Search Results (42,785)

Membrane separation technology has shown significant potential in the treatment of mixed dye wastewater. In this study, a reduced graphene oxide (AH-rGO) membrane was prepared using an amino-hydrothermal method and applied for the first time in mixed dye separation. These membranes can selectively recover high-value dyes while addressing the technical challenges of balancing permeability and selectivity in traditional membrane materials, which are often at odds with each other in the treatment of mixed dye wastewater. We simulated actual dye wastewater using four dyes: methyl orange (MO), methyl blue (MB), rhodamine B (RB), and Victoria Blue B (VBB). The four combinations of mixed dyes were MO/VBB, RB/VBB, MO/MB, and RB/MB, all of which demonstrated high water permeability and separation efficiency. Notably, the MO/VBB combination achieved a maximum water permeability rate of 118.79 L m⁻² h⁻¹ bar⁻¹ and a separation factor of 24.2. The AH-rGO membrane is currently the highest-permeability membrane available, achieving excellent separation results with typical mixed dye wastewater. Additionally, it demonstrates good stability. The experimental results indicate that the overall performance of the AH-rGO membrane is superior to that ofother graphene oxide (GO) membranes, which reveals the significant application potential of this membrane in the field of mixed dye wastewater treatment. Full article

Modern aerospace engineering places increasing emphasis on materials that combine low weight with high mechanical performance. Fiber metal laminates (FMLs), which merge metal layers with fiber-reinforced composites, meet this demand by delivering improved fatigue resistance, impact tolerance, and environmental durability, often surpassing the performance of their constituents in demanding applications. Despite these advantages, inspecting such thin, layered structures remains a significant challenge, particularly when they are difficult or impossible to access. As with any new invention, they always come with challenges. This study examines the effectiveness of the fundamental anti-symmetric Lamb wave mode (A0) in detecting weak interfacial defects within Carall laminates, a type of hybrid fiber metal laminate (FML). Delamination detectability is analyzed in terms of strong wave dispersion observed downstream of the delaminated sublayer, within a region characterized by acoustic distortion. A three-dimensional finite element (FE) model is developed to simulate mode trapping and full-wavefield local displacement. The approach is validated by reproducing experimental results reported in prior studies, including the author’s own work. Results demonstrate that the A0 mode is sensitive to delamination; however, its lateral resolution depends on local position, ply orientation, and dispersion characteristics. Accurately resolving the depth and extent of delamination remains challenging due to the redistribution of peak amplitude in the frequency domain, likely caused by interference effects in the acoustically sensitive delaminated zone. Additionally, angular scattering analysis reveals a complex wave behavior, with most of the energy concentrated along the centerline, despite transmission losses at the metal-composite interfaces in the Carall laminate. The wave interaction with the leading and trailing edges of the delaminations is strongly influenced by the complex wave interference phenomenon and acoustic mismatched regions, leading to an increase in dispersion at the sublayers. Analytical dispersion calculations clarify how wave behavior influences the detectability and resolution of delaminations, though this resolution is constrained, being most effective for weak interfaces located closer to the surface. This study offers critical insights into how the fundamental anti-symmetric Lamb wave mode (A0) interacts with delaminations in highly attenuative, multilayered environments. It also highlights the challenges in resolving the spatial extent of damage in the long-wavelength limit. The findings support the practical application of A0 Lamb waves for structural health assessment of hybrid composites, enabling defect detection at inaccessible depths. Full article

This review article focuses on the long-term durability challenges associated with bamboo fiber-reinforced polymer composites when subjected to various environmental aging conditions such as water immersion, hygrothermal fluctuations, ultraviolet (UV) radiation, soil burial, and refrigerated storage. The primary issue addressed is the degradation of mechanical and structural performance of bamboo fiber-reinforced polymer composites due to moisture absorption, fiber swelling, and fiber–matrix interface deterioration. To mitigate these aging effects, the study evaluates and compares multiple strategies, including chemical and physical fiber surface treatments, filler additions, and fiber hybridization, which aim to enhance moisture resistance and mechanical stability. These composites are relevant in automotive interiors, construction panels, building insulation, and consumer goods due to their eco-friendly nature and potential to replace conventional synthetic composites. This review is necessary to consolidate current knowledge, identify effective enhancement approaches, and guide the development of environmentally resilient bamboo fiber-reinforced polymer composites for real-world applications. Full article

