The Influence of Education and Motivation on Non-Adherence to Prep Use Among Men Who have Sex with Men in Bandar Lampung, Indonesia

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Introduction: Human Immunodeficiency Virus (HIV) remains a global health problem, especially in high-risk groups of men who have sex with men (MSM). Pre-Exposure Prophylaxis (PrEP) is an effective HIV prevention strategy, but its success is highly dependent on the level of adherence to use.

Objective: This study aims to analyse the effect of education and motivation on non-adherence to PrEP use in men who have sex with men in Kemiling District, Bandar Lampung, Indonesia.

Methods: This study used an observational analytical design with a cross-sectional approach conducted in January–February 2026 with a sample of 64 respondents selected using proportional random sampling. Data were collected through a structured questionnaire, with non-adherence measured using the MMAS-8, and analysed using the Chi-Square test.

Results: The results showed that education (p=0.016; OR=4.427; 95% CI: 1.441–13.602) and motivation (p=0.003; OR=6.240; 95% CI: 1.923–20.248) significantly influenced non-adherence to PrEP use. Respondents with higher education and good motivation tended to be more adherent compared to respondents with lower education and less motivation.

Conclusion: It can be concluded that education and motivation are important factors influencing non-adherence to PrEP use. Therefore, interventions that emphasize increasing health literacy and strengthening motivation through ongoing education and counselling are needed to improve PrEP adherence in the MSM population.

A Meta Analysis on the Impact of Social Media on Students Academic Performance

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This study presents a comprehensive meta-analysis examining the impact of social media use on students’ academic performance. Drawing on a wide range of empirical studies conducted across diverse educational contexts, the analysis synthesizes findings to determine the overall relationship between social media engagement and academic performance. The methodology accepted in earlier studies (2011-2021) on the relationship between teacher and student was consistent with the purposes and effects on students’ academic performances. Literature rooted in the relationship between teacher and student: around 80 scholars’ articles, summaries, and guides were composed for analysis purposes. A sum of 18 articles was finally nominated for systematic review of the relationship between teacher and student. These include 15 quantitative and 3 mixed-methods articles. The results reveal a nuanced effect: while excessive and non-academic use of social media is generally associated with lower academic performance, purposeful and educational use can have a positive influence by enhancing collaboration, access to information, and engagement. The study highlights the importance of balanced and guided use of social media in educational settings and offers recommendations for educators, policymakers, and students to maximize its benefits while minimizing potential drawbacks.

Examining the Impact of Assignments on Academic Achievement and Student Well-being in Public Universities of Afghanistan

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This quantitative study examines the impact of academic assignments on students’ academic achievement and well-being in public universities of Afghanistan. The study was conducted using a quantitative approach, used questionnaire to collect data from 218 students from 24 public universities in Afghanistan. The study participants were students from several public universities in Afghanistan who had both work and study experience. The findings indicate that while appropriately designed assignments can enhance students’ understanding, time management, and academic achievement, excessive workload and tight deadlines are significantly associated with increased stress, anxiety, and reduced well-being. The study highlights the need for balanced assignment practices that support learning outcomes without compromising students’ mental health. It recommends that university instructors adopt student-centered approaches, ensure reasonable workload distribution, and provide adequate feedback to optimize both academic success and well-being in the Afghan higher education context.

Do Tall Columns Truly Represent Industrial Heaps? A Critical Review of Nickel Heap Leach Testwork

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Tall column tests are widely used as an intermediate step between laboratory-scale experiments and industrial heap leaching, aiming to improve the reliability of scale-up predictions by capturing hydro-mechanical and geochemical processes under more representative conditions. However, their predictive value remains inherently limited. This review critically evaluates the extent to which tall columns (2–6 m) reproduce key mechanisms governing industrial heap performance, including progressive compaction, unsaturated flow, preferential pathways, and coupled transport–reaction phenomena. Evidence shows that while tall columns can partially capture vertical chemical gradients, permeability evolution, and delayed reagent consumption, they still fail to represent large-scale heterogeneity, long flow paths, and structural evolution typical of industrial heaps. As a result, the observed extraction kinetics reflect system-dependent effective rates rather than intrinsic reaction kinetics, and direct extrapolation to recovery, hydraulic stability, or long-term performance is unreliable. The analysis also identifies systematic limitations in experimental design, including insufficient column diameter, limited instrumentation, and short test durations, which further constrain data interpretation. A scale-aware framework is proposed that integrates mineralogical characterization, staged testing (from bottle roll to pilot), and coupled hydro-geochemical modeling to improve decision-making and reduce scale-up risk. Tall column tests are therefore best interpreted as diagnostic tools for mechanistic understanding and trend identification, rather than standalone predictors of industrial heap leaching performance.

