Search Results (29,755)

Background: Tartary buckwheat (Fagopyrum tataricum) serves as an excellent model for studying plant water adaptation mechanisms due to its exceptional drought tolerance. While aquaporins (AQPs) mediate the transmembrane transport of water and solutes in plants, their fine-tuned regulatory networks underlying stress resilience in Tartary buckwheat remain largely elusive. Methods: Here, we combined bioinformatics and transcriptomics to systematically identify 30 highly conserved FtAQP genes at the genome-wide level. Results: Cross-validated by qRT-PCR, our analysis revealed their distinct expression patterns across different organs. Based on our transcriptomic data, we hypothesize that FtAQP family members potentially participate in a coordinated whole-plant water management network through differential spatiotemporal expression. Specifically, the robust transcription of FtAQP8, FtAQP12, and FtAQP28 in roots is associated with the initial water uptake process. As water undergoes long-distance transport, the synergistic upregulation of FtAQP13, FtAQP17, FtAQP20, and FtAQP29 in the stem suggests a potential role in facilitating critical lateral water flow. Furthermore, during reproductive development, FtAQP27 exhibits extreme tissue specificity in floral organs, implying its possible involvement in maintaining local osmotic homeostasis. Furthermore, the promoter regions of FtAQPs are highly enriched with cis-acting elements responsive to light, abscisic acid (ABA), and cold stress, suggesting they are intimately regulated by a coupling of endogenous phytohormones and environmental cues. Conclusions: Ultimately, this study provides valuable insights into the potential molecular basis of multidimensional water regulation in Tartary buckwheat, and identifies candidate genetic targets for improving water use efficiency in dryland agriculture through the precise manipulation of aquaporins. Collectively, while these observational findings provide valuable predictive models, future in vivo experimental validations are required to confirm their exact biological functions. Full article

Fine particulate matter PM_2.5 continues to pose a critical public health risk in Northern Thailand, particularly in Chiang Mai, where traditional filtration methods often face limitations in cost and efficiency for large-scale applications. This study introduces a novel “Evaporator Condensation Growth Particle Scrubber (ECGP)” designed to enhance the collection efficiency of sub-micron particles by enlarging their physical size through a pressure-driven growth mechanism. The ECGP system utilizes synergistic effects between solid nuclei, high relative humidity, and mechanical pressure modulation. The ECGP system integrates solid nuclei, ~95% relative humidity and mechanical pressure modulation within a single chamber. Using incense smoke as a PM surrogate, the process utilizes controlled adiabatic cycles to induce stable heterogeneous condensation. The results indicate that the integrated process effectively shifts particle size distribution, reducing the PM_2.5/PM₁₀ mass ratio from 1.00 to 0.83. This indicates that approximately 17.5% (with a standard deviation < 1% across 10 trials, p < 0.05) of the fine mass successfully transitioned into the larger, more filterable PM₁₀ fraction and exhibited high physical stability and resistance to re-evaporation, effectively overcoming the low-efficiency threshold (typically <10%) of standard mechanical scrubbers and cyclones for sub-micron dust. This study concludes that ECGP technology offers a promising, cost-effective alternative for improving indoor air quality in large public infrastructures by leveraging particle inertia for enhanced removal, providing a scalable solution to the persistent smog crisis. Full article

Background/Objectives: Inguinal hernia and hydrocele are common pediatric surgical conditions resulting from failed obliteration of the processus vaginalis during fetal development. Although prenatal exposure to fine particulate matter (PM2.5) has been linked to adverse perinatal outcomes and congenital anomalies, its role in structurally defined pediatric surgical diseases remains unclear. We examined the association between maternal PM2.5 exposure during pregnancy and the risk of inguinal hernia or hydrocele in offspring. Methods: We performed a retrospective cohort study of 1093 mother–offspring pairs delivering at a tertiary referral center (July 2016–June 2019). Monthly residential PM2.5 levels were estimated at geocoded maternal addresses using kriging interpolation from fixed-site monitoring stations. Offspring diagnosed with inguinal hernia or hydrocele through March 2024 were identified using ICD-10 codes. Perinatal characteristics were compared using t-tests and chi-square tests, and multivariable logistic regression assessed trimester-specific PM2.5 exposure and risk. Results: During follow-up, 53 offspring (4.85%) developed inguinal hernia or hydrocele. Male sex (odds ratio [OR], 24.71; 95% CI, 5.95–102.54; p < 0.001) and second-trimester PM2.5 exposure (OR, 1.07 per µg/m³; 95% CI, 1.01–1.14; p = 0.028) were independent risk factors. A dose–response pattern was observed across quartiles of second-trimester exposure; an interquartile range increase was associated with a 64% higher risk (OR, 1.64). The model showed good discrimination (AUC, 0.804). Conclusions: Elevated maternal PM2.5 exposure during the second trimester was independently associated with increased risk of inguinal hernia or hydrocele in offspring. Prenatal air pollution may contribute to persistence of the processus vaginalis and represents a potentially modifiable environmental risk factor. Full article

