Search Results (11,268)

The growth of cloud services and immersive applications based on extended reality (XR), including Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), imposes increasingly demanding requirements on access networks. The large-scale integration of IoT devices in smart buildings further increases the need for high throughput, low latency and jitter, and reliable connectivity. Traditional Fiber-to-the-Home (FTTH) networks with a single access point (AP) become quite limiting when there are high performance requirements, with many users with indoor mobility and high device density. Fiber-to-the-Room (FTTR) is an extension of FTTH, which brings fiber optics to each room of the house through a Main FTTR Unit (MFU) and several Sub FTTR Units (SFU) along with the APs, with centralized device management. Green FTTR networks are characterized by their energy efficiency through centralized control of signal power and Dynamic Bandwidth Allocation (DBA) management. The fgONT architecture allows for deterministic network slicing, enabling the allocation of specific resources isolated from the rest of the network traffic, allowing for predictable bandwidth and QoS. This work presents a framework that allows for a comparative analysis of FTTR and FTTH networks in different scenarios in order to ensure a compromise between transmission quality, network energy efficiency, and the user’s perceived experience. The results obtained show that, in high device density scenarios, FTTR reduces the average packet loss from 52.69% to less than 0.08%, decreases the average latency from 151 ms to less than 2 ms, and maintains the overall QoE above 0.974, compared to 0.27 in FTTH with a single AP. Full article

Background: Artificial intelligence (AI) is increasingly being integrated into radiology and may improve diagnostic efficiency and workflows. Its successful adoption depends on workforce readiness, organizational capacity, and effective governance. This study evaluated radiology professionals’ perceptions of AI integration in Saudi Arabia; sustainability was considered only as a conceptual implementation context and was not directly measured. Methods: An online cross-sectional survey using convenience sampling was conducted among healthcare professionals involved in radiology services in Saudi Arabia, using an expert-reviewed and pilot-tested 30-item questionnaire covering knowledge, attitudes, implementation readiness, and perceived barriers. Responses from 295 healthcare professionals were analyzed using descriptive statistics, Cronbach’s alpha, Spearman correlation, and Kruskal–Wallis testing. Results: Participants demonstrated positive attitudes toward AI integration (3.57 ± 0.69) and moderate knowledge (3.35 ± 0.70), whereas implementation readiness was comparatively lower (3.04 ± 0.79). Perceived barriers showed the highest domain score (3.67 ± 0.64). Major barriers included implementation costs (3.98 ± 0.71), limited digital infrastructure (3.90 ± 0.75), insufficient staff training (3.84 ± 0.77), and lack of technical expertise (3.82 ± 0.78). Positive correlations between knowledge, attitudes, and implementation readiness were observed. Conclusions: Among participating respondents, support for AI integration was generally favorable, but lower perceived institutional readiness and organizational, infrastructural, and governance barriers may constrain implementation. Because the convenience sample lacked a sampling frame and a calculable response rate, the results should not be interpreted as national estimates. The survey also did not measure clinical, economic, resource-use, or environmental outcomes and therefore does not establish sustainability benefits. Future probability-based and implementation studies should evaluate representativeness and these outcomes directly. Full article

With the widespread adoption of cloud computing technology, modern cloud platforms have become increasingly complex and dynamic, posing significant challenges for efficient resource management. Accurate forecasting of cloud resource loads has therefore become essential for improving service quality, optimizing resource utilization, and reducing operational costs. To address the intrinsic characteristics of cloud load time series, including nonlinear fluctuations, multi-scale temporal dependencies, and redundant high-dimensional features, this paper proposes the MST-iTransformer model, which integrates multi-scale temporal encoding, sparse attention, and adaptive feature selection mechanisms. Specifically, a multi-scale temporal encoding module is developed to capture and fuse temporal dependencies across multiple periodic scales. Furthermore, an adaptive feature selection module is introduced to dynamically assign importance weights to resource features, enhancing informative variables while suppressing redundant ones. Meanwhile, a sparse attention mechanism is incorporated to reduce computational overhead while maintaining forecasting accuracy. The proposed model is evaluated on the Alibaba Cluster Trace dataset. Experimental results demonstrate that MST-iTransformer achieves MSE, RMSE, and MAE values of 0.5559, 0.7456, and 0.4968, respectively. Compared with the original iTransformer, the proposed model achieves simultaneous reductions in prediction errors and inference latency, validating the effectiveness of the multi-scale temporal encoding, sparse attention mechanism, and adaptive feature selection modules in improving forecasting accuracy and computational efficiency. These improvements provide reliable prediction support for resource scheduling and elastic scaling in cloud data centers. Full article

