Search Results (22,841)

Background/Objectives: Although upper extremity dexterity problems are frequently reported in people with Parkinson’s disease (PwPD), valid and reliable scales for assessing upper extremity function and dexterity are limited. The objective of this study was to investigate the reliability and validity of the Action Research Arm Test (ARAT) and the Jebsen–Taylor Hand Function Test (JTHFT) in PwPD. Methods: Seventy PwPD and thirty HC were recruited. The test–retest reliability was evaluated by determining the intraclass correlation coefficient (ICC). MDC₉₅ was calculated by using ICC results. The concurrent validities of JTHFT and ARAT were determined by investigating their relationship with the Nine-Hole Peg Test (9-HPT), Hoehn and Yahr scale (H & Y), Unified Parkinson’s Disease Rating Scale (UPDRS), and motor symptoms (UPDRS-III). The cut-off times that best discriminated between PwPD and HC were investigated by plotting receiver operating characteristic (ROC) curves. Results: The ARAT and JTHFT showed excellent test–retest reliability (ICC = 0.937 to 0.995). The MDC₉₅ values for the ARAT were 0.38 for the dominant hand and 0.58 for the non-dominant hand. MDC₉₅ values for the JTHFT subtests and total scores ranged from 0.38 to 4.71. The ARAT, JTHFT subtests, and total scores demonstrated a fair-to-strong correlation with other outcomes (p < 0.05). The cut-off times that best differentiated JTHFT subtests and total scores ranged from 3.56 to 64.23. Conclusions: The JTHFT is a reliable and valid measurement tool for the assessment of manual dexterity in PwPD, while the ARAT is a reliable assessment tool in PwPD but does not have discriminant validity. Full article

This systematic review assesses indoor air quality (IAQ) in tropical residences (Köppen Af/Am/Aw), explicitly linking IAQ to ventilation from in situ monitoring and, when relevant, occupant surveys (surveys synthesized qualitatively). This focus is warranted by the scarcity of tropical, housing-specific evidence. Searches were performed exclusively in Google Scholar (25 August 2024–5 August 2025; English/Spanish) under PRISMA, with documented queries/filters; eligible studies reported residential settings, tropical climate, and IAQ–ventilation linkage. Results show a regulatory mosaic with few binding residential limits and heterogeneous protocols that hinder comparison. Robust patterns include cooking-related particle peaks, penetration of traffic dust, humidity-driven VOC/formaldehyde emissions, and mold growth under deficient hygrothermal control. CO₂ is a useful operational indicator of ventilation yet insufficient for risk assessment without PM and VOC monitoring. Evidence supports source control, cross-ventilation and/or on-demand extraction/outdoor-air supply, humidity management, and filtration/purification to avoid particle ingress during ventilation. Reporting of sensor performance (calibration, drift, RH/T effects) is inconsistent, and targeted evaluations of TVOC/formaldehyde and window screens (mesh) are scarce. We conclude that tropical residential IAQ management requires multi-parameter, continuous monitoring, standardized reporting, and trials integrating ventilation, dehumidification, and filtration under real occupancy, alongside adaptive regulation and passive tropical design augmented by light mechanical support and informed occupant behavior. Full article

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical for anticipating battery failure and enabling effective health management. However, existing RUL prediction methods often suffer from several limitations, including the need for large volumes of training data, significant differences across datasets, and insufficient accuracy in long-term forecasting, which hinder their applicability in real world scenarios. To address these challenges, this paper proposes a hybrid model that integrates transfer learning (TL) and particle filtering (PF) with the Mogrifier LSTM (MLSTM) network. Specifically, the model first employs a transfer learning-based Mogrifier LSTM (TL-MLSTM) to perform long-term prediction of battery capacity, thereby enhancing the modelś generalization ability to accommodate RUL prediction under varying operating conditions. Subsequently, the capacity predictions generated by TL-MLSTM are used as observations in the PF algorithm, which iteratively updates the battery state parameters and refines the capacity predictions, thereby further improving accuracy. The proposed model is validated using publicly available datasets comprising multiple types of batteries under various operational conditions. Experimental results demonstrate that the model achieves an average RMSE of 0.0199, MAPE of 0.5803%, MAE of 0.0167 and APE of 11 cycles across multiple test groups. Compared with standalone models or purely data-driven approaches, the proposed method exhibits significant advantages in robustness and accuracy for long-term capacity degradation prediction. Full article

