Search Results (32,714)

Rotating packed beds (RPBs) have recently attracted significant attention as a promising approach to intensify the performance of traditional packed columns. Although many lab-scale experimental and numerical studies on RPBs are available in the literature, there is a scarcity of operational data for large-scale RPBs. In this research, high gas flow rates in large-scale RPBs are investigated using computational fluid dynamics (CFD) simulation to predict the dry pressure drop in a rotating bed. A 2D geometry with periodic boundary conditions was applied to simulate the turbulent gas flow in a rotating packed bed. The simulation results provide valuable insights into the gas flow dynamics within rotating beds, highlighting the pressure and velocity variations that occur at high rotational speeds. A semi-empirical correlation successfully replicated the results obtained in this study and can be utilized to predict the pressure drop in large-scale RPBs under operating conditions similar to those studied in this research. Full article

This paper investigates how higher education institutions (HEIs) in tourism, hospitality, and events in the United Kingdom (UK) are embedding decarbonisation and sustainability competencies within their curricula. Drawing on a 28-item survey distributed to 67 universities, this study explores the relationship between explicit decarbonisation learning outcomes, Sustainable Development Goal (SDG) alignment, and the breadth of decarbonisation practices taught. Twenty-one institutions responded (31%). Results show that only 19% of programmes explicitly reference decarbonisation in their learning outcomes, yet these programmes deliver substantially broader practice coverage. While SDG-aligned programmes were more liable to include such outcomes, this association was not statistically significant. Findings here highlight the gap between representative SDG alignment and operational curriculum reform. This study recommends embedding assessment-focused decarbonisation outcomes and strengthening training supported by targeted continuing professional development. Limitations include the small, self-reported sample and cross-sectional design. Future research could adopt longitudinal and comparative approaches to help examine how specific curriculum commitments translate into applied competencies over time. Full article

Automated valet parking (AVP) is a key component of autonomous driving systems. Its functionality and reliability need to be thoroughly tested before road application. Current testing technologies are limited by insufficient scenario coverage and lack of comprehensive evaluation indices. This study proposes an AVP testing and evaluation framework using OnSite (Open Naturalistic Simulation and Testing Environment) and Unity3D platforms. Through scenario construction based on field-collected data and model reconstruction, a testing scenario library is established, complying with industry standards. A simplified kinematic model, balancing simulation accuracy and operational efficiency, is applied to describe vehicle motion. A multidimensional evaluation system is developed with completion rate as a primary index and operation performance as a secondary index, which considers both parking efficiency and accuracy. Over 500 AVP algorithms are tested on the OnSite platform, and the testing results are evaluated through the Unity3D platform. The performance of the top 10 algorithms is analyzed. The evaluation platform is compared with CARLA simulation platform and field vehicle testing. This study finds that the framework provides an effective tool for AVP testing and evaluation; a variety of high-level AVP algorithms are developed, but their flexibility in complex dynamic scenarios has limitations. Future research should focus on exploring more sophisticated learning-based algorithms to enhance AVP adaptability and performance in complex dynamic environment. Full article

This paper examines the impact and mechanism of digital transformation on green innovation in state-owned enterprises. The study uses Chinese A-share listed state-owned enterprises from 2012 to 2023 as the sample, with the perspective of mixed ownership reform. The research sample is 4468 panel data in total. The study uses fixed effect regression analysis, Heckman two-stage test, and instrumental variable test methods, etc. The findings indicate that digital transformation positively influences the degree of green innovation within state-owned enterprises. Furthermore, the mixed ownership reform positively moderates this influence impact. Mechanism test results indicate that digital transformation fosters green innovation by enhancing the level of information disclosure and reducing management shortsightedness. The heterogeneity analysis reveals a more pronounced effect on the relationship among enterprises in the eastern region and those operating within the high-tech industry. The study not only contributes to theoretical implication of digital transformation but also provides empirical guidance for policy formulation. Full article

