Search Results (7,161)

The present study intends to evaluate the ratcheting of ER9 wheel medium carbon steel and austenitic steel samples at room and elevated temperatures subjected to uniaxial loading cycles through the use of the Ahmadzadeh–Varvani (A–V) kinematic hardening rule. The A–V framework incorporated an exponential function in the dynamic recovery term to account for the dynamic strain aging (DSA) phenomenon at temperatures where solute atoms and moving dislocations showed increased interaction. Within the DSA domain at 573K for ER9 wheel steel samples, and at 423K for austenitic steel samples, the collision of carbon and nitrogen solute atoms with moving dislocations resulted in the materials hardening, and promoted the yield strength. The Voyiadjis–Song–Rusinek (VSR) multivariable model was used to capture the evolution of yield strength with temperature. The predicted ratcheting results within the DSA temperature domain were in close agreement with those of measured values. Full article

Industrial gateways serve as critical data aggregation points within the Industrial Internet of Things (IIoT), enabling seamless data interoperability that empowers enterprises to extract value from equipment data more efficiently. However, their role exposes a fundamental trade-off between computational efficiency and prediction accuracy—a contradiction yet to be fully resolved by existing approaches. The rapid proliferation of IoT devices has led to a corresponding surge in network traffic, posing significant challenges for traffic forecasting methods, while deep learning models like Transformers and GNNs demonstrate high accuracy in traffic prediction, their substantial computational and memory demands hinder effective deployment on resource-constrained industrial gateways, while simple linear models offer relative simplicity, they struggle to effectively capture the complex characteristics of IIoT traffic—which often exhibits high nonlinearity, significant burstiness, and a wide distribution of time scales. The inherent time-varying nature of traffic data further complicates achieving high prediction accuracy. To address these interrelated challenges, we propose the lightweight and theoretically grounded DOA-MSDI-CrossLinear framework, redefining traffic forecasting as a hierarchical decomposition–interaction problem. Unlike existing approaches that simply combine components, we recognize that industrial traffic inherently exhibits scale-dependent temporal correlations requiring explicit decomposition prior to interaction modeling. The Multi-Scale Decomposable Mixing (MDM) module implements this concept through adaptive sequence decomposition, while the Dual Dependency Interaction (DDI) module simultaneously captures dependencies across time and channels. Ultimately, decomposed patterns are fed into an enhanced CrossLinear model to predict flow values for specific future time periods. The Dream Optimization Algorithm (DOA) provides bio-inspired hyperparameter tuning that balances exploration and exploitation—particularly suited for the non-convex optimization scenarios typical in industrial forecasting tasks. Extensive experiments on real industrial IoT datasets thoroughly validate the effectiveness of this approach. Full article

Chinese local governments have long financed public services through land-sale revenues. The shift from selling undeveloped land to redeveloping existing urban areas has disrupted this traditional financing model, exposing a critical tension between the pursuit of immediate revenue and the assurance of long-term fiscal health. The continued dependence on land-based finance has led many local governments to overlook long-term public service obligations and the long-term operating deficits associated with intensive urban development. Thus, by examining the relationship between the development rights allocation and the sustainable fiscal capacity of the government, the study evaluates both short-term revenue generation and long-term expenditure commitments in urban redevelopment contexts. However, existing research has yet to provide actionable tools to reconcile this structural mismatch between short-term revenues and long-term liabilities. We employ a comprehensive analytical framework that integrates fiscal impact modeling with the optimization of development rights allocation. Based on this framework, we construct a quantitative, dual-period fiscal balance model using mathematical programming to analyze various combinations of land development rights supply strategies for achieving fiscal equilibrium. Our results identify multiple feasible supply combinations that can maintain fiscal balance while supporting sustainable urban development. The findings demonstrate that strategic development rights allocation functions as an effective tool for balancing short-term revenue needs with long-term obligations in local land finance systems. Our study contributes to establishing a sustainable land finance framework, particularly for jurisdictions lacking comprehensive land value capture mechanisms. The proposed approach offers an alternative to traditional land rights transfer models and provides guidance for avoiding long-term fiscal distress caused by excessive land transfer. The framework supports more sustainable urban redevelopment financing while maintaining fiscal responsibility across temporal horizons. Full article