Water surface object detection in shipborne remote sensing is challenged by unstable wave-induced backgrounds, illumination variations, extreme scale changes with tiny objects, and limited annotations. Multimodal RGB–infrared (RGB–IR) sensing leverages complementary visible and infrared cues to enhance robustness. However, most existing RGB–IR methods rely on backbones pretrained on limited-scale data, which constrain their performance for complex water surface scenes. In this work, we propose the WS-R-IR Adapter, a parameter-efficient vision foundation model (VFM)-based framework for shipborne RGB–IR object detection. Instead of full fine-tuning, it adapts frozen VFM representations via lightweight task-specific designs. the WS-R-IR Adapter includes (1) a water scene domain-aware modal adapter that progressively guides frozen backbone features with evolving semantic cues, (2) a parallel multi-scale structural perception module for fine-grained, scale-sensitive modeling, (3) an adaptive RGB–IR feature modulation fusion strategy, and (4) a resolution-aligned context semantic and structural detail fusion module. Moreover, we introduce an object-guided global-to-local registration framework to address dynamic cross-modal misalignment, and construct modality-aligned PoLaRIS-DET and ASV-RI-DET datasets that cover diverse water surface scenes. On the two datasets, the proposed method achieves mAP@0.5:0.95 scores of 74.2% and 50.2%, respectively, significantly outperforming existing methods with only 11.9M additional parameters. These results demonstrate the effectiveness of parameter-efficient VFM adaptation for multimodal water surface remote sensing. Full article

To improve vehicle user service quality and address data privacy and security issues in intelligent transportation vehicle networking systems, a three-tier communication architecture with cloud-edge-end collaboration was designed in this paper. A Bayesian decision criterion was utilized to divide user data segments into fine-grained slices based on their privacy levels, and differential privacy techniques were applied to protect the offloaded data. To achieve multi-objective optimization between user service quality and data privacy and security, the problem was formulated as a constrained Markov decision process. A communication model, a caching model, a latency model, an energy consumption model, and a data-fragment privacy protection model were designed. Additionally, a deep reinforcement learning algorithm based on the actor–critic approach was proposed for the collaborative and centralized training of multiple intelligent agents (CTMA-AC), enabling multi-objective optimization decision-making for the protection of offloaded private user data. Simulation experiments demonstrate that the proposed multi-agent collaborative privacy data offloading protection strategy can effectively safeguard private user data while ensuring high service quality. Full article

The DPF-DETR model has been designed to address the challenges encountered in object detection within drone imagery, particularly in scenarios involving significant target scale variations, dense targets, and complex backgrounds. To overcome the limitations of traditional object detection methods, the Density Sensing Mechanism (DSM) and Adaptive Density Map Loss (AdaptiveDM Loss) have been incorporated into the model to provide fine-grained supervision signals. The DSM optimizes the query selection mechanism by utilizing density maps, enabling the number of queries to be adaptively adjusted based on the distribution density of targets, thus improving detection accuracy in dense regions. Furthermore, the precision of the model in detecting dense targets is enhanced by AdaptiveDM Loss, which dynamically adjusts the weights for object localization and classification. Multi-scale feature fusion capabilities are also improved by the Multi-Scale Feature Fusion Network (MSFFN) and the Selective Feature Integration Module (SFIM). The MSFFN refines the fusion of features, which improves the detection of targets across various scales, particularly in complex scenes. Additionally, SFIM enhances the detection accuracy for small targets and complex backgrounds by integrating low-level spatial features with high-level semantic information. The Context-Sensitive Feature Interaction Module (CSFIM) further optimizes multi-scale feature fusion through context-guided interactions, bridging the semantic gap between features of different scales, thus improving the robustness of the model in dense scenarios. Experimental results have shown that DPF-DETR outperforms traditional models and state-of-the-art detection methods across multiple datasets, demonstrating superior robustness and accuracy, especially in dense target detection and complex background scenarios. Full article

