Search Results (1,329)

Urban green spaces are essential for promoting human health and well-being, especially in cities facing increasing noise pollution and ecological stress. This study investigates the effects of audio-visual interaction on restorative outcomes across three soundscape types (park, residential, and street), focusing on the compensatory role of positive visual stimuli in low-quality soundscape environments. Thirty-two university students participated in a controlled evaluation using soundscapes and corresponding visual materials derived from 30 urban green spaces. A two-way repeated measures ANOVA revealed significant main effects of soundscape type and modality (auditory vs. audio-visual), as well as a significant interaction between these factors. Audio-visual conditions consistently outperformed auditory conditions, with the strongest restorative effects observed in noisy street soundscapes when paired with positive visual stimuli. Further analysis highlighted that visual cleanliness and structural clarity significantly enhanced restorative outcomes in challenging environments. These findings align with existing theories of sensory integration and extend their application to large-scale urban settings. This study shows that multi-sensory optimization can mitigate urban environmental stressors, supporting healthier, more resilient, and sustainable urban environments. Future research should explore long-term and cross-cultural applications to inform evidence-based urban planning and public health policies. Full article

Aiming at the problems of low efficiency, high energy consumption, and poor path quality during the multi-mechanism operation of bridge cranes in spatial tasks, an improved Rapidly exploring Random Tree (RRT) algorithm based on multi-strategy fusion is proposed for energy-efficient path planning. First, the improved algorithm introduces heuristic path information to guide the sampling process, enhancing the quality of sampled nodes. By defining a heuristic boundary, the search space is constrained to goal-relevant regions, thereby improving path planning efficiency. Secondly, focused sampling and reconnection strategies are adopted to significantly enhance path quality while ensuring the global convergence of the algorithm. Combined with line segment sampling and probability control strategies, the algorithm balances global exploration and local refinement, further optimizing path selection. Finally, Bezier curves are applied to smooth the generated path, markedly improving path smoothness and feasibility. Comparative experiments conducted on a constructed three-dimensional simulation platform demonstrate that, compared to other algorithms, the proposed algorithm achieves significant optimization in planning time, path cost, number of path nodes, and number of random tree nodes, while generating smoother paths. Notably, under different operational modes, this study provides a quantitative evaluation of operational efficiency and energy consumption based on energy efficiency trade-offs, offering an effective technical solution for the intelligent operation of bridge cranes. Full article

Symmetry is an essential characteristic in both solution spaces and cloud task scheduling loads, as it reflects a structural balance that can be exploited to enhance algorithmic efficiency and robustness. In recent years, with the rapid development of 6G networks, the number of tasks requiring computation in the cloud has surged, prompting an increasing number of researchers to focus on how to efficiently schedule these tasks to idle computing nodes at low cost to enhance system resource utilization. However, developing reliable and cost-effective scheduling schemes for cloud computing tasks in real-world environments remains a significant challenge. This paper proposes a method for cloud computing task scheduling in real-world environments using an improved dhole optimization algorithm (IDOA). First, we enhance the quality of the initial population by employing a uniform distribution initialization method based on the Sobol sequence. Subsequently, we further improve the algorithm’s search capabilities using a sine elite population search method based on adaptive factors, enabling it to more effectively explore promising solution spaces. Additionally, we propose a random mirror perturbation boundary control method to better address individual boundary violations and enhance the algorithm’s robustness. By explicitly leveraging symmetry characteristics, the proposed algorithm maintains balanced exploration and exploitation, thereby improving convergence stability and scheduling fairness. To evaluate the effectiveness of the proposed algorithm, we compare it with nine other algorithms using the IEEE CEC2017 test set and assess the differences through statistical analysis. Experimental results demonstrate that the IDOA exhibits significant advantages. Finally, to verify its applicability in real-world scenarios, we applied IDOA to cloud computing task scheduling problems in actual environments, achieving excellent results and successfully completing cloud computing task scheduling planning. Full article

