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Modeling behavioral intention of using health-related WeChat official accounts through ELM and SCT factors using the PLS-SEM approach
Get PDFHealth-related WeChat Official Accounts are widely used in China. However, limited research has explored how cognitive processing and psychological beliefs influence user behavior on these platforms. Previous studies have rarely examined the cognitive and psychological mechanisms that underlie users’ behavioral intention in this context. To address this gap, this study proposes a psychologically grounded dual-pathway model that integrates the Elaboration Likelihood Model (ELM) and Social Cognitive Theory (SCT) to explain how individual information processing and self-efficacy jointly shape user behavioral intention. Data were collected through an online survey (n = 434) conducted from April 11 to May 9, 2024. PLS-SEM was applied using SmartPLS 4.1. The results show that both central and peripheral processing routes significantly influence self-efficacy, which in turn mediates their effects on behavioral intention. Gender moderates the peripheral pathway, with female users more responsive to credibility cues. However, user experience did not have a significant moderating effect. This study extends the application of ELM and SCT in digital health communication by clarifying how different processing routes influence user behavior via self-efficacy. It offers practical insights for healthcare institutions, government health departments, and nonprofit organizations seeking to improve user engagement and satisfaction with WOAs
Prevalence of severe malnutrition in cancer patients: a systematic review and meta-analysis
Get PDFBackground: Cancer remains the foremost cause of mortality worldwide. Additionally, malnutrition frequently occurs among cancer patients and constitutes a significant factor contributing to adverse clinical outcomes and poor prognosis in this population. The present systematic review and meta-analysis study aimed to evaluate the prevalence of severe malnutrition in cancer patients. Method: This study was conducted based on data extracted from previous published studies reporting the prevalence of malnutrition in cancer patients. Statistical analysis of collected data was performed using Comprehensive Meta-Analysis software (v.2). Systematic searching was applied based on MeSH keywords in medical databases of ScienceDirect, Embase, Scopus, PubMed, Web of Science (WoS), MagIran, SID, and Google Scholar (by December 2024). Following the elimination of duplicate articles, further evaluation was applied through the assessment of Titles and Abstract. Then, the eligibility assessment was performed based on the inclusion and exclusion criteria by two reviewers, independently. The information extracted from Citation Management Software of EndNote was also added. In order to achieve the highest number of eligible articles, the references of all relevant studies were reviewed, manually. Results: Totally, 19 eligible studies were selected for data extraction and meta-analysis. According to the Random Effect Model, the prevalence of severe malnutrition among cancer patients was reported 19.3% (95% CI: 14.1–25.9%). Also, the results of meta-regression examining the effective factors on heterogeneity, it was found that the rate of malnutrition in cancer patients decreases with sample size, year of study, and the age of cancer patients (p < 0.05). Conclusion: Malnutrition is a common phenomenon in cancer patients. Continuous monitoring of nutritional status in cancer patients and associated economic and social factors are critical objectives during cancer therapies. According to the results of the present study, cancer therapy should be applied parallel to examination and attention to nutritional status in cancer patients
Enhancing peak performance forecasting in steam power plants through innovative AI-driven exergy-energy analysis
Get PDFThis study aims to investigate and predict the performance of a 400 MW steam power plant operating on the Rankine cycle through a combined exergy-energy analysis and an artificial intelligence-based random forest regression model. The primary objective is to assess component-wise inefficiencies, identify key parameters influencing plant performance, and develop an optimized predictive model for performance evaluation. A mathematical formulation of energy and exergy balance equations is developed for each plant component and analyzed using the Engineering Equation Solver (EES). The study investigates temperature and pressure gradients, as well as mass flow rates, across all integral components. A parametric analysis is conducted to evaluate the impact of operational parameters on cycle efficiency, exergy destruction, and exergy losses. The results indicate that the boiler experiences significant temperature and pressure gradients, leading to higher irreversibility, whereas the gland steam condenser exhibits lower gradients, resulting in reduced exergy destruction. Among the plant components, the intermediate pressure turbine demonstrates the highest exergetic efficiency (90–93 %), while the condensate extraction pump has the lowest (20–26 %). Similarly, energy efficiency is highest in the intermediate pressure turbine (90–92 %) and lowest in the condensate extraction pump (18–22 %). The study further reveals that steam quality and reheat pressure at the low-pressure turbine outlet significantly influence overall power output and plant efficiency. The mass flow rates of steam through the high, intermediate, and low-pressure turbines follow a ratio of 110:124.3:143.6, with corresponding pressure ratios of 20:2.1:0.071. To enhance predictive accuracy, a random forest regression model is employed to forecast various performance indicators of the steam power plant. The model utilizes 100 decision trees with a maximum depth of 10, enabled bootstrapping, a fixed random seed of 42, and a minimum sample split of 2. The model's predictions for energy and exergy efficiencies are validated against experimental data, with root mean square error (RMSE) and coefficient of determination (R2) computed for accuracy evaluation. The study highlights that the random forest regression model can be utilized to predict and optimize the performance of steam power plants, thereby enhancing their efficiency and minimizing exergy losses
Examining benign and malicious envy and flourishing among Muslim university students in Algeria: a quantitative study
