Search Results (75)

Iron-exchanged bentonites derived from a natural montmorillonite-rich clay (Taganskoe deposit, Kazakhstan) were prepared through a simple aqueous ion-exchange route using Fe(II) or Fe(III) inorganic salt precursors, yielding final Fe contents of ca. 5–7 wt.%, while preserving the smectite layered framework. A mild thermal treatment under air was applied to tune iron coordination without triggering major structural collapse. The resulting materials were characterized by ED-XRF, PXRD, FE-SEM/EDX, DLS/ζ-potential and DR UV–Vis–NIR spectroscopy, revealing predominantly exchanged Fe species with a limited fraction of surface iron-oxide clusters, whose contribution increases after activation. Structure–reactivity relationships were probed under mild conditions in liquid-phase ethyl acetate using dimethyl methylphosphonate (DMMP) and 2-chloroethyl ethyl sulfide (2-CEES) as organophosphorus and organosulfur hazardous chemicals and chemical warfare agent simulants, respectively. Fe(III)-bentonite enabled very fast DMMP removal (ca. 93% within 0.5 h) with a remarkable improved performance with respect to Fe(II)-bentonite and the pristine mineral clay. For 2-CEES, the presence of H₂O₂ markedly enhanced oxidation on Fe-containing clays, reaching quantitative abatement within 24 h (up to >90%), with strong retention of oxidized sulfur products by the clay matrix. These results highlight Fe-exchanged natural bentonites as robust, cheap and multifunctional adsorption/catalytic solids for decontamination and water-treatment applications. Full article

The accelerating generation of waste streams is observed globally. Spanning lignocellulosic biomass, plastic waste, sewage sludge, and industrial residues, this review presents both an urgent management challenge and a compelling materials opportunity. Carbon materials derived from these waste streams offer a sustainable route to functional carbons applicable in electrochemical energy storage, adsorption, heterogeneous catalysis, and high-temperature applications. Yet their rational design remains constrained by incomplete understanding of the relationships between feedstock composition, processing pathway, structural characteristics, and functional performance. This review provides an integrated analysis of waste-derived carbon materials from processing pathways to structure–property–function relationships. The principal feedstock categories are examined for their compositional characteristics and implications for carbon yield and structure. Five primary processing routes are assessed. The five routes examined are pyrolysis, hydrothermal carbonisation, physical and chemical activation, and microwave-assisted processing. They are assessed comparatively with emphasis on structural outcomes and governing parameters. The resulting structural characteristics are discussed. These are morphology, hierarchical pore architecture, surface chemistry, heteroatom doping, and crystallinity. They are discussed alongside their characterisation methods and known limitations as performance predictors. Structure–property relationships are examined quantitatively. Heteroatom-doped hierarchical porous carbons achieve 612 F/g specific capacitance. Turbostratic hard carbons deliver 450 mAh/g sodium storage with over 90% retention. Hierarchical porous carbons demonstrate CO₂ uptake of 5.0 mmol/g and dye adsorption exceeding 9000 mg/g under optimised laboratory conditions; these values reflect individual studies and are not directly comparable across systems. Biomass-derived sulfonated carbon catalysts sustain biodiesel yields above 90% over multiple cycles. Challenges of feedstock variability, process scalability, environmental compliance, and economic feasibility are addressed, and machine learning-guided design, standardised characterisation methodology, and circular economy policy frameworks are identified as key enablers for translating laboratory performance into industrial reality. Full article

In today’s tourism industry, service quality is essential for differentiation and achieving sustainable competitive advantage. While much research on service quality exists for the private sector, a notable gap remains regarding service dynamics in non-profit public recreational spaces in emerging economies. This study fills that gap by examining service quality (SQ) at National Botanical Gardens (NBGs) in South Africa, focusing on visitor perceptions of SQ importance and performance. Using an adapted Importance–Performance Analysis (IPA) framework, the research explores how visitor expectations compare with actual experiences. A quantitative approach was adopted, with structured questionnaires given to a convenience sample of visitors at key gardens like Pretoria and Walter Sisulu NBGs. Data analysis centred on correlation coefficients to assess the relationship between perceived importance and actual performance. The findings reveal a generally strong, statistically significant positive correlation; however, some attributes show a ‘weak correlation,’ indicating critical service gaps often overlooked by traditional management models. These insights demonstrate that, in natural attraction settings, visitor-perceived importance primarily drives satisfaction with performance. In addition to offering the South African National Biodiversity Institute (SANBI) a strategic plan to boost visitor retention, this study advances the academic discussion by challenging existing SQ models within non-profit recreational sectors. It lays a vital empirical groundwork for future research on public-sector service excellence in the Global South. Full article

