An expert-based multi-criteria evaluation of environmental management performance in major Turkish airports

Investigation of the fabrication and thermal management performance of three-dimensional hydroxylated boron nitride network/epoxy composites

Rice–fish coculture and its ecological indicators: Insights from Ziro Valley using LASSO regression model

Subcentimeter thyroid nodules: Diagnostic challenges, risk stratification and management in contemporary surgical oncology practice.

A biomass-derived carbon reinforced phase change aerogel with synergistic photothermal conversion and heat transfer: Performance and chip thermal management simulation

Performance management of safety instrumented systems (SIS) is essential for reducing major accident risk in oil and gas facilities, and has traditionally been governed by standards such as IEC 61508 and 61511. These standards prescribe how performance shall be managed for conventional systems, but provide little concrete guidance on how machine learning (ML) can be incorporated. As facilities move toward unmanned operation, where manual testing and inspection are infeasible, this lack of standardized rules for ML-based SIS becomes as challenging as the technical issues of data imbalance and sample selection bias. Without established rules, certification bodies have no framework for evaluating whether an ML-based SIS can be considered sufficiently reliable. This paper proposes an ML-based SIS advisor designed to improve operator awareness and decision support under varying operating conditions. To overcome data limitations, we introduce a simulation-based generative oversampling method (SIM-GEN) that produces abnormal-operation data from dynamic process models, thereby extending training coverage and addressing imbalance and support mismatch. Engineering priors are incorporated through HAZOP-based pseudo-labels to stabilize the training process. To address this lack of standardized measures, we further propose how the functional safety performance measure can be derived from the confusion matrix. These serve as dataset-level surrogates of the IEC 61508 failure rate categories. While these measures are presently static proxies, they can be extended in actual operation by integrating diagnostic functions and time-based monitoring, enabling convergence toward actual performance measures as defined in functional safety standards. Importantly, in the pre-deployment phase, these indices offer certification bodies a practical and reproducible basis for auditing ML model performance, supporting design verification and approval prior to operation. Experiments show that SIM-GEN improves robustness under imbalance and bias and outperforms conventional resampling techniques. The dynamic process simulator K-SPICE was used only to generate abnormal-operation scenarios during data preparation; the SIM-GEN method itself is independent of the particular simulation platform. • Formulates abnormal condition detection task in unmanned facilities as a machine learning classification problem, explicitly addressing class imbalance and sample selection bias, and linking functional safety practice with machine learning approaches. • Proposes a simulation-based generative oversampling method (SIM-GEN) that leverages dynamic process simulation to generate realistic abnormal operation data, thereby mitigating severe imbalance and sample selection bias in safety-critical training datasets. • Introduces functional safety–oriented performance indices ( η D , η S ), which, although not yet standardized, provide concrete numerical evidence beyond IEC 61508-2 guidelines and can serve certification bodies and practitioners as transparent benchmarks.

In an era marked by volatility, digital disruption, demographic change, and continuous organisational transformation, the ability of firms to compete increasingly depends on the quality of their human capability. While leadership scholarship has long recognised the importance of higher-order cognition, human resource management has given comparatively less explicit attention to thinking competence as a strategic capability that can be cultivated through talent systems, learning architectures, performance processes, and organisational culture. This paper argues that expert, critical, strategic, and systems thinking should be understood not merely as desirable individual traits, but as strategic human resource capabilities that enable organisations to attract, develop, deploy, and renew talent in ways that support sustained adaptability and performance. Drawing on the resource-based view, dynamic capabilities theory, organisational learning, and leadership research, the paper develops a framework for embedding thinking capability within HR strategy and practice. The paper shows how higher-order thinking can be advanced through recruitment and selection, leadership development, succession planning, performance management, employee voice, diversity and inclusion, and HR (Human Resource) information systems. In doing so, it repositions HR not as an administrative support function, but as a central architect of organisational adaptability, change readiness, and long-term capability development. The paper contributes to strategic HRM (Human Resource Management) by conceptualising thinking competence as a developable and institutionalisable dimension of workforce and leadership capability, with implications for talent management, organisational resilience, and competitive advantage.