Markerless human pose estimation is increasingly used for kinematic assessment, but evidence of its applicability to upper limb movements across different ranges of motion (ROM) remains limited. This study examined the accuracy and reliability of a markerless pose estimation system for shoulder, elbow and wrist flexion–extension analysis under full and partial ROM tasks. Ten healthy participants performed standardized movements which were synchronously recorded, with an optoelectronic motion capture system used as a reference. Joint angles were compared using RMSE, percentage RMSE (%RMSE), accuracy (Acc), intraclass correlation coefficients (ICC), and Pearson correlation of ROM values. The markerless system reproduced the temporal morphology of the movement with high coherence, showing ICC values above 0.91 for the elbow and 0.94 for the shoulder in full ROM trials. Wrist tracking presented the lowest RMSE values and low inter-subject variability. The main critical aspect was a systematic underestimation of maximum flexion, especially at the shoulder, indicating a magnitude bias likely influenced by occlusion and joint geometry rather than by temporal fluctuations. Despite this limitation, the system adapted consistently to different ROM amplitudes, maintaining proportional variations in joint excursion across tasks. Overall, the findings outline the conditions in which markerless pose estimation provides reliable upper limb kinematics and where methodological improvements are still required, particularly in movements involving extreme flexion and occlusion. Full article

The dynamics of open quantum systems play a crucial role in quantum information science. However, obtaining numerically exact solutions for the Lindblad master equation is often computationally expensive. Recently, machine learning techniques have gained considerable attention for simulating open quantum system dynamics. In this paper, we propose a deep learning model based on time series prediction (TSP) to forecast the dynamical evolution of open quantum systems. We employ the positive operator-valued measure (POVM) approach to convert the density matrix of the system into a probability distribution and construct a TSP model based on Transformer neural networks. This model effectively captures the historical evolution patterns of the system and accurately predicts its future behavior. Our results show that the model achieves high-fidelity predictions of the system’s evolution trajectory in both short- and long-term scenarios, and exhibits robust generalization under varying initial states and coupling strengths. Moreover, we successfully predicted the steady-state behavior of the system, further proving the practicality and scalability of the method. Full article

This study aims to address the safety and mobility challenges faced by visually impaired individuals. To this end, a lightweight, high-precision semantic segmentation network is proposed for scenes containing tactile paving and zebra crossings. The network is successfully deployed on an intelligent guide robot equipped with a high-definition camera and a Huawei Atlas 310 embedded computing platform. To enhance both real-time performance and segmentation accuracy on resource-constrained devices, an improved G-GhostNet backbone is designed for feature extraction. Specifically, it is combined with a depthwise separable convolution-based Coordinate Attention module and a redesigned Atrous Spatial Pyramid Pooling (ASPP) module to capture multi-scale contextual features. A dedicated decoder efficiently fuses multi-level features to refine segmentation of tactile paving and zebra crossings. Experimental results demonstrate that the proposed model achieves mPA of 97% and 93%, mIoU of 94% and 86% for tactile paving and zebra crossing segmentation, respectively, with an inference speed of 59.2 fps. These results significantly outperform several mainstream semantic segmentation networks, validating the effectiveness and practical value of the proposed method in embedded systems for visually impaired travel assistance. Full article

Background: People with Parkinson’s disease suffer from gait impairments. Clinical scales provide a limited and rater-dependent assessment of gait. Wearable sensors allow an objective characterization by capturing rhythm, pace, and signature patterns. This study investigated if sensor-derived gait parameters have prognostic value for short-term progression of gait impairments. Methods: A total of 111 longitudinal visit pairs were analyzed, where participants underwent clinical evaluation and a 4 × 10 m walking test instrumented with wearable sensors. Changes in the UPDRSIII gait score between baseline and follow-up were used to classify participants as Improvers, Stables, or Deteriorators. Baseline group differences were assessed statistically. Machine-learning classifiers were trained to predict group membership using clinical variables alone, sensor-derived gait features alone, or a combination of both. Results: Significant between-group differences emerged. In participants with UPDRSIII gait score = 1, Improvers showed higher median gait velocity ($0.81 \mathrm{m}/\mathrm{s}$) and stride length ($0.80 \mathrm{m}$) than Stables ($0.68 \mathrm{m}/\mathrm{s}$; $0.70 \mathrm{m}$) and Deteriorators ($0.59 \mathrm{m}/\mathrm{s}$; $0.68 \mathrm{m}$), along with lower stance time variability (3.10% vs. 4.49% and 3.75%; all $p<0.05$). The combined sensor-based and clinical model showed the best performance (AUC $0.82$). Conclusions: Integrating sensor-derived gait parameters with clinical score can support the identification of patients at risk of gait deterioration in the near future. Full article

