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In line with Saudi Arabia’s Vision 2030, this study reviews the green operations and environmental responsibility revolution in Saudi companies. The study also contributes new cross-sector empirical evidence from Saudi firms, a context that has received limited attention in previous green operations research. This study assesses sustainability advancements in 38 Saudi companies, employing a mixed-research approach. It examines the impact of (i) environmentally friendly technologies, (ii) employee training, (iii) assessing and reporting, (iv) transparent reports, and (v) adherence to recognized standards on sustainability practices through a detailed statistical and correlation analysis. To ensure thoroughness, the selected companies operate across various sectors, differ in sizes, and possess varying levels of experience. The analysis reveals how internal capabilities and governance mechanisms jointly support the operational adoption of sustainability practices in Saudi firms during the Vision 2030 transition. The findings presented have valuable insights for managers and policymakers seeking to promote sustainable practices within their organisations. Full article

Nitrous oxide (N₂O) emissions from agricultural soils are an important source of greenhouse gases and are strongly influenced by fertilization practices. In this study, a field experiment was conducted from 24 June to 12 October 2024, at a saline-alkali farmland site in Binzhou, Shandong Province, China, to evaluate the effect of Bacillus subtilis biofertilizer on N₂O emissions and to explore the underlying mechanisms. Compared with conventional chemical fertilization, the Bacillus subtilis biofertilizer treatment reduced the cumulative N₂O emission flux by 39%. At the N₂O emission peak, the emission flux under the biofertilizer treatment was 40.7%, 18.2% lower than that under the CF and CBF treatments, respectively. Functional gene analysis further showed that at the N₂O emission peak, the biofertilizer treatment reduced the copy number of Bacterial-amoA by 94% and 83% relative to CF and CBF, respectively, while the hao gene abundance in the CF treatment was 7.67, 24 times higher than that in the BF and CBF treatments, indicating that the reduction in N₂O emissions was closely associated with suppression of the nitrification process. In addition, the biofertilizer treatment showed the highest plant nitrogen uptake. All fertilization treatments significantly increased crop yield compared with the control, whereas there was no significant difference in yield among BF, CF, and CBF treatments (p > 0.05). These findings indicate that B. subtilis biofertilizer can mitigate N₂O emissions from saline-alkali farmland without reducing crop yield and may represent a promising strategy for sustainable agricultural management. Full article

A significant amount of recent scientific literature emphasizes the importance of mother tongue education, as minority languages continue to be underrepresented in formal schooling. While some progress has been made in integrating these languages into curricula, the situation varies widely across different regions. Ideally, populations would achieve proficiency in multiple languages, yet in practice, this phenomenon is relatively rare. This article examines the status of minority language education across five regions of the post-USSR. The analysis is conducted according to specific principles, with attention to demographic patterns, economic conditions, legislative frameworks, national and regional educational policy documents, and the types and outcomes of programs involving minority languages. Methodologically, the study employs a comparative qualitative approach, combining document analysis, secondary data review, and the synthesis of existing case studies. By applying these methods, the research seeks to identify correlations between the presence of minority languages in the public sphere and their incorporation into educational programs. Findings indicate that active use of minority languages in everyday life and public domains provides the strongest motivation for sustained investment in education. At the same time, the introduction of modern educational technologies offers promising opportunities to achieve more positive results in the future. Full article

Tourism’s digital transformation has reshaped how travelers search for and evaluate destinations. However, relatively little empirical work has examined how user engagement translates into booking intent, especially under the emergent discovery channels mediated by artificial intelligence (AI). This study tests an engagement-driven referral framework using longitudinal behavioral data from a Mediterranean destination portal (April 2022–January 2026; 1.6 million sessions). Engagement depth, measured as average session time, significantly predicts booking intent click rate. Mobile drives 83% of sessions, but desktop users convert at nearly twice the rate (5.69% vs. 3.37%). High traffic, as it turns out, does not equal high commercial intent. Lower-volume international markets routinely outperform the dominant domestic market. The most striking result concerns AI referrals. Traffic arriving from AI assistants converts at 8.26%, more than double the organic search rate of 3.88%, despite shorter sessions, a pattern consistent with compressed decision-making under generative AI. These findings, grounded in real travel portal data, extend engagement theory beyond transactional settings and shed early light on how referrals from AI assistants like ChatGPT or Gemini differ behaviorally from organic search, with practical implications for portal managers, destination marketing organizations (DMOs), and sustainable demand management. Full article

