Search Results (36,614)

Viticulture is highly vulnerable to weather variability and climate change. Growers increasingly face risks associated with extreme weather events, water scarcity, and emerging pests and diseases. To address these challenges, this study presents the development and implementation of the first operational digital decision support platform (DSP) tailored to Montenegrin vineyards within the MONTEVITIS project. The platform integrates IoT sensor data, national meteorological records and high-resolution global climate datasets to provide real-time monitoring and climate projections for vineyard management. The system was piloted in four vineyards representing diverse microclimatic and soil conditions of Montenegro. Key functionalities include phenology, irrigation and disease alerts supported by a user-friendly dashboard, map-based visualisation tools and data export functions. The pilot deployment demonstrated that combining heterogeneous data streams increases the reliability of outputs and enables timely, site-specific recommendations. Challenges identified during implementation include connectivity limitations, gaps in data and variable levels of digital expertise among growers; however, lessons learned point to the importance of continuous stakeholder engagement and institutional support for sustained use. The MONTEVITIS experience demonstrates how digital agriculture tools can bridge tradition and innovation in viticulture. By fostering collaboration between growers, researchers and policy makers, the platform enables adaptive strategies for climate resilience and sustainable vineyard management. Although the platform has been successfully deployed and tested under pilot conditions, a comprehensive long-term validation of its performance and impact on vineyard decision-making remains part of ongoing future work. Full article

Modern cyber–physical infrastructures rely heavily on alarm and notification systems to direct human attention when abnormal conditions occur. These mechanisms support timely and safe responses by informing operators and occupants about potential hazards. At the same time, research in human factors has shown that repeated or excessive alerts can weaken vigilance, slow reactions, and reduce confidence in warning systems. This behavioral pattern is commonly described as alarm fatigue. This paper examines how that vulnerability can be exploited intentionally. We refer to this adversarial strategy as alarm poisoning: the deliberate injection of false or misleading alerts in order to increase alarm pressure, erode trust in the monitoring infrastructure, and degrade organizational responsiveness over time. To study this process, we develop a stochastic Cybersecurity Dynamics model representing the interaction among attackers, defenders, alarm infrastructure, and a population of employees. Employee behavior is modeled through evolving trust and fatigue levels, while the overall system is formulated as a continuous–time Markov chain and simulated using the Gillespie Stochastic Simulation Algorithm. A Monte–Carlo campaign is used to analyze the resulting socio–technical dynamics under alternative attacker strategies. The study evaluates time-dependent trust, fatigue, and alarm-pressure trajectories, the distribution of times to behavioral collapse, and defender timing through Trust–Resilience–Agility–Mitigation (TRAM) metrics. The revised analysis also includes replication-sufficiency diagnostics, one-at-a-time sensitivity analysis, and threshold-robustness checks for the collapse criterion. The results show that false alarms with high perceived severity drive alarm pressure upward and degrade trust faster than nuisance-dominated campaigns, even when the total fake-alarm intensity is held constant across strategies. Collapse timing remains highly variable across stochastic realizations, and a non-negligible fraction of runs do not reach the collapse threshold within the simulation horizon. Sensitivity analysis indicates that the main qualitative ranking of attacker strategies is robust across most tested perturbations, with fatigue recovery and defender escalation emerging as particularly influential mechanisms. Overall, the findings support the view that alarm poisoning is a credible socio–technical attack vector and highlight the importance of rapid mitigation, robust alarm management, and human-centered defensive design in cyber–physical security systems. Full article

Speech alterations are early markers of neurodegenerative diseases. Transformer-based speech models such as Whisper have advanced automated speech assessment, but most systems rely on cloud-based computation, raising privacy concerns. On-device processing could offer a scalable and privacy-preserving alternative. This research’s objective was to evaluate whether a fully on-device speech analysis pipeline can achieve competitive accuracy in detecting Alzheimer’s disease and to quantify the contributions of acoustic, linguistic, and embedding features. Therefore, we developed an iOS application running all components, including acoustic analysis, two transformer-based speech-to-text modules (WhisperBase and quantized CrisperWhisper), linguistic feature extraction, and embedding generation, directly on the device. Using the ADReSS Challenge 2020 dataset (N = 156), we trained classical machine-learning classifiers across 20 configurations and evaluated them via a stratified 10-fold cross-validation. Area under the receiver operating curve (AUC), accuracy, precision, recall, and F1 scores were used as performance metrics. An ablation study examined the relevance of each feature group. The best-performing setup (Random Forest with CrisperWhisper transcription and Apple embeddings) achieved an accuracy of 85.4% and an AUC of 0.85. Performance was 5–7% below benchmark models relying on manual transcripts or server-based processing. Embedding features provided the strongest individual contribution, but the highest accuracy required combining acoustic, linguistic, and embedding information. A fully on-device pipeline for Alzheimer’s disease detection from speech is feasible and achieves competitive accuracy while maintaining strict data privacy. These findings highlight the potential of on-device transformer architectures for scalable, privacy-preserving digital screening. Future work should validate the approach in larger and more diverse cohorts. Full article