This research explores the impact of ownership structure on the financial performance of Initial Public Offerings (IPOs) in North African markets, a key emerging region that remains insufficiently examined in the academic literature. Drawing on agency theory, signalling theory, and liquidity theory, the study investigates how different shareholder configurations—particularly managerial shareholding, ownership concentration, institutional investor presence, and float—influence both initial underpricing and long-run market performance. Based on a sample of 228 IPO transactions conducted between 2005 and 2023 across six countries (Morocco, Egypt, Tunisia, Algeria, Libya, and Mauritania), the research adopts a quantitative methodology grounded in a hypothetico-deductive approach. The findings support the signalling theory premise that managerial retention constitutes a credible quality signal, showing a strong positive relationship between post-IPO managerial shareholding (MOWN) and long-run performance measured by the 36-month Buy-and-Hold Abnormal Return (BHAR). Ownership concentration (CONC) reduces underpricing while improving long-term performance, reflecting stronger governance discipline. Institutional investor presence (INST) exerts a significant direct effect on both performance dimensions. Conversely, firm size shows no direct significant effect, a result consistent with the institutional specificities of North African markets. These findings underscore the complex mechanisms behind IPO success in this context and offer practical and theoretical implications regarding governance practices and institutional frameworks. The study also outlines avenues for future research, including a deeper examination of regional governance dynamics. Full article

Human serum albumin (HSA) binding critically influences drug distribution and pharmacokinetics. In this study, HSA affinity chromatography was integrated with machine-learning-based quantitative structure–retention relationship (QSRR) modeling to elucidate structural determinants of albumin binding in a library of 115 fluoroquinolone (FQs) derivatives. Experimentally determined logk_HSA values were obtained using biomimetic chromatography, and these were then used as modelling endpoints. Following descriptor reduction via Least Absolute Shrinkage and Selection Operator (LASSO) and systematic benchmarking of 42 regression algorithms, support vector regression (SVR) and nu-support vector regression (ν-SVR) with radial basis function kernels demonstrated superior predictive performance. A parsimonious 12-descriptor ν-SVR model achieved strong calibration and validation metrics (R² = 0.916, Q²_test = 0.823, concordance correlation coefficient (CCC) = 0.899) and satisfied Organisation for Economic Co-operation and Development (OECD) criteria, including applicability domain assessment. Shapley Additive exPlanations (SHAP)-based interpretation revealed that albumin binding is governed by a balance between hydrophobic surface area and distributed electronic properties, whereas excessive localized polarity and quaternary ammonium functionalities reduce affinity. This experimentally anchored and interpretable modeling framework provides mechanistic insight into HSA binding in fluoroquinolones and offers a robust tool for rational pharmacokinetic optimization. Furthermore, in order to make the model easily accessible to users, we have packaged it in the form of an online application. Full article

This study examined how working memory, attention control, and frequency of ChatGPT-4 use are structurally associated with vocabulary retention in foreign language learning. A quantitative cross-sectional survey design was employed, with data collected from 1002 EFL learners via stratified random sampling. Validated self-report instruments measured working memory, attention control, frequency of ChatGPT use, and vocabulary retention (immediate recall, delayed retention, semantic integration, and productive use). Structural equation modelling was used to test the proposed model. The results showed that working memory was strongly associated with attention control and exerted a direct effect on vocabulary retention across all dimensions. Attention control explained a substantial share of the relationship between working memory and retention, indicating that regulatory allocation of attention, rather than memory capacity alone, governs whether lexical information is stabilised during ChatGPT-assisted learning. The frequency of ChatGPT use conditioned these cognitive pathways by strengthening links between working memory and attention control, and between attention control and vocabulary retention, at higher levels of engagement. Frequency did not predict retention independently, indicating that repeated use supports learning only to the extent that it reinforces cognitive regulation rather than increasing exposure. Vocabulary learning with AI relies more on cognitive regulation and engagement than exposure. Full article