Accurate classification of tomato ripening stages and quality estimation is pivotal for optimizing post-harvest management and ensuring market value. This study presents a rigorous comparative analysis of morphological and colorimetric features extracted via two state-of-the-art deep learning-based instance segmentation frameworks—Mask R-CNN and YOLOv8n-seg—and their efficacy in machine learning-driven ripening stage classification and quality prediction. Using 216 fresh-market tomato fruits across four defined ripening stages, we extracted 27 image-derived features per model, alongside 12 laboratory-measured physio-morphological traits. Multivariate analyses revealed that R-CNN features capture nuanced colorimetric and structural variations, while YOLOv8 emphasizes morphological characteristics. Machine learning classifiers trained with stratified 10-fold cross-validation achieved up to 95.3% F1-score when combining both feature sets, with R-CNN and YOLOv8 alone attaining 96.9% and 90.8% accuracy, respectively. These findings highlight a trade-off between the superior precision of R-CNN and the real-time scalability of YOLOv8. Our results demonstrate the potential of integrating complementary segmentation-derived features with laboratory metrics to enable robust, non-destructive phenotyping. This work advances the application of vision-based machine learning in precision agriculture, facilitating automated, scalable, and accurate monitoring of fruit maturity and quality. Full article

Steel surface defect detection is essential for maintaining industrial production quality and operational safety. However, existing deep learning-based methods often encounter high computational costs, hindering their deployment on mobile devices. To effectively address this challenge, we propose a lightweight and efficient steel surface defect detection network based on feature segmentation and partially connected structures, termed LESSDD-Net. In LESSDD-Net, we first introduce a lightweight downsampling module called the cross-stage partial-based dual-branch downsampling module (CSPDDM). This module significantly reduces the number of model parameters and computational costs while facilitating more efficient downsampling operations. Next, we present a lightweight attention mechanism known as coupled channel attention (CCAttention), which enhances the model’s capability to capture essential information in feature maps. Finally, we improve the faster implementation of cross-stage partial bottleneck with two convolutions (C2f) and design a lightweight version called the lightweight and partial faster implementation of cross-stage partial bottleneck with two convolutions (LP-C2f). This module not only enhances detection accuracy but also further diminishes the model’s size. Experimental results on the data-augmented Northeastern University surface defect detection (NEU-DET) dataset indicate that the mean average precision (mAP) of LESSDD-Net improves by 3.19% compared to the baseline model YOLO11n. Additionally, in terms of model complexity, LESSDD-Net reduces the number of parameters and computational costs by 39.92% and 20.63%, respectively, compared to YOLO11n. When compared with other mainstream object detection models, LESSDD-Net achieves top detection accuracy with the highest mAP value and demonstrates significant advantages in model complexity, characterized by the lowest number of parameters and computational costs. Full article

This paper outlines the design and testing of a horizontal-axis tidal turbine (HATT) at a scale of 1:20, employing numerical simulations and experimental validation. The design employed an in-house code based on the Blade Element Momentum (BEM) theory. As reliable lift and drag coefficients for this scale are not present in the literature due to the low Reynolds number of the airfoil, Computational Fluid Dynamics (CFD) simulations were conducted to generate accurate polar diagrams for the NACA 4412 airfoil. The turbine was then 3D-printed and the rotor tested in a subsonic wind tunnel at various fixed rotational speeds to determine the power coefficient. Fluid dynamic similarity was achieved by matching the Reynolds number and tip-speed ratio in air to their values in water. Three-dimensional CFD simulations were also performed, yielding turbine efficiency results that agreed fairly well with the experimental data. However, both the experimental and numerical simulation results indicated a higher power coefficient than that predicted by BEM theory. The CFD results revealed the presence of radial velocity components and vortex structures that could reduce flow separation. The BEM model does not capture these phenomena, which explains why the power coefficient detected by experiments and numerical simulations is larger than that predicted by the BEM theory. Full article

With the increasing number of highly airtight residences, concerns have risen that the negative pressure formed indoors during kitchen hood operation can reduce capture performance and cause unintended infiltration. This study experimentally and numerically (via CFD simulations) examined whether installing an air supply unit on the cooktop beneath a hood can stabilize hood performance and suppress infiltration in small residential spaces. Two cases were established depending on whether air was supplied: Case 1 (hood operation only) and Case 2 (simultaneous operation of the hood and the air supply unit). In the experimental setup, the hood exhaust flow rate, supply airflow rate, sink-drain infiltration rate, and temperature/humidity were measured. The period during which variations in measured values remained within 10% was defined as the steady state. In the CFD analysis, winter conditions were assumed, and the measured values were applied to the wall boundary, after which the temperature and velocity field were analyzed. In Case 2, by supplying 24.11 CMH of air, the hood flow rate remained stable at 75.72 CMH (98.8% of the initial level) throughout the test, and no infiltration was detected. The CFD analysis revealed that the air supply unit generated an “air curtain” effect, enabling rapid capture of hot airflow and reducing the high-temperature region. In conclusion, the interconnected operation of supply and exhaust systems was shown to be effective in enhancing hood exhaust stability, suppressing unintended infiltration, and improving capture reliability in highly airtight small residential buildings. Future studies should include further analyses, such as the effects of actual cooking behaviors and leakage path distributions. Full article