Background: This study introduces the Adaptive Graph Fusion Network, an interpretable graph-based learning framework developed for large-scale prediction of intensive care outcomes. The proposed model dynamically constructs patient similarity networks through a density-aware kernel that adjusts neighborhood size based on local data distribution, thereby representing both frequent and rare clinical patterns. Methods: To characterize physiological evolution over time, the framework integrates a short-horizon convolutional encoder that captures acute variations in vital signs and laboratory results with a long-horizon recurrent memory unit that models gradual temporal trends. The approach was trained and internally validated on the publicly available eICU Collaborative Research Database, which includes more than 200,000 admissions from 208 hospitals across the United States. Results: The model achieved a mean area under the receiver operating characteristic curve of 0.91 across six critical outcomes, with in-hospital mortality reaching 0.96, outperforming logistic regression, temporal long short-term memory networks, and calibrated Transformer-based architectures. Feature attribution analysis using SHAP and temporal contribution mapping identified lactate trajectories, creatinine fluctuations, and vasopressor administration as dominant determinants of risk, consistent with established clinical understanding while revealing additional temporal dependencies overlooked by existing scoring systems. Conclusions: These findings demonstrate that adaptive graph construction combined with multi-horizon temporal reasoning improves predictive reliability and interpretability in heterogeneous intensive care populations, offering a transparent and reproducible foundation for future research in clinical machine learning. Full article

Traditional multi-stage separated heat pipes (SHPs) face limitations in independently setting operation parameters for each stage. To address this issue, this paper presents a novel independent multi-stage microchannel Separated Gravity Heat Pipe (SGHP) for air compressor room cooling. The innovative structure and working principle of this novel multi-stage SGHP were introduced. Furthermore, numerical investigations on a single stage of the SGHP were then conducted to study the gas–liquid two-phase flow characteristics and phase-change heat transfer performance. Experimental research on a three-stage SGHP was carried out to further explore the impact of the filling ratio combinations and the temperature difference between the hot and cold ends on the heat transfer performance of the SGHP. The results show that the temperature difference between the hot and cold ends affects the flow pattern of the working fluid, which has a vital effect on the heat transfer performance of the SGHP. The optimum filling ratio combination of the three-stage SGHP depends on the temperature difference between the hot and cold ends. The optimum filling ratio combination is 37%/37%/30% at low temperature difference conditions and 43%/37%/37% at high temperature difference conditions, respectively. The highest heat transfer capacity of the three-stage SGHP reaches 15.3 kW, and the peak heat recovery efficiency is 74.0%. The findings provide a crucial foundation for developing novel independent multi-stage SGHP in compressor room cooling and similar industrial settings, promising high potential to reduce energy consumption and operational costs. Full article

Focusing on mediatized urban images, this examination of Jiangnan water towns analyzes 1000 user-generated posts on Xiaohongshu through word frequency statistics, content categorization, and textual interpretation to demonstrate how “Seeding Strategy” transforms the symbolic representation and cultural identity of ancient towns. The results reveal that mediatized conceptions of water towns operate within a four-dimensional symbolic framework—natural, cultural, interactive, and Sentiment symbols—shaped by user co-creation and local cultural assets. Through photo-taking and check-ins, users convert historic towns from static geographical locations into dynamic media environments with visual and emotional resonance. Platform algorithms amplify engaging content, reinforcing spatial imaginaries. The concept of “symbolic effects on media platforms” elucidates how local culture is reconstructed and disseminated within digital frameworks, offering theoretical insights and practical recommendations for cultural tourism branding and cross-platform place research in the digital age. Full article

This study presents a comparative performance modeling and optimization framework for a 5 kWp rooftop photovoltaic (PV) system in Qatar, using two widely adopted simulation tools, PVsyst and the System Advisor Model (SAM). The research addresses a key limitation in existing PV modeling practice: the restricted capability of standard software to represent site-specific soiling and dynamic albedo effects under arid climatic conditions. To overcome these limitations, the Humboldt State University (HSU) soiling model was calibrated using field measurements from a DustIQ sensor, and its parameters, rainfall cleaning threshold and particulate deposition velocity were optimized through a Differential Evolution algorithm. Additionally, the study utilized dynamic albedo inputs to better account for ground-reflectance effects in energy yield simulations. The optimized approach reduced the root mean square error (RMSE) of predicted soiling ratios from 7.30 to 1.93 and improved the agreement between simulated and measured monthly energy yields for 2024, achieving normalized RMSE values of 4.66% in SAM and 4.86% in PVsyst. The findings demonstrate that coupling data-driven soiling optimization with refined albedo representation modernizes the predictive capabilities of PVsyst and SAM, yielding more reliable performance forecasts. This methodological advancement supports better-informed design and operation of rooftop PV systems in desert environments where soiling and reflectivity effects are pronounced. Full article

