Search Results (63,822)

Naturally Occurring Retirement Communities Supportive Service Programs (NORC-SSPs) are one of the most popular models of aging in place. While the existing NORC literature focuses on the social and service environments of these programs, their built environments remain underexplored, particularly across housing tenures. This study is the first to explore the built environment, social integration, and socio-demographic factors among older people living in NORCs in New York, and their associations with health and well-being. The mixed-methods research included qualitative (interviews with NORC directors) and quantitative (151 resident surveys and an architectural assessment) data on 26 housing developments in New York, collected simultaneously using a convergent parallel design. The findings show that socialization and exercise improve the health and quality of life of NORC residents. The study also revealed that older people living in public housing have different needs than those in cooperative housing, namely a worse perception of their health and dwellings of a poorer physical condition. Therefore, the services offered by NORC programs should vary according to housing type, while management and NORC staff should improve coordination to address maintenance in public housing. Future research should examine interventions to improve the physical environments of NORC residents. Full article

We propose a coupled gas–particle two-phase model for particle transport in a compressible carrier gas with interphase momentum and energy exchange, and we incorporate a diffusion-based mechanism to represent gas–particle reactions. The governing equations are discretized in an Arbitrary Lagrangian–Eulerian (ALE) finite-volume framework using an HLLC-type two-dimensional Riemann solver (HLLC-2D). The solver employs a nodal-conservation construction that enforces consistency between numerical fluxes and nodal contact velocities, which helps reduce spurious oscillations near discontinuities on moving meshes. In addition, a particle-search-based Courant–Friedrichs–Lewy(CFL)-like time-step restriction is introduced to enhance robustness in coupled simulations. Numerical tests are presented to assess the method and to illustrate particle-induced modifications of wave dynamics, as well as reaction-driven variations in velocity and temperature fields. Full article

Elastocaloric cooling is an emerging solid-state refrigeration technology that leverages the latent heat exchange of shape memory alloys under mechanical stress. This study investigates the energy performance of a solid-to-solid elastocaloric cooling heat pump to enhance heat transfer efficiency and overall system performance. A Matlab-based numerical model, developed using the finite volume method, was employed to simulate the system. The energy performances of the elastocaloric heat pump are analyzed by varying the frequency of the cycle, the elastocaloric refrigerants, and the types of thermal diodes, from ideal up to realistic Peltier switches. The results demonstrate that the strategic use of thermal diodes significantly improves heat flow directionality, reducing thermal losses and enhancing the efficiency of the elastocaloric cooling process with a system that employs a realistic Peltier thermal diode, guaranteeing specific cooling powers up to 6500 W kg⁻¹. The maximum COPs of the system with ideal thermal diodes range from 60 to 10. These findings contribute to the development of more efficient solid-state cooling technologies, offering a viable alternative to conventional systems, especially for electronic circuit cooling applications. Full article

With renewable integration and zero-carbon microgrids achieving 100% penetration, converter-dominated systems exhibit millisecond-timescale transient synchronization, which challenges existing physical cognitive methods and cognitive methodology with the synchronous generator (SG). In this paper, in order to quantificationally analyze the transient synchronization, a unified framework has been proposed that combines the generalized participation factor (GPF) method and basin of attraction (BOA) boundary analysis using the manifold approach. According to the GPF and BOA analyses, the fourth-order models are essential for accurate stability quantification, with synchronization controls (PLL, VSG, and droop control) contributing greater than 70% to transient dynamics versus about 20% from power-balance interactions. Further, the dynamic security region (DSR) is redefined by two typologies. Type 1 DSR maps stability in active-power injection space, and Type 2 DSR (generalized DSR) delineates limits in the controllable parameter space. The estimation procedures are proposed for these two types of DSRs by the BOA method. Finally, electromagnetic transient simulations and critical clearing time validation are employed for fidelity verification of models and estimation approaches. To sum up, the proposed novel framework enables systematic DSR estimations for renewable-rich power systems, empowering grid operators to optimize converter-controllable parameters and system operation conditions. Full article