Electroporation-mediated gene delivery is a cornerstone of synthetic biology, offering several advantages over other methods: higher efficiencies, broader applicability, and simpler sample preparation. Yet, electroporation protocols are often challenging to integrate into highly multiplexed workflows, owing to limitations in their scalability and tunability. These challenges ultimately increase the time and cost per transformation. As a result, rapidly screening genetic libraries, exploring combinatorial designs, or optimizing electroporation parameters requires extensive iterations, consuming large quantities of expensive custom-made DNA and cell lines or primary cells. To address these limitations, we have developed a High-Throughput Microfluidic Electroporation (HTME) platform that includes a 384-well electroporation plate (E-Plate) and control electronics capable of rapidly electroporating all wells in under a minute with individual control of each well. Fabricated using scalable and cost-effective printed-circuit-board (PCB) technology, the E-Plate significantly reduces consumable costs and reagent consumption by operating on nano to microliter volumes. Furthermore, individually addressable wells facilitate rapid exploration of large sets of experimental conditions to optimize electroporation for different cell types and plasmid concentrations/types. Use of the standard 384-well footprint makes the platform easily integrable into automated workflows, thereby enabling end-to-end automation. We demonstrate transformation of E. coli with pUC19 to validate the HTME’s core functionality, achieving at least a single colony forming unit in more than 99% of wells and confirming the platform’s ability to rapidly perform hundreds of electroporations with customizable conditions. This work highlights the HTME’s potential to significantly accelerate synthetic biology Design-Build-Test-Learn (DBTL) cycles by mitigating the transformation/transfection bottleneck. Full article

This review investigates recent developments in cardiac mechano-electrical-fluid interaction (MEFI) modeling, with a focus on multiphysics simulation platforms and digital twin frameworks developed between 2015 and 2025. The purpose of the study is to assess how computational modeling methods—particularly finite element and immersed boundary techniques, monolithic and partitioned coupling schemes, and artificial intelligence (AI)-enhanced surrogate modeling—capture the integrated dynamics of cardiac electrophysiology, tissue mechanics, and hemodynamics. The goal is to evaluate the translational potential of MEFI models in clinical applications such as cardiac resynchronization therapy (CRT), arrhythmia classification, atrial fibrillation ablation, and surgical planning. Quantitative results from the literature demonstrate <5% error in pressure–volume loop predictions, >0.90 F1 scores in machine-learning-based arrhythmia detection, and <10% deviation in myocardial strain relative to MRI-based ground truth. These findings highlight both the promise and limitations of current MEFI approaches. While recent advances improve physiological fidelity and predictive accuracy, key challenges remain in achieving multiscale integration, model validation across diverse populations, and real-time clinical applicability. The review concludes by identifying future milestones for clinical translation, including regulatory model certification, standardization of validation protocols, and integration of patient-specific digital twins into electronic health record (EHR) systems. Full article

As a high-market-value sector, the consumer electronics industry is particularly vulnerable to reputational damage from surface defects in shipped products. However, the high level of automation and the short product life cycles in this industry make defect sample collection both difficult and inefficient. This challenge has led to a severe shortage of publicly available, comprehensive datasets dedicated to surface defect detection, limiting the development of targeted methodologies in the academic community. Most existing datasets focus on general-purpose object categories, such as those in the COCO and PASCAL VOC datasets, or on industrial surfaces, such as those in the MvTec AD and ZJU-Leaper datasets. However, these datasets differ significantly in structure, defect types, and imaging conditions from those specific to consumer electronics. As a result, models trained on them often perform poorly when applied to surface defect detection tasks in this domain. To address this issue, the present study introduces a specialized optical sampling system with six distinct lighting configurations, each designed to highlight different surface defect types. These lighting conditions were calibrated by experienced optical engineers to maximize defect visibility and detectability. Using this system, 14,478 high-resolution defect images were collected from actual production environments. These images cover more than six defect types, such as scratches, plain particles, edge particles, dirt, collisions, and unknown defects. After data acquisition, senior quality control inspectors and manufacturing engineers established standardized annotation criteria based on real-world industrial acceptance standards. Annotations were then applied using bounding boxes for object detection and pixelwise masks for semantic segmentation. In addition to the dataset construction scheme, commonly used semantic segmentation methods were benchmarked using the provided mask annotations. The resulting dataset has been made publicly available to support the research community in developing, testing, and refining advanced surface defect detection algorithms under realistic conditions. To the best of our knowledge, this is the first comprehensive, multiclass, multi-defect dataset for surface defect detection in the consumer electronics domain that provides pixel-level ground-truth annotations and is explicitly designed for real-world applications. Full article

