Epistemic Description of The Fruit Tree-Based Intercropped Milpa System (MIAF) in Veracruz, Mexico

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The Fruit Tree-Based Intercropped Milpa System (MIAF) is characterized by its systemic approach, which considers the interactions among the biological, physical, and socioeconomic components of the agroecosystem. The objective of this research was to analyze the MIAF system as a complex productive model, integrating into Rolando García’s theoretical framework of complex systems the agroecological principles underlying its design, as well as its conceptualization from complexity sciences and contemporary soil science theories. An information search was conducted, consisting of a state-of-the-art analysis and bibliometric maps of Complex Agrarian Systems (CAS) and the MIAF concept using the Scopus platform and VOSviewer. A total of 591 documents were found for the concept of Complex Agrarian System, and 53 for MIAF. For the first concept, it was found that it has been used from 1974 to 2024, primarily in 10 countries, with 65.5% being scientific articles and spanning 11 thematic areas. Meanwhile, the second concept has been used from 1985 to 2025 in 10 countries, with 90.6% being scientific articles and covering 11 thematic areas. Eighteen philosophers and 66 theories dating back to 1900, inherent to the Galilean tradition and focused on the MIAF system, were identified. Additionally, five concepts that have surpassed agroecology in contemporary discourse were identified and described. It is concluded that MIAF stands as a successful agroecological model by integrating traditional knowledge and technical innovation, enhancing biodiversity, food security, and climate resilience. Its study, framed within Rolando García’s and Bertalanffy’s complex systems theories, reveals that its sustainability depends on biological, physical, and socioeconomic interactions. Bibliometric analyses highlight a gap between the concepts of “Complex Agrarian System” and “MIAF,” pointing to opportunities for future research linking them. Therefore, INIFAP continues to advance its research while adopting the CAS framework to strengthen MIAF implementation, as this system not only represents an agroecological solution but also a practical example of the adaptability of systemic theories in sustainable agriculture.

Asset Optimization Through Utilizing of Vacant Land (Case Study: PT JKL Jakarta)

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The study aims to analyzed the optimization of vacant land assets owned by PT JKL, strategically located on Jalan Kayu Putih Raya in East Jakarta. The area is characterized by high mobility, making it essential to assess the land use for optimal efficiency. This assessment will identify the most suitable land use according to the highest and best use principle, which maximizes both building potential and profitability from permissible uses. The analysis resulted in a mixed-use building concept that integrates the functions of a hospital with service apartments. This combination is estimated to generate the highest and best increase in land value, at IDR 74.89 million per square meter, and a 222.79% increase in productivity after development. Over an investment period of more than ten years, the project is expected to generate a net present value of IDR 240.72 billion, an internal rate of return of 17.72%, and a payback period of 9 years and 11 days.

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.

Strategic Multi-Factor Root Cause Analysis for Lead Time Optimization in Jewellery Manufacturing

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One of the strengths that company need to have been the ability to release new models faster than competitors. It will give the opportunity for the company to capture the market share. However, company need to have a quick mass producing for the new models after prototype released to the market. Slow mass producing will open the window for the competitor to imitate the prototype and give the chance for them to capture all the markets if they have quicker mass-producing time. This issue happens in jewellery companies in Indonesia, one of the companies is XYZ Company. Prototype models from XYZ company are widely imitated by competitors who have faster production time. To overcome this issue, company need to analyse the root cause of slow mass production process in the company. Based on this analysis, recommendations can be given to the company and applied to accelerate the production process so the company will not lose the market anymore. The root cause analysis was conducted by using a fishbone diagram with a case study in XYZ company. Through the analysis, it can be seen that the root cause of the slow production time is due to lack of human resources thar involved in production. Based on this finding, series of measures such as hiring additional employees and implementing the overtime hours for the employee can be applied to speed up the production time and even increase the capacity.

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.