Search Results (2,392)

This paper considers geometry-aware ground user clustering and Cluster Center Optimization for beam pointing and scheduling in adaptive multibeam Geostationary Earth Orbit (GEO) satellite networks. By grouping ground users, beams can be directed toward user clusters to maximize satellite throughput. We propose GeoClust, a polynomial-time geometric user clustering algorithm for adaptive multibeam GEO satellite networks, using a geometric set-cover approach that explicitly balances link signal-to-interference-plus-noise ratio (SINR) and hopping overhead. The algorithm employs a Boyle–Dykstra projection-based cluster center update within an alternating optimization framework, combined with nearest-center membership updates, to enforce the cluster-radius constraint while ensuring feasibility and provable convergence. It also achieves near-linear throughput scaling with increasing number of RF chains. Numerical evaluations on real-world population data show that, under heavy traffic conditions, our approach more than doubles the zero outage and median user rates compared to benchmark schemes. Full article

This article introduces an intelligent framework using deep learning to recognize and classify different faults through the real-time detection of multiple faults in power distribution systems. A collection of data representing normal operating conditions, alongside various fault scenarios including line-to-ground (LG), line-to-line (LL), double line-to-ground (LLG), and three-phase line (LLL) faults, was created using three phase current signals obtained from the Real-Time Digital Simulator (RTDS) microgrid test system. To properly model the system dynamics, a feature extraction method that integrates phase currents, differential currents, summation currents and magnitude results was developed. The temporal features of the fault signals were identified by using a sliding window approach to fit the data. A one-dimensional convolutional neural network (CNN) was developed to identify different types of faults. This model performed well, obtaining nearly 96.15% accuracy while testing. In order to evaluate the feasibility of the approach, the trained model was loaded on Raspberry Pi 5, NodeMCU, ESP32 and existing sensing devices. The fault classification performed in real-time was time-sensitive. The proposed intelligent framework is applicable to low-scale operation for smart grid fault monitoring and protection and it is an economically viable solution. Full article

The double hidden-layer neural network has increasingly been applied in tree height modeling due to its superior performance. To improve the precision of tree height estimation, this study compared the performance of a double hidden-layer neural network with that of a nonlinear mixed-effects model, aiming to provide a new method for tree height prediction. Taking the Larix olgensis forest plantation in Jilin Province as the research object, a double hidden-layer back propagation (BP) neural network was established for tree height prediction by adopting trial and error, k-fold cross-validation, and near-domain optimization strategies. In constructing the nonlinear mixed-effects model, the overall and local differences in forest growth data, as well as the autocorrelation among the various levels of data, were considered. Accordingly, after determining the base model, random effects were introduced, the correlation variance–covariance matrix was calculated, and random parameters were estimated to compare the predictive performance of the two aforementioned models. For the mixed-effects model, the coefficient of determination R² was 0.8590, the root mean square error (RMSE) was 1.6230, and the mean absolute error (MAE) was 2.2658. For the double hidden-layer BP neural network, the R² reached 0.9068 (an increase of 5.56%), the RMSE was 1.3197 (a decrease of 18.69%), and the MAE was 1.2736 (a decrease of 43.79%). The results demonstrate that the double hidden-layer BP neural network is superior to the nonlinear mixed-effects model for tree height prediction. Therefore, the results provide a more accurate method for tree height prediction. Full article

Coherent optical communication serves as the backbone of long-haul, high-capacity optical networks, where polarization-insensitive 90° optical hybrids (OHs) are crucial for system simplification and robustness. This work presents a polarization-insensitive 90° OH based on asymmetric double-strip silicon nitride waveguides, designed for dual-polarization quadrature phase-shift keying (DP-QPSK) systems. The device consists of a cascaded polarization-insensitive structure incorporating one 1 × 2 and three 2 × 2 multimode interference (MMI) couplers, interconnected by four 90° bent waveguides. Optimized via 3D finite-difference time-domain (FDTD) simulations, the 1 × 2 MMI coupler exhibits insertion losses below 0.06 dB (TE) and 0.09 dB (TM), while each 2 × 2 MMI coupler shows insertion losses under 0.2/0.4 dB, amplitude imbalance below 0.05/0.18 dB, and phase error within ±0.5°/±1.5° for the TE/TM modes, respectively. Based on these components, the full device achieves polarization-insensitive operation across a 100 nm bandwidth (1500–1600 nm), with a phase error within ±1°, insertion loss below 0.3 dB (TE) and 0.5 dB (TM), and common-mode rejection ratio better than −40 dB (TE) and −30 dB (TM). Furthermore, the design demonstrates high fabrication tolerance, maintaining performance under manufacturing deviations of ±2 μm in MMI length and ±20 nm in waveguide spacing. This work provides a promising polarization-insensitive OH design and a viable route toward cost-effective mass production of next-generation high-speed coherent systems. Full article

