Search Results (3,485)

This study presents various microwave reactor designs specifically engineered for continuous microwave CO₂ desorption, marking a significant advancement in microwave-heating systems. This study explored both horizontal and vertical continuous microwave reactor configurations. The horizontal design incorporates a modified conveyor belt system with cleated belts and Teflon sidewalls, rendering it particularly suitable for the regeneration of gas. Conversely, the vertical design utilizes a cascade gate opening mechanism, facilitating precise control over the microwave intensity and exposure duration. The efficiency of microwave power utilization was enhanced through the numerical modeling and optimization of the reactor dimensions. This study assessed the impact of waveguide placement, cavity size, and sorbent material thickness on power absorption and heating. The findings indicate that strategic waveguide positioning and optimal cavity dimensions significantly influence the microwave energy distribution and absorption, leading to reduced hotspots and more uniform heating. This study offers valuable insights into the design and optimization of microwave reactors for CO₂ desorption, contributing to more efficient and effective commercial applications of this technology. These results underscore the potential of microwave technology to revolutionize desorption processes and pave the way for further advancements in this domain. Design 2 exhibited more uniform heating owing to its slower and controlled temperature increase, making it more suitable for applications requiring consistent thermal performance over extended periods. Full article

This study investigates the performance of a high-resolution Weather Research and Forecasting with large-eddy simulation (WRF-LES) model in simulating the strong wind of a realistic typhoon (Jebi, 2018). Multiple domains are nested to downscale the grid resolution from 4.5 km to 33.3 m, and grid size sensitivity is tested in the innermost WRF-LES domain. The commonly used 1.5-order turbulent kinetic energy (TKE) subgrid-scale (SGS) model is excessively dissipative near the ground; this causes overshoot in the mean velocity profile compared with the expected log-law profile, a phenomenon slightly amplified by finer grids. Horizontal roll structures in the typhoon boundary can be effectively resolved with the 100 m horizontal grid size ($\mathrm{\Delta }x$). However, higher resolution is needed to capture small-scale turbulence, and the effective mesh resolution for resolved turbulence is about 5–9$\mathrm{\Delta }x$ near the ground. The nonlinear backscatter and anisotropy (NBA) model significantly reduces the overshoot, and the resolved velocity structures are insensitive to the SGS model except for the lowest model level. Full article

This study presents a standardized method for multimodal monitoring of exercise execution in isolated, confined, and extreme (ICE) environments, addressing the need for reproducible assessment of neuromuscular and cardiovascular responses under space- and equipment-limited conditions. The method integrates wearable surface electromyography (sEMG), inertial measurement units (IMU), and electrocardiography (ECG) to capture muscle activation, movement, and cardiac dynamics during space-efficient exercise. Ten exercises suitable for confined habitats were implemented during analog missions conducted in the DeepLabH03 facility, with feasibility evaluated in a seven-day campaign involving three adult participants. Signals were synchronized using video-verified repetition boundaries, sEMG was normalized to maximum voluntary contraction, and sEMG amplitude- and frequency-domain features were extracted alongside heart rate variability indices. The protocol enabled stable real-time data acquisition, reliable repetition-level segmentation, and consistent detection of muscle-specific activation patterns across exercises. While amplitude-based sEMG indices showed no uniform main effect of exercise, robust exercise-by-muscle interactions were observed, and sEMG mean frequency demonstrated sensitivity to differences in movement strategy. Cardiac measures showed limited condition-specific modulation, consistent with short exercise bouts and small sample size. As a proof-of-concept feasibility study, the proposed protocol provides a practical and reproducible framework for multimodal physiological monitoring of exercise in ICE analogs and other constrained environments, supporting future studies on exercise quality, training load, and adaptive feedback systems. The protocol is designed to support near-real-time monitoring and forms a technical basis for future exercise-quality feedback in confined habitats. Full article