Land cover classification (LCC) is a fundamental task in remote sensing, which enables effective environmental monitoring, agricultural planning, and disaster management. The existing approaches often rely on fine-tuning pre-trained models, which are not specifically designed for LCC, which lead to suboptimal performance in complex scenarios. To address these limitations, we propose the Cross-Module Attention Land Cover Network (CALCNet), a novel architecture developed from scratch. CALCNet follows a contracting and restoration backbone, where the contracting path extracts progressively abstract semantic features while reducing spatial resolution, and the restoration path recovers fine-grained spatial details through upsampling and skip connections. In addition, CALCNet integrates a cross-module attention mechanism that combines spatial attention and multi-scale feature selection to enhance feature representation. Furthermore, we applied a differential evolution-based neuron pruning strategy to create a compressed CALCNet variant, which retains high classification performance while reducing computational cost. The CALCNet is evaluated on four benchmark LCC datasets, AID, UCMerced_LandUse, NWPU_RESISC45, and EuroSAT, demonstrating strong performance across all benchmarks. Specifically, the model achieves classification accuracies of 98.09%, 99.47%, 99.19%, and 99.19%, respectively. The compressed CALCNet variant reduces computational cost to 78.55 million floating point operations (FLOPs) with a model size of 43 MB, while achieving improved inference speeds (38.32 frames/sec on CPU and 118.3 frames/sec on GPU), representing approximately 45–50% reduction in FLOPs and model storage. These results highlight that CALCNet is both highly accurate and computationally efficient, making it well suited for real-world LCC applications. Full article

Lung nodule detection remains a critical challenge in clinical diagnostics due to the small size, weak contrast, and high background interference of nodules in CT scans. To address these issues, a novel deep neural network architecture, termed Daisy-Net, is proposed. This model incorporates dual attention mechanisms and inter-scale feature perception, consisting of two primary components: the Parallelized Patch and Spatial Context Aware (PPSCA) module and the Omni-domain Multistage Fusion (OMF) module. The PPSCA module enhances the extraction of fine-grained textures and boundary information through multi-branch patch perception and spatial attention. The OMF module employs omni-domain feature fusion and progressive stage-wise supervision to improve robustness and discrimination under complex conditions. The lung nodule detection task is formulated as a two-dimensional segmentation problem and evaluated on the LUNA16 dataset. In the post-binarization comparative evaluation, Daisy-Net achieves the best overall performance among all compared methods, with an Intersection over Union (IoU) of 81.41, a Dice coefficient of 89.75, a precision of 95.34, a sensitivity of 84.78, and a specificity of 99.9974. These findings indicate the model’s strong capability in detecting small pulmonary nodules accurately and reliably. Full article

Conserving biodiversity requires identifying and prioritizing critical habitats at a fine scale, as coarse-scale approaches often fail to address the needs of specialized and threatened species. This study applies a fine-scale prioritization approach across temperate and tropical regions of North America and the Caribbean using a detailed map of 636 ecosystem types and high-resolution Area of Habitat (AOH) data. We then evaluated the current protection status and risk of future land use changes for each habitat type and prioritized them for conservation. Our results revealed that 38% of the area was identified in the top quartile of high-priority habitats, with 56 (33%) of identified IUCN threatened ecosystem types captured within these areas. Top priority habitats include the Meso-American Premontane Semi-deciduous Forest, Central American Caribbean Evergreen Lowland Forest, and Guerreran Dry Deciduous Forest, all characterized by low protection, high projected land-use conversion, and large numbers of threatened and habitat-specialist species, highlighting their urgent conservation importance in Meso-American and Caribbean tropical forests. Our findings emphasize the need for targeted conservation strategies that consider finer-scale habitat classifications and species requirements to improve the precision of conservation planning, especially where already at-risk species and ecosystems are located, and human land use intensities are high. Full article