Sediment accumulation in reservoirs represents a critical challenge for sustainable water resources management in semi-arid regions. In Jordan, accelerated sedimentation threatens the operational capacity of major dams, including the King Talal Dam (KTD), which serves as a key water resource in the Amman–Zarqa Basin (AZB). This study assesses sediment yield and erosion risk at the catchment scale using the Soil and Water Assessment Tool (SWAT) integrated with the Modified Universal Soil Loss Equation (MUSLE). The AZB was subdivided into 31 sub-basins and 586 Hydrological Response Units (HRUs) based on land use, soil characteristics, topography, and slope. The model was calibrated for the period 1993–2002 and validated for 2003–2012 using hydrological and sediment observations from 17 monitoring stations. Long-term simulations covering more than two decades were conducted to quantify spatial and temporal sediment yield patterns across the basin. Results indicate a mean annual sediment yield of 2.79 t ha⁻¹ yr⁻¹, corresponding to approximately 0.59 MCM yr⁻¹ of sediment inflow to the reservoir. These estimates closely agree with bathymetric survey results reported by the Jordan Valley Authority, which indicate sedimentation rates of 2.59 t ha⁻¹ yr⁻¹ (0.55 MCM yr⁻¹). Overall, the model demonstrates strong agreement between observed and simulated sediment loads, confirming its reliability for sediment dynamics assessment. The findings are relevant to Sustainable Development Goals (SDGs) 6 (clean water and sanitation) and 15 (life on land) by informing sustainable watershed and soil erosion management practices. Full article

This study presents an investigation of the factors (driven by coupled multi-factor corrosion mechanisms) which contribute to the degradation of the spirally wound armored steel wires used to protect core-structured, unarmored optical-fiber submarine cables. The influences of the physical properties of deep-sea sediments on the durability of unarmored cables, as well as the impact of ionizing radiation on optical fibers, are also assessed. The objective of this paper is to establish a scientific basis for cable longevity by integrating theoretical insights with empirical evidence. Although the steel utilized in armored cables is cost-effective and durable, it remains vulnerable to corrosion. Since the inaugural practical deployment of submarine communication cables between the UK and France in the 1850s, only a small number of studies worldwide have examined the corrosion and durability of cable armor. There is also limited literature examining the physical characteristics of the deep-sea surface sediments that directly affect the service life of the cables’ mechanically fragile polyethylene sheathing. An in-depth analysis of the cable damage and environmental conditions observed during maintenance operations provides valuable insights into the key environmental factors that influence armor corrosion and cable longevity. This research aims to guide future design and support strategies to improve the sustainability and durability of cable systems in marine environments. Full article

In social science degree programs, where Statistics is not a core subject, students often experience anxiety and negative attitudes that influence their engagement and may hinder academic performance. This study examines the role of positive emotions in the teaching of Probability Calculus and Inferential Statistics in Business Administration and Management studies, analyzing their relationship with students’ engagement in Problem-Based Learning (PBL). The research is framed as an exploratory single-campus case study conducted with a modestly sized sample of undergraduate students from a single Faculty. Moving beyond traditional approaches that view emotions merely as outcomes of learning, our model assumes that positive emotions, both prior to and following the PBL experience, shape students’ perceptions of its usefulness, their collaborative behaviors, and their communication with instructors. Using Structural Equation Modeling (SEM) and Cluster Analysis, the findings show that positive emotions are a key driver of students’ predisposition toward and engagement with PBL, indicating that cultivating a supportive emotional climate enhances participation and deepens the understanding of statistical concepts. These results suggest that fostering emotional engagement is essential not only for improving motivation and academic outcomes in Statistics but also for developing transversal and sustainability-related competencies such as critical thinking, collaboration, communication, and evidence-based decision-making. The study contributes to current discussions on sustainable and inclusive teaching practices by highlighting the importance of integrating socio-emotional dimensions into active learning methodologies in higher education. Full article