The main objective of this study is to enhance the understanding of the physical and chemical processes operating in a still understudied basin and to establish methodologies for assessing the impacts of climate change in an arid region of southern Spain. The work aims to identify areas that are vulnerable, or potentially vulnerable, to climate change and to evaluate the system’s response in terms of both water quantity and quality. To this end, we analyze the evolution of streamflow, suspended sediments, and nitrates, using the SWAT (Soil and Water Assessment Tool) model. A clear lack of observed data was the main limitation improving river flow calibration; however, the validation process showed a very satisfactory coefficient of determination (R²) for the two stations considered (R² = 0.78 and R² = 0.70). Due to the limited water quality dataset, the calibration and validation of nitrates and suspended sediments were performed using the LOAD ESTimator (LOADEST) program. Satisfactory results were obtained at both stations during the validation period for nitrates (R² = 0.52 and R² = 0.92) and suspended sediment (R² = 0.83 and R² = 0.95) load. Finally, the model was applied under two climate change scenarios (Representative Concentration Pathways, RCP 4.5 and RCP 8.5). Reduced precipitation, combined with temperature increases exceeding 1 °C in some areas, leads to decreased flows along the main channel, affecting suspended sediment concentrations. Nitrate levels generally decrease across the basin, although they increase from October to April at the river mouth. This area emerges as highly vulnerable to climate change, particularly regarding alterations in water flow and nitrate concentration. Full article

Soft tissue sarcomas (STS) are rare and heterogeneous tumors for which achieving complete tumor resection with negative surgical margins remains the cornerstone of curative treatment and a key predictor of survival. Current intraoperative resection margin status assessment techniques remain limited, as traditional intraoperative frozen section analysis is of limited accuracy for most STS histological subtypes. This comprehensive review evaluates current and emerging margin assessment techniques used intra-operatively during STS resection. A systematic search of PubMed and PubMed Central databases from 2000 to 2025 identified studies using fluorescence imaging, spectroscopy, and ultrasound-based modalities. Indocyanine green (ICG) fluorescence-guided surgery appeared to be the closest to widespread use, with the most clinical evidence showing potential to reduce positive margins. Use of acridine orange (AO) as a fluorescent dye also showed potential in decreasing local recurrences, but it remains in the experimental stage of research with little clinical data available. Raman spectroscopy has recently shown high accuracy in identifying STS from healthy tissue, but the impact of its use on patient outcomes has not been studied yet. Other techniques, such as diffuse reflectance spectroscopy (DRS), rapid evaporative ionization mass spectrometry (REIMS), optical coherence tomography (OCT), and intraoperative ultrasound (IOUS) yielded encouraging results but still require further prospective studies to validate their safety, reproducibility, and clinical utility in improving surgical precision and patient outcomes. Full article

Maping Phosphate Mine operates as a large-scale mining complex characterized by a multi-mining area strip mining layout. This configuration exhibits expansive operational zones, numerous dispersed mining sites, and inherent systemic complexity, collectively complicating ventilation system management. The optimization of ventilation processes across multiple mining areas constitutes a critical measure for enhancing operational safety and efficiency within resource-constrained scenarios. This investigation specifically targets four adjacent mining zones—340B, 380B, 380C, and 420D—where three distinct ventilation schemes were formulated and evaluated. A process-oriented simulation-optimization model combining Ventsim and TOPSIS was developed to evaluate the ventilation systems. The ventilation network architecture and airflow distribution characteristics of the target mining areas were comprehensively simulated, establishing a decision optimization framework for the ventilation system that successfully identified the optimal solution. The results demonstrate minimal error between the simulated and measured data of the mine ventilation network model, validating the accuracy of its system parameter estimations. Simulations of diverse ventilation schemes generated airflow distribution parameters and dust concentration data for each mining area. Subsequently, a TOPSIS-integrated process optimization model was developed to comprehensively evaluate the ventilation schemes against eight quantitative indicators. Evaluation results identified Scheme Two as the optimal solution, as it demonstrates a balanced optimization of safety, efficiency, and cost-effectiveness. This scheme achieves a significant enhancement of the underground ventilation environment and a marked suppression of dust diffusion, with only a marginal increase in overall ventilation costs. By elevating the air volume from an initial less than 1.0 m³/s to a precisely regulated range of 5.0–13.0 m³/s, the scheme fundamentally eliminated ventilation dead zones. This intervention resulted in a significant reduction in dust concentrations across multiple working faces, consistently maintaining levels below the 4 mg/m³ national exposure limit (GBZ 2.1-2019), and ultimately ensured a safer and healthier working environment. The attainment of these practical outcomes, which directly correspond to the optimization objectives of the TOPSIS method, confirms its efficacy and practical value in guiding ventilation strategy selection. Full article