Efficient scheduling of agricultural machinery is critical for optimizing resource utilization and reducing operational costs in modern farming operations. This study proposes an Adaptive Genetic Algorithm integrated with Ant Colony Optimization (AGA-ACO) to solve the multi-task machinery scheduling problem. The problem is formulated as a Vehicle Routing Problem with Time Windows (VRPTW), considering time constraints, machinery heterogeneity, and task dependencies. The AGA-ACO algorithm employs a two-phase optimization strategy: genetic algorithms for global exploration and ant colony optimization for local refinement through pheromone-guided search. Experimental evaluation using real-world agricultural data from Hangzhou demonstrates that AGA-ACO achieves cost reductions of 5.92–10.87% compared to genetic algorithms, 5.47–7.75% compared to ant colony optimization, and 6.23–9.51% compared to particle swarm optimization, while converging with fewer iterations. The algorithm maintains stable convergence and high robustness across different farmland scales, reducing computational time while preserving solution quality. A scheduling management system integrating IoT sensors, MQTT protocols, and GIS technologies validates the practical applicability of the proposed approach. This research provides a replicable framework for agricultural machinery optimization, contributing to the advancement of sustainable and precision agriculture. Full article

Machine vision (MV) is reshaping numerous industries by giving machines the ability to understand what they “see” and respond without human intervention. This review brings together the latest developments in deep learning (DL), image processing, and computer vision (CV). It focuses on how these technologies are being applied in real operational environments. We examine core methodologies such as feature extraction, object detection, image segmentation, and pattern recognition. These techniques are accelerating innovation in key sectors, including healthcare, manufacturing, autonomous systems, and security. A major emphasis is placed on the deepening integration of artificial intelligence (AI) and machine learning (ML) into MV. We particularly consider the impact of convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer architectures on the evolution of visual recognition capabilities. Beyond surveying advances, this review also takes a hard look at the field’s persistent roadblocks, above all the scarcity of high-quality labeled data, the heavy computational load of modern models, and the unforgiving time limits imposed by real-time vision applications. In response to these challenges, we examine a range of emerging fixes: leaner algorithms, purpose-built hardware (like vision processing units and neuromorphic chips), and smarter ways to label or synthesize data that sidestep the need for massive manual operations. What distinguishes this paper, however, is its emphasis on where MV is headed next. We spotlight nascent directions, including edge-based processing that moves intelligence closer to the sensor, early explorations of quantum methods for visual tasks, and hybrid AI systems that fuse symbolic reasoning with DL, not as speculative futures but as tangible pathways already taking shape. Ultimately, the goal is to connect cutting-edge research with actual deployment scenarios, offering a grounded, actionable guide for those working at the front lines of MV today. Full article

Construction accidents continue to threaten worker safety despite advances in management systems. Existing research catalogs accident attributes but rarely explains how triggers like human error, equipment failure, or procedural lapses interact with project types and tasks. This limits recognition of high-risk scenarios and hampers targeted prevention. To address this, a two-step framework combining Multiple Correspondence Analysis (MCA) and Association Rule Mining (ARM) is proposed. Using the Korean Construction Safety Management Integrated Information (CSI) database, MCA reduces dimensionality and clusters similar accident cases, while ARM extracts context-specific rules linking accident types, causes, and activities. The analysis reveals the following key patterns: (i) worker negligence during setup or formwork often leads to tool-related cuts; (ii) poor judgment or inadequate waste removal during excavation heightens hit or stuck incidents; and (iii) negligence frequently triggers hit and fall accidents during transportation, dismantling, and finishing. By mapping causes to operational risk factors, the framework supports actionable guidance for daily risk assessments. Safety professionals can align planned tasks with identified risks, enabling proactive interventions such as focused training, stricter supervision, and engineering controls. Thus, the MCA–ARM method establishes a data-driven foundation for improving safety decision-making and reducing construction accidents. Full article

Winter road icing poses significant safety risks, particularly on steep urban slopes with vulnerable populations. While thermal-comfort indices such as UTCI, PMV, and PET have been used for summer conditions, this study focuses on operational indices that quantify road-icing risk. This study introduces and empirically validates two novel indices—Freezing Intensity (FI) and Cold Intensity (CI)—designed to quantify the likelihood and severity of road icing. A case study was conducted on Namgyeong-maeul Road in Daedeok-gu, Daejeon, South Korea, where IoT-based environmental monitoring, including automated weather stations, thermal cameras, and drone imaging, was deployed from December 2024 to January 2025. Results demonstrate that road heating systems (RHS) effectively increased surface temperatures by an average of 4.1 °C compared to non-heated segments, with maximum differences exceeding 12.5 °C. The FI of non-heated slopes reached critical levels above 2400, whereas heated roads reduced FI to near zero. Similarly, CI values dropped from hazardous levels (~12) to below 6 in heated zones, reducing icing severity by more than 50%. These findings confirm that FI and CI can serve as robust metrics for operational assessment of RHS performance, complementing traditional heat-related indices. By integrating FI and CI into monitoring and design, policymakers and engineers can establish data-driven activation thresholds, optimize energy efficiency, and ensure safer winter mobility for vulnerable groups. This research provides a structured operational framework for winter road icing quantification, advancing climate adaptation strategies equivalent in rigor to summer climate indices. Compared with temperature-only monitoring, FI and CI improved operational responsiveness and reduced residual icing duration by ≈50%. Full article