Purpose This study aims to investigate the relationship between artificial intelligence (AI) adoption and digital knowledge management (DKM) performance through the mediating role of digital knowledge capability. Additionally, it examines the moderation of digital culture on the association between digital knowledge capacity and DKM performance, as well as the moderation of AI familiarity on the association between AI adoption and digital knowledge capability. Design/methodology/approach Using a quantitative study design with a time-lag approach, data were gathered in two waves. Finally, 378 senior managers of palm oil-producing firms in Malaysia responded. Findings The findings show that AI adoption positively relates to DKM performance through the mediating role of digital knowledge capability. Furthermore, the association between digital knowledge competency and AI adoption is strengthened by AI familiarity. Additionally, the link between digital knowledge competency and DKM performance is considerably moderated by digital culture. Practical implications In this contemporary digital era, managers in the palm oil sector may benefit from the findings of this study by achieving greater digital knowledge capability and DKM performance while using advanced digital technologies. Originality/value The current research extends the existing knowledge-based view (KBV) theory regarding the effects of organizational knowledge and the adoption of AI. The study also enhances the dynamic capability theory by analyzing the role of knowledge processes induced by AI in organizational pervasiveness and agility in using digital knowledge assets.

This study examines the influence of government on the performance of public sector projects, with a specific focus on government-funded school construction projects in Morogoro Municipality, Tanzania. The study employs a cross-sectional research design and utilizes multivariate regression analysis on data collected from 194 sampled participants within the municipality. The findings reveal that adequate budgeting, regulatory compliance, efficient procurement processes, and supportive government policies significantly influence the performance of public sector projects managed by the Morogoro Municipality Council. Based on these findings, the study recommends the promotion of collaborative partnerships and coordinated efforts to continuously enhance public sector project performance management. Furthermore, local government authorities should adopt a more inclusive and participatory approach to project planning, implementation, and monitoring. This includes actively engaging and empowering local communities, as well as ensuring that government commitment to funding public school infrastructure is both visible and sustainable.

Architectural excellence has traditionally been associated with creativity, innovation, aesthetic quality, and conceptual strength. While these characteristics remain essential, contemporary project environments increasingly demonstrate that design quality alone is insufficient to guarantee successful outcomes. Many projects begin with ambitious architectural visions yet encounter challenges during technical development, construction execution, operational occupancy, or long-term asset management. As a result, a growing distinction has emerged between design excellence and execution excellence. The most successful architectural projects are not merely those that generate compelling concepts but those that effectively translate conceptual intent into built environments that perform successfully throughout their lifecycle. This article proposes a comprehensive framework for understanding architectural execution excellence as the integration of conceptual design, technical delivery, construction implementation, and operational performance. Rather than treating these stages as independent activities, the paper argues that they form a continuous system in which decisions made during one phase influence outcomes across all others. Architectural success therefore depends on the quality of coordination, communication, and decision-making throughout the entire project lifecycle. The study examines the mechanisms through which design intent is translated into executable architectural systems, explores the role of technical coordination in preserving project quality, and analyzes how construction processes influence the realization of architectural objectives. Particular attention is given to interdisciplinary collaboration, stakeholder management, digital technologies, lifecycle thinking, and operational performance evaluation. The paper further investigates how architects can strengthen project outcomes by expanding their focus beyond design production and engaging more actively with implementation and performance management. The findings suggest that execution excellence represents a strategic capability that bridges the traditional divide between design and delivery. Projects that successfully align architectural vision with technical feasibility, construction realities, and operational objectives are more likely to achieve long-term value, user satisfaction, and organizational success. The article concludes by presenting a holistic model that positions architectural execution excellence as a critical competency for contemporary architectural practice.

Wearable biochemical sensing is shifting sports physiology from intermittent laboratory sampling toward continuous, in situ readouts during training and competition. This review analyzes skin conformal systems that integrate capillary driven fluid routing, flexible sensor arrays, and wireless readout to capture dynamic changes in eccrine secretions relevant to performance management. We compare microchannel layouts that enable controlled filling, time resolved sampling, and evaporation suppression, and we discuss how material selection governs comfort and analytical fidelity, highlighting tradeoffs among polydimethylsiloxane, polyurethane, and polyethylene terephthalate substrates and their compatibility with scalable fabrication. Manufacturing pathways are assessed from soft lithography to laser cutting, three dimensional printing, and roll to roll processing for high throughput multilayer assembly. For transduction, we summarize enzymatic amperometric schemes based on lactate oxidase with hydrogen peroxide detection, including sensitivity, response time, and oxygen dependence, and we contrast these with emerging non enzymatic catalysts. We then detail potentiometric ion selective electrodes for sodium and potassium, focusing on ion selective membrane chemistry, solid state reference electrodes, Nernstian response, and dominant error sources such as drift and biofouling. System integration challenges, including chemical and electrical cross talk in multiplexed layouts, are linked to microfluidic isolation strategies and multiplexed electronics. Finally, we appraise validation practice, emphasizing the debated sweat to blood relationship, the need for synchronized comparative protocols, and the role of data analytics and machine learning for personalization, drift compensation, and prediction of thermoregulatory strain. Remaining barriers include long term stability, adhesion under motion, manufacturability, and regulatory evidence requirements in real world settings. Across these topics, the review emphasizes technology-specific limitations, engineering translation metrics, and realistic breakthrough directions rather than treating wearable sweat patches as a mature plug-and-play platform.