Hybrid hydro–solar microgrids offer a practical electrification option for remote and weak-grid communities by combining run-of-river hydropower with photovoltaic generation. However, their performance depends strongly on coordinated decisions across three layers: (i) system sizing and architecture, (ii) turbine selection and rating under variable river flow, and (iii) operational energy dispatch under time-varying solar resource and demand. This paper develops an optimization-driven planning framework for a run-of-river hydro–PV microgrid that co-optimizes component capacities and turbine-related design choices while enforcing time-series operational feasibility. Physics-based component models translate river discharge into hydroelectric output via turbine efficiency characteristics and operating limits, and compute PV generation and storage trajectories under dispatch and state-of-charge constraints. The planning problem is formulated as a multi-objective optimization that quantifies trade-offs among life-cycle cost, supply reliability (e.g., unmet-load metrics), and sustainability indicators (e.g., diesel-free operation or emissions when backup generation is present). A Pareto-optimal set of designs is obtained using a population-based multi-objective algorithm, and representative knee-point (balanced) solutions are selected to illustrate how turbine choice and dispatch strategy interact with seasonal hydrology and solar variability. The proposed approach supports transparent and robust design decisions for hybrid hydro–solar microgrids. Full article

Food waste presents a major challenge to global sustainability. Up to 60% of this waste occurs at the household level, at which point labeling confusion causes avoidable loss. The present study employed the motivation–opportunity–ability framework to conduct a narrative synthesis of 82 studies and pieces of gray literature, incorporating policies and industry practices to elucidate how food labeling modulates food waste behavior through interactions with consumer motivation, external opportunities, and individual abilities. Food labeling should be considered a systemic intervention tool spanning the entire food supply chain rather than mere carriers of information. The present findings indicate that although standardizing quality and safety label terminology mitigates cognitive confusion, it may have limited efficacy to reduce food waste. Extending shelf life and providing explicit storage guidance are critical strategies that are often undervalued and comparatively underexplored. Labels most effectively reduce waste when they simultaneously activate motivation, opportunity, and ability. When all three elements cannot be activated concurrently, stakeholders should prioritize improving external opportunities or enhancing individual abilities rather than stimulating motivation. Food labeling interventions can only be effective at waste mitigation if systemic and transdisciplinary synergy is achieved among all stakeholders in food supply chains. Full article

Green supply chain management (GSCM) has become essential for organizations seeking to balance environmental sustainability, regulatory compliance, and economic resilience. However, selecting appropriate green supply chain strategies constitutes a complex multicriteria decision-making (MCDM) problem due to diverse sustainability practices, conflicting objectives, dynamic market conditions, and significant uncertainty in expert evaluations. To address these challenges, this study proposes an intelligent multicriteria group decision-making (MCGDM) framework to assess 15 GSCM strategies across 15 environmental, operational, economic, and regulatory criteria. The framework employs complex fractional orthopair fuzzy sets (C_FOFS) to model uncertainty, expert hesitation, and complex-valued judgments. Expert weights are determined using the analytic hierarchy process (AHP), while criteria weights are derived objectively through the entropy method. A modified technique for order preference by similarity to the ideal solution (TOPSIS) is applied to obtain a robust ranking of alternatives. Evaluations from five multidisciplinary experts ensure practical relevance and validity. The results indicate enhanced uncertainty modeling, improved ranking stability, and greater interpretability compared with conventional fuzzy and deterministic approaches. The proposed framework provides a transparent and effective decision support tool for strategic GSCM planning. Full article

Aquaculture marks the transition from the simple activity of harvesting aquatic animal resources, carried out through the catching practices of fishing, to the farming of aquatic organisms in fresh, brackish and sea waters, carried out through human intervention aimed at increasing production. To date, research is proceeding towards expanding the range of species that can be farmed, improving the number and quality of products, and reducing the environmental impact of aquaculture activities; these efforts are supported by the improvement of our knowledge of the biology of the relevant species, the significant updating/upgrading of the rearing technologies, and the increasing awareness of the importance of water quality in optimising farming conditions. While necessarily dependent on market demand, aquaculture needs to fully leverage its environmental potential; and the relationship between aquaculture and the environment requires a system of production that combines eco-compatibility and eco-sustainability. Here, we report and analyse insights and perspectives in eco-sustainable aquaculture, spanning from sustainability and innovation processes in aquaculture to antibiotic control and aquaculture ecosystem services, in the context of the United Nations Sustainable Development Goals. Full article