Explainable AI for Foreign Direct Investment Analysis: Evidence from Central Asia

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Foreign direct investment (FDI) is an important factor in the economic development of Central Asian countries, where investment flows have traditionally been concentrated in resource-based sectors. In the context of a growing focus on diversification, the need to analyze and study the determinants of FDI is increasing.

This study examines the determinants of FDI inflows in Central Asian countries using machine learning methods (CatBoost) and explainable artificial intelligence (SHAP), and compares the results with a classical econometric approach based on a two-way fixed effects (TWFE) model. Given the limited availability of data, a transfer learning approach is applied: the model is first trained on a group of countries structurally similar to Central Asia and then fine-tuned on the regional sample.

The results show that key macroeconomic factors such as Trade (% of GDP), Current account balance (% of GDP), and several other macroeconomic variables remain significant across both methodologies. At the same time, ML identifies additional regional patterns, such as a higher importance for FDI of determinants including Adjusted savings: carbon dioxide damage (% of GNI), Urban population (% of total population), and Access to electricity (% of population), among others.

The findings indicate that XAI provides interpretable results that are consistent with classical methods and additionally allows for capturing nonlinearities and regional heterogeneity. The study extends the application of ML and XAI in data-constrained Central Asian settings and demonstrates the value of combining econometric and machine learning approaches in the analysis of FDI determinants.

Possibility of Using Agrivoltaics in Vineyards in The Island of Crete, Greece

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The clean energy transition in Europe and worldwide requires the generation of electricity from zero-carbon energy sources including solar and wind energy. Solar photovoltaics are in the forefront of clean energy technologies used in the decarbonization of the global power system. Agrivoltaics is an emerging solar energy technology that allows the dual production of electricity and agricultural products in the same land area. The possibility of installing agrivoltaics in vineyards in the island of Crete, Greece has been studied. Several published papers assessing the use of agrivoltaics in vineyards in several countries have been reviewed while their benefits and challenges have been stated. It has been estimated that installation of agrivoltaics in vineyards in Crete covering 1% of their surface with coverage ratio at 15% and 30% can generate electricity meeting 2.6% and 5.2% of Crete’ annual electricity demand respectively. The generated electricity can cover the electricity demand of 20,800 and 41,600 households respectively in Crete. Although there are not sufficient data assessing the use of agrivoltaics in vineyards it is concluded that, under specific conditions, they have many benefits regarding the dual production of electricity and grapes. Our results indicate that installation of agrivoltaics in Cretan vineyards, under limited shading, can offer an additional income to farmers improving the growth and yield characteristics of the cultivated vines. Our result could be useful to many stakeholders of Cretan viticulture.

A Hybrid “ARIMA–ML Regression” Model for Enhanced Predictive Analysis in Cyber-Physical Systems: Conceptual framework and Simulation Evaluation

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This paper presents a hybrid ARIMA–machine learning (ARIMA–ML) regression framework designed to improve predictive accuracy in cyber‑physical systems (CPS). The approach brings together the strengths of classical statistical time‑series modelling and modern data‑driven techniques, allowing the model to capture both linear structures and nonlinear dynamics that commonly arise in CPS environments. A simulation‑based evaluation was conducted using a multivariate dataset generated from a MATLAB/Simulink CPS model, complemented by Python‑based machine learning components. The results show that the hybrid model consistently outperforms standalone ARIMA and ML approaches across multiple operational scenarios, including normal operation, peak load, and early‑stage failure conditions. Improvements were observed not only in RMSE and MAE but also in residual stability, prediction interval reliability, and statistical significance as confirmed by the Diebold–Mariano test. These findings suggest that hybrid modelling offers a practical and effective pathway for enhancing predictive maintenance, anomaly detection, and decision‑support capabilities in complex CPS environments. Future work will explore real‑time deployment, integration with edge computing platforms, and the use of more advanced learning architectures to further strengthen model adaptability and performance.