Background: Rapid adoption of artificial intelligence (AI) in logistics enhances operational efficiency and firm performance; however, empirical evidence on its capability-driven impact remains limited, particularly in Saudi Arabia’s e-commerce sector. This study’s purpose is to examine the influence of AI on logistics firm performance through the mediation role of supply chain consistency and logistics capabilities. Methods: A quantitative study was conducted and data were collected using a convenience sampling technique from 275 employees working in the Saudi Arabian logistics firms. Partial Least Squares Structural Equation Modeling was used to perform data analysis. Results: The study findings indicated that AI usage has significant and positive influence on supply chain consistency (β = 0.290) and logistics capabilities (β = 0.303). Furthermore, supply chain consistency (β = 0.115) and logistics capabilities (β = 0.171) play mediating role between AI usage and firm performance. The research model exhibits substantial predictive capability, explaining 74.6% (R² = 0.746) of the variance in firm performance, while AI usage explains a smaller portion of the variance in supply chain consistency 8.4% (R² = 0.084) and logistics capabilities 9.2% (R² = 0.092). Conclusions: The findings demonstrate that AI-based logistics operations provide extensive support to streamline operations and reduce costs. Full article

Publicly available electricity datasets from operational industrial facilities remain limited due to instrumentation cost, retrofit complexity, and data governance constraints. This paper presents an openly accessible dataset of asset-level electrical energy measurements collected from a medium-scale industrial manufacturing facility over an approximately one-year observation window, with staged commissioning resulting in heterogeneous temporal coverage. The dataset includes time-series measurements from production machinery, auxiliary systems, and distribution-level assets instrumented using a heterogeneous fleet of Ethernet and RS-485 energy meters integrated via industrial gateways and programmable logic controllers. Measurements were acquired via a SCADA-based logging infrastructure and exported from an operational SQL historian. The publicly released dataset comprises fixed 15 min aggregated energy and power metrics derived from high-frequency SCADA telemetry. In its released ALL-phase representation, the dataset comprises measurements from 43 monitored assets and 1,039,873 15 min windows, corresponding to 2.96 GWh of measured electrical energy. Mean window-level data coverage is 99.99%, and 97.72% of ALL-phase windows satisfy the dataset’s reliability criterion. Interval records include energy consumption, demand, data coverage metrics, and reliability indicators. The dataset reflects real-world industrial monitoring conditions, including mixed communication pathways and irregular sampling behaviour, and is intended to support research in industrial energy analytics, data quality assessment, load profiling, and operational energy modelling. Full article

Ultra-large underground metal mines often have complex surrounding rock structures, making traditional assessment methods inadequate for warning against the sudden failure of highly brittle rock masses. To accurately identify high-risk rock layers, this study combines Brazilian splitting tests with acoustic emission (AE) monitoring on four typical surrounding rocks. A normalized damage–stress brittleness coefficient (NDBC) is proposed, and Gaussian mixture model (GMM) clustering is employed to analyze crack evolution mechanisms. Different from conventional brittleness indexes merely based on mechanical parameters, the proposed NDBC characterizes rock brittleness from the perspective of progressive damage evolution driven by acoustic emission microfracture information, providing a dynamic evaluation basis for sudden instability in highly brittle rock masses. The GMM clustering automatically identifies crack features and accurately quantifies the transition from tensile peak to increasing shear during the failure process. The research shows that: (1) AE characteristics during the failure stage are manifested as medium- to high-frequency signals caused by small-scale cracks. (2) Siliceous limestone exhibits extremely high brittleness (NDBC of 0.07) and sudden failure due to the difficulty of microcrack propagation, posing a greater risk of instability and potential overall collapse during mining; in contrast, granite (NDBC of 0.23) is more ductile, showing progressive damage accumulation. (3) Initial rock splitting failure is primarily tensile cracking, with shear cracking increasing as failure approaches, transitioning the failure mechanism to a tensile–shear composite mode. Therefore, establishing a differentiated monitoring and prevention system based on AE main frequency identification and GMM analysis, designating siliceous limestone surrounding rock areas as key prevention zones, can effectively reduce the risk of sudden instability and ensure safe mining operations. Full article