Background/Objectives: Nonunion bone healing results from a critical size defect that fails to bridge a bone injury to produce bony union. Novel approaches are critical for refining therapy in clinically challenging bone injuries, but the complex and coordinated nature of fracture callus tissue development requires study outside of the simple closed murine fracture model. Methods: We have utilized a three-dimensional printing approach to develop a scaffold construct with layers designed to sequentially release small molecule therapy within the tissues of a murine endochondral segmental defect to augment different mechanisms of fracture repair during critical stages of nonunion bone healing. Initially, a sonic hedgehog (SHH) agonist is released from a fibrin layer to promote chondrogenesis. A prolyl-hydroxylase domain (PHD)2 inhibitor is subsequently released from a β-tricalcium phosphate (β-TCP) layer to promote hypoxia-inducible factor (HIF)-1α regulation of angiogenesis. This sequential approach to therapy delivery is assisted by the inclusion of bone marrow stromal cells (BMSCs) to increase the cell substrate available for the small molecule therapy. Results: Immunohistochemistry of fracture callus tissue revealed increased expression of PTCH1 and HIF1α, targets of hedgehog and hypoxia signaling pathways, respectively, in the SAG21k/IOX2-treated mice compared to vehicle control. MicroCT and histology analyses showed increased bone in the fracture callus of mice that received therapy compared to control vehicle scaffolds. Conclusions: While our findings establish feasibility for the use of BMSCs and small molecules in the fibrin gel/β-TCP scaffolds to promote new bone formation for segmental defect healing, further optimization of these approaches is required to develop a fracture callus capable of completing bony union in a large defect. Full article

During coal mining, detecting subsurface structures (such as faults, voids, collapse columns, etc.) using radio waves in existing mines is hindered by the absence of effective three-dimensional coal seam medium models and simulation methods, adversely affecting the forward modeling of data analysis. This study establishes a Three-Dimensional Finite-Difference Time-Domain (3D-FDTD) radio wave penetration medium model based on coal seam tunnel penetration working conditions to simulate the electric field intensity characteristics of longitudinal and transverse waves in various coal rock mediums. Firstly, a higher-order finite difference method based on Maxwell’s equations is employed to analyze the electric field characteristics of gas-enriched areas under various geological conditions, enabling the exploration of the relationship between the position and size of the electromagnetic wave field strength in different areas. The electromagnetic wave field strength response data are then analyzed during the actual detection process to determine the specific location, shape, and size of the abnormal area. Finally, by comparing the simulation results with an actual engineering project, electromagnetic wave field strength attenuation data were collected from 158 measuring points at a working face of a coal mine in Anhui. The detection results clearly illustrate the changes in electric field intensity (with attenuation coefficients ranging from 0.41 to 0.77 dB/m) in anomalous areas, enabling the forward simulation to accurately determine the position and size of faults. The novelty of this study lies in the establishment of a conductivity-weighted 3D-FDTD model specifically calibrated for complex coal seam environments, which significantly improves the accuracy of fault boundary detection compared to traditional linear inversion methods. Full article

Monocular depth estimation is a fundamental task with broad applications in autonomous driving and augmented reality. While recent lightweight methods achieve impressive performance, they often neglect the interaction of mid-order semantic features, which are crucial for capturing object structures and spatial relationships that directly impact depth accuracy. To address this limitation, we propose MogaDepth, a lightweight yet expressive architecture. It introduces a novel Continuous Multi-Order Gated Aggregation (CMOGA) module that explicitly enhances mid-level feature representations through multi-order receptive fields. In addition, we present MambaSync, a global–local interaction unit that enables efficient feature communication across different contexts. Extensive experiments demonstrate that MogaDepth achieves highly competitive or superior performance on KITTI, improving key error metrics while maintaining comparable model size. On the Make3D benchmark, it consistently outperforms existing methods, showing strong robustness to domain shifts and challenging scenarios such as low-texture regions. Moreover, MogaDepth achieves an improved trade-off between accuracy and efficiency, running up to 13% faster on edge devices without compromising performance. These results establish MogaDepth as an effective and efficient solution for real-world monocular depth estimation. Full article

Wildfires are increasingly recognized as a climatological hazard, able to threaten industrial and critical infrastructure safety and operations and lead to Natech disasters. Future projections of exacerbated fire regimes increase the likelihood of Natech disasters, therefore increasing expected direct damage costs, clean-up costs, and long-term economic losses due to business interruption and environmental remediation. While large industrial complexes, such as oil, gas, and chemical facilities have sufficient resources for the implementation of effective prevention and mitigation plans, small-to-medium-sized industrial hubs are particularly vulnerable due to their scattered distribution and limited resources for investing in comprehensive fire prevention systems. This study targets the vulnerability of these communities by proposing the deployment of Wireless Sensor Networks (WSNs) as cost-effective Early Wildfire Detection Systems (EWDSs) to safeguard wildland and industrial domains. The proposed approach leverages wildland–industrial interface (WII) geospatial data, simulated wildfire dynamics data, and mathematical optimization to maximize detection efficiency at minimal cost. The WII delimits the boundary where the presence of wildland fires impacts industrial activity, thus representing a proxy for potential Natech disasters. The methodology is tested in Cocentaina, Spain, a municipality characterized by a highly flammable Mediterranean landscape and medium-scale industrial parks. Results reveal the complex trade-offs between detection characteristics and the degree of protection in the combined wildland and WII areas, enabling stakeholders to make informed decisions. This methodology is easily replicable for any municipality and industrial installation, or for generic wildland–human interface (WHI) scenarios, provided there is access to wildfire dynamics data and geospatial boundaries delimiting the areas to protect. Full article