Against the macro-background of balancing development and food security strategies, China has implemented a land-use regulation system centered on farmland protection. However, the economic impacts of such regulation lack sufficient quantitative evaluation. Using farmland retention targets at the county-level in the administrative region and combining them with relevant data, this study employs an Intensity Difference-in-Differences (Intensity DID) approach to examine how land-use regulation affects county-level economic growth and convergence. The findings reveal a U-shaped relationship between land-use regulation and county-level economic growth, suggesting that, at the current stage, the intensity of land-use regulation generally promotes economic growth. Heterogeneity analysis further indicates that county economies in major grain production areas (MGPAs) and main grain-producing counties (MGPCs) experience stronger negative constraints related to the policy, while MGPCs in non-major grain production areas (non-MGPAs) are most sensitive to land-use regulation. China’s county economies exhibit convergence; however, land-use regulation may reduce the growth rate of counties that were underdeveloped in the base period, thereby widening inter-county development disparities. This divergence is manifested in the lack of convergence between the clubs of MGPCs and non-MGPCs. Mechanism analysis suggests that differences in industrial structure, capital investment, and fiscal expenditure constitute the key focal points for addressing the issue. Policy implications indicate that China should strengthen land-use regulation on the premise of rationally determining the functions and scale of various land types, continue to advance market-oriented reforms of land factors, improve the vertical and horizontal interest compensation mechanism for MGPAs, and stimulate the endogenous development momentum of these regions. Full article

Conventional topical therapy for corneal and anterior segment diseases is limited by rapid tear clearance and multilayer corneal barriers, resulting in low bioavailability and the need for frequent dosing. Artificial intelligence (AI) is emerging as a complementary approach that learns quantitative relationships between molecular structure, formulation variables, and ocular performance. In corneal drug delivery, machine learning models have been used to optimize multicomponent formulations and processing conditions; predict key quality attributes such as particle size, zeta potential, encapsulation efficiency and release kinetics; and estimate corneal permeability, retention and ocular irritation risk, thereby reducing experimental burden and guiding safer design. AI can also be coupled with mechanistic ocular pharmacokinetic/pharmacodynamic models to translate formulation attributes into predicted tissue exposure. Finally, inverse design approaches enable the discovery of new carriers and devices, illustrated by machine learning-guided peptide carriers and smart contact lens platforms that combine sensing with on-demand drug release. Despite these advances, current datasets remain small and heterogeneous, external validation and benchmarking against conventional workflows are limited, and uncertainty quantification and interpretability must be addressed to enable clinical translation. This review summarizes corneal barriers and delivery platforms, critically evaluates where AI provides measurable value across design, characterization and performance and highlights data and validation priorities needed for trustworthy AI-enabled corneal therapeutics. Full article

Self-healing polymer nanocomposites are increasingly investigated as damage-tolerant materials for structural and functional applications; however, their engineering translation remains limited by the difficulty of achieving high mechanical reinforcement while retaining sufficient polymer mobility for effective repair. Previous reviews have largely summarized healing chemistries or nanofiller classes but have rarely established quantitative structure–property–healing relationships or resolved contradictory trends reported across studies. In this review, we develop an integrated framework that links polymer network architecture, nanofiller geometry/percolation behavior, and interfacial dynamics to healing kinetics, and we compile quantitative design windows for nanofiller loading, percolation thresholds, activation conditions, and durability metrics. The synthesis reveals that healing performance is maximized within intermediate filler contents near the percolation regime, whereas excessive nanofiller loading commonly suppresses healing by nanoscale confinement and interphase immobilization despite improving modulus and conductivity. Finally, we propose application-oriented design rules and benchmarking priorities, emphasizing standardized fracture/fatigue-based evaluation, multi-cycle healing retention, and scalable interphase engineering as the key pathways for translating self-healing nanocomposites from laboratory demonstrations to validated engineering systems. Full article