Instructional Leadership, Competency Skills, and Supervisory Practices toward the Development of Science, Technology, Engineering, and Mathematics (STEM) Learning Continuity Model

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Instructional leadership, competency skills and supervisory practices are crucial factors in ensuring STEM learning continuity during class disruptions, yet the correlation among these variables as predictors of learning continuity in STEM education need further explorations. In this study, the researcher investigates these dynamics among curriculum implementers in the City Schools Division of Cabuyao in the SY 2025-2026. Using a descriptive correlational research design, the study determines the level of instructional leadership, competency-skills, supervisory practices and how they affect the STEM Learning Continuity during class disruptions. Using purposive sampling, 340 curriculum implementers responded to a validated survey questionnaire which was analyzed Pearson moment correlation and multiple regression analysis using the SPSS software. The findings indicated a very high level of supervisory practices (mean=3.66, SD=0.28), followed by competency skills (mean= 3.52, SD=0.44), and instructional leadership (mean=3.57, SD=0.30), among curriculum implementers. The level of STEM learning continuity (mean=3.61, SD=0.27) was also found very high.  The test of significance unveiled a strong and significant correlation between instructional leadership and competency-skills (r = 0.620) and between instructional leadership and supervisory practices (r = 0.632), while a moderate yet significant correlation between competency skills and supervisory practices (r = 0.568) at p-value <0.001. Regression analysis revealed that instructional leadership, competency skills and supervisory practices are significant moderate predictors of STEM learning continuity (R2= 0.423, Adj.R2 = 0.418 at p-value <0.001).  It was further revealed that only instructional leadership (ꟕ=0.150, P-value = 0.11) and supervisory practices (ꟕ=0.472, P-value=<0.001) are significant predictors of STEM learning continuity during class disruptions. Based on these results, the researcher recommends implementing MLMN Model: A Systems and Leadership Approach on STEM Learning Continuity as a guide for curriculum implementers in ensuring STEM learning continuity during class disruptions.

The Multi-Prime RSA Permutation Crypto System Based on Clear Ring

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Cryptography secures information through encryption, allowing only authorized access. The RSA algorithm, which relies on the difficulty of factoring  where  and  are primes, is a popular public-key cryptosystem. Advances in factorization techniques and computing power necessitate improvements to methods for enhanced security. This study proposes a multi-prime RSA permutation cryptosystem based on the algebraic structure of a clear ring as a modification of RSA. It uses three primes  to form modulus , increasing modulus complexity and thus security. Permutation is applied in binary code form to produce more random ciphertext, alongside the application of a clear ring structure, specifically, the ring of integers modulo 256 with addition and multiplication modulo 256 based on ASCII. This ring allows each element to be expressed as a sum of a unit and a regular unit. The algorithm strengthens key generation and creates varied representations for the same plaintext through unit and regular unit addition, complicating cryptanalysis. Permutation further randomizes ciphertext. However, the method requires careful implementation to avoid errors. This innovation supports digital security.

Development of Geogebra-Assisted Learning Materials for Circle Topics Based on the van Hiele Model

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This study aims to develop GeoGebra-assisted instructional materials on circle topics based on the van Hiele model that meet the criteria of validity, practicality, and effectiveness. The study employed a Research and Development (R&D) approach using the 4D model, consisting of the Define, Design, Develop, and Disseminate stages. The participants were high school students divided into an experimental group and a control group. Data were collected through validation sheets, observation sheets, student response questionnaires, readability tests, and learning outcome assessments. The results indicate that the developed materials are valid, with average validity scores ranging from 3.58 to 3.85. Practicality was demonstrated by a high level of instructional implementation (3.75), very high student activity (93.2%), and highly positive student responses (92.68%). Effectiveness was confirmed by a classical mastery rate of 89%, a high N-Gain score (0.77), and a statistically significant difference between the experimental and control groups (p < 0.05). These findings suggest that GeoGebra-assisted instructional materials based on the van Hiele model are effective in improving students’ mathematics learning outcomes, particularly in geometry.