ABSTRACT The construction industry generates substantial amounts of waste, and ineffective waste management (WM) continues to pose serious challenges, particularly in developing countries. Despite the growing interest in smart waste management (SWM) within the construction sector, its effective implementation remains limited, especially in developing countries. This study systematically examines the main barriers to SWM performance in South Africa’s construction sector. A total of 25 potential barriers were identified and assessed using a structured questionnaire, yielding 195 valid responses from construction professionals selected via simple random sampling. The data were analyzed using Structural Equation Modeling (SEM) implemented via the AMOS software. The results revealed that all five categories of barriers significantly influence SWM performance at the 95% confidence level (p ≤ .05). Therefore, regulatory and policy barriers exert the strongest effect (β = 0.234), followed by technological barriers (β = 0.216), economic and financial barriers (β = 0.178), organizational barriers (β = 0.125), and social and behavioral barriers (β = 0.117). The study concludes that overcoming regulatory and policy constraints is critical to improving SWM performance, alongside investments in technology, financial support mechanisms, and organizational capacity. The study also offers practical recommendations for professionals and policymakers to address the main challenges identified.

Cloud computing is a key enabler of modern computing services, offering scalability and flexibility. However, efficient management of cloud resources remains challenging due to limited capacity and the increasing number of tasks requiring timely execution. An effective task scheduling strategy is therefore essential to improve resource allocation and utilization, reduce operational costs and energy consumption, and support high availability-especially for long-term jobs. In this paper, we propose a new scheduling approach that combines a Social-Based Algorithm (SBA) with Deep Reinforcement Learning (DRL), referred to as SBA-DRL. This method allocates tasks to resources by learning from workload patterns and adapting to workload characteristics in a batch scheduling context. We evaluate SBA-DRL using both a synthetic dataset and the real-world Google Cloud Jobs (GoCJ) under workloads ranging from 200 to 1,000 tasks. On the synthetic dataset, our method reduces cost by 20.21% and energy consumption by 25.31%, while improving resource utilization by 9.36%. On the GoCJ dataset, it achieves up to 28.94% lower cost, 8.16% less energy use, and a 14.04% increase in resource utilization. In both cases, SBA-DRL also demonstrates better performance in resource allocation and high-availability management compared to existing heuristics, meta-heuristics, hybrid, and machine learning-based schedulers. These results indicate that the proposed SBA-DRL approach effectively addresses key challenges in cloud task scheduling, offering a practical solution to enhance the efficiency and sustainability of cloud systems.

To address the issues of poor flexibility, insufficient capillary performance, and weak bending fatigue resistance of traditional wicks in thermal management of flexible electronic devices, this paper proposes the surface modification of carbon fiber cloth (CFC) using 1064 nm laser to prepare a wick with both high capillary performance and flexibility. By adjusting laser power and scanning spacing, the influence rules on CFC's surface morphology, chemical composition, wettability, and capillary performance were systematically explored, and its reliability was verified through bending cycle experiments. The results show that laser processing constructs micronano convex structures on the CFC surface and introduces oxygen-containing functional groups via the thermal oxidation effect, realizing a controllable transition from superhydrophobicity (contact angle 150.1°) to superhydrophilicity (contact angle 0°). When the laser power is 24 W and the scanning spacing is 0.1 mm, the capillary rise height reaches 150 mm, the capillary factor is 18.30 mm/s0.5, and the average rise rate is 3.91 mm/s. After 2000 bending cycles, the loss of capillary height and capillary factor is no more than 7%; even after 8000 bending cycles, more than 50% of the comprehensive capillary performance is retained, which is much better than that of traditional metal wicks. Compared with existing wicks, the laser-modified CFC wick achieves the synergistic optimization of "flexible adaptation" and "high-performance capillary transport", providing a solution for efficient thermal management of flexible electronic devices and also offering theoretical support for surface modification and performance optimization of flexible heat-dissipating materials.

