Search Results (2,118)

In the context of urban regeneration, identifying the nonlinear and interactive effects of the built environment on commercial vitality is essential for targeted spatial improvement. Using Xi’an’s central urban area as a case study, this study integrated multi-source data, including POI, AOI, street-view imagery, and mobile phone signaling data, to delineate commercial spaces via kernel density analysis. With actual service population density as the vitality indicator, a built-environment framework was constructed using 14 indicators across four dimensions: transport accessibility, functional diversity, street quality, and environmental capacity. Random forest regression and SHAP-based interpretable machine learning were employed to examine factor importance, nonlinear thresholds, and interactions. Results show that environmental capacity and transport accessibility are the dominant dimensions, with building density, road network density, and employment density contributing most. Built-environment variables generally exhibit nonlinear threshold effects; key thresholds include road network density > 8 km/km², building density > 40%, functional mix > 4.5, and sky view factor around 40%. Interactions involving building density are most pronounced, and its positive effect is significantly amplified under higher accessibility or employment density. These findings suggest prioritizing road network optimization and building coverage, while balancing functional mix and spatial scale in commercial space regeneration. Full article

Low-carbon operation and reasonable carbon responsibility allocation are essential for improving source-load coordinated emission reduction in park integrated energy systems (PIESs). Existing allocation methods usually trace carbon emissions or calculate marginal contributions, but they still have difficulty distinguishing heterogeneous park users with different load rigidity, demand response (DR) capability, payment capability and real carbon-reduction potential. To address this problem, this paper proposes a carbon responsibility allocation method for PIESs considering user heterogeneity and develops a carbon-cost-feedback-based bi-level low-carbon scheduling model. First, park users are classified into high-energy-consuming industrial users, commercial and public service users, and energy infrastructure users according to quantitative criteria related to energy consumption scale, load continuity, adjustable load proportion and distributed-resource interaction capability. A heterogeneity indicator system is then established, including DR elasticity, electricity utilization efficiency, payment capability, DR potential and actual carbon-reduction potential. Second, an improved Shapley value allocation model is constructed by combining coalition marginal contribution with entropy-weighted heterogeneity correction. The allocation results are converted into user-side carbon responsibility cost signals and embedded into a bi-level optimal scheduling model, where the upper level minimizes the system operating cost and the lower level minimizes users’ integrated energy-use cost. Case studies show that, compared with the conventional economic scheduling scenario, the proposed model reduces the total system cost from CNY 5.0782 million to CNY 4.3258 million and decreases carbon emissions from 14,994.39 t to 10,874.62 t, corresponding to reductions of 14.82% and 27.47%, respectively. The results indicate that the proposed method can coordinate fairness-oriented carbon responsibility allocation with incentive-oriented low-carbon scheduling, supporting both SDG 11 and SDG 12. Full article

With the rapid expansion of artificial intelligence infrastructure and cloud computing services, data centres are evolving from rigid electricity loads into flexible resources capable of contributing to renewable energy integration, grid regulation and cross-regional computing power allocation. Addressing the shortcomings in existing research regarding the differences between various types of computing tasks, the mechanisms of green migration under network constraints, and the characterisation of curtailment risks for renewable energy, this paper proposes a risk-aware collaborative optimisation and scheduling method for a power–computing–network–storage coupled system across remote data centres. Firstly, a hierarchical model of multi-type computing tasks is constructed, classifying data centre loads into fixed real-time tasks, online inference tasks, long-duration AI training tasks, and opportunistic elastic tasks, to characterise the differences between these tasks in terms of latency, time-shift, migration, and completion volume constraints. Secondly, a graph-attention-inspired green affinity prior is proposed, mapping grid topological distance, renewable energy availability, data centre PUE, and energy storage regulation capacity into interpretable migration signals, thereby guiding flexible computing power to migrate towards nodes with abundant green electricity and favourable grid support conditions. Subsequently, we introduce the CVaR metric to quantify the tail risk of renewable energy curtailment, establishing a multi-scenario stochastic linear optimisation model that incorporates DC power flow, unit output, renewable energy utilisation, campus energy storage, task SLAs, and cross-node migration constraints. A 24 h simulation based on the IEEE 10-machine, 39-node system demonstrates that the proposed method can reduce the expected curtailment volume from 176.939 MWh to 0 MWh, lower the CVaR curtailment risk from 694.085 MWh to 0 MWh, and increase the proportion of green computing power by 9.283 percentage points compared to the fixed-load baseline, whilst improving the five-tier collaborative score by 4.885 points. Full article