In the field of electronic support and radar warning, it is necessary to effectively detect and recognize the modulation types of non-cooperative radar signals, especially for radars with Low Probability of Intercept (LPI) waveforms. Multiple intelligent detection and recognition algorithms based on the Transformer architecture have been proposed, achieving good performance even under low signal-to-noise ratio (SNR). However, Transformer-based radar intelligent detection and recognition algorithms have a huge number of parameters coupled with complex structures, which will result in significant power consumption and computational latency when deployed on general computing platforms. To address the above issues, this paper proposes a lightweight design for Transformer-based radar signal intelligent detection and recognition networks. A Lightweight Joint Detection and Modulation Recognition Networks (JDMR-LNet) is designed. To enhance the feature extraction ability of lightweight networks, this paper designed a hybrid model distillation method. The experimental results demonstrate that, compared with the directly trained JDMR-LNet, the accuracy of automatic modulation type recognition of the JDMR-LNet after distillation is increased by 2.37% at −12 dB, and the signal detection is increased by 2.07% at −10 dB. The number of parameters of the JDMR-LNet has also decreased significantly. Compared with the original model, the JDMR-LNet is compressed by 11.18 times. Furthermore, this paper completed FPGA deployment of the JDMR-LNet model, with simulation verifying its functional correctness. Full article

Background/Objectives: To investigate and develop a non-invasive parametric radiomics model derived from dynamic contrast-enhanced MRI (DCE-MRI) time-intensity curve (TIC) kinetics for predicting breast cancer molecular subtypes (HR+/HER2−, HER2+ and triple-negative breast cancer). Methods: This multicenter retrospective study enrolled 935 female patients with histologically confirmed breast cancer who underwent pretreatment breast DCE-MRI from August 2017 to July 2022. Based on the wash-in rate (WIR) and the area under the TIC, the original multiphase DCE-MRI images were converted into two types of parametric images. Radiomics features were extracted from TIC-WIR and TIC-Area images and analyzed using low variance filtering, the elimination of highly correlated features, and the least absolute shrinkage and selection operator regression. The categorical boosting algorithm was employed to develop multiclass prediction models for breast cancer molecular subtyping. A TIC-Combined model was further established by integrating the calibrated probability outputs of the TIC-WIR and TIC-Area models using a decision-level fusion strategy. The discrimination, calibration, and interpretability of the models were evaluated in the study datasets. Results: The TIC-Combined model achieved superior predictive performance in both the internal validation set (micro-average AUC: 0.79, macro-average AUC: 0.77) and the external validation set (micro-average AUC: 0.77, macro-average AUC: 0.75). For subtype-specific classification by the TIC-Combined model, the highest one-vs-rest AUCs were 0.81 for triple-negative breast cancer in the internal validation set and 0.76 for HER2+ breast cancer in the external validation set. The TIC-Combined model also showed good calibration and high interpretability which ensured reliable predictions and provided clear insights into feature importance. Conclusions: Interpretable parametric radiomics from TIC-derived parametric maps links kinetic features to molecular phenotypes, enabling accurate and non-invasive classification of breast cancer molecular subtypes. Full article

Hooked-head spur dikes are a specialized type of spur dike, where their geometry significantly influences flow diversion, sediment transport, and bank protection. This study establishes a three-dimensional numerical model utilizing the renormalization group (RNG) k-ε turbulence closure and the volume of fluid (VOF) method to explore the effects of hook angle (90°, 120°, and 150°) and hook-length ratio (L/D = 1/2, 1/3, and 1/4) on the flow structure surrounding a hooked-head spur dike. The study comprises nine simulation cases, and the distributions of mainstream velocity and turbulent kinetic energy (TKE) are analyzed. The results demonstrate that a hook angle of 120° yields the greatest increase in the mean dimensionless mainstream velocity (V*), corresponding to enhancements of 4.26% and 9.09% relative to the angles of 90° and 150°, respectively. When the hook angle is fixed at 120°, increasing the hook length enhances the mainstream velocity; specifically, at L/D = 1/2, the mean V* increases by 10.58% and 14.64% compared to at L/D = 1/3 and 1/4, respectively. Meanwhile, the TKE in the downstream recirculation zone decreases as either the hook angle or the hook length increases. At a hook angle of 90°, the mean dimensionless TKE (E*) is 8.80% and 10.65% higher than at 120° and 150°, respectively. For a fixed hook angle of 120°, the mean E* at L/D = 1/2 decreases by 3.46% and 9.35% compared to at L/D = 1/3 and 1/4, respectively. In summary, the appropriate selection of hook angle and hook length can effectively guide flow toward the channel center, increase conveyance capacity, and enhance hydraulic performance for river regulation. Full article