Dynamic Wireless Power Transfer (DWPT) systems require analytical models capable of capturing time-varying coupling and multi-transmitter interactions. However, most existing formulations address only static Wireless Power Transfer (WPT) or single-transmitter configurations, offering limited applicability to realistic DWPT scenarios. This paper addresses this gap by developing a comprehensive analytical framework for Series–Series (SS) compensated DWPT systems, supporting general n-transmitter/m-receiver architectures. The model is derived from coupled RLC circuit equations and expressed in normalized time- and frequency-domain forms, enabling analysis of resonance behavior, transient dynamics, and mutual-inductance variations during vehicle motion. To represent the continuous receiver motion, we establish a coupling-coefficient distribution covering the operating range of $k=0.11$ to $k=0.581$. The framework is then applied to three representative cases: a dynamic $1\times 1$ baseline, sequential transmitter activation, and simultaneous multi-transmitter activation. The study investigates system performance across varying operating frequencies and receiver positions to evaluate efficiency characteristics for $1\times 1$, $n\times 1$, and $n\times m$ wireless power transfer configurations. The proposed analytical framework provides a scalable basis for control development, transmitter coordination, and future real-time DWPT implementation. Full article

Background: Airline pilots and cabin crew are exposed to multiple occupational stressors, including circadian disruption, irregular meal timing, cabin environment and radiation, which collectively affect sleep, metabolism and overall health. This study aimed to evaluate the health implications of shift work in aviation by combining self-reported experiences with existing scientific evidence. Methods: A cross-sectional survey of 101 airline personnel was conducted to assess sleep patterns, fatigue, nutrition-related challenges and health symptoms. The survey findings were integrated with a literature review to contextualize observed health outcomes within known effects of circadian disruption and aviation-related stressors. Results: Sleep disturbances (71%) and fatigue (89%) were the most prevalent symptoms, while 60% of respondents reported weight fluctuations and 50% limited access to nutritious food during duty. Appetite alterations, reduced taste perception and frequent melatonin use indicated behavioral adaptation to circadian misalignment. Among female aircrew (63%), thyroid and reproductive concerns were reported, aligning with documented impacts of radiation exposure and endocrine disruption. The findings correspond with existing evidence linking aviation-related circadian stress to cardiometabolic, endocrine and gastrointestinal imbalance. Conclusions: Shift work and occupational exposures in aviation contribute to significant disturbances in sleep, metabolism and overall health among aircrew. Preventive strategies should integrate fatigue risk management, circadian-aligned scheduling, improved in-flight nutrition and comprehensive occupational health surveillance to safeguard crew well-being and operational safety. Full article

Digital Twins (DTs) are increasingly used to support real-time decision making in connected mobility systems, where network latency and uncertainty limit the effectiveness of conventional control strategies. This paper proposes a conceptual model for a DT-driven Co-Pilot designed to provide adaptive speed recommendations in congested urban traffic. The system combines live data from a mobile client with a prediction engine that executes multiple short-horizon SUMO simulations in parallel, enabling the DT to anticipate local traffic evolution faster than real time. A lightweight clock-alignment mechanism and latency evaluation over LAN, Cloudflare-tunneled connections, and 4G/5G networks demonstrate that the Co-Pilot can operate reliably using existing communication infrastructures. Experimental results show that moderate speeds (35–50 km/h) yield throughput and delay performance comparable to higher speeds, while improving flow stability—an important property for safe platooning and collaborative driving. The parallel execution of ten SUMO instances completes within 2–3 s for a 600 s simulation horizon, confirming the feasibility of embedding domain-specific ITS logic into a predictive DT architecture. The findings demonstrate that Digital Twin–based anticipatory simulation can compensate for communication latency and support real-time speed coordination, providing a practical pathway toward scalable, deployable DT-enabled traffic assistance systems. Full article