The proliferation of Unmanned Aerial Vehicles (UAVs) in various applications has created a pressing need for robust and efficient communication systems. Fifth-generation (5G) networks can support UAV connectivity through high bandwidth and low-latency communication; however, rapid three-dimensional UAV mobility creates handover-management challenges that can increase signalling overhead, service interruption, and Quality of Service (QoS) degradation. This paper presents an integrated framework that combines LSTM-based multi-UAV trajectory prediction with proactive handover optimization using an Advantage Actor–Critic (A2C) Deep Reinforcement Learning (DRL) agent. The LSTM predictor is evaluated on a real-world UAV trajectory dataset and reports a root mean square error (RMSE) of 4.37 m over a 5 s prediction horizon after conversion to a local East–North–Up coordinate frame. A lightweight simulation-level coordination mechanism is included to reduce simultaneous target-cell contention among multiple UAVs; it is not claimed as a new standardized 3GPP signalling procedure. Handover performance is evaluated by replaying 180 held-out flight trajectories in a controlled 5G simulation across ten independent random seeds. Under these stated assumptions, the proposed framework achieves a handover success rate of $94.2\pm 0.8$%, an average SINR of $15.8\pm 0.2$ dB, a handover delay of $45.2\pm 1.1$ ms, and a handover frequency of $0.85\pm 0.05$ HOs/min, outperforming the tuned 3GPP A3, reactive SINR, and CASH baselines in the reported simulation results (Wilcoxon signed-rank test, $p<0.01$, Bonferroni-corrected). The experimental setup is described in detail to support methodological transparency and facilitate future replication, but the handover results should be interpreted as simulation-based evidence rather than live-network validation. Full article

Accurate assessment of winding condition for power transformers is critical for ensuring the stable operation of modern power systems. Vibration signal has been regarded as an effective and promising evaluator for winding diagnosis. While on-line vibration monitoring offers the continuous, non-invasive and in-service assessment for winding condition, establishing precise correlations between the variable vibration patterns and specific winding condition remains challenging. To this end, an off-line vibration response analysis (VRA) technique was presented in the paper. Specifically, vibration frequency response (VFR) curves, indicating the winding response, were first obtained when the transformer was excited by the developed vibration response testing system, consisting of constant current variable-frequency power supply, intermediate transformer, accelerometers, data acquisition, control and analysis system. The VFR curves were then quantitatively and comprehensively described through four kinds of correlation indices. Finally, hierarchical integration strategy was proposed to aggregate those indices into quantitative criterion for condition assessment. The proposed method was validated on a real transformer under both normal and fault conditions, demonstrating superior performance. Notably, a 10% decrease in the evaluation criterion indicates an incipient winding looseness, while a reduction of 25% or more suggests severe looseness, prompting timely maintenance recommendations. Full article

