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.

Free Radical Scavenging Potential of Cobalt Nanoparticles Synthesized from Ipomoea Batatas Leaves Extract

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Synthesizing nanoparticles with high antioxidant capacity through green routes is critical for creating biocompatible antioxidants. This present study investigated the free-radical scavenging potential of cobalt nanoparticles (CoNPs) synthesized from Ipomoea batatas leaf extract. Air-dried leaf powder was macerated separately with distilled water and ethanol for 48 h, respectively. The filtrates were lyophilized to give aqueous and ethanol extracts. Total phenolic, flavonoid and tannin contents were quantified on both extracts, followed by DPPH scavenging assay. The ethanol extract that exhibited higher activity was utilized in the synthesis of CoNPs. The NPs were characterized via UV, Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscopy (SEM-EDX) analysis. The results showed that the ethanol extract demonstrated higher DPPH scavenging activity (IC50 66.49 µg/ml) than aqueous extract (IC50: 641.35 µg/ml) and CoNPs (IC50: 175.18 µg/ml). The CoNPs also competed favorably with ascorbic acid for ferric reducing potential. UV absorption peak was observed at 220 nm, corresponding to the surface plasmon resonance of CoNPs. The FT-IR showed characteristic peaks at <800 cm⁻¹ which is characteristic of cobalt oxide bond. The XRD and SEM-EDX analyses showed that the CoNPs were nanocrystalline, spherical, and well-dispersed with an average size of 17–22 nm. The study concludes that the synthesized CoNPs exhibited significant in vitro antioxidant potential which could be further explored for in vivo antioxidant potential.

Attitudes Towards E-Learning and Internet Usage: Their Impact on Students’ Study Habits – A Conceptual Exploration

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In today’s digital world, students’ attitudes toward e-learning and how they use the internet play a big role in forming good study habits needed for school success. This conceptual paper looks closely at these links. It shows how positive views of e-learning help students learn on their own and use resources better. Smart internet use also improves thinking skills and memory. But problems like too much social media or tech issues can hurt study routines. Based on simple theories like the Technology Acceptance Model (TAM), Uses and Gratifications Theory (UGT), and Self-Regulated Learning (SRL), this paper gives clear ideas for teachers. It fits well for high school and college students, including those in places like Jammu and Kashmir, India, where internet access varies. The paper suggests easy steps like digital training and mixed online-offline classes to make technology help build strong study habits for long-term learning.

The Effect of Environmental Performance, Liquidity, and Leverage on Profitability with Firm Size as a Moderating Variable: Evidence from Mining Companies Listed on the IDX and SET (2018–2024)

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This study aims to examine the impact of environmental performance, liquidity, and leverage on profitability, with firm size acting as a moderating variable, among mining sector companies listed on the Indonesia Stock Exchange (IDX) and the Stock Exchange of Thailand (SET). Profitability serves as a critical indicator for assessing overall corporate performance.

A quantitative approach was employed, utilizing panel data regression analysis. Data processing and analysis were conducted using EViews version 13. The research sample comprised 28 mining companies listed on the IDX and 25 mining companies listed on the SET, covering the observation period from 2018 to 2024.

The empirical results reveal that environmental performance has a positive and significant effect on profitability for mining companies listed on the IDX; however, it does not significantly affect the profitability of those listed on the SET. Liquidity demonstrates a positive and significant impact on profitability across mining companies in both the IDX and the SET. Conversely, leverage exerts a negative and significant influence on profitability for companies in both markets. Furthermore, firm size fails to moderate the relationship between environmental performance and profitability in both the IDX and SET contexts. Firm size significantly moderates the effect of liquidity on profitability for companies listed on the IDX, but this moderating effect is absent for those listed on the SET. Finally, firm size is unable to moderate the impact of leverage on profitability for mining companies listed on either exchange.

Sonographic Study of Adnexal Masses Using Gynecologic Imaging-Reporting and Data System (GI-RADS)

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Background and Objective: The Gynecology Reporting and Data System (GI-RADS) is a standardized framework designed to improve adnexal mass characterization and streamline clinician-radiologist communication. By utilizing morphological features and Doppler vascularity, GI-RADS reduce subjectivity in ultrasound interpretation. This study evaluated its diagnostic performance in predicting malignancy risk and providing clear clinical pathways for patient management.

Methodology: A clinical study was conducted at Al-Auda Medical Center, Saudi Arabia, The study included 300 female patients (ages 17+) undergoing ultrasound evaluation for adnexal lesions. Data were analyzed using SPSS version 20.

Results: The mean age was 35.7±10.3 years. Masses were most prevalent in premenopausal married women aged 26–45 (58%), with pelvic pain as the primary indicator (61.3%). Analysis showed 85% of masses had regular capsules and 93.7% were well-defined. Septation occurred in 13.3%, while posterior shadowing and vascularization were noted in 40.3% and 25.7%, respectively. A strong significant correlation (P=0.000) was found between GI-RADS grades and sonographic parameters. Simple ovarian cysts were the most frequent diagnosis (25%), while malignant outcomes were minimal (1.3%). Distribution showed GI-RADS 3 was most frequent (51.7%), followed by GI-RADS 2 (36.3%), GI-RADS 4 (8%), GI-RADS 5 (2%), and GI-RADS 1 (2%).

Conclusion: GI-RADS is a reliable tool for preoperative assessment, effectively distinguishing benign (GI-RADS 2/3) from suspicious (GI-RADS 4/5) lesions. Implementing this standardized language enhances diagnostic accuracy and optimizes surgical management. The researcher recommends mandatory inclusion of GI-RADS scores in all pelvic ultrasound reports to guide clinical decision-making.