Get PDFEnvy is a social emotion that arises from comparing ourselves to others, and it can significantly affect our flourishing in competitive environments such as universities. In Muslim societies, where cultural and religious values emphasize contentment and avoiding harmful emotions, envy's impact may differ. Understanding how harmless and harmful envy can affect students' flourishing is important, as this emotion can either motivate personal growth or hinder it. Therefore, this study aimed to explore how benign and malicious envy influence the flourishing of university students in Muslim society. A cross-sectional design was employed, and data were collected from a sample of 401 Algerian university Muslim students (86.5% female, mean age 21.63 years) from the University of Chlef, with varied economic backgrounds and academic levels ranging from first-year undergraduates to doctoral students. The study utilized the Benign and Malicious Envy Scale and the Flourishing Scale. Multiple regression analysis revealed that benign envy was a significant positive predictor of flourishing, while malicious envy had a significant negative effect. The regression model indicated that only the academic year significantly impacted flourishing, while gender, age, and economic status did not significantly predict flourishing. In terms of benign and malicious envy, gender, age, family economic status, and academic year did not significantly impact either. In conclusion, this study emphasizes the complex impact of envy on people's flourishing, highlighting the different effects of benign and malicious envy. It suggests that fostering benign envy could help individuals thrive while reducing malicious envy, which is important for improving flourishing. The findings offer important insights for educators and policymakers aiming to support students in academic settings, especially in Muslim societies
The effect of Latin dance on social physique anxiety in middle school girls: a pilot study
Get PDFIntroduction: Social physique anxiety (SPA) is a prevalent psychological issue among adolescents, particularly among female middle school students. SPA is characterized by fear of negative evaluation based on physical appearance and can significantly impact self-esteem and social interactions. This study aimed to investigate the effects of a 4-week Latin dance intervention on reducing SPA in middle school girls. Methods: A total of 40 female middle school students were randomly assigned to either the experimental group (n = 20) or the control group (n = 20). The experimental group participated in Latin dance training, consisting of two 40-minute sessions per week for four weeks. The control group engaged in traditional physical education classes, focusing on basketball. SPA was measured using the Social Physique Anxiety Scale (SPAS) before and after the intervention, assessing three dimensions: Negative Evaluation (NE), Self-performance (SP), and Social Comparison (SC). Results: The results indicated that the Latin dance intervention significantly reduced SPA across all three dimensions, with the most significant improvements observed in NE and SP (p < 0.05). The experimental group showed greater reductions in SPA compared to the control group (p = 0.004). Discussion: These findings suggest that Latin dance is an effective intervention for reducing SPA in adolescents. The improvements in SPA, particularly in NE and SP, highlight the potential of Latin dance to promote positive body image and enhance self-esteem. This study contributes to the growing body of research on the mental health benefits of dance and provides insights into integrating physical activity into psychological interventions aimed at improving adolescent well-being
Modified Mann-Kendall with higher-order statistics for trend analysis
Get PDFTrend analysis of rainfall events is important to understand changes in precipitation patterns over time, which may affect climate adaptation planning. The Mann–Kendall (MK) trend test is preferred by researchers for its robustness in handling non-normal data with extreme outliers. However, the assumption of independence is often not fulfilled. Modifications have been introduced to improve MK performance under autocorrelation, but the issue of nonlinearity is still not widely discussed. In this study, a modified MK called Mann–Kendall with Third-Order Cumulant (MKC3) is proposed. A simulation study was conducted
with comparisons made against MK and other MK variants to provide practical guidance for practitioners, followed by a case study of rainfall in Peninsular Malaysia. The results show that MK, TFPW, SMK, MKRD, and MKC3 have different strengths: MK for independent data, TFPW preserves trends in autocorrelated series, SMK performs well for weak autocorrelation with small sample size, MKRD is robust for strong autocorrelation and sinusoidal models, and MKC3 performs well in bilinear and nonlinear models. The case study shows increasing trends during the Northeast Monsoon (NEM) and decreasing trends during the Southwest Monsoon (SWM). Overall, MKC3 shows promising robustness, but the selection remains a trade-off
4,316 New Students Join UPM for 2025/2026 Academic Session
Get PDFSERDANG, 5 Oct – Universiti Putra Malaysia (UPM) continues to cement its reputation as a premier destination for higher education, welcoming 4,316 new students for the 2025/2026 academic session. The intake reinforces UPM’s role as a national leader in agriculture and food security, while offering one of the best campus learning and living experiences in the country
Putra Academia Month 2025 Reflects UPM’s Aspiration to Nurture Competitive Academia
Get PDFSERDANG, 8 October – Universiti Putra Malaysia (UPM) has officially launched the Putra Academia Month 2025, an annual celebration that recognises the roles, contributions and achievements of its academic community in advancing the pursuit and stature of knowledge
Aquaculture areas extraction model using semantic segmentation from remote sensing images at the Maowei Sea of Beibu Gulf
Get PDFThe extraction of aquaculture areas from high-resolution remote sensing images is crucial for effective coastal management and resource preservation. This study introduces SwinNet, a semantic segmentation model leveraging multi-scale feature fusion to enhance the extraction of aquaculture areas, particularly in the Maowei Sea of the Beibu Gulf, China. Utilizing the Swin Transformer backbone and a novel Parallel Pooling Attention Module (PPAM), SwinNet minimizes background noise and improves segmentation accuracy. SwinNet achieved a pixel accuracy of 96.53% and an intersection over the union of 93.07% on an aquaculture dataset, demonstrating superior performance in overcoming noise and accurately extracting aquaculture areas. SwinNet offers an effective solution for large-scale, high-precision monitoring of coastal aquaculture, with potential broader applicability in aquatic resource conservation and management