Cloud task scheduling is a critical component of cloud computing systems because it directly affects resource allocation, workload distribution, execution efficiency, and service cost. However, many metaheuristic algorithms suffer from population diversity loss, premature convergence, and an inadequate balance between global exploration and local exploitation when solving complex and large-scale optimization problems. To address these limitations, this study develops an Enhanced Human Urbanization Algorithm (EHUA) for numerical optimization and cloud task scheduling. Inspired by the collaborative resource-allocation behavior of modern logistics networks, three coordinated mechanisms are reformulated within the adventurer–city–citizen structure of the original Human Urbanization Algorithm: a logistics-hub-guided adaptive exploration mechanism, a supply–demand-based dynamic redistribution mechanism, and a cooperative logistics delivery exploitation mechanism. These mechanisms reduce excessive dependence on a single capital, adaptively regulate city search ranges, and strengthen citizen-level solution refinement. The performance of EHUA is evaluated on the CEC2014 and CEC2020 benchmark suites using convergence analysis, box plots, numerical statistics, Wilcoxon signed-rank tests, Friedman rankings, and ablation experiments. EHUA obtains the best mean fitness values on 20 of the 30 CEC2014 functions under both 30- and 50-dimensional settings, on 8 of the 10 CEC2020 functions at 10 dimensions, and on all 10 functions at 20 dimensions, demonstrating strong overall competitiveness and repeatability without implying universal superiority on every problem. EHUA is further applied to cloud task scheduling under workload scales ranging from 100 to 10,000 tasks. Considering comprehensive cost, monetary cost, execution time, and load cost, the proposed method consistently achieves low comprehensive scheduling costs and maintains favorable trade-offs among individual objectives as the workload increases. These results indicate that EHUA provides an effective and scalable optimization framework for complex benchmark problems and cloud task scheduling applications. Full article

Electric-shovel top sheave bearings with sensor-embedded slots operate under harsh service loads, making them prone to fatigue crack initiation and propagation. Accurate predictions of crack growth within the bearing body are therefore essential for intelligent bearing design and reliability assessments because the bearing integrity directly affects shovel service life and safety. This paper presents a sub-modeling-based method that embeds initial cracks while preserving actual roller-ring boundary conditions and ensuring computational efficiency via adaptive mesh refinement. A global model first identifies critical crack-prone zones, after which the sub-model systematically examines the effects of the initial crack angle and sensor-embedded slot depth on the propagation behavior. The results indicate that both factors significantly increased the stress intensity factor (SIF). Among the evaluated designs, the 15 mm -deep slot produced the highest SIFs and the shortest predicted crack-propagation life, indicating that slot depth was a key design parameter under the investigated conditions. The findings provide theoretical support for the structural design and fatigue evaluation of intelligent electric-shovel top sheave bearings. Full article