The increasing size of recent deep neural networks, particularly when applied to learning across multiple domains, limits their deployment in resource-constrained environments. To address this issue, this study proposes a lightweight neural architecture with a parallel structure of convolutional layers to enable efficient and scalable multi-domain learning. The proposed network includes an individual feature extractor for domain-specific features and a common feature extractor for the shared features. This design minimizes redundancy and significantly reduces the number of parameters while preserving classification performance. To evaluate the proposed method, experiments were conducted using four image classification datasets: MNIST, FMNIST, CIFAR10, and SVHN. These experiments focused on classification settings where each image contained a single dominant object without relying on large pretrained models. The proposed model achieved high accuracy while significantly reducing the number of parameters. It required only 3.9 M parameters for learning across the four datasets, compared to 33.6 M for VGG16. The model achieved an accuracy of 98.87% on MNIST and 85.83% on SVHN, outperforming other lightweight models, including MobileNet v2 and EfficientNet v2b0, and was comparable to ResNet50. These findings indicate that the proposed architecture has the potential to support multi-domain learning while minimizing model complexity. This approach may be beneficial for applications in resource-constrained environments. Full article

With the explosive growth in the quantity of electrical equipment in distribution networks, traditional manual inspection struggles to achieve comprehensive coverage due to limited manpower and low efficiency. This has led to frequent equipment failures including partial discharge, insulation aging, and poor contact. These issues seriously compromise the safe and stable operation of distribution networks. Real-time monitoring and defect identification of their operation status are critical to ensuring the safety and stability of power systems. Currently, commonly used methods for defect identification in distribution network electrical equipment mainly rely on single-image or voiceprint data features. These methods lack consideration of the complementarity and interleaved nature between image and voiceprint features, resulting in reduced identification accuracy and reliability. To address the limitations of existing methods, this paper proposes distribution network electrical equipment defect identification based on multi-modal image voiceprint data fusion and channel interleaving. First, image and voiceprint feature models are constructed using two-dimensional principal component analysis (2DPCA) and the Mel scale, respectively. Multi-modal feature fusion is achieved using an improved transformer model that integrates intra-domain self-attention units and an inter-domain cross-attention mechanism. Second, an image and voiceprint multi-channel interleaving model is applied. It combines channel adaptability and confidence to dynamically adjust weights and generates defect identification results using a weighting approach based on output probability information content. Finally, simulation results show that, under the dataset size of 3300 samples, the proposed algorithm achieves a 8.96–33.27% improvement in defect recognition accuracy compared with baseline algorithms, and maintains an accuracy of over 86.5% even under 20% random noise interference by using improved transformer and multi-channel interleaving mechanism, verifying its advantages in accuracy and noise robustness. Full article

Fucheng-1 is China’s first commercial synthetic aperture radar (SAR) satellite equipped with interferometric capabilities. Since its launch in 2023, it has demonstrated strong potential across a range of application domains. However, a comprehensive and systematic evaluation of its overall performance, including its time-series monitoring capability, is still lacking. This study applies the Small Baseline Subset (SBAS-InSAR) method to conduct the first systematic processing and evaluation of 22 Fucheng-1 images acquired between 2023 and 2024. A total of 45 potential landslides were identified and subsequently validated through field investigations and UAV-based LiDAR data. Comparative analysis with Sentinel-1 and ALOS-2 indicates that Fucheng-1 demonstrates superior performance in small-scale deformation identification, temporal-variation characterization, and maintaining a high density of coherent pixels. Specifically, in the time-series InSAR-based potential landslide identification, Fucheng-1 identified 13 small-scale potential landslides, whereas Sentinel-1 identified none; the number of identifications is approximately 2.17 times that of ALOS-2. For time-series subsidence monitoring, the deformation magnitudes retrieved from Fucheng-1 are generally larger than those from Sentinel-1, mainly attributable to finer spatial sampling enabled by its higher spatial resolution and a higher maximum detectable deformation gradient. Moreover, as landslide size decreases, the advantages of Fucheng-1 in deformation identification and subsidence estimation become increasingly evident. Interferometric results further show that the number of high-coherence pixels for Fucheng-1 is 7–8 times that of co-temporal Sentinel-1 and 1.1–1.4 times that of ALOS-2, providing more high-quality observations for time-series inversion and thereby supporting a more detailed and spatially continuous reconstruction of deformation fields. Meanwhile, the orbital stability of Fucheng-1 is comparable to that of Sentinel-1, and its maximum detectable deformation gradient in mountainous terrain reaches twice that of Sentinel-1. Overall, this study provides the first systematic validation of the time-series InSAR capability of Fucheng-1 under complex terrain conditions, offering essential support and a solid foundation for the operational deployment of InSAR technologies based on China’s domestic SAR satellite constellation. Full article