Urban acoustic environments and performing-arts spaces strongly influence speech communication quality, acoustic comfort, and public wellbeing, particularly in noise-exposed shared environments such as transport hubs, campuses, healthcare spaces, public service facilities, music-education settings, and rehearsal or performance-related spaces. To address speech–noise separation in low signal-to-noise ratio and acoustically complex scenarios, this study proposes a lightweight two-stage deep learning framework termed LCGRU–Wave-SkipConvNet. In the preprocessing stage, a Lightweight Convolutional Gated Recurrent Unit (LCGRU) model is employed to achieve preliminary separation of target speech and background noise by capturing both spatial and temporal acoustic features. In the post-processing stage, a Wave-SkipConvNet model is introduced to further suppress residual noise and enhance speech quality. Experimental results demonstrate that the proposed framework achieves superior performance under different signal-to-noise ratios, sound-source angles, and target angle errors. For example, in the preprocessing stage, the LCGRU model achieved a perceptual evaluation of speech quality (PESQ) score of 2.64 at source angles between 0° and 30°, outperforming the convolutional neural network-long short-term memory (CNN-LSTM) model by 1.17. In the post-processing stage, the Wave-SkipConvNet model achieved higher short-time objective intelligibility (STOI) and segmental signal-to-noise ratio (segSNR) values than the comparison models under different SNR conditions. The proposed framework provides an effective and deployment-oriented AI solution for improving speech accessibility and acoustic comfort in urban acoustic environments and performing-arts spaces. Beyond speech enhancement, it offers practical potential for supporting healthier, more inclusive, and more equitable acoustic environments in noise-sensitive public and educational spaces. It should be noted that this study focuses on the objective acoustic environment and signal-level speech enhancement, rather than subjective soundscape perception, musical perception, or human perceptual evaluation. Full article

Wireless Body Area Networks (WBANs) have become an essential component of modern Internet of Medical Things (IoMT) healthcare systems, enabling continuous monitoring of patient physiological signals through wearable sensors. Despite their advantages, WBAN environments remain highly prone to cyber threats, privacy breaches, and single points of failure. To address these risks, this work proposes a Hybrid Multi-Metric Anomaly Detection (HM-MAD) framework deployed on the NodeMCU-32S platform with BLE 5.0 connectivity for secure continuous glucose monitoring (CGM) data transmission. The detection model simultaneously analyses physiological signals, system-level parameters, and network-level communication metrics, enabling the reliable identification of multiple cyberattacks. The proposed system focuses on securing data transmission against relay attacks, where attackers induce communication delay without modifying payloads, potentially leading to false glucose readings, improper insulin dosage delivery, unauthorized control or denial-of-service. The Convolutional Neural Network (CNN) and Bi-Directional Long Short Term Memory (BiLSTM) model classifies attack types including timing manipulation, replay attacks, power glitches, firmware tampering, and sensor spoofing. Experimental evaluation demonstrates that the proposed CNN + BiLSTM framework achieves 94.6% detection accuracy with an average inference latency of 15 ms, representing a 50% latency reduction compared to Transformer-based intrusion detection models (30 ms), while simultaneously reducing computational overhead by 28% in terms of floating-point operations and memory utilization. These results indicate that the HM-MAD framework provides an effective and scalable solution for protecting resource-constrained IoMT healthcare systems against emerging cyber threats. Full article

Background: Large language models and other generative artificial intelligence (AI) tools are increasingly being embedded in digital healthcare services, including mobile health applications, telerehabilitation, remote monitoring, and hybrid care pathways. In this review, digital healthcare refers to technology-mediated healthcare services in which digital platforms, mobile applications, wearables, remote communication, and AI-enabled interfaces support health assessment, self-management, rehabilitation, clinical decision support, or service delivery. When AI-generated exercise guidance moves from general education to individualized recommendations about dose, progression, contraindications, or rehabilitation, it may become directly actionable and safety-relevant. Objectives: This review aimed to clarify when AI-generated exercise guidance in digital healthcare may warrant safety-relevant governance attention and to outline implementation considerations for explainability, human oversight, and service-level governance. It addresses a gap in the literature: general AI-governance and exercise-prescription discussions rarely specify how point-of-use explanations, review thresholds, and escalation safeguards can be organized for directly actionable AI exercise guidance. Methods: We conducted a governance-oriented narrative review of peer-reviewed literature and representative regulatory or guidance documents. This review was not designed as a systematic review, scoping review, or exhaustive evidence map; transparent source mapping was used to support conceptual synthesis. Searches and source mapping focused on generative AI, large language models, explainable AI, clinical decision support, digital health, mobile health, exercise prescription, rehabilitation, trust, automation bias, and human oversight. Sources were included when they informed the safety, explainability, governance, or real-world implementation of patient-facing AI-generated exercise guidance. Extracted material was grouped by evidentiary role and synthesized through framework synthesis and governance mapping to distinguish literature-supported observations, author interpretation, and proposed implementation tools. Results: The included sources were first organized into five thematic groups: digital exercise delivery and exercise-prescription evidence; explainability, trust, and automation bias literature; professional responsibility, ethics, and patient disclosure literature; regulatory and policy documents; and digital literacy, patient/clinician attitudes, and equity literature. The synthesis then proceeded from safety relevance to explanation needs, human oversight and escalation needs, and selected regulatory and policy signals before translating these strands into conceptual and implementation-oriented outputs rather than empirically validated instruments. AI-generated exercise guidance was most safety-relevant in scenarios involving individualized dose, progression, contraindication-sensitive action, or rehabilitation strategy. Across the included sources, generic transparency alone was not sufficient to support reviewable use; relevant explanation elements included evidence sources, risk warnings, reasoning paths, and reasonable alternatives. Oversight considerations varied with embodied risk, clinical ambiguity, user vulnerability, and likelihood of direct enactment. Implementation considerations linked interface design, clinical review, escalation, auditability, and post-deployment monitoring. Conclusions: AI-generated exercise guidance in digital healthcare may warrant governance attention as a patient-safety and accountability issue when it influences actionable exercise decisions. The proposed framework offers a conceptual basis for designing more reviewable and accountable mobile and remote exercise-support services. Future work can validate these outputs in patient-facing services, clinician review workflows, usability studies, implementation pilots, and safety evaluations. Full article