Multi-UAV communication networks face significant challenges in achieving high energy efficiency and low communication latency under dynamic topology and interference conditions. This paper proposes a Dueling Deep Q-Network (DQN) framework for joint resource optimization in 6G-enabled multi-UAV systems. The proposed approach jointly optimizes transmit power allocation, inter-UAV link association, and adaptive graph density within a unified reinforcement learning framework. By employing a dueling value–advantage decomposition, the proposed model improves learning stability and convergence compared to conventional DQN and Double DQN (DDQN) schemes. Simulation results under varying network densities and UAV scales show that the proposed Dueling DQN achieves up to 15% higher energy efficiency and 12% lower end-to-end latency, while maintaining robust performance in dense connectivity scenarios. These results demonstrate the effectiveness and scalability of the proposed framework for energy- and latency-sensitive UAV communication applications. Full article

Utilizing food-derived bioactive polysaccharides in advanced biomedical applications offers significant potential. To effectively harness the inherent bioactivity of Poria cocos, a renowned edible and medicinal fungus, we developed a multifunctional double-network composite hydrogel (CPS) via a feasible one-pot strategy. This was achieved by incorporating functional carboxymethyl pachymaran (CMP) into a matrix of food-grade sodium alginate (SA) and polyacrylamide (PAM). This formulation endows the hydrogel with excellent extensibility, rapid self-healing capabilities, and strong tissue adhesion, all while preserving the biological activity of the natural macromolecules. In a mouse full-thickness skin defect model, the CPS significantly accelerated wound recovery, achieving a healing rate of 51.17 ± 4.87% by day 7. Mechanistically, the food-derived CMP synergistically promoted skin tissue regeneration by downregulating the expression of the early pro-inflammatory cytokine TNF-α and upregulating the angiogenic marker CD31, thereby actively modulating the local microenvironment. Ultimately, these findings demonstrate the viability of using edible fungal polysaccharides as primary bioactive components in advanced wound dressings, providing a novel approach for utilizing food macromolecules in biomedicine. Full article

The transition toward capacity-based network tariffs shifts the primary role of battery energy storage systems (BESS) from traditional energy arbitrage to active peak shaving. This paper presents a mixed-integer linear programming (MILP) optimization model for the co-optimization of both BESS size and operation scheduling for multiple prosumers operating individually and within an energy community (EC). Battery aging is accounted for in the optimization model through the state of health (SOH). The framework is evaluated by a comprehensive techno-economic analysis of BESS integration under Slovenia’s multi-block tariff structure. The results demonstrate that while individual distributed BESS integration is highly profitable, centralized EC BESS financially underperforms. Because centralized BESS cannot directly reduce individual contracted power limits, its profitability relies on energy arbitrage, making the initial investment and double grid fees the primary barriers. Conversely, integrating distributed storage with peer-to-peer (P2P) trading minimizes the required BESS capacity while maintaining profitability. The evaluation also reveals that ECs do not automatically act as socio-economic equalizers, indicated by a stable Gini coefficient. However, a break-even analysis reveals the necessary reduction in capital costs to overcome these hurdles, confirming the strong future viability of centralized EC BESS. Full article

The efficient recovery of coalbed methane (CBM) is critically constrained by the inherent low permeability of coal reservoirs, a challenge predominantly attributed to mineral blockages within the pore-fracture structure. In this study, the deashing efficacy of several acid solutions (HCl, HNO₃, HF, and CH₃COOH) on bituminous coals from the Yushuwan (YSW) and Jiangna (JN) mines was initially assessed to determine the optimal acidizing system. Subsequently, the multi-scale evolution of pore-fracture structures and the fractal characteristics of coal samples treated with the optimized acids were systematically investigated. A multi-analytical approach, integrating scanning electron microscopy (SEM), X-ray diffraction (XRD) with microcrystalline peak-fitting, and low-temperature nitrogen gas adsorption (LT-N₂GA), was employed to quantitatively elucidate the underlying transformation mechanisms. The experimental results indicate that HCl and HNO₃ emerged as the most effective agents for the YSW and JN coals, respectively. Optimized acidification achieved significant reductions in ash content (specifically, an ash removal efficiency of 83.99% for HCl-treated YSW coal) through the selective dissolution of carbonate and clay minerals, thereby facilitating the exposure of the organic matrix and the induction of extensive dissolution pits and secondary fractures. Although the dissolution-induced collapse of mineral-supported fine pores led to a reduction in both total pore volume and BET specific surface area, the average pore diameter undergoes a substantial increase (e.g., nearly doubling from 9.0068 nm to 16.5126 nm for the JN coal). Furthermore, the reduction in Frenkel–Halsey–Hill (FHH) fractal dimensions (D₁ and D₂) indicates a decrease in pore-surface complexity and structural heterogeneity. These findings reveal that optimized acidification induces significant alterations in pore structure and mineral composition. The treatment facilitates the conversion of isolated pores into interconnected networks, accompanied by an increase in pore volume and a shift in pore size distribution toward larger dimensions. This research elucidates the mechanisms of mineral dissolution and pore expansion, providing a fundamental characterization of the microstructural evolution of coal in response to acid treatment. Full article