Structural health monitoring (SHM) based on ultrasonic-guided waves has been widely investigated for aerospace, transportation, marine engineering, petrochemical equipment and large-scale civil infrastructure. As the core component for guided-wave excitation and signal reception, the transducer directly affects electromechanical conversion efficiency, modal selectivity and service stability. Interdigital transducers (IDTs) are characterized by a lightweight structure, designable wavelength, tunable operating frequency and good array compatibility, and therefore show considerable potential for curved structures, composite components and large-area online monitoring. The periodically repeated interdigital electrode pattern represents a basic form of structural symmetry in IDTs, providing the geometric basis for wavelength matching and frequency-selective response, while substrate properties govern the electromechanical conversion efficiency and stability of the device. This review focuses on the research progress of substrate materials for flexible IDTs. The material characteristics and application status of inorganic piezoelectric materials, piezoelectric polymers, piezoelectric composites and heterogeneous integrated substrates are summarized. The differences among typical substrate systems are compared in terms of electromechanical coupling, flexible conformability, thermal stability, acoustic loss and integration process. Recent applications of IDTs in guided-wave damage detection, flexible sensing, high-temperature monitoring and on-chip acoustic devices are also discussed. The main challenges and future directions of IDT substrate materials are analyzed to provide guidance for material selection, device design and SHM applications. Full article

Road tunnels are highly carbon-intensive due to material use, energy-intensive construction, and long service lives, yet major gaps remain regarding emission hotspots, construction method comparisons, and regional differences, particularly in Central and Eastern Europe. This article combines a PRISMA 2020-guided systematic literature synthesis with a Polish case-study life-cycle assessment (ISO 14040/14044, cradle to grave, functional unit of 1 m of tunnel, 100-year horizon) using Ecoinvent factors and the Polish energy mix, covering material production, construction, operation, maintenance, and end of life. The literature synthesis found substantial variability in tunnel carbon emissions, ranging from 1500 to 22,062 t CO₂-eq per lane-kilometer depending on the construction method, tunnel type, and region. Material production was the largest contributor to construction-phase emissions (70–95%), with concrete and steel responsible for over 90% of material-phase impacts and 75–80% of construction-phase emissions, while operational energy use dominates over the full life cycle. Concrete and steel substitution (e.g., GFRP bars and calcium sulfoaluminate cement) offers the greatest construction-phase reduction potential, while operational measures, such as LED lighting, demand-controlled ventilation, and renewable energy, can cut long-term energy use by 30–50%. For Poland, low-carbon concrete, prefabrication, and renewable electricity could reduce tunnel emissions by 40–60%. These findings highlight pathways for decarbonizing tunnel infrastructure through material innovation, energy-efficient operation, and circular economy principles. Full article

The growing adoption of electric vehicles (EVs) and the increasing need for coordinated charging and energy management have highlighted the importance of vehicle-to-everything (V2X) technologies within battery management systems (BMSs). However, existing studies often treat EVs as idealized storage systems, overlooking battery and BMS-related operational constraints, and typically analyze driving, charging, and bidirectional energy exchange in isolation, limiting realistic, end-to-end evaluation of daily operation scenarios. This paper addresses these gaps by analyzing how advanced, BMS-integrated V2X capabilities can be deployed in real-world EV operation, focusing on battery utilization, operational performance, and system-level energy interactions. A unified, scenario-based methodology combines mobility demand, AC/DC charging behavior, and bidirectional V2X services within a single daily operational framework. Representative use cases for both passenger EVs and electric light commercial vehicles (eLCVs) are developed to capture realistic driving patterns, environmental conditions, and energy exchange scenarios. The results indicate that V2X operation can provide substantial gross economic value in the investigated scenarios. For the eLCV cases, the estimated increase in equivalent full cycle (EFC) throughput rate ranges from approximately 14.3% to 30.3%, while combined summer–winter cumulative avoided electricity purchase cost reaches approximately EUR 4033 for the higher-power charging strategy, equivalent to 57.0% of the adopted battery cost reference. The analysis also highlights the strong influence of ambient temperature and usage patterns on energy consumption, charging strategies, and overall system performance. Overall, this work provides a holistic and practical evaluation framework for V2X-enabled BMS operation, demonstrating its potential to improve grid support, enhance energy efficiency, and support sustainable EV integration while balancing economic and battery-lifetime trade-offs. Full article