Indoor environmental quality in offices, comprising thermal, acoustic, lighting, and air quality domains, is known to influence human comfort, yet the relative importance of their sub-factors—particularly in severe cold regions—remains unclear. This study addresses this gap by integrating objective (Criteria Importance Through Intercriteria Correlation, CRITIC) and subjective (Analytic Hierarchy Process, AHP) weighting methods, supported by field measurements and questionnaire surveys in open-plan offices in three provinces in northeastern China. Cluster analysis categorized acoustic sub-factors into outdoor traffic, outdoor entertainment, people conversation, burst sound, and people movement. Results show that temperature is the dominant thermal comfort driver (39.7% CRITIC; 45.5% AHP), exceeding air velocity and humidity, which had nearly equal influence. Indoor sound exerted greater impact than outdoor sound, with people conversation ranked highest among indoor noise sources, and burst sound and movement showing similar but slightly lower weights. Natural light outweighed artificial light in importance (54.2% CRITIC; 61.0% AHP), while air freshness and pollution were nearly equally influential. Compared to CRITIC, AHP produced more dispersed weights, reflecting subjective bias toward pronounced differences. These findings provide a quantitative basis for prioritizing environmental design interventions—such as controlling indoor conversational noise, optimizing natural lighting, and stabilizing temperature—to enhance comfort in offices in severe cold regions. Full article

In this paper, we propose a novel image restoration framework that integrates optimal control techniques with the Hamilton–Jacobi–Bellman (HJB) equation. Motivated by models from production planning, our method restores degraded images by balancing an intervention cost against a state-dependent penalty that quantifies the loss of critical image information. Under the assumption of radial symmetry, the HJB equation is reduced to an ordinary differential equation and solved via a shooting method, from which the optimal feedback control is derived. Numerical experiments, supported by extensive parameter tuning and quality metrics such as PSNR and SSIM, demonstrate that the proposed framework achieves significant improvement in image quality. The results not only validate the theoretical model but also suggest promising directions for future research in adaptive and hybrid image restoration techniques. Full article

Urban Waterlogging Susceptibility Assessment (UWSA) is vital for resilient urban planning and disaster preparedness. Conventional methods depend heavily on Historical Waterlogging Records (HWR), which are limited by their reliance on extreme rainfall events and prone to human omissions, resulting in spatial bias and incomplete coverage. While hydrodynamic models can simulate waterlogging scenarios, their large-scale application is restricted by the lack of accessible underground drainage data. Recently released flood control plans and risk maps provide valuable physics-informed priors (PI-Priors) that can supplement HWR for susceptibility modeling. This study introduces a dual-source integration framework that fuses HWR with PI-Priors to improve UWSA performance. PI-Priors rasters were vectorized to delineate two-dimensional waterlogging zones, and based on the Three-Way Decision (TWD) theory, a Multi-dimensional Connection Cloud Model (MCCM) with CRITIC-TOPSIS was employed to build an index system incorporating membership degree, credibility, and impact scores. High-quality samples were extracted and combined with HWR to create an enhanced dataset. A Maximum Entropy (MaxEnt) model was then applied with 20 variables spanning natural conditions, social capital, infrastructure, and built environment. The results demonstrate that this framework increases sample adequacy, reduces spatial bias, and substantially improves the accuracy and generalizability of UWSA under extreme rainfall. Full article

This article presents an extensive experimental investigation of the dynamic characteristics of three-arch historical masonry bridges, using Operational Modal Analysis (OMA). The research thoroughly characterizes the dynamic behavior of four representative masonry bridges from the Apulia Region in Southern Italy through detailed experimental campaigns. These campaigns employed calibrated and optimally implemented accelerometric monitoring systems to acquire high-quality dynamic data under controlled excitation and environmental conditions. The selected bridges include the Santa Teresa Bridge in Bitonto, the Roman Bridge in Bovino, the Roman Bridge in Ascoli Satriano and a moderner road bridge on the Provincial Road SP123 in Troia; they span almost two millennia of construction history. The experimental framework incorporated several non-invasive excitation methods, including controlled vehicle passes, instrumented hammer impacts and ambient vibration tests, strategically chosen for optimal signal quality and heritage preservation. This investigation demonstrates the feasibility of capturing the dynamic behavior of these complex and specific historic structures through customized sensor configurations and various excitation methods. The resulting natural frequencies and mode shapes are accurate, robust, and reliable considering the extended data set used, and have allowed a rigorous seismic assessment. Eventually, this comprehensive data set establishes a fundamental basis for understanding and predicting the seismic response of several three-span masonry bridges to accurately identify their long-term resilience and effective conservation planning of these valuable and vulnerable heritage structures. In conclusion, the data comparison enabled the formulation of a predictive equation for the identification of the first natural frequency of bridges from geometric characteristics. Full article