Generative artificial intelligence (GenAI) shows transformative potential in mathematics education. However, empirical findings remain inconsistent, and a systematic synthesis of its effects across distinct engagement dimensions is lacking. This preregistered meta-analysis (INPLASY2025110051) systematically reviewed 22 empirical studies (46 independent samples, N = 5232) published between 2023 and 2025. The results indicated that GenAI has a moderate positive impact on students’ mathematics learning outcomes (g = 0.534). Moderation analysis further revealed that the level of GenAI integration in teaching, sample size, and learning content are the primary factors influencing this effect. The study found that the effect was most pronounced under the creative transformation (CT) integration mode, was significant when applied to geometry learning, and was stronger in studies with small samples or small class sizes; collaborative learning approaches also significantly enhance these mathematics learning outcomes. By contrast, educational stage and intervention duration did not show significant moderating effects. The GRADE assessment indicated that while the overall evidence is supportive, the certainty of evidence is stronger for cognitive outcomes than for non-cognitive domains. The findings also offer a reference for future research on constructing a human–machine collaborative learning environment. Full article

Large multi-UAV mission systems operate over time-varying communication graphs with heterogeneous platforms, where classical distributed task assignment may incur excessive message passing and suboptimal task–resource matching. To address these challenges, this paper proposes CLAC-CBBA (Centrality-Driven and Load-Aware Adaptive Clustering CBBA), an enhanced variant of the Consensus-Based Bundle Algorithm (CBBA) for large heterogeneous swarms. The proposed method is biomimetic in the sense that it integrates swarm-inspired self-organization and load-aware self-regulation to improve scalability and robustness, resembling decentralized role emergence and negative-feedback workload balancing in natural swarms. Specifically, CLAC-CBBA first identifies key nodes via a centrality-based adaptive cluster-reconfiguration mechanism (CenCluster) and partitions the network into cooperation domains to reduce redundant communication. It then applies a load-aware cluster self-regulation mechanism (LCSR), which combines resource attributes and spatial information, uses K-medoids clustering, and triggers split/merge reconfiguration based on real-time load imbalance. CBBA bidding is executed locally within clusters, while anchors and cluster representatives synchronize winners/bids to ensure globally consistent, conflict-free assignments. Simulations across diverse network densities and swarm sizes show that CLAC-CBBA reduces communication overhead and runtime while improving total task score compared with CBBA and several advanced variants, with statistically significant gains. These results demonstrate that CLAC-CBBA is scalable and robust for large-scale heterogeneous UAV task allocation. Full article