The lightweight design of box-type double-girder overhead crane main girders is important for improving load-carrying capacity, reducing energy consumption, and enhancing transportation efficiency. However, the structural optimization of crane main girders involves multiple constraints and strong nonlinearity, which often leads to slow convergence and premature stagnation when using traditional optimization methods. To address these issues, a multi-strategy improved dung beetle optimization algorithm (MSIDBO) is proposed for the lightweight design of overhead crane main girders. First, the search mechanism and inherent limitations of the standard dung beetle optimization (DBO) algorithm are analyzed. Subsequently, several enhancement strategies are introduced, including hybrid chaotic population initialization; reflective boundary handling; adaptive quantum jump updating; adaptive hybrid updating; and a staged control strategy for search intensity. These strategies are designed to enhance population diversity and achieve a better balance between global exploration and local exploitation. The performance of MSIDBO was evaluated on 29 CEC2017 benchmark functions. The results show that MSIDBO generally converges faster on 25 functions and reaches the global optimum on 24 functions among the compared algorithms. Finally, based on mechanical analysis and design specifications of overhead crane main girders, a constrained structural optimization model is established. The lightweight design optimization is carried out, and finite element simulations were conducted using ANSYS Workbench to verify the effectiveness and engineering feasibility of the optimized design. The results show that the proposed MSIDBO algorithm exhibits enhanced stability and convergence performance, achieving a weight reduction of 19.4% in the main girder under the specified design configuration, meeting satisfying strength and safety requirements. Full article

Logistics parks are increasingly acting as coordination hubs in cross-border agricultural supply chains (CASCs), yet evidence on how park-enabled governance mechanisms translate into sustainability remains limited. This study examines the drivers of CASC sustainability within the context of logistics parks in Henan, China, and assesses whether the dominant park type conditions these effects. A total of 385 valid questionnaire responses were analyzed using structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). SEM results show that symbiotic environment cultivation is the strongest predictor of sustainability, while interface mediation efficiency and safety also significantly support cross-border circulation. The moderating role of dominant park type is supported only for the interface and sustainability link. fsQCA further identifies three equifinal configurations leading to high sustainability, indicating that strong environmental cultivation and interface efficiency can compensate for weaker elements under certain combinations. These findings clarify how logistics parks enable economic, environmental, and social value creation in CASCs and provide actionable levers for park management and policy design. Full article

Automatic sprinkler systems are widely used for fire protection in various buildings, with deluge valves serving as the core component of these systems. Traditional deluge valves employ a diaphragm-type design (Zoning Sprinkler Fire Monitor, ZSFM), which is prone to significant safety hazards such as corrosion and damage due to uneven pressure distribution on the diaphragm. This study modified a 150 mm diameter ZSFM to a non-pressure diaphragm type, establishing and validating a CFD model of the internal flow field. Based on the original structure, six drag reduction optimization cases are designed. Among these, case 5 exhibits the minimum inlet-to-outlet pressure drop of 0.050 MPa under rated operating conditions, meeting and significantly exceeding the fire protection industry standard (≤0.08 MPa). Full article

Given the rising global demand for environmentally sustainable energy sources, solar photovoltaic (PV) power generation has emerged as a pivotal component of the energy transition. In PV systems, power conversion efficiency is degraded and operational lifespan reduced due to the presence of defective modules. Consequently, achieving accurate and efficient defect detection during PV module manufacturing is critical to ensuring product quality and reliability. To address this challenge, we propose MambaVSS-YOLOv11n, an electroluminescence (EL) image-based multi-defect detection method for PV modules. Our study utilizes a dataset containing six types of defects—Broken Gate, Cold Solder Joint, Black Spot, Scratch, Microcrack, and Suction Mark—to construct 692 labeled EL images of defective PV modules. The model integrates the Vision State Space (VSS) module from Mamba and optimizes the C3k2 Bottleneck structure to enhance fine-grained feature extraction, while employing Space-to-Depth Convolutional (SPD-Conv) Layer for downsampling to improve computational efficiency. Additionally, to address YOLOv11n’s limited generalization capability for small objects and complex backgrounds, we adopt the Inner Mask Distance Penalized Intersection over the Union (Inner-MDPIoU) loss function, which enhances detection accuracy and mitigates the impact of low-quality samples. Experimental results demonstrate that compared to YOLOv11n, MambaVSS-YOLOv11n reduces the number of parameters by 18.1%, while improving mAP@0.5 to 0.869 and mAP@0.5:0.95 to 0.637. This achieves model lightweighting while enhancing detection performance. These findings indicate that the model is well-suited for real-time defect detection in PV module production lines, providing PV manufacturers with a lightweight yet accurate and reliable solution for PV module defect inspection. Full article