Indoor air quality (IAQ) is a critical determinant of health, comfort, and productivity, and is strongly connected to building energy demand due to the role of ventilation and air treatment in HVAC systems. This review examines recent applications of Artificial Intelligence (AI) and Machine Learning (ML) for IAQ prediction across residential, educational, commercial, and public environments. Approaches are categorized by predicted parameters, forecasting horizons, facility types, and model architectures. Particular focus is given to pollutants such as CO₂, PM2.5, PM10, VOCs, and formaldehyde. Deep learning methods, especially the LSTM and GRU networks, achieve superior accuracy in short-term forecasting, while hybrid models integrating physical simulations or optimization algorithms enhance robustness and generalizability. Importantly, predictive IAQ frameworks are increasingly applied to support demand-controlled ventilation, adaptive HVAC strategies, and retrofit planning, contributing directly to reduced energy consumption and carbon emissions without compromising indoor environmental quality. Remaining challenges include data heterogeneity, sensor reliability, and limited interpretability of deep models. This review highlights the need for scalable, explainable, and energy-aware IAQ prediction systems that align health-oriented indoor management with energy efficiency and sustainability goals. Such approaches directly contribute to policy priorities, including the EU Green Deal and Fit for 55 package, advancing both occupant well-being and low-carbon smart building operation. Full article

Objectives: We conducted a systematic review of clinical trials and case reports analyzing the safety of the currently approved BRAF and MEK inhibitors in adults with cutaneous melanoma (CM), and a meta-analysis to estimate the pooled prevalence of treatment-related adverse events (TRAEs). Methods: We systematically searched six databases for studies published since 2009. The TRAE absolute frequencies reported in primary studies were aggregated using the Metaprop command in Stata 17, which calculates 95% confidence intervals (CIs) incorporating the Freeman–Tukey double arcsine transformation of proportions to stabilize variances within random-effect models. Methodological quality was assessed using the RoB 2 tool for randomized controlled trials (RCTs) and the ROBINS-I tool for non-randomized studies. Results: Twelve RCTs, thirteen prospective cohort studies (PCSs), and ten case reports were included. Meta-analysis was feasible for two regimens: vemurafenib 960 mg monotherapy and dabrafenib 150 mg twice daily plus trametinib 1–2 mg daily. The most common TRAEs during vemurafenib treatment were musculoskeletal and connective-tissue disorders (24%, 95% CI: 6–41%, p = 0.01), with arthralgia as the most prevalent (44%, 95% CI: 29–59%, p < 0.001), followed by rash (39%, 95% CI: 22–56%, p < 0.001). The most common TRAEs during dabrafenib plus trametinib were constitutional toxicities (classified in CTCAE as ‘General disorders and administration site conditions’; 25%, 95% CI: 14–37%, p < 0.001), with fatigue as the most prevalent (47%, 95% CI: 38–56%, p < 0.001), followed by pyrexia (40%, 95% CI: 26–54%, p < 0.001). Squamous cell carcinoma and keratoacanthoma were among the most frequent grade ≥ 3 cutaneous adverse events observed with vemurafenib therapy. Conclusions: Although additional large-scale studies are needed to corroborate these findings, each treatment has a distinct toxicity profile that should be considered when developing personalized risk-stratified treatment plans and in guiding healthcare resource allocation in melanoma care. Full article