Electroencephalogram (EEG) signals serve as the core information carrier for brain–computer interfaces (BCIs); however, their highly non-stationary nature, extremely low signal-to-noise ratio, and significant inter-individual variability pose considerable challenges for signal decoding. Existing deep learning methods struggle to strike a balance between multi-scale, fine-grained feature extraction and efficient long-range temporal modeling. To overcome this limitation, this study proposes a novel deep cascaded architecture, MSResBiMamba, which deeply integrates multi-scale spatiotemporal feature learning with cutting-edge long-sequence modeling techniques. The model first utilizes an enhanced multi-scale spatiotemporal convolutional network (MS-CNN) combined with a SE-channel attention mechanism to adaptively extract local multi-band features and dynamically suppress redundant artefacts. Subsequently, it innovatively introduces an enhanced bidirectional Mamba (Bi-Mamba) module to efficiently capture non-causal long-range temporal dependencies with linear computational complexity, whilst cascading multi-head self-attention mechanisms to establish global higher-order feature interactions. Extensive experiments on the BCI Competition IV-2a dataset demonstrate that MSResBiMamba achieves outstanding classification performance in multi-class motor imagery tasks, significantly outperforming traditional methods and existing state-of-the-art neural networks. Ablation studies and t-SNE visualisations further confirm the model’s robustness in feature decoupling and cross-subject applications, providing a high-precision, high-efficiency decoding solution for BCI systems. Full article

Full fine-tuning of large-scale vision-language models for video action recognition incurs prohibitive computational cost and often degrades pre-trained spatial representations. To address this, we propose VETA-CLIP, a Video Efficient Temporal Adaptation framework that enhances temporal modeling while preserving cross-modal alignment. By incorporating lightweight adapters into a frozen backbone, VETA-CLIP introduces only 3.55M trainable parameters (a 98% reduction compared to full fine-tuning). Our approach features two key innovations: (1) an Efficient Spatio-Temporal Attention (ESTA) mechanism with a parameter-free boundary replication temporal shift (BRTS) module, which explicitly decouples spatial and temporal attention heads to capture inter-frame dynamics while minimizing disruption to the pre-trained spatial representations; and (2) a novel Variation Loss that maximizes both local inter-frame differences and global temporal variance, encouraging the model to focus on action-related changes rather than static backgrounds. Extensive experiments on HMDB-51, UCF-101, and Something-Something v2 demonstrate that VETA-CLIP achieves competitive performance across zero-shot, base-to-novel, and few-shot protocols, while and remains competitive on the Kinetics-400 dataset. Notably, our eight-frame variant requires only 4.7 GB of peak GPU memory and 2.47 ms of inference per video, demonstrating exceptional computational efficiency alongside consistent accuracy gains. Full article

To address the accumulation of construction and demolition waste (W&D), this study recycled it into regenerated fine aggregate and prepared 3D-printed mortars with replacement ratios ranging from 0% to 100%. The mechanical properties of hardened specimens were tested, and the degradation mechanisms of mechanical performance were investigated through SEM, MIP, and microhardness analysis. The carbon emissions of the materials were evaluated. The results indicated that while the 3D-printed mortar exhibited excellent buildability, its compressive strength, flexural strength, and interlayer bond strength gradually decreased with increasing replacement ratio. MIP results showed that as the replacement ratio of the W&D increased from 0% to 100%, the total porosity of the 3D-printed specimens significantly increased from 14.7433% to 27.5903%. SEM and microhardness images confirmed severe ITZ deterioration, and the average ITZ width increased from 31 to 79 μm. As the W&D replacement ratio increased from 0% to 100%, the total GWP decreased from 0.4043 to 0.3800 kg CO₂-eq/kg mortar. Maximizing the utilization of W&D is key to achieving efficient utilization of solid waste. Considering printability, mechanical performance, interlayer behavior, microstructural characteristics, and environmental impact in a comprehensive manner, the 80% W&D replacement ratio can be regarded as a relatively balanced and promising selection. This work not only suggests the technical feasibility of recycling W&D in 3D printing mortar, but also proposes a sustainable pathway to reduce carbon emissions in construction. Full article