The accurate characterization of human immunodeficiency virus (HIV) genetic diversity and drug resistance is critical for effective surveillance and treatment, yet current sequencing technologies face limitations in sensitivity and scalability for community-level implementation. We present G-HIV, an integrated platform combining long-read sequencing (G-seq500) with an automated bioinformatics pipeline. G-HIV processes raw FastQ data to generate automated reports on point mutations, drug resistance predictions, viral quasispecies diversity, and haplotype networks via a two-step analytical approach. Applied to 44 HIV-1 plasma samples (42 used in the final comparison after excluding 2 samples with low-quality Sanger chromatograms), G-HIV detected 3–48 candidate minority variants per sample that were not observed by Sanger sequencing, identifying drug-resistant quasispecies in two samples with undetectable Sanger signals, and revealed mixed infection cases (e.g., inter-subtype CRF07_BC/CRF08_BC) through phylogenetic analysis. G-HIV addresses an integration of long-read sequencing with a fully automated, one-stop bioinformatics pipeline designed for frontline laboratories without specialized bioinformatics expertise—providing a scalable solution for community-based resistance surveillance and personalized therapy optimization in resource-limited settings. This research addresses an integrated long-read sequencing and automated bioinformatics platform for rapid and precise HIV-1 surveillance. G-HIV surpasses conventional approaches like Sanger sequencing in resolution, efficiency, and accessibility for community-level surveillance. By integrating long-read sequencing, streamlining workflows and eliminating the need for specialized bioinformatics expertise, G-HIV is positioned to become a new solution, providing more effective one-stop services for HIV-1 prevention and control. Full article

In recent years, interest in artificial intelligence has grown significantly, particularly in the development of advanced computational models for supply chain decision-making. Inventory management is one of the areas in which intelligent algorithms can support demand forecasting, replenishment, stock control, and operational optimization under uncertainty. This study presents an updated systematic literature review of intelligent algorithms applied to inventory management. The review followed PRISMA 2020 guidelines and combined database searches in Scopus, ScienceDirect, Web of Science, IEEE Xplore, SpringerLink, Taylor & Francis, and complementary manual searching. The original search covering January 2020 to December 2024 was updated in July 2026 to include studies published or available online up to June 2026. After applying strict eligibility criteria, 37 primary studies with quantitative evidence were included. The updated corpus confirms the predominance of deep learning, reinforcement learning, and hybrid intelligent models, while also showing the recent emergence of Transformer-based, graph neural network, multi-agent reinforcement learning, and prescriptive analytics approaches. The most frequent application areas were inventory control, inventory optimization, replenishment decision-making, and demand forecasting. Reported improvements were mainly associated with cost efficiency, service level, stockout reduction, and system performance; however, the magnitude of improvement varied across algorithms, data sources, sectors, and simulation or real-world settings. Overall, intelligent algorithms represent a relevant tool for improving inventory management, but their adoption requires careful validation, transparent reporting, and alignment with the operational context. Full article

Emerging industrial agglomeration drives green transformation through knowledge spillovers and economies of scale. However, its effects exhibit pronounced nonlinearity and heterogeneity, shaped by spatial externalities and development stages. This paper investigates 19 Chinese urban agglomerations over the period 2014 to 2023. Kernel density estimation based on enterprise-level Point-of-Interest (POI) data is used to characterize spatial agglomeration patterns across eight emerging sectors. A two-stage dynamic network super-efficiency SBM model decomposes Green Transition Efficiency (GTE) into resource utilization and pollution control sub-stages. An XGBoost-SHAP-GEO analytical framework, combined with partial dependence analysis, then identifies nonlinear driving mechanisms. The main findings are as follows: First, emerging industrial agglomeration intensifies and polarizes toward the eastern coast, whereas GTE displays a “high-west, low-east” pattern. This produces a significant spatial mismatch, rooted in the near-saturation of environmental carrying capacity in eastern regions, where congestion effects exceed knowledge spillover dividends. Second, geographic characteristics constitute the primary factor shaping GTE and operate through nonlinear interactions with industrial agglomeration and R&D investment. Notably, their moderation direction is reversible, suggesting that geographic endowments should be understood as “conditional assets” rather than fixed advantages. Third, nonlinear patterns across sectors are highly heterogeneous. The bio-industry is the only sector to achieve a J-shaped positive breakthrough. Information technology and new materials exhibit persistent inhibition, while related services display an extremely narrow threshold window with the deepest negative reversal. Thus, “moderate agglomeration” is a multidimensional concept that shifts dynamically with industry type and regional endowment. Fourth, driving mechanisms display stage-dependent evolution. The incubation stage relies on natural endowments and basic industrial pull, with the green bottleneck residing in resource utilization efficiency. The growth stage faces multiple tensions from coexisting positive and negative effects. The optimization stage shifts toward R&D innovation and industrial greening, marking a qualitative transformation from MAR externalities to Jacobs externalities. In addition, the non-significant linear coefficient in the 2SLS instrumental variable test is consistent with the inverted U-shaped nonlinear finding, further validating the necessity of a nonlinear analytical framework. These findings provide differentiated governance evidence for balancing industrial agglomeration with green sustainable development. Full article