Purpose The purpose of this study is to examine the role of strategic management accounting (SMA) in facilitating strategic decisions and organizational performance. Drawing on contingency theory, the authors examine how two elements of the decision support system for strategic decisions – managerial reliance on SMA and accountants’ involvement in strategy processes – mediate the relationship between three distinct organizational strategic choices (strategy deliberation, market orientation and nonfinancial priorities) and organizational performance. Design/methodology/approach The study uses a cross-sectional survey design. The proposed conceptual model is tested with partial least squares structural equation modeling analysis on a sample of 138 Czech firms. Findings Support is provided for four of the six hypothesized mediation effects. The test of simple mediation reveals that managerial reliance on SMA positively mediates the relationship between two strategic choices (strategy deliberation and nonfinancial priorities) and performance, but not for the strategic choice of market orientation. The same pattern of mediation effects was also observed for serial mediation. Practical implications Firms pursuing deliberate strategies and nonfinancial priorities can benefit from involving accountants in strategy processes and from managers relying on information gathered through SMA. These decision support system elements collectively enhance decision quality and, in turn, improve performance. Originality/value The study advances SMA literature in two important ways. First, it introduces a novel SMA construct – managerial reliance on SMA – which adopts the perspective of information users rather than information preparers, unlike prior conceptualizations. Second, it advances a contingency model of SMA by theoretically proposing and empirically demonstrating that SMA is a powerful mediator between strategic choices and performance.

Glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase L1 (UCH-L1) are internationally validated blood biomarkers for the management of mild traumatic brain injury (mTBI). Capillary blood sampling would facilitate point-of-care testing for dual GFAP/UCH-L1 determination, but it is more sensitive to pre-analytical variables such as hemolysis than venous blood measurements. The aim of this study was to compare capillary and venous assays and assess the impact of hemolysis onthem. Paired venous and capillary samples were collected from healthy volunteers and neurocritical care unit patients. GFAP and UCH-L1 were measured using the i-STAT® Alinity system. Red blood cell hemolysate spiking experiments were performed on different analytical platforms able to measure GFAP/UCH-L1. A human albumin matrix experiment was also conducted to look for intra-erythrocytic presence of GFAP and UCHL-1. GFAP showed excellent correlation between capillary and venous samples (r=0.99; 95 % CI 0.98-1.0; p<0.001) with no potential clinically relevant impact on diagnostic thresholds. By contrast, UCH-L1 showed poor correlation (r=0.28; 95 % CI 0-0.57; p=0.058) and wide limits of agreement. Hemolysis caused marked overestimation of UCH-L1 using different analytical platforms, whereas GFAP remained stable. Spiking experiments confirmed that UCH-L1 overestimation was related to its presence in red bloodcells. Capillary UCH-L1 is highly sensitive to hemolysis, limiting the reliability of its assay in capillary samples. By contrast, capillary GFAP assay displays robust analytical performance for mTBI management.

This study investigates the role of complementary clusters of human resource management (HRM) practices in explaining the persistent productivity variations across English NHS hospitals. While prior research has highlighted the importance of individual HRM practices, their impact is often indeterminate. A critical gap remains in the systematic analysis of complementarity, non-linearity, and the interdependencies between these practices. To address this gap, we draw on complementarity theory to propose and test a novel framework. This framework posits that hospital performance is shaped by three distinct effects: indeterminacy, where isolated practices have an inconsistent impact; non-linearity, where practices are effective only above a certain intensity threshold; and complementarity, where synergies between practices amplify their collective impact. We use this framework to explain the significant productivity dispersion observed across hospitals. Using panel data from English NHS hospitals, we employ machine learning techniques to identify distinct HRM clusters and test their significance using correlated random effects models. Our findings confirm that individual HRM practices often have no significant effect until their intensity surpasses a critical threshold. Crucially, we identify several distinct complementary clusters involving incentives, workplace flexibility, training, team quality, and job design. High-performing hospitals are distinguished by their adoption of these synergistic bundles-such as combining job design, team quality, and incentives-whereas persistent low performance is linked to the adoption of incomplete clusters. For instance, workplace flexibility or training only boosts productivity when supported by an effective incentive system, underscoring the powerful non-linear and interdependent effects at play. This study advances the HRM and healthcare management literature by systematically demonstrating the non-linear and complementary nature of HRM practices and by introducing data-driven methods for their analysis. The key implication for policymakers and hospital administrators is the need to prioritise bundles of complementary practices and holistic organisational change over isolated, piecemeal initiatives.