Crack opening and reinforcement stress are two complementary indicators of the service state of reinforced concrete hydraulic structures, yet they are often predicted separately. This study develops a data-driven multi-task temporal fusion framework for joint 48 h ahead prediction of dam crack responses and rebar stress using multi-source monitoring data. The measured data comprise five crack-monitoring series, five rebar stress series, local temperature channels, reservoir water level, antecedent rainfall, and an auxiliary environmental signal over approximately four years. Target responses are aligned only at common measured timestamps; no synthetic target observations are introduced. A simplified engineering layout and plan-based crack–rebar distances are further used to examine whether an explicit spatial prior can strengthen the shared temporal representation without introducing synthetic target values. A residual multi-task temporal fusion network (MTTF-Net) is proposed with a shared Transformer encoder, attention pooling, task-specific decoders, and a response-continuity regularization term. The model is compared with persistence, Ridge regression, random forest, Extra Trees, XGBoost, and GRU baselines under a chronological train/validation/test split. For the independent test period, Ridge regression obtains the lowest overall RMSE (2.2968), whereas MTTF-Net provides the lowest crack RMSE (0.0141), the lowest overall MAE (1.0035), and the second-best overall RMSE (2.3813). Distance-informed ablation, denoted as MTTF-Net-S, remains close to MTTF-Net in macro-averaged $R^2$ but is not superior in the overall test metrics, indicating that the available horizontal distances are valuable engineering metadata but cannot replace richer three-dimensional structural connectivity. These results indicate that the monitoring data contain a strong linear autoregressive component, while multi-task temporal fusion improves nonlinear crack response prediction and remains competitive for stress forecasting. The source code is prepared as a public implementation package, whereas the measured monitoring dataset is subject to data owner restrictions. Full article

Efficient data processing and signal acquisition are becoming increasingly critical. Pipeline networks present unique topological constraints that complicate the balance between signal sampling efficiency and data-transmission reliability. In this paper, we propose a bi-objective optimization model for the urban pipeline network (UPN). The model optimizes autonomous mobile sensor (AMS) path planning using an Euler path scheme and communication node (CN) deployment using a deterministic deployment scheme. The model aims to minimize both monitoring time (MMT) and data delay (MDD). These two indicators are used as quality of service (QoS) metrics for communication and sensing. By representing the UPN as a graph structure, we establish two mathematical models for the MMT and MDD problems. Then, we introduce a topology-guided heuristic virtual-edge strategy to construct an Euler traversal for the MMT problem. An adaptive simulated annealing (ASA) algorithm is designed to solve the MMT problem. On this basis, the MDD problem is solved using an enhanced ant colony optimization (EACO) algorithm. Simulation results show that the proposed scheme achieves shorter monitoring times and lower data delays. Specifically, the Euler path scheme for the AMS reduces MMT by more than 43.26%, and the deterministic CN-deployment scheme reduces MDD by more than 44.10%. Full article

