Morphological Forms and Metaphorical Dynamics of Banana Lexicon in Balinese Language: An Ecolinguistics Study

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This paper aims at (1) describing and analyzing the morphological forms of the Balinese lexicon on bananas, (2) analyzing the categories’ and revealing their dynamics, and (3) describing metaphors related to bananas and revealing their dynamics. Structural linguistic and Ecolinguistics theories proposed by Haugen are used for the analysis which refer to the three objectives. The data was collected through interview and observation methods; qualitative method was applied on the analysis of banana forms, the categories, as well as its metaphors. The dynamics of the lexicon were analyzed based on quantitative methods and descriptive-analytic technique. The results of the analysis were presented by using formal and informal methods completed with inductive and deductive techniques. The results of the analysis show (1) the morphological forms of the banana lexicon in Balinese are base forms (free morphemes), derived forms (affixed, reduplications, compound words, and phrasal forms); (2) the vocabulary of the banana lexicon consists of categories such as: 16 nouns, 16 numbers, 21 verbs, and 25 adjectives. Traditionally there are 18 types of bananas. In relation to the dynamics of banana lexicon there are found 4new names of bananas. The knowledge of the banana’s terms shows a decrease from generation to generation. This is evidenced by the results of the questioners for the older generation to adults, for the adolescents both the terms, the category of bananas and the knowledge of the Balinese metaphor for bananas.

Marketing Management of Tourist Destinations in the Context of a Sustainable Model of Their Future Development

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This study examines the marketing management of tourist destinations in the context of a sustainable model of their future development. In the context of increasing competition, a dynamic tourist environment and increased environmental and social challenges, sustainability is being established as a key factor for effective management of tourist resources. The emphasis is placed on the collaboration between tourist destination management algorithms and marketing strategies.

Organizational Governance and Business Conduct in Public Management: The Case of CTT

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The main objective of this work is to analyze the importance of business conduct in the management of organizations, seeking to understand how this standard contributes to the quality of governance, organizational sustainability, and the creation of institutional trust. More specifically, the aim is to: understand the evolution of the concept of organization and management; analyze the relevance of ethics, responsibility, and governance in modern management; define and characterize the concept of business conduct; and evaluate the practical application of these principles through a case study of CTT – Correios de Portugal (Portuguese Post Office).

The analysis revealed that CTT has a robust formal framework regarding ethics and business conduct, supported by codes, internal policies, and control mechanisms, reflecting a consistent concern for integrity, transparency, and organizational sustainability. Simultaneously, challenges inherent to the complexity of its business model and the reconciliation between economic objectives and public responsibilities were identified.

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.