Background/Objectives: Single-sided deafness (SSD) impairs speech perception, reduces spatial hearing, decreases quality of life, and is frequently accompanied by tinnitus. Cochlear implantation (CI) has become an established treatment option, but long-term prospective evidence across multiple functional and psychological domains remains limited. This study investigated auditory performance, subjective hearing outcomes, tinnitus burden, and psychological well-being over a two-year follow-up in a large SSD cohort. Methods: Seventy adults with SSD underwent unilateral CI. Assessments were conducted preoperatively and at 6 months, 1 year, and 2 years postoperatively. Outcome measures included the Freiburg Monosyllable Test (FS), Oldenburg Inventory (OI), Nijmegen Cochlear Implant Questionnaire (NCIQ), Tinnitus Questionnaire (TQ), Perceived Stress Questionnaire (PSQ), Generalized Anxiety Disorder scale (GAD-7), and General Depression Scale (ADS-L). Longitudinal changes were analyzed using Wilcoxon signed-rank tests with effect sizes; Holm-adjusted p-values were applied for baseline-to-follow-up comparisons. Results: Speech perception improved markedly within the first 6 months and remained stable through 2 years, with large effect sizes. All OI subdomains demonstrated early and sustained improvements in subjective hearing ability. Several hearing-related quality-of-life domains assessed by the NCIQ, particularly social interaction, self-esteem, and activity participation, showed medium-to-large long-term improvements. Tinnitus severity decreased substantially, with marked reductions observed by 6 months and maintained thereafter; the proportion of tinnitus-free patients increased at follow-up, although tinnitus symptoms persisted in a substantial subset of participants. Perceived stress was reduced initially at the early follow-up and remained below baseline thereafter. Anxiety and depressive symptoms mostly stayed within nonclinical ranges, showing no lasting changes after adjusting for multiple comparisons. Conclusions: In this prospective cohort, cochlear implantation was associated with durable improvements in auditory outcomes, tinnitus burden, and selected patient-reported quality-of-life domains over two years. Although significant functional and patient-centered improvements were noted, persistent tinnitus and diverse psychosocial outcomes underscore the need for personalized counseling and comprehensive follow-up that incorporate patient-reported outcomes and psychological assessments. Full article

Wearable inertial measurement units (IMUs) provide an accessible means of monitoring fatigue-related changes in running biomechanics, yet most existing methods rely on limited feature sets, lack personalization, or fail to generalize across individuals. This study introduces a bioinformatics-inspired stride sequence modeling framework that integrates spectral–entropy features, sample entropy, frequency-domain descriptors, and mixed-effects statistical modeling to detect fatigue using a single lumbar-mounted IMU. Nineteen recreational runners completed non-fatigued and fatigued 400 m runs, from which we extracted stride-level features and evaluated (1) population-level fatigue classification via global leave-one-participant-out (LOPO) models and (2) individualized fatigue detection through supervised participant-specific models and non-fatigued-only anomaly detection. Mixed-effects models revealed robust and multidimensional fatigue effects across key biomechanical features, with large standardized effect sizes (Cohen’s d up to 1.35) and substantial variance uniquely explained by fatigue (partial R² up to 0.31). Global LOPO machine learning models achieved modest accuracy (55%), highlighting strong inter-individual variability. In contrast, personalized supervised Random Forest classifiers achieved near-perfect performance (mean accuracy 97.7%; mean AUC 0.997), and NF-only One-Class SVMs detected fatigue as a deviation from individual baseline patterns (mean AUC 0.967). Entropy and stride-to-stride variability metrics further demonstrated consistent fatigue-linked increases in movement irregularity and reduced neuromuscular control. These findings show that IMU stride sequences contain highly informative, fatigue-sensitive biomechanical signatures, and that combining bioinformatics-inspired sequence analysis with hybrid statistical and personalized AI models enables both robust population-level insights and highly reliable individualized fatigue monitoring. The proposed framework supports future integration into sports analytics platforms, digital coaching systems, and real-time wearable fatigue detection technologies. This highlights the necessity of personalized fatigue-monitoring strategies in wearable systems. Full article

Coastal wetland vegetation exhibits pronounced spectral mixing, complex mosaic spatial patterns, and small target sizes, posing considerable challenges for fine-grained classification in high-resolution UAV imagery. At present, remote sensing classification of ground objects based on deep learning mainly relies on spectral and structural features, while the frequency domain features of ground objects are not fully considered. To address these issues, this study proposes a vegetation classification model that integrates spatial-domain and frequency-domain features. The model enhances global contextual modeling through a large-kernel convolution branch, while a frequency-domain interaction branch separates and fuses low-frequency structural information with high-frequency details. In addition, a shallow auxiliary supervision module is introduced to improve local detail learning and stabilize training. With a compact parameter scale suitable for real-world deployment, the proposed framework effectively adapts to high-resolution remote sensing scenarios. Experiments on typical coastal wetland vegetation including Reeds, Spartina alterniflora, and Suaeda salsa demonstrate that the proposed method consistently outperforms representative segmentation models such as UNet, DeepLabV3, TransUNet, SegFormer, D-LinkNet, and MCCA across multiple metrics including Accuracy, Recall, F1 Score, and mIoU. Overall, the results show that the proposed model effectively addresses the challenges of subtle spectral differences, pervasive species mixture, and intricate structural details, offering a robust and efficient solution for UAV-based wetland vegetation mapping and ecological monitoring. Full article