The stability of modern power systems has become critically dependent on precise inertia estimation of synchronous generators, particularly as renewable energy integration fundamentally transforms grid dynamics. Increasing penetration of converter-interfaced renewable resources reduces system inertia, heightening the grid’s susceptibility to transient disturbances and creating significant technical challenges in maintaining operational reliability. This paper addresses these challenges through a novel Bayesian inference framework that synergistically integrates PMU data with an advanced MCMC sampling technique, specifically employing the Affine-Invariant Ensemble Sampler. The proposed methodology establishes a probabilistic estimation paradigm that systematically combines prior engineering knowledge with real-time measurements, while the Affine-Invariant Ensemble Sampler mechanism overcomes high-dimensional computational barriers through its unique ensemble-based exploration strategy featuring stretch moves and parallel walker coordination. The framework’s ability to provide full posterior distributions of inertia parameters, rather than single-point estimates, helps for stability assessment in renewable-dominated grids. Simulation results on the IEEE 39-bus and 68-bus benchmark systems validate the effectiveness and scalability of the proposed method, with inertia estimation errors consistently maintained below $1\%$ across all generators. Moreover, the parallelized implementation of the algorithm significantly outperforms the conventional M-H method in computational efficiency. Specifically, the proposed approach reduces execution time by approximately $52\%$ in the 39-bus system and by $57\%$ in the 68-bus system, demonstrating its suitability for real-time and large-scale power system applications. Full article

Background: This systematic review examines artificial intelligence (AI) applications in neuroimaging for autism spectrum disorder (ASD), addressing six research questions regarding biomarker optimization, modality integration, social function prediction, developmental trajectories, clinical translation challenges, and multimodal data enhancement for earlier detection and improved outcomes. Methods: Following PRISMA guidelines, we conducted a comprehensive literature search across 8 databases, yielding 146 studies from an initial 1872 records. These studies were systematically analyzed to address key questions regarding AI neuroimaging approaches in ASD detection and prognosis. Results: Neuroimaging combined with AI algorithms demonstrated significant potential for early ASD detection, with electroencephalography (EEG) showing promise. Machine learning classifiers achieved high diagnostic accuracy (85–99%) using features derived from neural oscillatory patterns, connectivity measures, and signal complexity metrics. Studies of infant populations have identified the 9–12-month developmental window as critical for biomarker detection and the onset of behavioral symptoms. Multimodal approaches that integrate various imaging techniques have substantially enhanced predictive capabilities, while longitudinal analyses have shown potential for tracking developmental trajectories and treatment responses. Conclusions: AI-driven neuroimaging biomarkers represent a promising frontier in ASD research, potentially enabling the detection of symptoms before they manifest behaviorally and providing objective measures of intervention efficacy. While technical and methodological challenges remain, advancements in standardization, diverse sampling, and clinical validation could facilitate the translation of findings into practice, ultimately supporting earlier intervention during critical developmental periods and improving outcomes for individuals with ASD. Future research should prioritize large-scale validation studies and standardized protocols to realize the full potential of precision medicine in ASD. Full article