Accurate prediction of asphalt mixture density is critical for quality control in pavement engineering. This study develops a novel dielectric-based predictive framework by applying the Lichtenecker–Rother (L-R) dielectric mixing theory to asphalt composites. The model’s key microstructural parameter, the geometric arrangement factor c, was optimized to 0.3 using a combined experimental dataset: laboratory measurements on AC (asphalt concrete) mixtures produced in this study, supplemented with published data from open-graded friction course (OGFC), stone mastic asphalt (SMA), and asphalt mixture (AM) types reported in the literature. The resulting model, termed the Geometric Arrangement Optimization (GAO) model, was systematically compared against three established dielectric models: the complex refractive index method (CRIM), the Rayleigh mixing model, and the Bottcher-type model adapted by Leng et al. (denoted ALL). Validation on a total of 34 sets of laboratory specimens showed that GAO achieved the highest prediction accuracy, with a mean relative error of 1.83% and a coefficient of determination R² of 0.91. When tested on eight independent field cores, GAO maintained reliable performance, yielding a mean relative error of 3.01%. These results indicate that the GAO model provides a physically grounded and practically applicable approach for asphalt mixture density estimation, contributing a useful tool for pavement performance evaluation and quality assurance. Full article

Tiles are commonly used as architectural finishing materials, but are prone to debonding defects due to construction and environmental factors in engineering applications. Therefore, effective detection of tile debonding holds significant engineering relevance. This study proposes a tile debonding detection method based on impact sound signal features and a dual-branch convolutional neural network. The sound signals collected through tapping are transformed into two types of two-dimensional feature maps using Mel-frequency cepstral coefficients (MFCCs) and continuous wavelet transform (CWT), which are then fed in parallel into the dual-branch convolutional neural network for feature extraction and fusion. Finally, tile debonding classification is performed in the classifier module. Experimental results show that the proposed model achieves a classification accuracy of 98.5% under laboratory conditions. Moreover, it demonstrates strong robustness under varying noise levels and sound pressure conditions, maintaining an accuracy of 82% in a 75 dB human voice noise environment. Field validation in real-world engineering environments yields an accuracy of 91.5%. These findings indicate that the proposed method, which combines MFCC and CWT features with a dual-branch convolutional neural network architecture, enables high-precision identification of tile debonding defects. Full article

Tracking structural behavior is critically important to reduce maintenance and repair costs. Structural Health Monitoring (SHM) aims to evaluate the structural integrity, detect damage or abnormalities, and estimate overall safety. The integration of Machine Learning techniques has significantly advanced SHM by enabling the identification of deterioration patterns through sensor data analysis. This study focuses on classifying different vibration patterns recorded under various excitation scenarios (ambient, transient, and forced) using sensors installed directly on a 3-DoF structure. The proposed approach used a two-dimensional convolutional neural network (2D-CNN) trained on vibration image patterns generated from vibration signal scalogram images. To address dataset imbalance, stratified 5 × 3 Nested cross-validation and multiple performance metrics were computed to ensure robust evaluation. The proposed method was compared with single-sensor scalogram approaches and baseline models, including Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), One-Dimensional Convolutional Neural Network (1D-CNN), and Long Short-Term Memory (LSTM) models, incorporating class-weighting strategies. Additionally, the contribution of the Total Energy Delivered by Sensor (TES) feature was evaluated for SVM, RF, and XGBoost models. The 2D-CNN model achieved superior performance in identifying excitation types associated with structural dynamic behavior, highlighting its effectiveness for structural vibration pattern recognition in SHM applications. Full article

Numerical simulations of (1) two aerosol types such as organic carbon (i.e., spherical) and dust (i.e., non-spherical) particles, and (2) their mixtures are carried out. Optical and microphysical parameters of these aerosols in our simulations are provided by MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2). The inversion routine is performed with TiARA (Tikhonov Advanced Regularization Algorithm) using the Lorenz–Mie (i.e., spherical) light-scattering model in unsupervised and automated, i.e., autonomous mode. The results of our numerical simulations show that the accuracy of the inversion results for the aerosol mixtures from synthetic optical data perturbed by ±10% random error is comparable to the accuracy observed for the inversion results of the “pure” spherical particles. In particular, the retrieval uncertainties of effective radius, and number, surface-area, and volume concentrations of these mixtures are ±30%, ±10%, between –50% and +100% and ±30%, respectively. However, we need to apply a modified version of the gradient correlation method (GCM) to stabilize the inversion results. The results of this study will form the baseline for future work, where we plan to apply TiARA to optical data products obtained from real lidar observations in the framework of the SCC (Single Calculus Chain) of EARLINET (European Aerosol Research Lidar Network). Full article