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.

Background: Chronic kidney disease (CKD) affects over 35.5 million US adults. Serum α1-acid glycoprotein (α1-AGP), an acute-phase protein, exhibits anti-inflammatory properties in animal models, but its association with CKD in younger women remains underexplored. This study investigated the relationship between serum α1-AGP and CKD risk in US women aged 20–49 years. Methods: This nationally representative cross-sectional study used data on female adults in the US aged 20–49 years from the National Health and Nutrition Examination Survey 2015–2018 cycles. 2,137 individuals were included in the study after excluding individuals without serum α1-AGP, urine albumin, and creatinine data. Multivariate logistic regression models evaluated the association between serum α1-AGP and CKD. Moreover, we performed stratified and interaction analyses to see if the relationship was stable in different subgroups. Results: Among 2,137 participants (mean age 34.6 years, mean eGFR 111.7 mL/min/1.73 m²), CKD prevalence was 8.8% (n=188). Higher serum α1-AGP levels were associated with lower CKD risk in the fully adjusted model (OR 0.37, 95% CI 0.16–0.84, P = 0.017), with a dose-response trend across quartiles (P = 0.041). The association was stronger in women aged 40–49 years (OR 0.20, 95% CI 0.05–0.76) and Mexican Americans (OR 0.07, 95% CI 0.01–0.56), though interaction terms were not significant (P > 0.05). Conclusions: Higher serum α1-AGP levels are associated with lower CKD prevalence in young women, suggesting a protective role. Longitudinal studies are needed to confirm causality and explore α1-AGP as a biomarker for CKD risk stratification.

Background: Dysphagia is a common functional impairment in elderly populations, often leading to severe complications such as malnutrition and aspiration pneumonia, significantly increasing healthcare burdens. Currently, effective prognostic assessment tools are lacking. The absolute lymphocyte count (ALC), a biomarker reflecting immune-nutritional status, has potential predictive value in this context, though its role in dysphagia prognosis remains unclear. Methods: This retrospective cohort study included 253 dysphagic patients who received percutaneous endoscopic gastrostomy (PEG) or total parenteral nutrition (TPN) between 2014 and 2017. Five patients with missing ALC were excluded. Cox regression models assessed the association between ALC and mortality. ALC was analyzed as both continuous variable (using restriocted cubic splines) and categorical tertiles, with additional threshold analyses to assess non-linearity. Kaplan–Meier survival curves and subgroup analyses were also performed. Results: Lower ALC was associated with poorer nutritional status, higher inflammatory markers, and greater comorbidity burden. Higher ALC was independently associated with reduced mortality (adjusted HR: 0.60; 95% CI: 0.44–0.83; p = 0.002). Patients in the highest tertile had significantly better survival than those in the lowest (HR: 0.37; 95% CI: 0.23–0.59; P < 0.001). A non-linear threshold effect was identified at ALC = 1.899×10⁹/L (p for non-linearity = 0.009). Kaplan–Meier analysis confirmed improved survival with higher ALC (p < 0.0001). Subgroup analyses showed the protective effect of higher ALC was consistent across age, sex, BMI, PEG use, and comorbidity strata, with no significant interactions. Conclusions: ALC is an independent, non-linear predictor of mortality in older dysphagic patients and may aid clinical risk stratification across diverse patient subgroups.

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.

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.

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: Maternity leave, whether paid or unpaid, is a critical resource that can significantly impact maternal well-being and newborn outcomes. However, its availability and utilization among mothers living in poverty remain understudied. Education is widely recognized as a key factor that increases access to both paid and unpaid leave. However, the theory of Minorities’ Diminished Returns (MDRs) posits that structural racism, segregation, and labor market discrimination limit the benefits of socioeconomic resources, such as education, for Black and Latino individuals. This suggests that the effects of education on maternity leave may not be uniform across racial and ethnic groups. Objective: This study aimed to examine the MDRs of education on access to unpaid and paid maternity leave among Black and Latino mothers compared to White mothers giving birth while living in poverty. Methods: We utilized baseline data from the Baby’s First Years Study (BFY), a longitudinal investigation of the effects of poverty on child development. The sample consisted of 1,050 mothers living in poverty who had recently given birth. Maternity leave (paid and unpaid) was assessed via self-report, and educational attainment was measured in years of schooling. Structural equation modeling (SEM) and interaction terms were employed to analyze racial and ethnic differences in the relationship between education and access to maternity leave. Results: Educational attainment was positively associated with access to unpaid maternity leave for the overall sample of mothers giving birth in poverty, but this association was weaker for Black and Latino mothers compared to non-Latino White mothers. Education did not significantly increase the likelihood of paid maternity leave, and there were no group differences for this association. Conclusion: This study highlights the urgent needs to address structural racism, labor market discrimination, and residential segregation that diminish the impact of education on living conditions for Black and Latino mothers, compared to non-Latino White mothers, even for those living under poverty. Policymakers and practitioners should develop targeted interventions to reduce racial and ethnic disparities in access to paid and unpaid maternity leave and other critical resources, particularly for new mothers living in poverty. Addressing these inequities is essential for improving maternal and newborn health outcomes and promoting social justice.

