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.