Digital governance in higher education represents a complex systemic challenge, shaped by the intricate interplay of socio–economic–political contexts, technological infrastructures, and multiple stakeholders. Yet existing scholarship tends to examine these factors in isolation, lacking an integrated theoretical lens capable of capturing their systemic interdependencies and dynamic interactions. This study addresses this gap by drawing on the Social–Ecological Systems (SES) framework—a well-established systems theory for analyzing coupled social and ecological dynamics—to construct an integrated analytical framework for university digital governance. The framework organizes governance into three interconnected dimensions: external contexts, internal systems, and interaction effects. External contexts—including technological ecosystems and socio–economic–political factors—shape opportunities and constraints for universities. Internal systems, comprising resource systems, resource units, governance structures, and actors, form a complex network through information flows, resource flows, and institutional arrangements. Interaction effects emerge from these networks and are observed in both social outcomes and ecological outcomes, encompassing both positive and negative dimensions. The framework advances theory by extending the SES perspective to higher education, integrating multiple governance elements, and operationalizing core variables for measurement. Practically, it provides universities with a systematic tool for diagnosing digital governance performance, identifying gaps, and guiding optimization, while also supporting cross-institutional benchmarking and longitudinal monitoring. Future research should empirically test the framework, refine the operational indicators, and explore its applicability across diverse institutional and cultural contexts. Full article

Tube boilers are extensively employed in oil and gas refineries, as well as in petroleum, energy, and power generation industries, where they serve critical functions in local steam-generation units and combined-cycle gas turbine (CCGT) plants. However, these boilers are prone to defects arising from waterside corrosion (e.g., thinning of U-bend tubes), fireside corrosion, and material degradation caused by stress or creeping. Among these issues, wall thinning of tube bends is particularly severe, as it results in localized metal loss, reduced structural integrity, and an elevated risk of tube rupture or failure under high-temperature and high-pressure operating conditions. Such failures can significantly compromise boiler safety and efficiency, potentially leading to forced outages, costly unplanned repairs, or catastrophic damage if not detected in time. The current condition-monitoring policy for U-bends relies on scheduled preventive maintenance and unscheduled corrective interventions. In practice, this involves randomly checking approximately 10–20% of the tubes through spot scanning, partial scanning, or full scanning, with repairs typically carried out only after an undetected failure occurs. Such maintenance strategies generally require plant shutdowns, making the process time-consuming, labor-intensive, and ultimately not cost-effective. This paper reviews existing solutions, technologies, and research addressing the problem, and introduces femtosecond laser micromachined fiber optic sensors as a transformative approach for real-time monitoring of wall thickness reduction in U-bend boiler tubes, thereby opening pathways for further research. Full article

With globalization and rapid changes in the international research environment from technological advancement, political instability, and economic crisis, knowledge management (KM) is crucial to help research institutions operate international research collaboration (IRC) effectively and sustainably. This study uses systematic literature review to extract key KM factors for IRC enhancement. Exploratory factor analysis and structural equation modeling methods are performed to confirm KM factors and explore how key KM and IRC factors relate to each other. Several KM strategies are established based on study results to achieve sustainable IRC development. The results show that five key KM factors, including knowledge sharing (KS), knowledge creation (KC), knowledge retention (KR), knowledge storage (KT), and knowledge utilization (KU), influence each other. They have both direct and indirect impacts on IRC. The KU factor is crucial for immediate IRC improvement. Research institutions can use existing knowledge and resources to address current IRC opportunities. For example, personnel with IRC experience can act as coaches and mentors to facilitate activities, and integrating IRC into career paths can be beneficial. Activities related to KC, KR, and KT should support KU implementation. Setting up a task force, improving organizational structure, and engaging management in KM can help achieve better IRC performance. The KS factor should be emphasized for sustaining IRC. The plan should involve activities to raise the processes of knowledge sharing effectively. The study results provide guidelines for research institutions aiming for sustainable IRC in the long term. Full article