The automated classification of Hebrew, a morphologically rich language (MRL), presents unique challenges, particularly when high-quality labeled data are scarce. This study investigates the interplay between handcrafted feature engineering and transformer-based representations in a low-resource news classification setting (n = 306). We evaluate a multi-task classification across four distinct dimensions: domain, sentiment, gender, and source. Our methodology employs an extensive feature space of 2149 stylistic and content-based attributes, optimized through a systematic Hill-Climbing selection process. We contrast five classical machine learning architectures with five BERT-based models, integrating five oversampling strategies to mitigate class imbalance. The results reveal that in scenarios of extreme data scarcity, the performance gap between deep learning and optimized classical ML becomes marginal, with stylistic features providing critical stability and interpretability. This study contributes a curated Hebrew news dataset and establishes a robust benchmark, demonstrating that linguistically aware feature engineering remains a vital component for MRL processing when large-scale fine-tuning is impractical. Full article

Hybrid metal–polymer components are used in many industries, such as in aerospace, automotives, and electronics, due to the possibility of reducing the weight of the final part while maintaining mechanical properties comparable to components made entirely of metal. Conventional 3D printing processes do not enable the direct fabrication of hybrid structures consisting of solid metal and polymer parts due to the significant differences in the processing temperatures of both materials. A solution to this problem is the integration of two processes, electrodeposition and photopolymerization, which allow fabrication to be carried out at room temperature. This paper presents preparatory studies aimed at developing a new 3D printing technology that uses the simultaneous application of electrodeposition and photopolymerization to manufacture hybrid metal–polymer elements in a single, integrated 3D printing process. Here, a hybrid metal–polymer element is defined as a component composed of at least two bonded parts, including at least one metal part fabricated by electrodeposition and at least one polymer part produced by photopolymerization. Thus, it is not a polymer component merely coated with an electrodeposited metal layer, but a true hybrid structure consisting of functional metallic and polymeric parts. Such components can be manufactured using the world’s first hybrid 3D printer, which integrates electrodeposition and photopolymerization to produce metal–polymer hybrid parts within a single 3D printing process (the device has been submitted to the Polish Patent Office). However, its design and operating principle are beyond the scope of this paper. The presented research focuses on initial study of selected feedstock materials for this printer, namely photocurable resins and electroplating baths. Since the entire hybrid printing process occurs in an electroplating bath environment, studies of these materials for 3D printing under such conditions were essential. This work includes a screening study of photocurable formulations with respect to rheological properties, 3D printing tests in a model copper electroplating bath, and selection of a suitable bath brightener to maximize the quality (fine grain size, homogeneous grain distribution) of additively deposited copper layers. The study was conducted using copper electrodeposition and acrylate resin photopolymerization as model processes for evaluating the proposed hybrid metal–polymer 3D printing technology. Finally, the most suitable feedstock materials for producing metal–polymer hybrid parts via the proposed 3D printing method were selected. Full article

Metal artifacts generated by dental implants significantly degrade cone-beam computed tomography (CBCT) volumes, obscuring critical anatomical structures and compromising diagnostic precision. To address this, an unsupervised deep learning framework has been proposed for Metal Artifact Reduction (MAR) utilizing a Cycle-Consistent Adversarial Network (CycleGAN) optimized for high-fidelity restoration. Unlike supervised methods that rely on unattainable voxel-aligned paired datasets, the proposed approach leverages an unpaired dataset of approximately 4000 images, curated from the public ToothFairy dataset. The architecture integrates U-Net-based generators and PatchGAN discriminators, specifically tuned to mitigate generative hallucinations and preserve morphological integrity. Quantitative benchmarking on a held-out test set demonstrates a 34.6% improvement in the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) score, a substantial reduction in Fréchet Inception Distance (FID) from 207.03 to 157.04, and a superior Structural Similarity Index Measure (SSIM) of 0.9105. The framework achieves real-time efficiency with a 3.03 ms inference time per slice, effectively suppressing artifacts while preserving anatomical detail. Expert validation confirms high fidelity; however, to ensure reliability in extreme cases, the architecture is recommended as a clinical decision-support tool under human-in-the-loop oversight. By enhancing diagnostic clarity via a scalable software pipeline, this study provides a robust solution for high-fidelity dental implant imaging. Full article