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.

This study aimed to assess the knowledge level of street food vendors on hygiene in the Tamale metropolis in the Northern Region of Ghana. The study employed the health belief model as the theoretical basis. Quantitatively, the study employed a descriptive cross-sectional study design to examine the microbial load of street-cut fruits and assess the knowledge and practice of vendors of cut fruits on personal and food hygiene in the study setting. The population consists of cut and vented pawpaw, watermelon, and street fruit vendors registered with the health directorate in the Tamale Metropolis. A convenient sampling technique was used to select 113 respondents for the study. The Yamane formula was used to determine the sample size to select one hundred and thirteen participants (113) out of one hundred and fifty-eight street fruit vendors in the Tamale Metropolis. The main instrument for data collection was a questionnaire. A questionnaire had close-ended questions which were developed using a 'Yes' and 'No' response, and a four-point Likert-type scale ranging from 1=Strongly Disagree (SD), 2=Disagree (D), 3=Agree (A) and 4= Strongly Agree (SA). The data were analysed using descriptive statistics (frequency, percentages, means and standard deviation). The findings revealed that the overall knowledge level of respondents is low. The findings also indicate that vendors do not control the rate at which their customers touch their vended fruits. It is recommended that Street fruit vendors and handlers be educated on fruit hygiene practices through engagement by the Health Directorate Unit of Tamale Metropolis and the Ministry of Health. To keep consumers safe, the Tamale Metropolitan Assembly must strictly enforce compliance with regulations on operation permits and health clearance certificates. Metropolitan sanitation officers must regularly monitor fruit vendors to ensure compliance with goods.

The Chinese film Wolf Warrior II floats all the way at the domestic box office, and jumps into the top 100 of the world's film box office rankings. It has achieved great economic success and ratings are overwhelmingly positive in China. Nevertheless, in stark contrast to this, Wolf Warrior II is cold at the box office abroad, and the word of mouth is not satisfactory. Transcreation is the re-creation or adaptation of content for a group of specific target audience. As an inter-related process of translation, a successful and holistic transcreation can arouse the same emotions as well as connotations produced in the target language as the source language. There are different perspectives to detailed translation analysis of China’s identity as a prominent character of contemporary society. Insofar as this research probes into the branding and in subtitle translation, it also constructs a binary theoretical model based on triadic signs of intersemiotic translation and metafunctional framework of multimodal analysis to testify China’s core values in this film and beyond.

