The Influence of Product Quality and Promotion on Revisit Decisions Mediated by Outpatient Satisfaction at St Elisabeth Hospital Bekasi

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This study investigates the influence of product quality and promotion on outpatient revisit decisions, with patient satisfaction serving as a mediating variable, at St. Elisabeth Hospital in Bekasi. Employing a quantitative research approach, data were analyzed using the Partial Least Squares – Structural Equation Modeling (PLS-SEM) technique. A total of 160 respondents were selected through purposive sampling. The findings reveal that both product quality and promotion exert a positive and significant impact on patient satisfaction and revisit decisions. Furthermore, patient satisfaction significantly mediates the relationship between product quality and promotion on the decision to revisit. These results highlight the critical role of delivering high-quality healthcare services and implementing effective promotional strategies to foster patient satisfaction and loyalty. The study provides practical implications for hospital management in formulating marketing strategies and enhancing service delivery to sustain and increase patient visit.

Artificial Intelligence and Machine Learning- Driven Pharmaceutical Industry

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Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing the pharmaceutical sector at every stage—drug discovery, development, regulatory affairs, quality control, and post-marketing surveillance. These technologies improve data processing, accuracy, and timelines by using complex algorithms and large volumes of healthcare data. AI helps in drug target identification, drug design, prediction of toxicity, and pharmacokinetics modeling, as well as improving regulatory processes and pharmacovigilance. Though they have their benefits, there are still challenges such as data privacy, algorithmic bias, explainability, and accountability. Regulatory structures and ethical implications need to keep pace so that AI can be used safely and fairly in pharmaceuticals. This article discusses the existing applications, advantages, risks, and future possibilities of AI and ML in transforming drug development and healthcare outcomes.

Numerical Solution of Non-Linear Equation in MATLAB

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Since Non-linear equations are significant across many disciplines—including physics, engineering, economics, and other sciences—but solving them analytically can be quite challenging. This article explores the application of MATLAB to analyze three numerical methods: False Position, Newton-Raphson, and Secant. Each method is demonstrated through examples implemented in MATLAB, with error graphs provided to assess their accuracy. The study aims to assist in identifying the most appropriate method for solving particular types of nonlinear problems.

Integrating Advanced API Solutions into Full-Stack Web and Mobile Applications to Optimise User Experience

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The impact of integrating Stripe, Firebase and OpenAI in advanced API solutions, which is investigated in this research, is to optimize user experience (UX) in web and mobile applications. Based on a case study analysis of actual applications that have already adopted these APIs, the study uses empirical data collection, performance metrics analysis, and user feedback analysis to test whether these APIs are effective or not. Experiments are performed to analyze API integration, and the performance of the application before and after API integration is compared. The success rate of transactions, data synchronization speed, and AI-powered engagement metrics were used as KPIs to measure the impact on UX. To complete the evaluation, data was gathered from application logs, user interaction reports and developer insights. The study defines UX optimization as loading speed, ease of navigation, transaction speed, real-time responsiveness, and engagement levels. According to our results, Stripe reduces the checkout abandonment rate by 40% and improves the transaction success rate by 30%, thereby boosting user trust in transactions as well as transaction efficiency in terms of finance. This cuts down around 70% for data synchronization latency, which gives for a smoother app experience and better retention rates. The AI models from Open AI enhance the session times by 25-40% and grow engagement by virtue of engaging the user better with a more personalized experience for the user. Science verifies the particular advantages of integrating API, including latency reduction, improved interactivity, and speed of application processes. All of these are highlighted as integration challenges in the research, as well as the best practices for future API implementations. This study is a good start to suggest ways of optimizing UX with the adoption of APIs.

Physicochemical Evaluation of Used Frying Oils Through Determination of Saponification, Acid, Peroxide, And Iodine Values

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 This study investigates the degradation of frying oils used in local food establishments through the analysis of key quality parameters. Oil samples, collected after frying common food items such as samosas, Manchurian, chicken, medu vada, jalebi, and momos for prolonged periods (8–9 hours), were examined. Palm and vegetable oils were analysed for acid value, saponification value, peroxide value, and iodine value using standard titrimetric techniques. Acid-base titration methods were applied for acid, peroxide, and saponification values, while iodometric titration was used for iodine value. The comparative assessment highlights the chemical changes occurring in reused oils, emphasizing the necessity of regular monitoring to ensure safety and suitability for continued use in food preparation.

Prevalence of Digital Burnout among Medical Science Students of a Private College, Saudi Arabia

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Background: University students are more likely to experience digital burnout as they utilize and are exposed to digital gadgets regularly in both academic and personal contexts.

Purpose: To assess the prevalence of digital burnout among medical science students and correlate the digital burnout levels with various demographic variables.

Methods: Through convenient sampling, a descriptive cross-sectional study was conducted among 300 students (86.3%, males 13.7 %) from all programs and levels. The tools used to collect data were Tool 1 – Demographic Data and Tool 2 – Digital Burnout Scale (DBS).

Results: The results showed that 75% of the students reported moderate to slight burnout. Overall, and across all subcategories, mean scores indicate moderate degrees of burnout. A significant difference in digital burnout was observed across age groups (F=4.62, p=0.011), with individuals aged 24 and older reporting the highest levels of burnout compared to their younger counterparts. A statistically significant difference was found in the digital burnout scores among groups based on time spent online, i.e., more than 6 hours (F=4.52, p=0.007).  Overall, the study indicates that the students experience moderate burnout, which is related to age and time spent on the devices.