Situated within Saudi Arabia’s Vision 2030 transformation agenda, this study examines the performance implications of sustainable logistics practices (SLPs) and the mediating role of Management Information Systems (MIS). Although achieving a “double bottom line” is a central premise of sustainable supply chain management, its realization in state-driven emerging economies remains unclear. Drawing on the Natural Resource-Based View and Stakeholder Theory, a structural equation model is tested using survey data from 372 logistics and supply chain professionals in Saudi Arabia. The model assesses the effects of Green Transportation, Sustainable Packaging, and Sustainable Waste Management on Environmental Sustainability and Economic Performance. The results reveal a clear “Economic Performance paradox.” While all three practices significantly enhance Environmental Sustainability, only Sustainable Waste Management directly improves Economic Performance. Moreover, Green MIS significantly mediates the relationship between sustainable logistics practices and Environmental Sustainability but shows no direct or mediating effect on Economic Performance. This indicates a prevailing compliance-oriented use of MIS, where firms prioritize environmental monitoring and reporting over operational optimization. This study demonstrates that the double bottom line is not automatic, but contingent on practice type and institutional context. By providing firm-level evidence from Saudi Arabia, the study extends sustainable logistics and information systems research and offers contextually grounded insights for managers and policymakers. Full article

Eutrophication driven by excess nitrogen (N) and phosphorus (P) remains a pervasive global water-quality challenge, necessitating scalable nutrient recovery strategies that extend beyond conventional treatment approaches. This review synthesizes the emerging literature on algae-based systems as dual-purpose platforms for nutrient mitigation and biomass valorization. We examine systems including seaweed bioextraction, integrated multi-trophic aquaculture, algal turf scrubbers, and wastewater phycoremediation, while highlighting reported nutrient removal efficiencies and operational constraints. Beyond remediation, the spectrum of valorization pathways considered ranges from biofertilizers, feed, bioenergy, and materials to nutraceuticals, cosmetics, biomedical materials, biomanufacturing, and methane-mitigating livestock additives. The review emphasizes the economic and logistical challenges linking remediation-scale biomass production to commercial markets, including the contamination risk, processing intensity, regulatory classification, and scale mismatch. We propose an integrated remediation–valorization framework to guide research, policy, and industry toward nutrient-circular, economically viable restoration strategies. Full article

Urban emergency shelters constitute essential spatial elements within the framework of urban disaster prevention and mitigation. Addressing the shortcomings of existing evaluation methods, which often overlook the relationship between shelters and their served populations, this study utilizes Xi’an as a case study to develop a resilience assessment model that integrates supporting facilities, operational efficiency, and safety performance. To link this model to the served population, the research incorporates the service population pressure index and employs the Gini coefficient alongside the Lorenz curve to assess the congruence between shelter resilience and population distribution. Moreover, the introduction of the intervention priority index and population vulnerability index facilitates a comprehensive determination of shelter intervention priorities. The results reveal that emergency shelters in Xi’an display a spatial pattern characterized by a “single core with multiple centers,” with higher resilience levels, service pressures, and intervention priorities concentrated in the central urban area and lower values observed in peripheral zones. Additionally, a significant spatial mismatch is identified between shelter resilience and population service demands. Despite relying on static population data and not accounting for the effects of population migration, the evaluation framework presented in this study offers a transferable methodological reference for the comprehensive evaluation of shelters in densely populated urban areas, contributing to sustainable urban development. Full article

Human motivation is governed by a long-memory cognitive process in which the depth of temporal integration—how far into the past the system draws upon accumulated experience—is not fixed, but dynamically compressed under cognitive stress. Despite extensive empirical evidence that acute stress impairs working memory and narrows temporal integration in decision-making, no existing mathematical framework has formally coupled the memory depth of the governing operator to a physiologically grounded stress indicator. To address this gap, we propose a stress-adaptive variable-order fractional model for motivational intensity $M\left(t\right)$, in which the Caputo fractional order $\alpha \left(t\right)$ varies inversely with an aggregated stress indicator $\sigma \left(t\right)$ through the Hill-type coupling $\alpha \left(t\right)={\alpha }_{min}+({\alpha }_{max}-{\alpha }_{min})C/(C+\sigma \left(t\right))$, thereby encoding the empirically documented shift from deep integrative to shallow heuristic processing as cognitive load increases. Rather than deriving the model by algebraic manipulation of a differential equation, we formulate it directly as a causally consistent type-III Volterra integral equation, in which the memory kernel is evaluated at the history time s, ensuring that the weight assigned to each past state reflects the memory depth that was physiologically active when that state was experienced. Well-posedness is established rigorously via the Banach fixed-point theorem with explicit contraction constants, uniform boundedness and non-negativity of solutions are derived through the fractional Gronwall inequality, and numerical solutions are computed using an Adams–Bashforth–Moulton predictor–corrector scheme adapted to the variable-order kernel. Five numerical experiments demonstrate that stress-induced variation in $\alpha \left(t\right)$ produces qualitatively richer dynamics compared with the tested constant-order baselines: the proposed model achieves a steeper peak decline rate (0.48 versus 0.19–0.45), a larger burnout gap (3.15 versus 1.92–2.81), and faster recovery to ninety percent of peak motivation (4.2 versus 3.9–7.3 time units), while the empirically observed numerical convergence approaches $O\left(h^2\right)$ for sufficiently small step sizes. The framework offers a principled phenomenological substrate for memory-adaptive cognitive modelling, with direct implications for stress-aware intelligent tutoring systems that are capable of inferring $\alpha \left(t\right)$ in real time from biometric signals such as heart rate variability or galvanic skin response, and adjusting instructional complexity accordingly. Empirical calibration against learning-analytics and psychophysiological datasets, together with stochastic extensions for probabilistic burnout-risk prediction, are identified as immediate priorities for future research. Full article