Loss models of fixed capacity constitute a fundamental tool in teletraffic theory, with the classical Erlang loss model being widely used for dimensioning purposes. In practical communication systems, however, server failures and repairs introduce time-varying capacity, significantly affecting call blocking probabilities (CBPs). This paper studies an Erlang loss system where busy servers may fail. Failed servers are repaired by either a shared or a non-shared repair facility, while in-service calls are lost upon server failure. Three approaches for determining CBP in the shared and non-shared repair cases are examined. The first provides exact results by solving a 2D Markov chain but becomes computationally demanding for large systems. The second, known as the performability method, offers a simple approximation but allows failures of idle servers. The third approximate approach employs state aggregation while restricting failures to busy servers. These approximate solutions offer computational efficiency, but they cannot ensure consistently accurate performance. To circumvent this limitation, we propose a novel method for the exact and efficient determination of CBPs. Analytical comparisons show that: (1) the third approach consistently outperforms the performability method and (2) the proposed method outperforms the approximate methods in both the shared and the non-shared repair cases. Full article

Pedestrian-induced vibration is a critical serviceability concern for flexible structures such as footbridges and long-span floors. Existing human–structure interaction models commonly rely on single-degree-of-freedom simplifications and time-domain simulations, making them less suitable for frequency-domain analysis. This paper proposes a spectral analysis model for crowd-structure interaction vibration under unrestricted pedestrian traffic. The structure was formulated as a multi-degree-of-freedom modal system, whereas each pedestrian is represented by an independent spring–mass–damper system. To address the time-varying nature of moving crowds, a piecewise stationary assumption was introduced: the continuous walking path was discretized into fixed position groups, within each of which a time-invariant coupled equation of motion was established. The response spectra obtained for different position groups were combined using residence-time weighting, thereby allowing nonuniform walking speeds to be considered. The corresponding frequency response function was derived using the state–space method, and the structural acceleration power spectral density and root mean square responses were obtained by incorporating an unrestricted crowd walking load spectral model. Comparisons with field measurements from two footbridges demonstrated reasonable agreement. The resulting framework offers an efficient frequency-domain approach for vibration serviceability assessment under unrestricted pedestrian traffic. Full article

The automatic inspection of airport infrastructure, specifically horizontal surface markings and Airfield Ground Lighting (AGL), is a critical task for maintaining aviation safety and operational efficiency. As the aviation industry shifts from manual surveys to automated visual inspections utilizing unmanned aerial vehicles (UAVs) and smart service vehicles, the demand for robust, real-time computer vision systems has surged. However, deploying computationally intensive deep learning models in the field introduces severe Size, Weight, and Power (SWaP) constraints. This paper presents a comprehensive framework for the semantic segmentation of runway/taxiway markings and the point-localization of AGL lamps, specifically focusing on the deployment paradigm shift from the expensive, GPU-accelerated heterogeneous system on chip (SoC) to highly efficient, dedicated Neural Processing Units (NPUs). We evaluate the performance of U-Net, LinkNet, U-Net-Point and HRNet-Lite-Point architectures trained on a custom dataset from the Poznań-Ławica Airport. Crucially, this study conducts a rigorous comparative hardware analysis between the flagship NVIDIA Jetson AGX Orin and a highly cost-effective heterogeneous setup comprising a Raspberry Pi 5 augmented with an NPU Hailo-8 AI accelerator. Experimental results demonstrate that while both platforms achieve real-time inference, the Hailo-8 integration fundamentally disrupts the traditional cost-to-performance ratio. Furthermore, the Hailo-8 configuration consumed less electrical power and memory footprint required by the Jetson, proving that dedicated NPUs are vastly superior for continuous, battery-operated edge deployment in autonomous airport maintenance systems. Specifically, the Raspberry Pi setup with the Hailo-8 accelerator demonstrated superior energy efficiency, requiring a significantly lower energy consumption per processed video frame compared to the Jetson platform. Full article

