Search Results (11,721)

Whether compact urban development can achieve a spatial balance in the supply and demand of ecosystem services remains unclear amidst rapid urbanization. Understanding this relationship is critical for territorial spatial planning. Using Guanzhong Plain Urban Agglomeration as a case study, we applied local spatial autocorrelation to reveal spatial trade-offs and synergies, and employed ordinary least squares and geographically weighted regression models to analyze the underlying mechanisms. Results demonstrate that urban compactness is significantly negatively correlated with supply–demand gaps for carbon storage (r = −0.66), habitat quality (r = −0.58), recreation services (r = −0.60), and water yield (r = −0.63), while positively correlated with gaps for grain production and soil conservation. The GWR model outperformed the ordinary least squares model, with improvements in adjusted R² ranging from 0.0019 to 0.13. Land use intensity and GDP emerged as the dominant drivers of spatial heterogeneity in the ecosystem service supply–demand ratio, accounting for 66.21% and 51.08% of the variance, respectively. These findings provide a scientific basis for integrating compact urban form with ecosystem management in sustainable landscape planning. Full article

Diffusion probabilistic models are powerful generative tools but are purely data-driven, limiting their ability to incorporate domain knowledge—such as physical laws, degradation trends, or engineering priors—in scientific and engineering applications. We introduce Prior-Guided Diffusion Processes (PGDPs), a unified mathematical framework that integrates arbitrary differentiable prior knowledge into the reverse diffusion dynamics by augmenting the score function with a guidance term derived from a prior potential V(x,t) and weighted by a time-dependent strength γ_t. This formulation subsumes existing mechanisms (classifier guidance, model-based diffusion, physics-informed corrections) as special cases. We analyze the guided path measures, providing an upper bound on the Kullback–Leibler divergence between guided and unguided marginals (Theorem 1), quantifying the inherent trade-off between data fidelity and prior satisfaction. Experiments on synthetic data confirm the predicted dependence on γ_t. On the NASA C-MAPSS turbofan benchmark, we enforce compressor-oriented physical constraints (e.g., speed–pressure consistency, monotonicity) within PGDP; remaining useful life scores are reported only as reference metrics under transparent protocols. A cross-domain study on the NASA IGBT accelerated aging dataset, using the same backbone with a replaced physics module, achieves a 99.98% reduction in monotonicity loss, demonstrating generality across distinct degradation mechanisms. PGDP provides a principled, extensible template for knowledge-informed generative modeling with theoretical guarantees and verifiable physical consistency. Full article

Hyperspectral image classification remains challenging because robust recognition requires preserving spatial–spectral coupling, extracting complementary spectral and spatial cues, and fusing heterogeneous features without excessive redundancy. To address this issue, a dual-branch convolutional neural network (CNN) with depthwise separable fusion, termed DSFA-CNN, is developed. The network combines a 3D convolution branch for coupled spatial–spectral representation learning with a 1D+2D branch for efficient spectral and spatial modeling. A convolutional block attention module (CBAM) is introduced in the decomposed branch to emphasize informative spectral responses and salient spatial regions, and a depthwise separable fusion module is used to improve cross-branch integration while limiting fusion-stage redundancy and the risk of overfitting. Experiments on Indian Pines, University of Pavia, Salinas, and Houston2013 yield overall accuracies of 95.62 ± 0.13%, 99.25 ± 0.13%, 99.89 ± 0.11%, and 97.62 ± 0.23%, respectively. The gains are most evident on the more challenging Indian Pines and Houston2013 scenes. Ablation results show that the dual-branch design provides complementary information, whereas CBAM and the fusion module further improve representation selectivity and feature integration. Computational cost analysis further indicates that DSFA-CNN achieves a more favorable trade-off between classification accuracy and computational efficiency than several recent competitive baselines. These results demonstrate the effectiveness of parallel coupled–decomposed modeling with efficient feature fusion for robust hyperspectral image classification. Full article