Background: Financial well-being is a key domain of overall well-being, encompassing an individual's ability to meet financial obligations, secure their financial future, and maintain a sense of financial freedom. Education is often viewed as a critical pathway to enhancing financial well-being. However, the returns of education on financial well-being are not uniform across racial, ethnic, and nativity groups. The theory of Minorities’ Diminished Returns (MDRs) suggests that the positive effects of education on outcomes such as income and financial security are weaker for marginalized groups, including Black individuals, Latinos, and immigrants. Objective: This study examines the diminished returns of education on financial well-being among Black, Latino, and immigrant populations in the United States. We aim to investigate how structural inequalities contribute to weaker financial returns on education for these groups compared to their White and native-born counterparts. Methods: We utilized data from the Understanding America Study (UAS 2014) to conduct a cross-sectional analysis of adult respondents. The study assessed financial well-being outcomes (e.g., income, savings, and financial security) and their association with educational attainment across racial, ethnic, and nativity groups. Regression models were employed to test for interaction effects between education and race/ethnicity/nativity, adjusting for sociodemographic factors such as age, gender, employment, and family structure. Results: Our analysis included 8,121 individuals. The mean age of the respondents was 48 years (SD = 16). High education was associated with higher financial well-being (B = 1.284, 95% CI: 1.157, 1.410). The interaction terms between education and immigrant status (B = -0.507, 95% CI: -0.930, -0.084), race (Black) (B = -0.770, 95% CI: -1.208, -0.331), and ethnicity (Hispanic) (B = -0.589, 95% CI: -0.969, -0.210) were all significant, suggesting that immigrant, Black, and Hispanic individuals experience diminished returns on education in terms of financial well-being, relative to US-born non-Hispanic White individuals. The significant negative interactions between education and minority statuses (Black, Hispanic, and immigrant) indicate that while education generally improves financial well-being, the magnitude of this improvement is substantially smaller for these marginalized groups. Conclusion: Understanding how education translates to financial well-being across different racial, ethnic, and nativity groups is critical for addressing persistent financial disparities.

The Gambia uses the Primary Health Care model with no trauma response system. Trauma patients are transferred through multiple levels of health care facilities before definitive care hospitals. This study was conducted to identify predictors of injury factors associated with transfer. In this study, we examined characteristics of transferred patients compared to those directly admitted in definitive care hospitals. The study was conducted in two major trauma hospitals in The Gambia. 251 road traffic injury (RTI) patients were either transferred (84%) from lower-level health centers or directly admitted (16%) to one of the study hospitals. Transferred patients were more likely to have been pedestrian/bicyclists (aOR = 1.81; 95% CI = 0.86 – 3.80). Administration of antibiotics was significantly associated with direct admit than transferred patients (aOR = 6.84; 95% CI = 2.38 – 19.68). Transferred patients were more likely to receive intravenous fluid compared to direct admits (aOR = 0.03; 95% CI = 0.01 – 0.08). The study results have implications for policies and planning in the healthcare setting in The Gambia and other LMICs with similar settings. Based on the findings of this study, it is essential that hospital management teams adapt to increasing reliance of RTI patients on lower-level healthcare facilities. The study results suggest increased burden on lower-level health care facilities. Efforts and resources should focus more on supporting lower-level facilities.

Biodiversity is related to a global problem: its destruction, a fact supported by scientific authorities. It is not trivial that educational dimension has been contemplated as one of the strategies for its conservation. Since 1992 global initiatives such as the Convention on Biological Diversity postulates concepts that linked education and nature conservation. The main objective of this research work is to test the level of assimilation of extracurricular scientific knowledge by primary school pupils. The method chosen for the content was, on the one hand, a master class intervention with an interactive presentation on a digital whiteboard. Third cases were chosen. Each case consisted of a presentation of the current status of a species of fauna present in Spain. On the second part, students were asked to write an essay and to illustrate the experience during the presentation. Regarding the essays, students showed that they were more attracted to the first species that was presented (Iberian lynx) in a proportion of over 45% of cases. The “Endangered species” concept appeared in more than 77% of the texts reviewed. In terms of drawings, almost 55% of the responses seem to devote more attention to the second species described (Testudo graeca). An attempt was made to offer rigorous, structured information related to different aspects of natural reality in order to contemplate the broadest possible vision. The example of an instructional intervention presented here aims to be an alternative to other transmissive teaching models. Likewise, the linking of abstract concepts with socio-cultural reality proved to be a successful strategy to reinforce knowledge about natural biodiversity, endangered species or threat factors.

The price of grains like maize and sorghum is subject to significant fluctuations, which can have a significant impact on a country's economy and food security. The aim of the study is to model sorghum and maize price volatility in Nigeria. The data utilized in the study was extracted from World Bank Commodity Price Data (WBCPD), 2022. The data consists of monthly prices in nominal US dollars for maize and sorghum from January 1960 – August 2022. The Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models were utilized for capturing the two-grain price volatility. Two types of conditional heteroscedastic models exist, the first group uses exact functions to control the evolution of , while the second group describes with stochastic equations. It is inferred from the result that inherent uncertainties and fluctuations existed in the prices of maize and sorghum in Nigeria which implies that the price volatility is positive and statistically significant suggesting that historical information and past shocks play a crucial role in determining the volatility observed in the grains. It is recommended that the ARCH, GARCH, EGARCH, TGARCH, PARCH, CGARCH, and IGARCH models should be employed for modeling and managing the volatility of maize and sorghum prices in Nigeria. These models have shown effectiveness in capturing different aspects of volatility, including the impact of past shocks, conditional volatility, asymmetry, and other relevant factors.