This study was conducted in response to the challenges posed by the heterogeneity of ground station resources and the dynamic nature of tasks in satellite observation missions. To combat these issues, we propose a resource scheduling method based on a dynamic coalition algorithm. The method involves constructing a five-dimensional evaluation system including spatial proximity, energy sufficiency, equipment integrity, load balancing, and continuous observation capability, which is combined with an improved simulated annealing algorithm to achieve global optimization of the coalition structure. Then, an energy allocation strategy based on demand is designed to enhance system sustainability. An experiment comparing the greedy, particle swarm, genetic, and simulated annealing algorithms was conducted. The results showed that the task completion rate of the dynamic coalition algorithm reached 93.8%; the resource utilization rate was 85.7%; the energy consumption standard deviation was 18.7; and the convergence speed was 45 iterations for the proposed method. These results were significantly better than those of other algorithms used for comparison. The innovative aspects of this study include ① a dynamic energy allocation model based on normalized priority; ② a simulated annealing optimization framework with hybrid neighborhood operations; and ③ the deep integration of multi-dimensional evaluation metrics and dynamic coalition construction mechanisms. This research provides theoretical support and technical solutions for task scheduling in wireless sensor networks under complex dynamic scenarios. Full article

Food loss and food waste (FLFW) constitute a major global challenge with profound economic, environmental, and social consequences. This study examines how businesses in Greece’s food service and retail sectors perceive and manage food waste, integrating the triple bottom line framework—economic, environmental, and social sustainability—as the guiding analytical lens. The research aims to: (1) analyze perceptions, practices, and barriers to food waste reduction among businesses; (2) explore the relationship between awareness, business policies, technological adoption, and consumer-oriented strategies; and (3) situate the Greek experience within broader European and international contexts to identify transferable lessons for policy and business innovation. Drawing on a structured survey of 250 industry representatives and comparative international evidence, the study finds that although awareness of food waste is widespread, it remains weakly connected to structured policies, technology adoption, or operational practices. Businesses recognize economic opportunities in waste reduction—such as supply chain optimization and near-expiry discounting—but these remain underexploited due to a lack of strong regulatory and financial incentives. The findings highlight that addressing food waste is not only an environmental and ethical necessity but also a strategic opportunity to enhance economic resilience, competitiveness, and sustainability within the agri-food sector. Full article

 Purpose: The research explores the impact of change management practices—leadership support, employee involvement, and regulatory compliance —on the practice of sustainable healthcare in Saudi Arabia. Operational efficiency is treated not as a management practice but as a key outcome of effective change management. The research also examines patient readiness as a mediator influencing awareness, participation, and satisfaction. Design/methodology/approach: The study used a quantitative Saudi Arabian healthcare consumer survey. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to analyze change management, patient readiness, and sustainable healthcare relations adoption. Findings: Findings indicate that change management plays a strong role in increasing patient adoption (β = 0.322; p = 0.083), but with large effects on awareness (β = 0.873; p < 0.001), engagement (β = 0.841; p < 0.001), and satisfaction (β = 0.881; p < 0.001), as adoption reflected through awareness, engagement, and satisfaction. Patient readiness as a mediator was significant with strong effects between change management and adoption (β = 0.571; p < 0.001). Originality/value: This research expands the Theory of Planned Behavior (TPB) by synthesizing it with strategic change management to predict patient readiness as a mediator of long-term adoption of healthcare in the Arab environment. Patient readiness is hypothecated as an observable behavioral construct to mediate organizational change practices—leadership, communication, and regulation—with individual adoption outcomes. The research provides theoretical and practical contributions for evidence-based healthcare policy and patient-led healthcare revolution. In addition, the study conforms with the United Nations Sustainable Development Goals (SDGs) including SDG 3 (Gsssssssood Health and Well-being), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 12 (Responsible Consumption and Production), and shows how effective change management not only assists national healthcare reforms but also global sustainability goals. Full article