Deploying modern deep learning-based vision systems on humanoid robots remains challenging due to limited onboard computational resources and legacy software constraints. This paper presents a modular vision system for practical object detection on resource-constrained humanoid platforms, based on the YOLOv9 framework. The proposed architecture adopts a dual-environment design, decoupling the perception pipeline from the robot control system to enable compatibility between modern deep learning libraries and a ROS-based platform. To support efficient deployment, task-specific lightweight models are trained and integrated into a modular pipeline optimized for CPU-only inference. The system is evaluated across multiple task scenarios derived from the FIRA RoboWorld Cup (Hurocup) competition, including Marathon, Basketball, and Archery. Performance is assessed in terms of detection accuracy and computational efficiency, demonstrating that reliable perception can be achieved at 4–8 FPS under constrained hardware conditions. The results show that the proposed approach improves robustness compared to traditional geometric vision methods, particularly in dynamic and visually complex environments, while maintaining practical responsive task-level perception for robotic decision-making. The work highlights the trade-offs between accuracy, computational cost, and system responsiveness and demonstrates the feasibility of deploying modern object detection models on embedded humanoid platforms. Full article

This paper proposes an experimental, intelligent optimization approach to improve the thermal cooling performance of an overclocked graphics processing unit (GPU). A closed-loop liquid-cooling system was built and tested utilizing deionized water and a silver (Ag) nanofluid coolant (0.015% vol.) across a variety of microchannel heat sink topologies with varying fin spacing. Key thermal performance indicators, including GPU temperature, coolant outlet temperature, and thermal resistance, were measured at different coolant flow rates. Experiments revealed that raising the flow velocity and decreasing the fin gap considerably enhanced cooling performance, while the Ag nanofluid consistently lowered GPU temperature by 1–3 °C compared to water. An Artificial Neural Network (ANN) surrogate model was constructed and trained using experimental data to support predictive analysis and system optimization, achieving excellent predictive accuracy with low RMSE. The trained ANN model was combined with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to perform multi-objective optimization, aiming to minimize GPU temperature and thermal resistance while improving heat removal. The Pareto-optimal solutions revealed that nanofluid-based cooling offered the best trade-off circumstances, with optimal designs occurring at moderate flow rates and small fin spacing. The ANN-NSGA-II multi-objective optimization results indicated that the best thermal performance of the GPU cooling system was achieved when using Ag nanofluid (0.015 vol.%) as the coolant, with an optimal coolant flow rate in the range of 1.30–1.84 LPM and an optimal fin/channel spacing of 0.57–0.71 mm, producing GPU temperatures of 29.18–29.66 °C, coolant outlet temperatures of 29.06–29.41 °C, and a minimized thermal resistance of 0.0106–0.0152 °C/W; thus, overall, the suggested ANN-NSGA-II framework works well as a practical design tool for improving GPU cooling systems and may be used to other high-heat-flux electronic thermal management applications. Full article

This investigation examines the decline in breaking performance observed in 550 kV high-speed circuit breakers, tracing the cause to insufficient coordination between the operating mechanism and the arc-extinguishing chamber. It proposes a coordinated adjustment of the buffer strategy mechanism and the structural parameters of the arc-extinguishing chamber, revealing their interaction under high-speed opening conditions. To address the impact loads and unstable airflow field during the mechanism’s high-speed opening, the buffer strategy was revised by increasing the gaps in the first four steps by 0.3 mm and 0.5 mm in two respective optimization schemes. Set the step size to 3 mm, and assign a decrease of zero for each of the final three steps. A 1 mm gap reduces the pressure drop near the end of the opening phase. The axial airflow velocity and the breaking performance were compared at the moment of 1 ms before current zero for three nozzle throat lengths (L_u): 22 mm, 27 mm, and 32 mm. Nozzle throat length has a clear effect on the main parameters of short-arc quenching. With a 27 mm throat length, the measured values remain relatively high. The proposed length scheme achieves a balanced trade-off between the airflow velocity distribution and the efficiency of arc cooling. Downstream of the nozzle, the axial airflow velocity is 18% higher than in the 32 mm scheme, and the pressure decays 22% more slowly than in the 22 mm scheme. This improves heat removal from the arc and shortens the short-arc phase to under 6 ms. Prototype tests provided by the manufacturer indicate that the circuit breaker with a 27 mm nozzle throat can achieve a minimum arcing time of approximately 6 ms, which is consistent with the simulation prediction. Full article