Real-time precise point positioning (PPP) is increasingly supported by open satellite-broadcast state-space representation (SSR) services, yet standalone operation with a single service remains vulnerable to limited constellation support, correction outages, latency variations, and service-dependent modeling inconsistencies. In the Asia-Pacific region, MADOCA-PPP and PPP-B2b provide two publicly accessible and complementary SSR sources, but their consistent fusion before user-level PPP estimation remains insufficiently investigated. This paper proposes a correction-domain fusion framework that combines MADOCA-PPP and PPP-B2b orbit and clock corrections before PPP estimation, rather than merging final positioning solutions. Inter-service discrepancies and unknown cross-correlations are handled by a bias-state-aware structured covariance intersection strategy, in which the relative weighting is derived from the respective correction information (inverse variance), preserving statistical consistency and avoiding overconfident fusion. A unified multi-GNSS PPP scheme further supports signal-priority harmonization, broadcast-ephemeris adaptation, correction-age control, and GLONASS inter-frequency and differential code bias handling. Static-station per-epoch (pseudo-kinematic) and offshore kinematic experiments validate the framework. In the static-station test, fusion raised the mean number of valid satellites from 21.98 and 14.98 to 26.56 and improved the horizontal RMS to 0.033 m—better than either standalone service (0.037 m, 0.079 m)—confirming a genuine combination rather than source selection, while the 3D RMS (0.068 m) matched the best standalone service (0.066 m). In the offshore test, fusion achieved the best overall accuracy (0.232 m horizontal, 0.290 m 3D, versus 0.332 m and 0.313 m for the standalone services) and the most satellites (25.4). It also degraded most slowly with increasing elevation cut-off, outperforming both services about threefold at 40°. A normalized-innovation-squared check confirmed the fused covariance is consistent and not overconfident (median ≈ 1.1; within the 99% bound in 100% of epochs). Under single-service outages from 30 s to 600 s, fusion maintained 100.0% availability, confirming its advantage in redundancy, continuity, and resilience. Full article

The rapid proliferation of cloud data centers has intensified concerns over carbon emissions, energy efficiency, and sustainability. Virtual machine (VM) placement is a pivotal control lever, yet existing methods rarely couple carbon intensity signals with computationally tractable multi-objective optimization. In this paper, we propose CASO (Carbon-Aware Surrogate-Guided Optimization), a novel framework that integrates an online adaptive Radial Basis Function (RBF) surrogate model with a self-adaptive hybrid PSO-DE swarm optimizer for real-time VM placement in geo-distributed edge cloud environments. CASO simultaneously minimizes carbon emissions, energy consumption, SLA violation rate, and network latency under strict host capacity and Quality-of-Service (QoS) constraints. Three key innovations differentiate CASO: (i) an online surrogate update mechanism that refines fitness approximations incrementally as workload patterns evolve; (ii) a carbon intensity weighting scheme anchored to real-time Grid Emission Factor (GEF) signals; and (iii) an adaptive parameter controller that autonomously tunes swarm exploration–exploitation trade-offs without hand-crafting. Experiments on the publicly available Alibaba Cluster Trace (cluster-trace-v2026-GenAI) dataset within a CloudSim-Plus environment show that CASO reduces carbon emissions by up to 31.4%, energy consumption by 27.9%, and SLA violations by 18.8% compared to the strongest baseline while converging 3.8× faster than the strongest baseline (ADEDL). Full article