Sudden tailings dam breaches trigger large-scale heavy metal compound pollution in coupled surface water–groundwater systems, requiring systematic full-cycle ecological damage quantification tools applicable to diverse contamination types. This study constructs an integrated full-cycle ecological damage assessment framework for sudden water pollution accidents, integrating three core modules: multi-model pollutant migration prediction, multi-scale aquatic biological damage diagnosis, and three-dimensional ecological-economic loss accounting. The framework adopts a modular design that can potentially accommodate heavy metals (Cd, Cr, As, Pb) and organic pollutants such as polycyclic aromatic hydrocarbons (PAHs), with standardized molecular, individual, and population-level biological endpoints and corresponding pollutant dose–response templates reserved as reference calculation modules. However, applicability beyond this case has not been validated and requires case-specific calibration. To verify the operability and accuracy of the proposed integrated system, a typical tailings dam leakage incident dominated by hexavalent chromium (Cr(VI)) and arsenic (As) pollution was selected as the practical validation case; all field monitoring, pollutant simulation, and final economic loss quantification in this case exclusively rely on on-site measured Cr(VI) and As data, while Cd and PAH-related biological response curves and remediation cost formulas retained in the manuscript only serve as illustrative universal template components of the framework rather than case-measured results. For the Cr(VI)/As pollution case, the advection–diffusion model simulation revealed that the Cr(VI) contamination plume horizontally spread 250 m within 48 h and extended to 560 m after seven days, and anaerobic groundwater environments drove the transformation of toxic mobile trivalent arsenic (As(III)) from primary pentavalent arsenic. The calibrated SWAT model achieved Nash–Sutcliffe efficiency (NSE) coefficients of 0.75 for dissolved Cr(VI) and 0.68 for particulate As. The graph theory-based rapid prediction model cut computation duration down to minutes; when validated against independent field monitoring data, it yielded an average relative error of 14.2%, and its consistency with the SWAT model reached 10.5% relative deviation, satisfying the accuracy requirement for emergency early warning. Field biological monitoring demonstrated substantial ecological impairment: metallothionein (MT) expression in fish tissues was markedly elevated (the reported 6.2-fold induction value derives from standard Cd exposure template tests within the framework, with analogous MT upregulation also observed for field Cr(VI)/As co-stress), and benthic community Shannon diversity declined by over 50% in polluted river reaches. The standardized Ecological Damage Index (EDI) of the case was calculated as 480.2, indicating severe aquatic ecosystem damage, with total comprehensive ecological and economic losses reaching 17.25 million CNY. This study innovatively couples high-precision physical transport models with fast emergency prediction algorithms and establishes a complete multi-tier biological indicator chain linking molecular biomarkers to community integrity metrics; the three-dimensional loss accounting system integrating ecosystem service impairment, restoration expenditure, and post-pollution recovery loss realizes closed-loop full-cycle damage evaluation. The proposed framework, demonstrated for Cr(VI) and As pollution, has a modular design that may potentially be extended to other pollutants such as Cd and PAHs by adjusting model parameters, providing a quantitative reference for emergency disposal, pollution remediation, and ecological compensation of water contamination accidents, although further validation across different pollutants and hydrological settings is required. Full article