A linear frequency modulation (LFM) signal is widely used in radar systems. However, its inherently high autocorrelation sidelobes can degrade weak-target detection, while amplitude and phase distortions caused by transmitter systems may further elevate sidelobe levels. To address these issues, a joint pre-compensation and windowing optimization framework is proposed for a transmitter-distorted LFM signal. First, a regularized pre-compensation filter with gain constraints is constructed to compensate for transmitter-induced distortions and restore the waveform. Considering that the system frequency response is difficult to estimate accurately in practice, amplitude and phase perturbations are introduced, and a pre-compensation filter under perturbation is derived to improve robustness. To overcome the limited flexibility of fixed windows, a parameterized cosine-series window is employed, and the firefly algorithm is employed to jointly optimize the window coefficients and width, achieving a better trade-off among peak sidelobe ratio, integral sidelobe ratio, main lobe width, and peak-to-average power ratio. Simulation results demonstrate that the proposed method compensates transmitter distortions, significantly suppresses autocorrelation sidelobes, and maintains favorable performance under perturbations. Full article

High penetration of wind and photovoltaic (PV) generation increases renewable uncertainty and real-time balancing pressure in active distribution networks. To address this problem, this paper proposes a two-stage robust optimization method for day-ahead and real-time scheduling of a flexibly interconnected multi-microgrid (MMG) system enabled by a flexible interconnection device (FID). The proposed framework jointly optimizes power purchase from the upper-level distribution network, micro-gas turbine output, energy storage system (ESS) operation, and FID-based bidirectional power exchange, thereby coordinating local temporal flexibility and inter-microgrid spatial flexibility. A polyhedral uncertainty set is used to model wind and PV forecast errors, and the problem is solved by the column-and-constraint generation (C&CG) algorithm. Case studies on a two-microgrid system show that, compared with independent operation under traditional robust optimization, the proposed method reduces real-time balancing cost, wind and PV curtailment, and total operating cost by 98.96%, 95.84%, and 0.59%, respectively. Sensitivity analysis further verifies the economy–robustness trade-off under different uncertainty budgets and forecast deviation levels. Full article

Sybil attacks remain a primary challenge for Proof-of-Stake (PoS) blockchain systems, as low-cost identity creation can distort validator participation and limit consensus reliability. This study proposes a hybrid participation–governance framework that integrates Attribute-Based Access Control (ABAC) and Reputation-Based Access Control (RpBAC) with a trust-based PoS workflow to reduce the influence of suspicious identities during validator selection and block validation. The proposed framework also incorporates graylisting and dynamic reward–penalty updates to support adaptive participation control. The strategy was evaluated in a simulation environment informed by Ethereum-derived block metadata, using network sizes ranging from 100 to 1000 nodes and Sybil attack ratios of 30%, 40%, and 50%. Its performance was compared with PoS-only and PoS + ABAC baselines using both security and performance indicators. The results show that the full ABAC + RpBAC configuration achieved the strongest and most stable security performance across the evaluated settings while introducing additional overhead at larger network sizes. These findings suggest that combining policy-based eligibility control with behavior-based reputation control strengthens the resilience against Sybil in PoS-like blockchain environments. However, this improvement requires a measurable trade-off between security and performance. Full article