Conclusion: Targeted approaches are required to address digital burnout, especially in seniors and those who spend an immense amount of time online. Institutions should study in deep implementing interventions to promote healthier digital habits and provide resources to support students’ well-being in increasingly digital academic environments.

Comparison of Problem-Based Learning Model with Direct Instruction in Mathematics Learning Towards the Development of Critical Thinking Skills

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The selection and application of learning models to develop critical thinking skills is a problem in learning mathematics in elementary schools. The purpose of this study was to determine the difference between Problem Based Learning model and Direct Instruction model in mathematics learning on the development of critical thinking skills. This research is a quasi – experimental research, using quantitative data analysis and sampling using cluster random sampling. Data collection with tests. Data analysis of this research includes: prerequisite analysis test, two-way variance analysis test of unequal cells, and further analysis of variance test. The results of the calculation of the analysis of variance test of two-way unequal cells obtained data that Fcount (5.36) > Ftable (3.91), and in the Anava further test, obtained data that the marginal mean of the Problem Based Learning model is 88.58 greater than the Direct Instruction model which has a mean of 80.05. The conclusion of the research is that the effectiveness of the Problem Based Learning model is better than the Direct Instruction model in developing critical thinking.

Fear of Missing Out (FOMO) Scale in Emerging Adulthood

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Fear of Missing Out (FoMO) is a significant psychological phenomenon among young adults, particularly influenced by social media. This study explores FoMO in the context of emerging adulthood, a developmental phase from ages 18 to 25 marked by identity exploration and decision-making.  To fill this gap, we developed a comprehensive FoMO measurement tool based on Przybylski et al.’s (2013) framework. This tool underwent a two-phase assessment process: first, through Content Validity Index (CVI) evaluations by experts, and second, through item discrimination testing with emerging adults. The findings aim to provide deeper insights into the emotional, behavioral, and social dimensions of FoMO, ultimately contributing to better mental health outcomes for young adults navigating this critical developmental stage.

Effect of Russian Current Along with Structured Exercises in Improving Knee Range of Motion, Isometric Muscle Strength and Functional Status Following ACL Reconstruction

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Background: The most common ACL injuries occur during sports and are caused by twisting or pivoting movements. Decreased ROM, along with reduced muscle strength and functional status is more common in individuals with ACL reconstruction. Russian current is an emerging treatment in musculoskeletal physiotherapy.

Objective: To assess the effect of Russian current along with structured exercise in improving knee function following ACL reconstruction.

Methodology: This study included 44 individuals with ACL reconstructions, through random allocation they were divided into Group A (n=22) who received structured exercise alone and Group B (n=22) who received Russian current along with structured exercise over 6 weeks. The knee range of motion, isometric muscle strength and function were assessed by universal goniometer, hand held dynamometer, lysholm knee scores respectively. The assessment was performed 6 weeks after treatment.

Result:  Between group comparison was analyzed using unpaired t-test, showed statistically significant (p<0.00) improvement in Knee flexion (t=29.41) & extension range(t=2,08), isometric quadriceps (t=10.04) and hamstring muscle strength (t=17.81) and functional status (t=13.74) in group which received russian current and structured exercises compared to structured exercises alone.

Conclusion: The finding suggest that Russian current along with structured exercise  significantly improved knee ROM, muscle strength and functional status in individual with ACL reconstruction than structured exercise alone.

Adaptive Approaches to Software Testing with Embedded Artificial Intelligence in Dynamic Environments

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Artificial intelligence (AI) is rapidly being integrated into application domains such as autonomous vehicles, health care, and cybersecurity; therefore, the requirements for dependable and robust AI-embedded systems are more pressing in these dynamic environments characterized by unpredictable variations in operational conditions. The traditional software testing methodologies that depend on static test cases and a predetermined set of scenarios usually fail to tackle the complexity of modern AI applications, resulting in undetected defects and security vulnerabilities. This study will evaluate adaptive test methods based on reinforcement learning (RL), fuzz testing, and other hybrid strategies for their application in software reliability assurance across environments such as stable, low-resource, high-load, and adversarial. The research is built upon a series of experiments on conversational chatbots, fraud detection systems, and autonomous navigation modules, demonstrating that RL-adaptive testing methods improve defect detection by 35-47% in dynamic environments compared to static testing methods and achieve 40-50% greater stability against stress (concerning the system itself). For the traditional testing methods, RL-based methods reduced failure rates by 75%; fuzz testing proved effective in detecting edge cases but was less stable when the same edge cases were instantiated in adversarial conditions.

Furthermore, the paper identifies prominent challenges in AI Software Testing, like environmental drifts and non-deterministic outputs, which are seen to be better adapted through RL-based methods. Although there is a trade-off regarding explainability and computational overhead, the data demonstrates that adaptive testing can transform safety-critical applications and highlights hybrid approaches combining the dynamic optimization of RL with the anomaly detection of fuzz testing. The description of the application areas presented in this document offers concrete recommendations to developers and engineers, enabling safer and more dependable AI in real systems.