Physical AI enables reliable and timely operations of autonomous systems such as robots and smart manufacturing equipment under diverse and dynamic execution environments. In these environments, computing resources are often limited, shared among tasks, and fluctuate over time. This makes it difficult to guarantee that tasks meet timing constraints. As a result, resource-aware execution time prediction becomes essential for efficient resource management in physical AI systems. However, existing methods typically assume specific environments or static resource usage and often fail to generalize to new environments. In this paper, we propose CARE-D (Calibration-Assisted Resource-aware Execution time prediction), which trains a deep neural network to model the nonlinear relationships among hardware characteristics, resource levels, and task features across environments. The model predicts the execution time of tasks under diverse hardware and dynamically allocated computing resources, using a few execution records from new environments. CARE-D applies few-history-based calibration using only 1 to k execution records from target environments to adjust predictions without retraining the model. Experiments show that CARE-D improves prediction accuracy by about 7.3% over zero-history predictors within a 10% relative error and outperforms regression and deep learning baselines, using only one to five records per target environment. Full article

The apparel industry plays a critical role in the global economy but continues to face persistent challenges related to fit accuracy, overproduction, inefficiencies, and limited digital integration. These issues are particularly evident in made-to-measure (MtM) manufacturing, where manual processes, fragmented digital tools, and weak data continuity hinder scalability and sustainability. This study aims to identify the key barriers to MtM 4.0 adoption and propose a digitally integrated workflow capable of supporting efficient, sustainable, and customer-centric apparel production. A systematic review of Industry 4.0 technologies and MtM practices is conducted to structure the problem and derive the requirements for a next-generation workflow. Based on these insights, a three-stage MtM 4.0 workflow (connecting design, product development, and production) is developed and operationalized in a functional prototype, MtM Lusitano 4.0. The prototype integrates a web configurator, a rule-based pattern engine, and ERP/MES connectivity, enabling full digital continuity from customer input to shop-floor execution. Results from industrial deployment confirm functional improvements, including increased measurement accuracy, reduced manual interventions, and stable production release flows. The study concludes that the proposed MtM 4.0 workflow strengthens operational efficiency, supports sustainability goals, and provides a structured pathway for digital transformation in the apparel sector. Full article

Amid global efforts toward carbon neutrality, carbon emission accounting in the construction sector has become essential for sustainable design. Public buildings, with complex energy systems and high operational loads, are major carbon emitters. However, early design stages often provide only low-dimensional parameters—such as floor area, number of floors, and location—limiting conventional regression methods. This study develops a lightweight prediction framework using a multilayer perceptron (MLP) neural network. Feature engineering constructs composite indicators—layers per unit area (LPA) and height-to-area ratio (HAR)—to quantify spatial compactness and vertical density. A three-layer MLP with Swish activation, adaptive L2 regularization, and Dropout reduces overfitting and improves generalization. Tests show the model achieves a mean absolute error of 4160 tCO₂ and R² of 0.966, reducing prediction error by 54.7% compared to linear regression. For high-rise buildings (>15 floors), error remains below 8.1%. SHAP analysis highlights floor area as the dominant factor (51.2%), while HAR and LPA jointly improve accuracy by 5.8%. A Python-based tool is developed for rapid emission estimation during design. Using 150 samples and 10-fold cross-validation, this work demonstrates the potential of deep learning in low-dimensional carbon prediction, offering a practical reference for early-stage green building design, though generalizability requires further validation with larger datasets. Full article