Microbial inoculation is widely used to improve composting performance, yet its effectiveness hinges on inoculum composition, substrate characteristics, and composting technology, which remain poorly understood. This study compared single versus mixed inoculants across different substrates and assessed their interactions with biochar amendment and nanomembrane covering, focusing on organic matter transformation, inorganic nutrient dynamics, and biological pollution control. Mixed inoculation significantly improved heating performance in cattle manure compost compared to single strains (p < 0.05) and sustained thermophilic conditions in sludge-sawdust compost, but showed limited impact in chicken manure-sludge compost. It reduced humic acid (HA) accumulation in chicken manure-sludge compost (14.29% to −39.28%) while increasing HA content in sludge-sawdust compost (3.55–5.41 g/kg, p < 0.05). Inorganic nitrogen retention was enhanced; specifically NO₃⁻-N concentrations rose by 175.1–222.6% in the chicken manure-sludge and by 6.7–17.9% in the sludge-sawdust compost. Microbial community analysis indicated enrichment of inoculant strains during the thermophilic phase, supporting nitrogen conservation and humification. However, inoculation increased potential pathogenic bacteria by over 51.2% across all composts and enriched predicted antibiotic resistance genes (ARGs) by 9.9–22.96% in chicken manure-sludge compost, while reducing the membrane covering’s inhibitory effect on predicted ARGs (rebound by 29.5%). Moreover, we found that the predicted ARG profiles, derived from 16S-based PICRUSt2 functional inference, covaried strongly with microbial community structure, with environmental factors such as organic carbon shaping predicted ARG dynamics mainly through indirect effects on microbial communities. These findings highlight that while mixed inoculation boosts composting efficiency, it also raises biosafety concerns. Thus, a comprehensive evaluation integrating organic, inorganic, and biological perspectives is essential before promoting thermophilic inoculants. Full article

Recent paradigms in traumatic brain injury have transitioned from focal-lesion models to an emphasis on diffuse axonal injury and white matter disruption as the primary drivers of cognitive morbidity. This selective review frames information processing speed as the functional signature of this connectivity loss. While processing speed is often theorized as a “cognitive bottleneck” that constrains higher-order functions, we identify critical methodological and conceptual pitfalls in the existing literature. Specifically, we argue that current research is frequently confounded by: (1) measurement impurity, where tasks like the SDMT and TMT-B recruit executive and mnemonic variance; (2) circularity, where speed measures are used to predict time-dependent outcomes; and (3) the neglect of speed–accuracy trade-offs, which may mask volitional cautiousness as neurobiological incapacity. To resolve these challenges, we offer evidence-based recommendations for the clinical setting, including the integration of construct-pure chronometric measures and dual-scoring protocols. We conclude that because white matter integrity functions as a rate-limiting substrate, processing speed must be prioritized as a primary target in early neurorehabilitation. By isolating processing speed from focal-driven deficits, clinicians can more accurately map the path from microstructural disruption to functional recovery. Recognizing this infrastructure is essential to understanding the full scope of cognitive consequences. Full article

Background: Acute knee trauma is a common reason for emergency department visits, yet early identification of patients requiring surgical intervention remains challenging. Most existing prediction studies focus on discrimination metrics and provide limited guidance for clinical decision-making. Methods: We conducted a retrospective study of 905 patients presenting to the emergency department with acute knee trauma. Prediction models were developed using logistic regression, random forest, and extreme gradient boosting (XGBoost) based on routinely available clinical variables. Model performance was evaluated in terms of discrimination (AUROC, AUPRC), calibration, and clinical utility. Threshold-specific performance metrics and decision curve analysis were used to assess clinical applicability, and patients were stratified into risk groups based on predicted probabilities. Results: Among 905 patients, 163 (18.0%) underwent surgical intervention. Logistic regression and random forest demonstrated comparable performance (AUROC 0.748 and 0.744, respectively), whereas XGBoost showed lower discrimination (AUROC 0.632). Calibration was acceptable overall but less stable at higher predicted probabilities. Threshold-specific analysis demonstrated meaningful trade-offs between sensitivity and specificity across probability thresholds. Decision curve analysis showed that the model provided greater net benefit than default strategies within a threshold range of approximately 0.05–0.25. Risk stratification showed increasing surgical rates across risk groups, although the degree of separation was modest. Conclusions: Prediction models based on routinely available clinical variables can support early risk assessment in acute knee trauma. Their clinical usefulness depends on threshold-specific evaluation and decision-analytic approaches rather than overall performance metrics alone. These findings highlight the importance of interpreting prediction models within a clinical decision-making framework to facilitate real-world application. Full article