Advancements in collaborative robotics have significantly enhanced the potential for human–robot collaboration in manufacturing. To achieve efficient and user-friendly collaboration, prior research has predominantly focused on the robot’s perspective, including aspects such as planning, control, and adaptation. A key approach in this domain has been the recognition of human intentions to inform robot actions. However, true collaboration necessitates bidirectional communication, where both human and robot are aware of each other’s intentions. A lack of transparency in robot actions can lead to discomfort, reduced safety, and inefficiencies in the collaborative process. This study investigates the communication of robot intentions to human operators through mixed reality (MR) and evaluates its impact on human–robot collaboration. A laboratory-based physical human–robot assembly framework is developed, integrating multiple MR-based intention communication strategies. Experimental evaluations are conducted to assess the effectiveness of these strategies. The results demonstrate that conveying robot intentions via MR enhances work efficiency, trust, and user comfort in human–robot collaborative manufacturing. Furthermore, a comparative analysis of different MR-based communication designs provides insights into the optimal approaches for improving collaboration quality. Full article

Objectives: This review seeks to evaluate the effectiveness of electrical stimulation (ES) in improving upper limb function in children and young people (CYP) with hemiplegic cerebral palsy (HCP). Methods: A systematic literature search from inception until May 2025 was conducted. Various study designs comparing the effect of different ES techniques such as functional electrical stimulation (FES), transcutaneous electrical nerve stimulation (TENS), neuromuscular electrical stimulation (NMES), transcutaneous spinal cord stimulation (TSCS), and transcranial direct current stimulation (tDCS) on upper limb function in CYP with HCP were included. Results: Eighteen studies were selected for review and quality assessment, comprising twelve randomised controlled trials (RCTs) and six non-RCTs. FES was shown to improve upper limb function, though more rigorous and controlled research is needed. Both TENS and NMES demonstrate potential to improve upper limb function, particularly when combined with other interventions. The analysis suggests that variability in reporting tDCS outcomes hinders assessment of its potential benefits for improving upper limb function. Conclusions: Current research suggests ES may support upper limb rehabilitation in CYP with HCP, though the overall evidence remains limited. Most studies are small, underpowered, and lack long-term follow-up, limiting confident conclusions. ES should therefore be applied cautiously and only as part of a comprehensive rehabilitation plan. Full article

Infectious diseases remain a global threat, with low- and middle-income countries disproportionately affected due to socio-economic and demographic vulnerabilities. Robust laboratory systems are critical for early detection, outbreak containment, and guiding effective interventions. This study aimed to map and evaluate Jordan’s laboratory diagnostic network for communicable diseases, identify gaps, and recommend strategies to strengthen capacity, harmonization, and alignment with international standards. A multi-method approach was employed in 2023 through collaboration between the Jordan Center for Disease Control and the Health Care Accreditation Council. Data were collected via (i) a desktop review of 226 national and international documents; (ii) 20 key informant interviews with stakeholders from the public, private, military, veterinary, and academic sectors; and (iii) 23 field visits across 27 laboratories in four Jordanian governorates. Data were analyzed thematically and synthesized using the LABNET framework, which outlined ten core laboratory capacities. Findings were validated through a multi-sectoral national workshop with 90 participants. The mapping revealed the absence of a unified national laboratory strategic plan, with governance dispersed across multiple authorities and limited inter-sectoral coordination. Standard operating protocols (SOPs) existed for high-priority diseases such as T.B, HIV, influenza, and COVID-19 but were lacking or outdated for other notifiable diseases, particularly zoonoses. Quality management was inconsistent, with limited participation in external quality assurance programs and minimal accreditation uptake. Biosafety and biosecurity frameworks were fragmented and insufficiently enforced, while workforce shortages, high turnover, and limited specialized training constrained laboratory performance. Despite these challenges, Jordan demonstrated strengths including skilled laboratory staff, established reference centers, and international collaborations, which provide a platform for improvement. Jordan’s laboratory network has foundational strengths but faces systemic challenges in policy coherence, standardization, quality assurance, and workforce capacity. Addressing these gaps requires the development of a national laboratory strategic plan, strengthened legal and regulatory frameworks, enhanced quality management and accreditation, and integrated One Health coordination across human, animal, and environmental health sectors. These measures will improve diagnostic reliability, preparedness, and alignment with the global health security agenda. Full article

