as using medications (14.5%) or hot compression (7.2%) for drying the cord, were reported. Conclusion: The study reveals significant gaps in maternal knowledge regarding umbilical cord care in Sherpur District, driven by socio-demographic disparities and cultural practices. Targeted health education programs, emphasizing evidence-based cord care practices and leveraging local social structures, are urgently needed to improve neonatal health outcomes in similar resource-limited settings. Future research should evaluate the effectiveness of these interventions to inform policy and practice.

Introduction: Pain is considered as the fifth vital sign that should be considered in assessing patients. For clinicians to evaluate and determine the right pain interventions, there should be parameters such as pain scale. Our objective in this study is to determine factors affecting pain scale preferences in suburban and rural populations. The pain scales used in this study are FPS-R (Faces Pain Scale-Revised), VRS (Verbal Rating Scale), VAS (Visual Analogue Scale), and NRS (Numering Rating Scale). Method: This study uses observational design with an interview approach and a cross-sectional study. Areas covered are within Indonesia, which are marginal areas of Tangerang district border, and two rural areas in Serukam, West Kalimantan, and Soe, East Nusa Tenggara. Data collected will be analyzed using SPSS 25 software. Result: Populations within the suburban areas prefer NRS (52.08%) as their pain scale, and populations in rural areas prefer FPS-R 76.92%). Factors affecting pain scale preferences are location areas, as well as last education, with statistical significance of p<0.05. Discussion: Our study showed that the choice of several pain scales is not appropriate for specific demographics due to the complexity of these scales. Factors that should be considered are the location areas and education level, as some population in remote areas have better understanding of simpler pain scales. Conclusion: Complexity or simpler components may be an underlying reason for the preference of score selection to assess pain scales in some population. Therefore, the selection of pain scales should be adjusted to specific demographics so that clinicians can provide appropriate management with appropriate pain scales.

Background: Nursing students are exposed to significant stress due to academic and clinical demands, which can adversely affect their mental health, academic performance, and future clinical competence. Despite the global acknowledgment of this issue, limited research has been conducted to explore the prevalence and determinants of stress among nursing students in Bangladesh. Methods: This cross-sectional study was conducted from December 2023 to February 2024 among 372 nursing students enrolled in selected nursing colleges in Bangladesh. A purposive sampling technique was used, and data was collected using a semi-structured questionnaire. The questionnaire assessed socio-demographic characteristics, academic challenges, and psychological symptoms, with mental health stress measured using a Likert scale. Descriptive statistics and Chi-square tests were used to analyze the data, with a 95% confidence interval applied to all analyses. Results: The findings revealed that 31.7% of nursing students experienced severe stress, 23.9% reported moderate stress, and 16.7% had mild stress. Age, academic semester, and course load difficulties were significantly associated with stress levels (p < 0.05). Psychological symptoms such as anxiety, difficulty concentrating, and loss of interest in activities were also significantly linked to higher stress levels. Notably, students in their first semester and those reporting harder course loads were more likely to experience stress. However, gender was not significantly associated with stress levels. Conclusions: This study underscores the high prevalence of stress among nursing students in Bangladesh, driven by academic and clinical challenges and psychological symptoms. The findings highlight the need for targeted interventions, such as stress management training, enhanced mental health support, and policies to alleviate academic pressures. Future research should explore longitudinal trends in stress and evaluate the effectiveness of interventions to support a resilient nursing workforce.

The integration of LiDAR and Artificial Intelligence (AI) has revolutionized feature detection in urban environments. LiDAR systems, which utilize pulsed laser emissions and reflection measurements, produce detailed 3D maps of urban landscapes. When combined with AI, this data enables accurate identification of urban features such as buildings, green spaces, and infrastructure. This synergy is crucial for enhancing urban development, environmental monitoring, and advancing smart city governance. LiDAR, known for its high-resolution 3D data capture capabilities, paired with AI, particularly deep learning algorithms, facilitates advanced analysis and interpretation of urban areas. This combination supports precise mapping, real-time monitoring, and predictive modeling of urban growth and infrastructure. For instance, AI can process LiDAR data to identify patterns and anomalies, aiding in traffic management, environmental oversight, and infrastructure maintenance. These advancements not only improve urban living conditions but also contribute to sustainable development by optimizing resource use and reducing environmental impacts. Furthermore, AI-enhanced LiDAR is pivotal in advancing autonomous navigation and sophisticated spatial analysis, marking a significant step forward in urban management and evaluation. The reviewed paper highlights the geometric properties of LiDAR data, derived from spatial point positioning, and underscores the effectiveness of machine learning algorithms in object extraction from point clouds. The study also covers concepts related to LiDAR imaging, feature selection methods, and the identification of outliers in LiDAR point clouds. Findings demonstrate that AI algorithms, especially deep learning models, excel in analyzing high-resolution 3D LiDAR data for accurate urban feature identification and classification. These models leverage extensive datasets to detect patterns and anomalies, improving the detection of buildings, roads, vegetation, and other elements. Automating feature extraction with AI minimizes the need for manual analysis, thereby enhancing urban planning and management efficiency. Additionally, AI methods continually improve with more data, leading to increasingly precise feature detection. The results indicate that the pulse emitted by continuous wave LiDAR sensors changes when encountering obstacles, causing discrepancies in measured physical parameters.

Artificial Immune Systems (AIS) are bio-inspired computational frameworks that emulate the adaptive mechanisms of the human immune system, such as self/non-self discrimination, clonal selection, and immune memory. These systems have demonstrated significant potential in addressing complex challenges across optimization, anomaly detection, and adaptive system control. This paper provides a comprehensive exploration of AIS applications in domains such as cybersecurity, resource allocation, and autonomous systems, highlighting the growing importance of hybrid AIS models. Recent advancements, including integrations with machine learning, quantum computing, and bioinformatics, are discussed as solutions to scalability, high-dimensional data processing, and efficiency challenges. Core algorithms, such as the Negative Selection Algorithm (NSA) and Clonal Selection Algorithm (CSA), are examined, along with limitations in interpretability and compatibility with emerging AI paradigms. The paper concludes by proposing future research directions, emphasizing scalable hybrid frameworks, quantum-inspired approaches, and real-time adaptive systems, underscoring AIS's transformative potential across diverse computational fields.

Background: The COVID-19 pandemic posed significant psychological challenges to frontline healthcare workers (HCWs), including anxiety, stress, and emotional strain. Aim: This study investigates the psychological impact on HCWs during the pandemic and explores coping strategies employed to manage distress. Methods: An integrative review was conducted using 24 studies published between January and December 2020. These studies were analyzed to identify common psychological outcomes and coping mechanisms among HCWs. Results: Healthcare workers experienced significant psychological distress during the COVID-19 pandemic, including anxiety, stress, insomnia, and depression. Anxiety was the most commonly reported issue, particularly among women, younger healthcare workers, and frontline staff. Stress levels were heightened by high workloads, exposure to COVID-19 patients, and inadequate protective measures. Coping strategies and self-care behaviors, such as seeking social support and utilizing institutional resources, varied in effectiveness across populations. Conclusion: The findings highlight the urgent need for targeted mental health support and resilience programs for HCWs, ensuring they are better equipped to face future health crises.