Background and Objectives: Traumatic dentoalveolar injuries (TDI) in children often require urgent stabilization using splints. Titanium trauma splints (TTS) represent a practical option; however, pediatric evidence from hospital-based emergency settings remains limited. This study describes the clinical and contextual characteristics of children treated with TTS and explores factors associated with early complications and splint stability. Materials and Methods: A retrospective observational cohort study was conducted at a Pediatric Dentistry Service, including children with TDI managed with TTS and followed for a minimum of three months. Clinical records were reviewed to collect demographic, contextual, and clinical variables. Early complications and the need for re-splinting were recorded, and associations between selected variables and outcomes were analyzed. Results: Seventy-three patients (64.4% male; mean age 10.29 ± 2.99 years) and 127 traumatized teeth (98.4% permanent) were included. A predominance of school-based injuries was observed (52.1%). The most frequent injury types were subluxation (39.1%), avulsion (26.6%), and extrusion (16.4%). A longer interval between trauma and splint placement was associated with inflammatory root resorption (p = 0.011), although this finding should be interpreted with caution given the limited number of events. Mixed-dentition splints showed a higher likelihood of requiring re-splinting (OR = 12.23; 95% CI: 1.18–126.60); however, this estimate was imprecise and should be interpreted as an exploratory signal. Overall, 90.4% of patients completed treatment with a single splint. Conclusions: Within the limitations of this retrospective observational cohort, TTS showed satisfactory short-term clinical stability in pediatric traumatic dental injuries. Longer time between trauma and splint placement was associated with inflammatory root resorption, while mixed-dentition splints emerged as a potential signal of increased re-splinting. These findings are exploratory and hypothesis-generating and require confirmation in future studies. Full article

Urban traffic congestion remains a persistent global challenge, contributing to significant economic inefficiencies, elevated greenhouse gas emissions, and diminished quality of life. This paper presents a real-world video-based traffic monitoring study combined with a proposed adaptive signal control framework. In the monitoring component, YOLOv11 object detection was applied directly to footage recorded from an overhead bridge position on a 40 km/h road. The model successfully detected and tracked multiple road-user categories, including cars, trucks, buses, motorcycles, cyclists, and pedestrians, yielding 1041 vehicle detections across 25 unique tracked objects. Vehicle speeds were estimated from inter-frame centroid displacement, and a Region of Interest (ROI) occupancy model was used to classify congestion states as High, Medium, or Free Flow using thresholds grounded in Highway Capacity Manual (HCM) level-of-service criteria. The system detected 11 high-congestion frames (3.8%), 184 medium-congestion frames (63.9%), and 93 free-flow frames (32.3%), consistent with moderate congestion observed during the recording period. In the proposed control component, a Proximal Policy Optimisation (PPO)-based reinforcement learning signal controller is designed around the YOLOv11 detection outputs as its state representation. Based on comparable adaptive traffic signal control studies in the literature, the proposed framework is projected to achieve approximately 25% higher peak-hour throughput, 35% shorter queue lengths, and 32% lower average waiting times relative to a fixed-time signal baseline. The detection accuracy (mAP@0.5 = 93.2%) and inference speed (32 FPS) cited are published YOLOv11 benchmarks used as indicative performance references. This work bridges real-world perception and proposed intelligent control, providing a transparent and reproducible methodology for next-generation smart city traffic management. Full article

Military burn pits used during post-9/11 U.S. military deployments functioned as uncontrolled combustion systems and were widely utilized to dispose of large volumes of outdoor waste by burning. Burn pits involved heterogeneous waste materials burned under variable temperature and oxygen conditions. These combustion environments generated complex, toxic, multipollutant airborne emission mixtures that included particulate matter (PM_2.5), polycyclic aromatic hydrocarbons (PAHs), and volatile organic compounds (VOCs). This narrative review synthesizes epidemiologic, experimental, and mechanistic evidence linking burn pit emissions to disruption of the lung–brain axis and adverse neurological outcomes. We specifically aim to address a critical gap in understanding how combustion-derived toxicants impact brain health and are associated with unfavorable neuropsychiatric outcomes, including increased risk of post-traumatic stress disorder (PTSD) and depression. Combustion-related exposures promote pulmonary inflammation and system-wide immune signaling that propagate to the central nervous system, contributing to neuroinflammation and dysregulation of the hypothalamic–pituitary–adrenal (HPA) axis. These interconnected mechanisms are associated with toxic encephalopathy and related cognitive and mood disturbances, underscoring the need to integrate fire science with military and environmental health services research to better define the systemic and neurological consequences of acute and chronic fire-derived inhalation exposures. Full article