Precise segmentation of flotation froths is a critical bottleneck to achieving intelligent perception and optimal control of process operations. Traditional convolutional neural networks (CNNs) are inherently limited by local receptive fields, making it challenging to accurately segment adhesive and multi-scale froths. To address this fundamental issue, this paper proposes a deep segmentation network with integrated global context awareness, termed GC-FSegNet, which establishes a new paradigm capable of jointly modeling macro-level structures and micro-level details. The proposed GC-FSegNet innovatively integrates the Global Context Network (GCNet) module into both the encoder and decoder of a Nested U-Net architecture. The GCNet captures long-range dependencies between froths, enabling macro-level modeling of clustered foam structures, while the Nested U-Net preserves high-resolution boundary details. Through their synergistic interaction, the model achieves simultaneous and efficient representation of both global contours and local details of froth images. Furthermore, the Mish activation function is employed to enhance the learning of weak boundary features, and a combined Dice and Binary Cross-Entropy (BCE) loss function is designed to optimize boundary segmentation accuracy. Experimental results on a self-constructed copper–lead flotation froth dataset demonstrate that GC-FSegNet achieves an mDice of 0.9443, mIoU of 0.8945, mRecall of 0.9866, and mPrecision of 0.9705, significantly outperforming mainstream models such as U-Net and DeepLabV3+. This study not only provides a reliable technical solution for high-adhesion froth segmentation but, more importantly, introduces a promising “global–local collaborative modeling” framework that can be extended to a wide range of complex industrial image segmentation scenarios. Full article

Compared to the traditional non-load-bearing multi-legged robots, the heavy-duty multi-legged robots typically not only have larger body weight, larger volume, and larger load ratio but also require greater energy dissipation. Traditional path planning often focuses on the problem of finding the shortest path. However, the substantial load capacity and multi-jointed structure of heavy-duty multi-legged robots impose stringent requirements on path smoothness. Consequently, the smoothness requirement makes the traditional A* algorithm unsuitable for applications where low-energy operation is critical. An improved low-energy path planning method based on the A* algorithm is presented for an electrically driven heavy-duty six-legged robot. Then, the environment is discretized by using the grid method to facilitate path searching. To address the path zigzagging problem caused by the traditional A* algorithm, the Bézier curve smoothing technique is adopted. The continuous curvature transitions are employed to significantly improve the smoothness of path. The heuristic function in the A* algorithm is enhanced through a dynamic weight adjustment mechanism. The nonlinear suppression strategy is introduced to prevent data changes and improve the robustness of the algorithm. The effectiveness of the proposed method is verified through the MATLAB simulation platform system. The simulation experiments show that, in various environments with different obstacle densities (0.17–0.37%), compared with the traditional A* algorithm, the method proposed in this paper reduces the average path length by 7.2%, the number of turning points by 25.9%, and the energy consumption by 5.75%. The proposed improved A* algorithm can significantly overcome the problem of insufficient smoothness in traditional A* algorithms and reduce the number of nodes generated by the control data stack, which improves the optimization efficiency during path planning. As a result, the heavy-duty six-legged robots can walk farther and operate for longer periods of time while carrying the limited energy sources. Full article

The increasing penetration of renewable energy resources is central to global sustainability and decarbonisation goals, yet it introduces intermittency and voltage instability in modern distribution networks. Ensuring stable operation while maximising renewable utilisation is critical for achieving long-term energy sustainability, reduced carbon emissions, and efficient grid performance. This study proposes a sustainability-oriented, Reinforcement Learning (RL)-driven voltage control framework that enables reliable and energy-efficient operation of wind-integrated distribution systems. A Deep Q-Network (DQN) agent formulates voltage regulation as a Markov Decision Process (MDP) and autonomously learns optimal control policies for on-load tap changers (OLTCs) and capacitor banks under highly variable wind and load conditions. Using the IEEE 33-bus test system with realistic stochastic wind and ZIP-load models, the results show that the proposed controller maintains voltages within statutory limits, reduces total active power losses by up to 18%, and enhances the network’s capacity to host renewable energy. These improvements translate to increased energy efficiency, reduced technical losses, and greater operational resilience, key enablers of sustainable energy distribution. The findings demonstrate that intelligent RL-based frameworks offer a scalable and model-free tool for advancing sustainable, low-carbon, and resilient power systems. Full article