Employer-funded education and training (EFET) has gained increasing attention as a strategic human resource practice for developing human capital and enhancing organizational performance. However, empirical evidence on its effectiveness remains limited in emerging economies, particularly within the Kingdom of Saudi Arabia (KSA), where workforce localization and human capital development are central to Vision 2030. This study examines the associations between EFET participation and three key employee outcomes: motivation, retention intention, and productivity. Using a quantitative research design, data were collected from 200 employees and managers across multiple sectors in KSA through a structured questionnaire. Structural Equation Modeling (SEM) was employed to test the hypothesized relationships while controlling for gender, age, sector, and years of experience. The results indicate that EFET participation is positively and significantly associated with employee motivation, retention intention, and self-reported productivity, with the strongest association observed for retention intention. Model fit indices demonstrate an excellent overall fit, supporting the proposed model’s robustness. By integrating Human Capital Theory with empirical evidence from the Saudi context, this study contributes to the literature by extending understanding of how employer-funded education functions within a non-Western labor market. The findings offer practical implications for organizations and policymakers seeking to optimize education and training investments in support of sustainable workforce development and Vision 2030 objectives. Full article

This study investigates the influence of banking service quality and customer trust on customer retention behavior, considering the mediating role of customer satisfaction and the moderating role of FinTech. In light of the growing digitalization in the banking sector, the study aims to understand how these constructs interact to drive long-term customer loyalty. A quantitative research approach was adopted using data collected through a structured questionnaire administered to banking customers. The relationships among variables were examined using Partial Least Squares Structural Equation Modeling (PLS-SEM), assessing both direct and indirect effects. The results show that banking service quality and customer trust significantly enhance customer satisfaction, which in turn positively influences customer retention behavior. Moreover, satisfaction was found to mediate the relationships between both service quality and trust with retention. FinTech demonstrated a strong direct effect on retention and also significantly moderated the satisfaction–retention link, amplifying its impact when FinTech services are effectively utilized. This study contributes to the relationship marketing literature by introducing FinTech as a novel moderating variable in the satisfaction–retention framework. It offers practical insights for banks aiming to enhance retention by improving service quality, fostering trust, and leveraging digital technologies to strengthen customer relationships. Full article

Background: The implementation of shared governance within the nursing practice results in heightened satisfaction among nurses and enhances the quality of care provided. Shared governance fosters collaborative and proactive relationships among nurses and healthcare providers, while also cultivating a sense of confidence among nurses. However, evidence suggests that a lack of awareness, reliance on traditional governance, and inadequate shared governance structures among nurses continue to exist at the unit-based council (UBC) level, including those in the Kingdom of Saudi Arabia (KSA). Purpose: The present study aimed to assess the extent of clinical nurses’ perceptions concerning shared governance at the UBC level, and to examine the variations and relationships in their perceptions based on demographic and work-related characteristics. It further explored the demographic and work-related factors that affect the overall perceptions of shared governance among clinical nurses. Methods: This quantitative study utilized a cross-sectional design and was carried out in three governmental hospitals in the KSA. The sample comprised 669 nurses, who were selected using a convenience sampling method. The Index of Professional Nursing Governance (IPNG) tool was utilized for data collection conducted between February 2025 and April 2025. Descriptive statistics alongside the t-test and analysis of variance (ANOVA), Pearson-r correlation coefficient, and multiple linear regression were utilized for data analysis. Significant findings were drawn when p ≤ 0.05. Results: The average perception of shared governance among clinical nurses at the UBC level was 180.42 out of 430, suggesting that decision-making occurs collaboratively between nurses and management. Significant differences in the average level of clinical nurses’ perceptions of shared governance were noted in relation to their educational qualifications (F = 5.015, p = 0.001) and nursing units (F = 4.157, p = 0.010). The hospital in which clinical nurses were employed (r = 0.098, p = 0.037) and nursing units (r = 0.087, p = 0.020) exhibited significant correlations with their overall shared professional governance. Furthermore, the hospital where clinical nurses were employed (β = 0.406, p = 0.001, 95% confidence interval [CI] = 0.166, 0.646) and nursing units (β = 0.326, p = 0.038, 95% CI = 0.018, 0.314) served as predictors of their overall professional shared governance. Conclusions: Clinical nurses in this study showed an initial or relatively low level of shared governance at the UBC level. The overall finding highlights a critical need for nursing managers and leaders to enhance the level of professional shared governance among clinical nurses, which may result in improved nurse retention and overall quality of nursing care. It is crucial to consider clinical nurses’ educational qualifications and working environment at the UBC level when aiming to enhance their level of professional shared governance. Full article