The accurate estimation of Individual Wave Components (IWC) is crucial for automated diagnosis of the human digestive system in a clinical setting. However, this process can be challenging due to signal contamination by other signal sources in the body, such as the lungs and heart, as well as environmental noise. To address this issue, various denoising techniques are commonly employed in bowel sound signal processing. While denoising is important, it can increase computational complexity, making it challenging for portable devices. Therefore, signal processing algorithms often require a trade-off between fidelity and computational complexity. This study aims to evaluate an IWC parameter extraction algorithm that was previously developed and reconstruct the IWC without denoising using synthetic and clinical data. To that end, the role of a reliable model in creating synthetic data is paramount. The rigorous testing of the algorithm is limited by the availability of quality and quantity recorded data. To overcome this challenge, a mathematical model has been proposed to generate synthetic bowel sound data that can be used to test new algorithms. The proposed algorithm’s robust performance is evaluated using both synthetic and clinically recorded data. We perform time-frequency analysis of original and reconstructed bowel sound signals in various digestive system states and characterize the performance using Monte Carlo simulation when denoising is not applied. Overall, our study presents a promising algorithm for accurate IWC estimation that can be useful for predicting anomalies in the digestive system.

Background: The COVID-19 pandemic disrupted routine osteoporosis care due to clinic closures and limited in-person consultations. Telehealth emerged as an alternative model enabling remote care delivery and monitoring. However, previous reviews on telehealth either did not include the pandemic period or had a limited focus in scope. Evidence synthesized specifically for osteoporosis care during the pandemic is needed but lacking. Methods: We systematically searched PubMed, MEDLINE, EMBASE, PsycINFO, Web of Science, and CINAHL for studies on telehealth for osteoporosis published between January 2021 and March 2023. Five studies met the inclusion criteria of: osteoporosis population, telehealth intervention, and COVID-19 pandemic timeframe. Data was extracted on study characteristics, COVID-19 outcomes, osteoporosis status, telehealth purpose, patient satisfaction, and clinical outcomes. Result: The five studies showed telehealth was used for monitoring data, delivering test results, adjusting medications, and assessments. Osteoporosis prevalence among telehealth users ranged 30-100%. High patient satisfaction was reported with telehealth versus in-person care. No major differences occurred in medication delays or fractures between telehealth and in-person groups. Conclusion: This review found telehealth enables effective osteoporosis care and monitoring during the pandemic, with high patient and provider satisfaction. However, more robust randomized controlled trials are needed to establish stronger evidence around telehealth's impacts on clinical osteoporosis outcomes. Implications: Though promising, further high-quality studies will help clarify telehealth's role in improving osteoporosis care and outcomes. Findings inform guidelines on integrating telehealth into routine management. Evidence on user perspectives optimizes telehealth implementation policies.

In this paper, we found a new model for a compact star with charged anisotropic matter distribution considering the generalized Chaplygin equation of state. The Einstein-Maxwell field equations have been solved with a particular form of metric potential and electric field intensity. The plots show that physical variables such as radial pressure, energy density, charge density, anisotropy, radial speed sound, and the mass are fully well defined and are regular in the star's interior. We obtained models consistent with stellar objects such as GJ 832, LHS 43, SAO 81292, GJ 380, GJ 412, and SAO 62377.

With the rapid growth of online communication platforms, the identification and management of toxic comments have become crucial in maintaining a healthy online environment. Various machine learning approaches have been employed to tackle this problem, ranging from traditional models to more recent attention-based transformer networks. This paper aims to compare the performance of attention-based transformer networks with several traditional machine learning methods for toxic comments classification. We present an in-depth analysis and evaluation of these methods using a common benchmark dataset. The experimental results demonstrate the strengths and limitations of each approach, shedding light on the suitability and efficacy of attention-based transformers in this domain.