Digital transformation has profoundly reshaped caregiving practices, yet its influence on family cohesion within disability contexts remains underexplored, particularly in Arab societies. This qualitative phenomenological study examines how digital technologies shape family cohesion among Jordanian caregivers of children with disabilities. In-depth, semi-structured interviews were conducted with 22 primary caregivers, and data were analyzed using reflexive thematic analysis. The findings reveal a central tension of being “between connectivity and care,” articulated through four interrelated themes: (1) a digital double-bind in which online support networks function as a vital “virtual village” while simultaneously contributing to intra-familial fragmentation; (2) the reconfiguration of care labor, whereby digital management emerges as an invisible and gendered form of caregiving work, often positioning mothers as primary digital coordinators; (3) the translation of traditional social capital (wasta) into digital spaces to navigate systemic resource constraints, producing new moral and emotional burdens; and (4) the strategic use of digital platforms to preserve cultural, religious, and familial identity in the face of stigma, thereby reinforcing internal cohesion. These findings suggest that digital technologies do not merely facilitate connection but actively reconfigure family dynamics through ongoing negotiation between support and strain. The study underscores the need for family-centered digital inclusion policies and support interventions that mitigate digital burdens while harnessing technology’s potential to strengthen culturally grounded resilience among families of children with disabilities. Full article

Single-event effects (SEEs) present a significant challenge to the radiation reliability of integrated circuits. Conventional SEE analysis methods for single-photon avalanche diode (SPAD) devices primarily rely on Sentaurus Technology Computer-Aided Design (TCAD) numerical simulation, which is computationally intensive and time-consuming. In this study, we propose a generalized deep learning (DL) model, using a silicon-based SPAD device with a double-junction double-buried-layer (DJDB) structure fabricated in 180 nm CMOS process as the research subject. By incorporating key parameters that influence SEEs as model inputs, the proposed approach enables rapid prediction of critical parameter metrics, including transient current peaks and dark count rates. Experimental results show that the DL model achieves a prediction accuracy of 97.32% for transient current peaks and 99.87% for dark count rates, demonstrating extremely high prediction precision. To further validate the generalization capability of the proposed network, the model is applied to predict the detection performance of the DJDB-SPAD device. The prediction accuracies for four key performance parameters all exceed 97.5%, further confirming the accuracy and robustness of the developed model. Meanwhile, compared with the conventional Sentaurus TCAD simulation method, the proposed method achieves a 336-fold improvement in computational efficiency. Overall, this method realizes the dual advantages of high precision and high efficiency, which provides an efficient and accurate technical solution for the rapid characteristic analysis and reliability evaluation of SPAD devices under single-event effects (SEEs). Full article

Residential air conditioning systems are a major contributor to household electricity consumption in tropical regions, where environmental factors such as climate variability and particulate pollution (PM10) can further increase cooling demand and accelerate equipment degradation. This study proposes an Enhanced Deep Q-Network (Enhanced DQN) for energy-efficient and maintenance-aware control of residential split-type air conditioners under dynamic environmental conditions. The proposed method integrates several stability-oriented reinforcement learning mechanisms, including Double Q-learning, a dueling architecture, prioritized experience replay, multi-step returns, Bayesian-style regularization via Monte Carlo dropout, and entropy-aware exploration. The framework is evaluated through a two-stage process consisting of a diagnostic benchmark on LunarLander-v3 to assess learning stability, followed by a realistic 365-day simulation driven by Thai weather and PM10 data. Compared with a fixed 25 °C baseline, the proposed controller reduced annual electricity consumption from 5116.22 kWh to as low as 4440.03 kWh, corresponding to a saving of 13.22%. The learned policy also exhibited environmentally adaptive behavior under high PM10 conditions, indicating maintenance-aware characteristics. These findings demonstrate that reinforcement learning can provide robust, adaptive, and sustainable control strategies for residential cooling systems in tropical environments. Full article