Zooplankton is an essential functional component of the aquatic food web, reflecting, through its structure and biomass, the impact of anthropogenic pressures on ecosystems. In this study, we investigated the traits of the Rotifera and Crustacea communities along a rural–urban gradient in the Colentina River system. The results revealed a partial separation between rotifers and crustaceans, with distinct distributions determined by trophic conditions and habitat type. Trophic indices (Carlson’s TSI, TSI_ROT, TSI_CR) indicated increased eutrophication in peri-urban and urban areas (Fundeni, Plumbuita) compared to rural reference ecosystems (Colentina, Crevedia). The relationships between Resource Use Efficiency (RUE) and trophic indices were positive and significant in rural areas, indicating a balanced ecosystem, but were decoupled in urbanised sectors, where high RUE values were driven by increased biomass of opportunistic species, whereas TSI indicated eutrophic conditions. The results confirm the role of zooplankton as a sensitive bioindicator, capable of capturing both the impact of eutrophication and the capacity of urbanised ecosystems to maintain trophic functionality. The integration of zooplankton-based metrics into monitoring schemes offers a complementary perspective on ecological resilience in aquatic ecosystems under urban pressures. Full article

Understanding human movement in industrial environments requires more than simple step counts—it demands contextual information to interpret activities and enhance workflows. Key factors such as location and process context are essential. However, research on context-sensitive human activity recognition is limited by the lack of publicly available datasets that include both human movement and contextual labels. Our work introduces the DaRA dataset to address this research gap. DaRA comprises over 109 h of video footage, including 32 h from wearable first-person cameras and 77 h from fixed third-person cameras. In a laboratory environment replicating a realistic warehouse, scenarios such as order picking, packaging, unpacking, and storage were captured. The movements of 18 subjects were captured using inertial measurement units, Bluetooth devices for indoor localization, wearable first-person cameras, and fixed third-person cameras. DaRA offers detailed annotations with 12 class categories and 207 class labels covering human movements and contextual information such as process steps and locations. A total of 15 annotators and 8 revisers contributed over 1572 h in annotation and 361 h in revision. High label quality is reflected in Light’s Kappa values ranging from $78.27$% to $99.88$%. Therefore, DaRA provides a robust, multimodal foundation for human activity and context recognition in industrial settings. Full article

This study conducts both experimental and statistical analyses of microinverter performance within a compact AC-PV module that integrates a PV panel and a microinverter without battery integration. Using measurement data in combination with correlation analysis, derived thermal indicators, and quadratic regression modeling, the research provides a comprehensive quantitative assessment of microinverter behavior under practical operating conditions. A central finding is that the PV module’s temperature rise above ambient, ΔT_module, serves as the most reliable single predictor of output power with a coefficient of determination of R² = 0.85. The coefficient determination of ΔT_module surpasses even solar irradiance and the microinverter temperature rise, ΔT_micro, with R² = 0.80 and R² = 0.75, respectively. This underscores the excess thermal loading of the module, rather than the absolute temperature alone. In contrast, ambient temperature (R² = 0.04) proves to be a negligible variable for output power prediction. Also, comparing experimental temperatures with semi-empirical models showed that the PV temperature formula captures key thermal behavior, and the difference between theoretical and measured values is around 12%. From a design standpoint, these results highlight that enhancing thermal management at the module–inverter interface can directly improve output stability and ensure battery integration in the long-term reliability of an AC-PV module in future studies. Full article

The widespread adoption of electricity market trading platforms has enhanced the standardization and transparency of trading processes. As markets become more liberalized, regulatory policies are phasing out protective electricity pricing mechanisms, leaving retailers exposed to price volatility risks. In response, demand for risk management tools has grown significantly. Futures contracts serve as a core instrument for managing risks in the energy sector. This paper proposes a futures-based risk hedging model grounded in electricity price forecasting. A price prediction model is constructed using historical data from electricity markets and energy futures, with SHAP values used to analyze the transmission effects of energy futures prices on monthly electricity trading prices. The Monte Carlo simulation method, combined with a t-GARCH model, is applied to calculate CVaR and determine optimal portfolio weights for futures products. This approach captures the volatility clustering and fat-tailed characteristics typical of energy futures returns. To validate the model’s effectiveness, an empirical analysis is conducted using actual market data. By forecasting electricity price trends and formulating futures strategies, the study evaluates the hedging and profitability performance of futures trading under different market conditions. Results show that the proposed model effectively mitigates risks in volatile market environments. Full article