Background/Objectives: GK plan quality is strongly affected by lesion size and shape, and the same evaluation metrics may not be directly comparable across patients with different anatomies. This study proposes a deep learning-based method to predict achievable, clinically acceptable plan quality from patient-specific geometry. Methods: A hierarchically densely connected U-Net (HD-U-Net) was trained at the lesion level to predict 3D dose distributions for the estimation of plan quality metrics, including coverage, selectivity, gradient index (GI), and conformity index at a 50% prescription dose (CI50). To improve the prediction accuracy of plan quality metrics, Dice similarity coefficient losses for the 100% and 50% isodose lines were incorporated with conventional mean squared error (MSE) loss. Results: Ten-fold cross-validation on 463 brain metastases (BMs) from 175 patients showed that our method achieved smaller mean absolute errors across all four metrics than the HD-U-Net baseline trained with MSE loss. Improvements were pronounced in all metrics for small metastases, and were observed primarily in GI and CI50 for medium and large lesions. Paired Wilcoxon signed-rank tests confirmed the statistical significance of these improvements (p < 0.05). Conclusions: The proposed method outperformed the baseline model in capturing overall trends, improving per-lesion accuracy, and enhancing robustness to dataset variability. It can serve as a pre-planning tool to guide planners in constraint setting and priority tuning, a post-planning quality control tool to identify subpar plans that could be substantially improved, and as a foundation for developing deep reinforcement learning-based automated planning of GK treatments for brain metastases. Full article

Microbial contamination in frozen beverages threatens public safety and food quality. By systematically analyzing safety incidents, potential microbial hazards can be identified. This study reviewed 155 microbial safety incidents related to frozen beverages reported by nine international regulatory agencies from January 2015 to December 2024, as well as 903 incidents published by the State Administration for Market Regulation of China. The results indicate a higher risk in Southeast Asia, particularly in Malaysia (16.13%) and Thailand (11.61%). In China, the risks are concentrated in South China (Guangdong, 14.52%), Northeast China (Liaoning, 10.20%; Heilongjiang, 9.87%), and the Huang-Huai-Hai region (Henan 6.87%; Shandong 5.99%). Statistical analysis reveals that non-compliance incidents related to coliforms account for 67.7% globally, while incidents involving pathogens such as Listeria monocytogenes, Staphylococcus aureus, Salmonella, and Norovirus account for 6.4%. The characteristics in the Chinese market align with global trends, with the highest proportion of coliform exceedance (41%), while the incidence of pathogenic contamination remains relatively low (0.6%). Further analysis of different types of frozen beverages (ice cream, ice milk, ice frost, ice lolly, sweet ice, edible ice, and others) and their association with microbial hazards reveals significant issues with ice cream products globally; however, in the Chinese market, the contamination problems with ice milk and ice lolly are more severe. This study provides regional and category-specific data for the microbial risk assessment of frozen beverages and offers guidance for regulatory agencies and enterprises to implement targeted control measures, including optimizing sampling plans, enhancing hygiene controls during production processes, and promoting compliance in cold chain management. Consequently, this approach effectively reduces the risk of foodborne diseases and enhances the overall safety level of the industry, demonstrating significant practical application value and public health significance. Full article

Fabric defect detection is essential for quality assurance in textile manufacturing, where manual inspection is inefficient and error-prone. This paper presents a real-time deep learning-based system leveraging YOLOv11 for detecting defects such as holes, color bleeding and creases on solid-colored, patternless cotton and linen fabrics using edge computing. The system runs on an NVIDIA Jetson Orin Nano platform and supports real-time inference, Message Queuing Telemetry (MQTT)-based defect reporting, and optional Real-Time Messaging Protocol (RTMP) video streaming or local recording storage. Each detected defect is logged with class, confidence score, location and unique ID in a Comma Separated Values (CSV) file for further analysis. The proposed solution operates with two RealSense cameras placed approximately 1 m from the fabric under controlled lighting conditions, tested in a real industrial setting. The system achieves a mean Average Precision (mAP@0.5) exceeding 82% across multiple synchronized video sources while maintaining low latency and consistent performance. The architecture is designed to be modular and scalable, supporting plug-and-play deployment in industrial environments. Its flexibility in integrating different camera sources, deep learning models, and output configurations makes it a robust platform for further enhancements, such as adaptive learning mechanisms, real-time alerts, or integration with Manufacturing Execution System/Enterprise Resource Planning (MES/ERP) pipelines. This approach advances automated textile inspection and reduces dependency on manual processes. Full article