In massive multiple-input multiple-output (MIMO) joint communication and sensing (JCAS) systems, hybrid beamforming (HBF) has attracted much attention because it can provide a favorable tradeoff between beamforming gain and hardware cost. However, HBF design in MIMO-JCAS systems is highly challenging. The main reasons are the strong coupling between the analog and digital precoders in joint communication-sensing optimization and the high-dimensional search space caused by large-scale antenna arrays. In this paper, a state-driven adaptive deep-unfolded hybrid beamforming algorithm is proposed for MIMO-JCAS systems. Specifically, the analog precoder update is redesigned in a manifold-based form to better match the geometry of the constant-modulus constraint, while the digital precoder update is enhanced by a learnable gradient-balancing mechanism to alleviate the dynamic imbalance between the communication-rate gradient and the sensing-error gradient. Furthermore, a lightweight state-driven control network is introduced to generate scaling factors for the hyperparameters according to the current iteration state, so that the unfolded model can adapt its update behavior during optimization. Different from conventional deep-unfolded methods with static hyperparameters during inference, the proposed method provides a more effective optimization strategy for the dynamic communication-sensing tradeoff in MIMO-JCAS hybrid beamforming. Simulation results demonstrate the effectiveness of the proposed state-driven adaptive deep-unfolded method. Compared with the conventional deep-unfolded projected gradient ascent (PGA) algorithm with 20 inner iterations, the proposed method improves the joint objective, while achieving faster convergence and stronger robustness. Full article

Recognition of the American Sign Language (ASL) alphabet is of utmost importance in bridging the communication gap between the hearing-impaired and the hearing. However, robust classification remains difficult because some hand gestures are morphologically very similar. To address this problem, this study presents CT-Net, a hybrid deep learning architecture that integrates ConvNeXt-Tiny with a lightweight Transformer encoder. CT-Net combines convolutional feature extraction and self-attention mechanisms, which enable it to capture fine-grained local patterns and long-range spatial dependencies effectively. The proposed model was extensively compared with various architectures including traditional CNNs, Transformer-based models, hybrid machine-learning approaches and recent lightweight hybrid networks. The experimental results show that CT-Net achieved the best overall performance with a peak accuracy of 95.67% on the enhanced ASL dataset. Ablation studies demonstrate the effectiveness of our design choice. CT-Net achieves a strong trade-off between recognition accuracy and computational efficiency with an inference rate of 163.55 Frames Per Second (FPS). These findings highlight the potential of hybrid frameworks as a powerful tool for fine-grained gesture recognition tasks. Full article

The basic renovation of old urban communities is an important livelihood project for urban renewal, but there are many problems in the decision-making of renovation schemes, such as strong dependence on experience, lack of quantitative basis for multi-objective trade-off, and difficulty in describing residents’ risk attitude. Combining Case-Based Reasoning (CBR) and utility theory, this paper constructs a set of intelligent decision support models driven by data and knowledge. First of all, through literature analysis and expert investigation, a decision-making index system is established, which includes four dimensions and 16 quantitative indicators: policy and financial support, residential conditions and needs, residents’ consensus and social coordination, and implementation management and long-term maintenance. Secondly, the framework representation method is used to describe the reconstruction case, a hybrid retrieval strategy combining inductive retrieval and nearest-neighbor retrieval is designed, and the subjective and objective data combination weights are calculated by using AHP and the entropy method. On this basis, a loss utility function and risk aversion coefficient based on accident and public opinion data (a = 0.02) are introduced to modify the similarity calculation results to describe the risk avoidance behavior of decision-makers. Through 40 real renovation projects, a case base is built, and two types of target cases, “typical inclusive” (F5) and “key renovation” (F35), are selected for empirical verification. The results show that the model can effectively retrieve similar cases, and the similarity ranking changes in line with risk aversion expectations after utility correction. Taking F5 as an example, by reusing and revising the reconstruction scheme of a similar case, targeted suggestions are generated, which give consideration to safety, economy and operability. This model provides a new quantifiable and reusable method for scientific decision-making in basic renovation of old residential areas. Full article