Urban and regional ecosystems are increasingly challenged by the compounded effects of climate change and intensive land use. In this study, a predictive assessment framework for ecosystem resilience in the Beijing–Tianjin–Hebei region was developed by integrating multi-source remote sensing data, with the aim of quantitatively evaluating the coupled effects of climate change and land use change on future ecosystem resilience. In the first stage of the study, the SD-PLUS coupled modeling framework was employed to simulate land use patterns for the years 2030 and 2060 under three representative combinations of Shared Socioeconomic Pathways and Representative Concentration Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Building upon these simulations, ecosystem resilience was comprehensively evaluated and predicted on the basis of three key attributes: resistance, adaptability, and recovery. This enabled a quantitative investigation of the spatio-temporal dynamics of ecosystem resilience under each scenario. The results reveal the following: (1) Temporally, ecosystem resilience exhibited a staged pattern of change. From 2020 to 2030, an increasing trend was observed only under the SSP1-2.6 scenario, whereas, from 2030 to 2060, resilience generally increased in all scenarios. (2) In terms of scenario comparison, ecosystem resilience typically followed a gradient pattern of SSP1-2.6 > SSP2-4.5 > SSP5-8.5. However, in 2060, a notable reversal occurred, with the highest resilience recorded under the SSP5-8.5 scenario. (3) Spatially, areas with high ecosystem resilience were primarily distributed in mountainous regions, while the southeastern plains and coastal zones consistently exhibited lower resilience levels. The results indicate that climate and land use changes jointly influence ecosystem resilience. Rainfall and temperature, as key climate drivers, not only affect land use dynamics but also play a crucial role in regulating ecosystem services and ecological processes. Under extreme scenarios such as SSP5-8.5, these factors may trigger nonlinear responses in ecosystem resilience. Meanwhile, land use restructuring further shapes resilience patterns by altering landscape configurations and recovery mechanisms. Our findings highlight the role of climate and land use in reshaping ecological structure, function, and services. This study offers scientific support for assessing and managing regional ecosystem resilience and informs adaptive urban governance in the face of future climate and land use uncertainty, promotes the sustainable development of ecosystems, and expands the applicability of remote sensing in dynamic ecological monitoring and predictive analysis. Full article

The intensifying global climate warming caused by human activities poses severe challenges to ecosystem stability. Constructing an ecological security pattern can identify ecological land supply and an effective spatial distribution baseline and provide a scientific basis for safeguarding regional ecological security. This study analyzes land-use data from 2000 to 2020 for Golog Tibetan Autonomous Prefecture. The PLUS model was utilized to project land-use potential for the year 2030. The InVEST model was employed to conduct a comprehensive assessment of habitat quality in the study area for both 2020 and 2030, thereby pinpointing ecological sources. Critical ecological restoration zones were delineated by identifying ecological corridors, pinch points, and barrier points through the application of the Minimum Cumulative Resistance model and circuit theory. By comparing ecological security patterns (ESPs) in 2020 and 2030, we proposed a dynamic restoration framework and optimization recommendations based on habitat quality changes and ESPs. The results indicate significant land-use changes in the eastern part of Golog Tibetan Autonomous Prefecture from 2020 to 2030, with large-scale conversion of grasslands into bare land, farmland, and artificial surfaces. The ecological security pattern is threatened by risks like the deterioration of habitat quality, diminished ecological sources as well as pinch points, and growing barrier points. Optimizing the layout of ecological resources, strengthening barrier zone restoration and pinch point protection, and improving habitat connectivity are urgent priorities to ensure regional ecological security. This study offers a scientific foundation for the harmonization of ecological protection and economic development and the policy development and execution of relevant departments. Full article

Ordinal regression combines classification and regression techniques, constrained by the intrinsic order among categories. It has wide-ranging applications in real-world scenarios, such as product quality grading, medical diagnoses, and facial age recognition, where understanding ranked relationships is crucial. Existing models, which often employ a series of binary classifiers with ordinal consistency loss, effectively enforce global order consistency but frequently encounter misclassification errors between adjacent categories. Achieving both global and local (neighbor-level) ordinal consistency, however, remains a significant challenge. In this study, we propose a hybrid ordinal regression model that addresses global ordinal structure while enhancing local consistency between neighboring categories. Our approach leverages ordinal metric learning to generate embeddings that capture global ordinal relationships and extends consistent rank logits with a neighbor order penalty in the loss function to reduce adjacent category misclassifications. Experimental results on multiple benchmark ordinal datasets demonstrate that our model significantly minimizes neighboring misclassification errors and global order inconsistencies, outperforming existing ordinal regression models. Full article

Single-image super-resolution (SISR) plays a critical role in enhancing visual quality for real-world applications, including industrial inspection and embedded vision systems. While deep learning-based approaches have made significant progress in SR, existing lightweight SR models often fail to accurately reconstruct high-frequency textures, especially under complex degradation scenarios, resulting in blurry edges and structural artifacts. To address this challenge, we propose a Dense Residual Fused Attention Network (DRFAN), a novel lightweight hybrid architecture designed to enhance high-frequency texture recovery in challenging degradation conditions. Moreover, by coupling convolutional layers and attention mechanisms through gated interaction modules, the DRFAN enhances local details and global dependencies with linear computational complexity, enabling the efficient utilization of multi-level spatial information while effectively alleviating the loss of high-frequency texture details. To evaluate its effectiveness, we conducted ×4 super-resolution experiments on five public benchmarks. The DRFAN achieves the best performance among all compared lightweight models. Visual comparisons show that the DRFAN restores more accurate geometric structures, with up to +1.2 dB/+0.0281 SSIM gain over SwinIR-S on Urban100 samples. Additionally, on a domain-specific rice grain dataset, the DRFAN outperforms SwinIR-S by +0.19 dB in PSNR and +0.0015 in SSIM, restoring clearer textures and grain boundaries essential for industrial quality inspection. The proposed method provides a compelling balance between model complexity and image reconstruction fidelity, making it well-suited for deployment in resource-constrained visual systems and industrial applications. Full article