Ports are complex infrastructure systems operating under adverse marine environments, diverse loading regimes, and significant economic pressures. Among their critical assets are pavement infrastructures that serve multiple functional domains, including container handling and storage areas, internal circulation corridors, passenger–vehicle interfaces, and auxiliary parking zones. However, existing port pavement research remains predominantly concentrated on heavy-duty container applications, while other functional categories are comparatively underexplored. This study develops a structured engineering synthesis of port pavement infrastructure assets by integrating bibliometric mapping, conducted using Scopus-indexed publications, with a functional–structural analysis of worldwide practices. Following the identification of research trends, additional insights from engineering-oriented studies and technical guidance documents were incorporated to strengthen the practical relevance of the investigation. These findings indicate that functional classification should precede structural design decisions, enabling the systematic identification of loading conditions, serviceability requirements, and transition demands across port environments. Heavy-duty operational zones require high-stiffness systems capable of resisting concentrated and repetitive loads, while circulation areas are particularly sensitive to low-speed traffic effects. In contrast, passenger and mixed-use zones necessitate hybrid design strategies that balance structural adequacy with serviceability and long-term durability under marine exposure, whereas auxiliary areas are primarily governed by cost-efficiency and maintenance considerations. The overall research provides a rational basis for investment prioritization, material selection, lifecycle planning, and performance-based pavement management within multifunctional port environments. Full article

Under dry sowing and wet emergence conditions in Xinjiang, cotton planting with extra-wide film mulching and sowing faced challenges including low soil moisture content and poor soil plasticity. These conditions resulted in inadequate film edge laying, seed exposure, and unstable sowing depth. This study focused on an extra-wide film mulch planter, conducting operational process analysis and parameter optimization experiments. The research first analyzed the soil layer structure required for a high-quality cotton seedbed, described the structural composition and working principle of the extra-wide film mulch planter, and examined the interaction between key components and soil during operation. The primary factors affecting machine performance were identified, and a soil-deflecting device was added to mitigate rapid soil backflow. A coupled MBD-DEM model was developed to simulate the operation of key components, and simulation experiments were conducted. The optimal parameter combination obtained through optimization was as follows: furrowing disc deflection angle of 11°, primary soil-covering disc deflection angle of 20°, operational speed of 3.5 km/h, longitudinal blade height of 16 mm, and spring stiffness of 14 N/mm. Simulation validation under these parameters yielded the following results: covering soil amount ranged from 3.22 kg/m to 3.67 kg/m, with a mean of 3.43 kg/m; seeding qualification rate ranged from 94.97% to 97.52%, with a mean of 96.3%; film hole length ranged from 43.14 mm to 46.86 mm, with a mean of 45.18 mm; and cotton seed sowing depth ranged from 29.51 mm to 31.82 mm, with a mean of 31.23 mm. These simulation results met the operational requirements for extra-wide film mulching and sowing. Field validation experiments were conducted using the optimal parameter combination. The results showed a mean soil-covering thickness of 35.1 mm, mean soil-covering width of 65.3 mm, mean film hole length of 45.7 mm, and mean cotton seed sowing depth of 29.1 mm, with coefficients of variation of 5.1%, 2.6%, 4.7%, and 5.8%, respectively. The field results were generally consistent with the simulation results, confirming the reliability of the simulation model and demonstrating improved operational performance of the extra-wide film mulch planter, making it more suitable for the dry sowing with wet emergence technique. Twenty days after sowing, the mean emergence rate reached 93.3% with a coefficient of variation of 1.0%, indicating stable emergence, which preliminarily validated the effectiveness of the constructed seedbed in promoting cotton growth. Full article