To address the reduced accuracy of stereo matching networks near object boundaries and disparity discontinuities, a double cost–volume stereo matching network with entropy-difference-guided fusion is proposed. The proposed network was built based on RAFT-Stereo. It employs a pretrained backbone to extract multi-scale features and uses deformable attention for cross-scale feature fusion. A shallow image-guided branch was used to generate pixel-wise constraint information to limit the magnitude of sampling offsets and alleviate cross-structure sampling. Based on the extracted features, a group-wise correlation cost–volume and a normalized correlation cost–volume were constructed. Both cost–volumes were regularized by 3D Hourglass networks, and a structure-consistent intra-scale aggregation module was introduced during the regularization of the group-wise correlation cost–volume. The two aggregated results were then fused by the entropy-difference-guided fusion module to obtain the final cost–volume. The experimental results show the effectiveness of the proposed network in the Scene Flow, KITTI, and ETH3D datasets, achieving an endpoint error of 0.45 px and a >3 px error rate of 2.41% on the Scene Flow dataset. Full article

Road safety studies commonly use machine learning to predict crashes or to estimate crash-based treatment effects. This study instead audits the modelled fatal-and-serious-injury (FSI) risk produced by the iRAP ViDA risk engine. We analyse 147,466 segments (100 m each) from 12 surveys grouped into four European reporting groups. In Stage 1, gradient-boosted trees reproduce the engine’s risk surface under road-grouped cross-validation(R² ≈ 0.92 with flows and survey identifiers), and Shapley-based attributions identify which coded attributes drive modelled risk at 396 hotspots (top-three segments per road). In Stage 2, a causal-forest double machine learning estimator adjusts for 38 covariates to estimate segment-level conditional contrasts between modelled risk and six retrofittable treatments across all eligible segments. Simple absolute and relative reduction thresholds translate these associations into 1170 association-based candidate upgrades. On 321 over-lapping hotspots, the candidate upgrades show moderate agreement with iRAP’s Safer Roads Investment Plan (Recall = 0.77; Precision = 0.66; Cohen’s κ = 0.40). All results are conditional associations on a calibrated risk engine whose totals are anchored to project- or network-level fatality totals or fatality estimates used in calibration, not causal effects on observed crashes. Full article

Firefighters must locate victims reliably to carry out rescue operations within burning structures during urban firefighting events. Low visibility, reduced oxygen levels, weakened structural rigidity, and dense smoke make it difficult to locate victims. In addition to these challenges, victims may be unconscious and unable to report their locations to firefighters. This research work explores the Double Deep Q-Network (Double DQN), Dueling Deep Q-Network (Dueling DQN), and Dueling Double Deep Q-Network (D3QN) agents for an unmanned aerial vehicle (UAV) to navigate around a structure and locate trapped victims within it. The UAV’s position, Light Detection and Ranging (LiDAR), and infrared camera data are utilized as inputs for the Deep Q-Networks. The PER is used to store transitions and sample them according to priority for training. Python’s Pygame library is used in this research to create a simulated environment in which infrared camera and LiDAR data are simulated. The performance of the UAV agent is evaluated using cumulative maximum reward, reward distribution histogram, Temporal Difference (TD) error over time, and number of successful episodes. Among the three DQN UAV agents, the Dueling DQN and Double DQN have potential for real-world applications in firefighting. Full article

Convolution is still the main method for hyperspectral image classification, since it takes into account both spatial and spectral characteristics. However, the convolution relies on local perceptual computation, ignoring the effective discriminant of context association for classification. In this paper, we propose a Double-Attention Context Interactive Network (DACINet) for hyperspectral image classification. Specifically, a Context Interaction Fusion Module (CIFM) is designed to enhance long-range contextual dependencies. By stacking multiple 3D convolutional layers, the module progressively enlarges its receptive field, while cross-layer residual connections facilitate the integration of features from different contextual scales, thereby strengthening the model’s ability to capture complex relationships within the hyperspectral data. Then, a Channel–Spatial Double-Attention (CSDA) mechanism based on 3D is proposed for enhancing the two-dimensional spatial features and one-dimensional spectral features, respectively, and fusing the enhanced features. Furthermore, we also construct a hybrid convolutional layer, which combines 2D and 3D convolution to further enhance spectral bands on the basis of three-dimensional understanding. Extensive experiments on the widely used IP, UP, SA and HU datasets show that the proposed DACINet achieves superior classification accuracy, reaching Overall Accuracies of 96.78%, 97.77%, 99.53% and 86.67% respectively, outperforming other state-of-the-art models. Full article