The challenge of achieving high-precision direction-of-arrival (DOA) estimation with enhanced degrees of freedom (DOFs) under a limited number of physical array elements remains a critical issue in array signal processing. To address this limitation, this paper makes the following three key contributions: (1) a novel moving sparse array synthesis model incorporating time-frequency-spatial joint processing for coprime frequencies signal sources; (2) an optimized coprime frequencies-based unmanned aerial vehicle array (CF-UAVA) configuration with derived closed-form expressions for the distribution of synthesized array; and (3) two DOA estimation methods: a group sparsity-based approach universally applicable to the proposed aperture synthesis model and a joint group sparsity and virtual array interpolation tailored for the proposed CF-UAVA configuration. Comprehensive simulation results demonstrate the superior DOA estimation accuracy and increased DOFs achieved by our proposed aperture synthesis model and DOA estimation algorithms compared to conventional approaches. Full article

To address the challenges of low recognition accuracy and substantial model complexity in crop disease identification models operating in complex field environments, this study proposed a novel hybrid model named ConvTransNet-S, which integrates Convolutional Neural Networks (CNNs) and transformers for crop disease identification tasks. Unlike existing hybrid approaches, ConvTransNet-S uniquely introduces three key innovations: First, a Local Perception Unit (LPU) and Lightweight Multi-Head Self-Attention (LMHSA) modules were introduced to synergistically enhance the extraction of fine-grained plant disease details and model global dependency relationships, respectively. Second, an Inverted Residual Feed-Forward Network (IRFFN) was employed to optimize the feature propagation path, thereby enhancing the model’s robustness against interferences such as lighting variations and leaf occlusions. This novel combination of a LPU, LMHSA, and an IRFFN achieves a dynamic equilibrium between local texture perception and global context modeling—effectively resolving the trade-offs inherent in standalone CNNs or transformers. Finally, through a phased architecture design, efficient fusion of multi-scale disease features is achieved, which enhances feature discriminability while reducing model complexity. The experimental results indicated that ConvTransNet-S achieved a recognition accuracy of 98.85% on the PlantVillage public dataset. This model operates with only 25.14 million parameters, a computational load of 3.762 GFLOPs, and an inference time of 7.56 ms. Testing on a self-built in-field complex scene dataset comprising 10,441 images revealed that ConvTransNet-S achieved an accuracy of 88.53%, which represents improvements of 14.22%, 2.75%, and 0.34% over EfficientNetV2, Vision Transformer, and Swin Transformer, respectively. Furthermore, the ConvTransNet-S model achieved up to 14.22% higher disease recognition accuracy under complex background conditions while reducing the parameter count by 46.8%. This confirms that its unique multi-scale feature mechanism can effectively distinguish disease from background features, providing a novel technical approach for disease diagnosis in complex agricultural scenarios and demonstrating significant application value for intelligent agricultural management. Full article

Interest in germanium electronic devices is experiencing a comeback thanks to their suitability for a wide range of new applications, like CMOS transistors, quantum technology or infrared photonics. Among these applications, Ge-based thermophotovoltaic converters could become the backbone of thermo-electrical batteries. However, these devices are still far from the efficiency threshold needed for industrial deployment, with surface recombination as the main limiting factor for the material. In this work, we discuss the main passivation techniques developed for germanium photovoltaic and thermophotovoltaic devices, summarizing their main advantages and disadvantages. The analysis reveals that surface recombination velocities as low as 2.7 cm/s and 1.3 cm/s have already been reported for p-type and n-type germanium, respectively, although improving surface recombination velocities below 100 cm/s would result in marginal efficiency gains. Therefore, the main challenge for the material is not reducing this parameter further but developing robust and reliable processes for integrating the current techniques into functional devices. Full article