This study examines the role of artificial intelligence (AI) technologies in identifying and sustaining leadership talent within the educational sector in Oman, addressing the increasing demand for evidence-based and innovative approaches to leadership development. A qualitative phenomenological research design was employed to explore how AI experts and educational leaders perceive, evaluate, and conceptualize AI-driven tools for leadership talent identification and sustainability. In-depth semi-structured interviews were conducted with 25 participants from three major Omani educational institutions. Data were analyzed using thematic analysis, allowing systematic identification of recurring patterns, conceptual relationships, and shared professional insights. The findings indicate that AI applications—including big data analytics, behavioral assessment tools, competency identification platforms, and predictive analytics—provide effective mechanisms for early detection and assessment of leadership potential. Furthermore, integrating AI into personalized professional development programs and continuous performance evaluation contributes to the long-term sustainability and strategic utilization of leadership talent. This study underscores the potential of AI to enhance strategic leadership planning within educational institutions. The results expand our empirical understanding of AI-driven leadership development and offer practical insights for implementing AI-informed strategies in Oman and the broader Gulf region. Full article

A water company’s water supply network is responsible for distributing good-quality water in quantities that meet customer needs, ensuring proper operation of the water supply network to ensure adequate pressure at the receiving points, efficiently repairing faults, and planning and executing maintenance, modernization, and expansion work. Managing a water supply network is a complex and complex process. A crucial challenge in water company management is detecting and locating hidden water leaks in the water supply network. Leak location in water distribution networks is a key challenge for utilities, as undetected leaks lead to water losses, increased energy consumption, and reduced service reliability. With the development of cyber-physical systems (CPSs), the integration of physical infrastructure with real-time digital monitoring has enabled more adaptive and responsive water operations. Data-driven decision-making in CPS in the water industry leverages classification and regression trees (C&RTs) to analyze real-time sensor data—such as pressure, flow, and consumption—to classify system states and predict potential faults. By transforming operational data into interpretable decision rules, C&RTs enable automated and timely maintenance actions that improve reliability, reduce water loss, and support intelligent infrastructure management. The aim of this study is to develop and evaluate AI-based optimization methods to enhance sustainability, efficiency, and resilience in the water industry by enabling autonomous, data-driven decision-making within CPSs, supporting smart industry practices, and addressing practical challenges associated with the actual implementation of smart water management solutions using simple solutions such as C&RTs. The accuracy of the best classifier was 86.15%. Further research will focus on using other types of decision trees that will improve classification accuracy. Full article

Generative Artificial Intelligence (GenAI) is reshaping how students engage with learning both within and beyond traditional classroom settings. In a time when the development of transferable skills is essential for enabling students to thrive in varied and rapidly evolving environments, the potential of GenAI to enhance learning engagement remains insufficiently understood. Despite rising interest in interactive, personalised learning companions that enable deep engagement and ongoing skills development, scholarly research remains limited. This gap constrains effective institutional use of GenAI, reinforces black-box thinking, and restricts understanding of meaningful student engagement and skills acquisition. This paper investigates how a GenAI-enabled AI teacher supports student learning engagement, focusing on behavioral engagement as evidenced by learner interaction and participation patterns across diverse international higher education institutions. Using a combination of quantitative engagement metrics and qualitative learner reflections, the study examines how GenAI supports personalised learning, sustained interaction, autonomy, and cognitive engagement among students with varying educational backgrounds. The findings demonstrate that GenAI-based teaching systems can promote meaningful learning engagement, enhance motivation, and strengthen the development of transferable and employability skills. The study contributes empirical evidence to current debates on GenAI integration, teacher practices, and student engagement, offering implications for curriculum design and institutional adoption of GenAI-enabled learning tools. Full article

Public transportation (PT) is key to breaking the vicious cycle of private vehicles, a critical sustainability challenge in developing countries. The increase in population raises the number of private cars, and this trend continues. PT plays a vital role in reducing car use, traffic congestion, and environmental pollution. Fare is crucial to the system’s ability to encourage passengers to use PT. It affects mobility, the quality of life, and the sustainability of the system. This study aims to examine Istanbul’s optimal fare system using the BWM (Best–Worst Method) for PT fare for the first time. Furthermore, it is the first study to compare fare structures and criteria for Istanbul, Europe’s second-largest city, where transportation affects quality of life. The most frequently used fare structures and criteria in the literature and practice were weighted by experts using BWM surveys for the Istanbul Metro. The results show that distance-based fare (DBF) (43.7%) is the best fare structure, while flat fare (FF) (12.2%) is the weakest. For the criteria weightings, benefit received (24.4%) and social equity (22.7%) are seen as superior. Finally, the impact of the criterion on the fare structure was demonstrated through analysis, and its importance for experts in evaluating PT was highlighted. Full article