This study introduces a Fuzzy–AHP–based analytical model for the quantitative assessment of authenticity loss in adaptive reuse practices, addressing a persistent gap in heritage research—the lack of reproducible mathematical frameworks capable of linking authenticity evaluation with sustainability indicators. Unlike previous studies that approach authenticity conceptually or qualitatively, this research develops a hybrid decision-support system that translates both intangible and tangible heritage attributes into measurable linguistic variables, enabling systematic and comparable authenticity assessments. The model was applied to ten traditional houses in Alanya, Türkiye, representing different adaptive reuse types (residential, cultural, commercial, and touristic). A total of 17 experts contributed to the Analytic Hierarchy Process (AHP) weighting stage, producing a Consistency Ratio of 0.0156 (<0.10), and 8 experts provided scoring inputs for the fuzzy system. The fuzzy inference system was implemented in MATLAB R2023a, incorporating seven main criteria and three subcriteria, nine input variables, five linguistic categories, and a rule base of 3400 fuzzy rules. Membership functions were defined within the 0–100 numerical range, and the centroid defuzzification method was used to compute final authenticity values. Model reliability was confirmed through Kendall’s W = 0.87, demonstrating strong inter-rater agreement. Results show that buildings retaining their original residential function achieved the highest authenticity scores (Final Score ≈ 86), while structures converted into boutique hotels or restaurants exhibited substantial authenticity losses (Final Score range: 25–45), especially within Group 2 criteria (environment, function, spirit, and intangible cultural heritage). This divergence illustrates a sustainability paradox: although adaptive reuse prolongs building life cycles and reduces embodied carbon, it may simultaneously undermine cultural sustainability when authenticity is significantly compromised. The proposed Fuzzy–AHP authenticity model provides a replicable, transparent, and empirically validated tool for evaluating the effects of functional transformation within a sustainability framework. By quantifying the relationship between adaptive reuse types and authenticity retention, the study contributes to sustainable heritage management research and supports the implementation of SDG 11—Sustainable Cities and Communities. Full article

Efforts to improve student retention and degree achievement in STEM disciplines has increasingly highlighted the importance of faculty professional development and the adoption of evidence-based teaching practice. Despite teaching being a core faculty responsibility, formal pedagogical training is rarely required, and many faculty develop their teaching approaches based on their experiences as students or graduate teaching assistants. This quantitative study examines STEM faculty perceptions of receiving encouragement as students and their relationship with dimensions of their professional identity. Our findings suggest that encouragement aligns with faculty student- and self-related professional identity dimensions. These results underscore the need for structured professional development and early training in evidence-based pedagogy, including the cultivation of encouragement practices, to foster more inclusive, supportive, and effective learning environments in STEM. Full article

River–lake systems in the middle and lower reaches of the Yangtze River Basin function as critical ecological interfaces for maintaining regional water security and biodiversity. However, the complex interplay between environmental factors and biological communities in these systems remains poorly understood, limiting evidence-based management strategies essential for achieving sustainable development goals. This study investigated the spatiotemporal heterogeneity of environmental variables and their relationships with biological communities across 36 sampling sites (4 rivers, 5 lakes) from January to November 2022. Significant spatial differences were observed between river and lake systems, with lakes exhibiting higher concentrations of biochemical oxygen demand (BOD₅), chlorophyll-a, permanganate index (CODMn), loss on ignition (LOI), sediment total nitrogen (STN), total organic carbon (TOC), and turbidity, while rivers showed elevated total nitrogen levels. These patterns reflected fundamental differences in hydrodynamic mechanisms, particularly water retention time and sedimentation processes. Environmental parameters displayed distinct seasonal variations, with BOD₅ increasing markedly in autumn, and chlorophyll-a showing system-specific peaks in lakes (May) and rivers (September). Multivariate analyses revealed that water temperature, organic matter, and nutrients (particularly phosphorus) were key drivers shaping both phytoplankton and benthic communities. The structural equation model identified a strong cascade pathway from turbidity through phosphorus to phytoplankton richness, and uncovered a “phosphorus paradox” wherein total phosphorus exhibited contrasting effects on different biological components—positive for phytoplankton richness but negative for benthic richness. The positive relationship between phytoplankton and benthic macroinvertebrate richness provided evidence for benthic–pelagic coupling in this river–lake system. These findings advance our understanding of the complex mechanisms linking physical factors, nutrient dynamics, and biological communities across river–lake continuums, providing a quantitative framework for ecosystem-based management that supports sustainable development in the Yangtze River Basin and similar freshwater systems globally. Full article