Professional tennis involves repeated short-duration, high-intensity anaerobic efforts interspersed with brief recovery intervals. Powerful serves, explosive sprints, rapid directional changes, and muscular contractions place substantial physiological demands and may lead to distinct long-term adaptations at both neuromuscular and biochemical levels, enabling efficient fatigue management and sustained high-level performance during training and competition. This study aimed to analyse the effect of a long-term professional tennis training adaptations both on neuromuscular and biochemical level in context of the fatigue induced by maximal anaerobic effort (MAnE). 14 professional tennis players (TP) (20.00 ± 0.96 years), and 15 physically active men (PAM) (20.07 ± 1.59 years) finished the study. The testing protocol consisted of MAnE in form of 2 × 30-second Wingate Anaerobic Test and maximal voluntary isometric contractions (MVICs) and submaximal isometric contractions at 20% and 50% of MVIC of the knee extensors and flexors with a surface electromyography evaluation before and after MAnE. Blood samples were collected at baseline, immediately after, and 3 and 24h after MAnE to assess serum markers, including 8-hydroxy-2'-deoxyguanosine, albumin, interleukins (IL-6, IL-10, IL-15), and total antioxidant capacity (TAC). TP exhibited superior absolute (7.8%, p = 0.05) and relative peak power (10.1%, p < 0.01) during MAnE in compare with PAM. After the MAnE, TP were able to maintain their performance in form of peak rate of torque development in knee flexion of MVIC, while PAM showed it reduction by 26% (p < 0.05). In addition, rectus femoris muscle in TP showed increase in muscle activity (52-62%, p < 0.05) after MAnE in both knee flexion and extension at 20% of MVIC. In context of biochemical analysis, TP had higher TAC levels at baseline (47.8%, p < 0.01) and 24h after MAnE (34.7%, p < 0.01) compared to PAM. Additionally, while IL-10 concentrations were significantly higher in TP at baseline (22.0%, p < 0.05),at 3h (14.2%, p < 0.05) and 24h after MAnE (26.8%, p < 0.05), IL-6 showed attenuated increase after MAnE and its concentrations immediately after (19.8%, p < 0.01) and 3h (27.5%, p < 0.01) were lower than PAM. The findings indicate that, compared with physically active men, elite tennis players exhibit distinct neuromuscular and biochemical responses to standardized MAnE, which may reflect long-term training-related physiological adaptations.

Hudy, A, Kraemer, WJ, Shah, J, Burland, JP, Glaviano, NR, Huggins, RA, Fields, JB, Baldwin, AJ, and Casa, DJ. The adaptive spiral of collegiate basketball performance: a multiseason framework integrating load, recovery, and return-to-performance. J Strength Cond Res XX(X): 000-000, 2026-Basketball performance emerges from dynamic interactions among mechanical load, tissue remodeling, neuromechanical regulation, and psychological readiness. Traditional load-management and return-to-play models provide structural guidance but often lack integration across biological and contextual systems within season-dense environments. The purpose of this manuscript is to present and operationalize the Adaptive Spiral (AS) and Return-to-Performance (RTPerf) framework as a monitoring-informed model of readiness regulation in NCAA Division I women's basketball. Across three competitive seasons, longitudinal external-load metrics (e.g., PlayerLoad·min-1, monotony), neuromechanical indices (e.g., countermovement jump variability, sway velocity), and perceptual measures were integrated within NCAA seasonal constraints to examine phase-based adaptation. The AS conceptualizes readiness as a recursive process unfolding through Restore, Build, Prime, Compete, and Recalibrate phases, whereas RTPerf operationalizes daily regulation of capacity, resilience, and performance expression. Longitudinal patterns demonstrated sustained high-intensity exposure with stabilized monotony and reduced neuromechanical variability across competitive phases, reflecting improved alignment between load progression, recovery distribution, and competitive readiness. These findings support a history-dependent model in which protection and performance are coregulated rather than sequential. By integrating objective monitoring, perceptual readiness, and contextual demands, the AS/RTPerf framework provides a structured approach for synchronizing adaptation within season-dense collegiate basketball environments, shifting performance management from reactive adjustment to proactive regulation.

ABSTRACT This study examines how psychological fit and entrepreneurial orientation shape managerial performance in rural enterprises. In resource-constrained and decentralized contexts, effective leadership depends on how individual attributes are translated into managerial action. Using a quantitative explanatory design, data were collected through a cross-sectional survey of rural enterprise managers and analyzed using partial least squares structural equation modeling. The findings show that person–job fit and entrepreneurial orientation improve managerial performance indirectly through managerial behavior, whereas person–organization fit mainly strengthens organizational commitment without directly improving performance. Organizational commitment also shows limited explanatory power in informal rural enterprise settings. These results identify managerial behavior as the central mechanism linking individual attributes to performance. The study contributes to fit–performance and entrepreneurial orientation research in small business contexts and offers practical guidance for improving managerial selection, leadership development, and behavior-based performance evaluation in rural enterprises.