Search Results (9,243)

Self-incompatibility (SI) is a key reproductive mechanism in angiosperms that prevents self-fertilization and promotes genetic diversity while limiting breeding efficiency. Akebia trifoliata is a recently domesticated economic crop native to East Asia with medicinal, edible, and oil-producing value. However, its late-acting self-incompatibility (LSI) severely limits genetic improvement and commercial development. To investigate the molecular basis of LSI, we conducted comparative transcriptomic analyses of pistils at 48, 96, 144, 192, and 240 h after self- and cross-pollination, identifying 1552, 2954, 1302, 814, and 1978 differentially expressed genes (DEGs), respectively. DEGs were consistently enriched in mitogen-activated protein kinase (MAPK) signaling, plant hormone signal transduction, and ubiquitin-mediated proteolysis pathways, with clear transcriptional differences before and after 96 h. Compared with cross-pollinated pistils, self-pollinated pistils showed restricted pollen tube spread, and genes related to pollen recognition and tube development showed differential expression at 48 and 96 h, indicating that LSI probably occurs within the pollen tube. Collectively, these results indicate that pistils of A. trifoliata exhibit distinct early responses to self- and cross-pollination, and that DEG-enriched pathways are similar to those involved in S-RNase-mediated SI. These results provide new insights into the molecular basis of LSI and suggest potential targets for overcoming the SI barrier. Full article

To address the significant decline in fracture conductivity after cross-layer fracturing in the L3 sand–mudstone interbedded reservoir of the WZ Oilfield, which restricts efficient development, this study investigates three typical fracture types formed after fracturing: simple fractures in muddy siltstone, simple fractures in mudstone, and complex fractures in muddy siltstone. Based on downhole full-diameter cores, fracture conductivity plates were prepared, and long-term (50 h) conductivity evaluation experiments were conducted under a simulated formation closure pressure of 28 MPa. The interaction modes between fracture surfaces and proppants, as well as the conductivity evolution laws of different fracture types were systematically analyzed. The results indicate that the interaction modes between proppants and fracture walls vary significantly with lithology and fracture morphology. Specifically, proppant embedment dominates in simple muddy siltstone fractures, whereas hydration-induced embedding and wrapping by swelled clay particles dominate in mudstone fractures. The conductivity evolution of simple fractures in muddy siltstone and mudstone follows an exponential decay law, with attenuation amplitudes of 35% and 98% after 50 h, respectively. Complex fractures in muddy siltstone exhibit a staged decay pattern with an attenuation amplitude of 92%, and their long-term conductivity primarily depends on shear-induced self-support. The overall conductivity of cross-layer fractures is controlled by the minimum conductivity among the intersected layers. Under the specific experimental conditions of 28 MPa closure pressure and 30/50 mesh ceramic proppant, the poor long-term conductivity of mudstone simple fractures (only 2% of the initial value) becomes the key bottleneck restricting productivity. This study characterizes the evolutionary features of conductivity evolution of cross-layer fractures in sand–mudstone interbedded reservoirs and provides theoretical support and engineering guidance for optimizing fracturing fluid systems to inhibit hydration and refining stage isolation strategies in similar reservoirs. Full article

Healthcare systems worldwide face increasing pressure to deliver accurate, affordable, and scalable diagnostic services while maintaining long-term sustainability. Chest X-ray screening is considered one of the most cost-effective methods for detecting lung disease. However, many deep learning approaches are computationally intensive and difficult to interpret, which limits their adoption in high-throughput, resource-constrained clinical settings. This study proposes a hybrid CNN–tree framework for automated lung disease classification from chest X-ray images, which targets COVID-19, pneumonia, tuberculosis, lung cancer, and normal cases. To ensure robustness and generalization, four publicly available chest X-ray datasets from different sources are merged into a unified five-class dataset, which introduces realistic variations in imaging conditions and patient populations. A ResNet-18 model is fine-tuned to extract domain-specific deep feature representations. Feature dimensionality and redundancy are reduced using Principal Component Analysis, while class imbalance is addressed through the Synthetic Minority Over-sampling Technique. The resulting compact feature vectors are used to train interpretable tree-based classifiers, which include Decision Tree, Random Forest, and XGBoost. Experiments conducted using five-fold stratified cross-validation demonstrate substantial and consistent performance gains. When trained on fine-tuned and preprocessed deep features, all evaluated tree-based classifiers achieve weighted F1-scores between 0.977 and 0.982 using five-fold cross-validation, with a significant reduction in inter-class confusion. In addition, the proposed framework maintains low per-sample inference latency, which supports energy-efficient and scalable deployment. These results indicate that combining deep feature learning with interpretable tree-based models provides a practical and reliable solution for sustainable chest X-ray screening in real-world clinical environments. Full article

The use of experimental animals with unified quality standards is an important condition for ensuring the effectiveness of scientific research. Ectromelia virus (ECTV), murine hepatitis virus (MHV), reovirus type 3 (Reo-3), and murine parvoviruses (MUV) are the four pathogens that need to be eliminated from SPF (Specific Pathogen-Free) level mice. These four pathogens present fast transmission and high pathogenicity, making it difficult to control. The previously described detection methods present substantial limitations in efficiency and accuracy. Thus, there is an urgent need for rapid and precise diagnostic methods to improve prevention and diagnosis efforts. In this study, we developed a one-step multiplex real-time PCR (mrt-PCR) detection method that can simultaneously detect four key viral pathogens causing diseases in laboratory mice without cross-reactivity with other mouse susceptible pathogens. We tested 128 suspected diseased mouse tissue samples collected from Beijing, and the results showed that this new method has higher sensitivity and specificity than ordinary PCR. The detection limit for ECTV, MHV, and MUV was determined to be 1.08 × 10¹ copies/μL, 1.14 × 10¹ copies/μL, 2.38 ×10¹ copies/μL, and 1.08 × 10¹ copies/μL, respectively. In addition, the assay showed excellent reproducibility, with a coefficient of variation below 1.5%, strong linear correlation (R² > 0.996), and amplification efficiency between 90% and 100%. In summary, the mrt-PCR serves not only as a rapid and accurate detection and early prevention method for laboratory mice but also constitutes a robust tool for microbial quality control in laboratory mice. Full article

The prevalence of ground fissures in deformation-affected areas has intensified, presenting serious risks to both operational safety and the local natural environment. Fissures in these disturbed terrains are typically characterized by elongated morphologies and large-scale variations, which pose substantial challenges to accurate feature extraction. To address these complexities, this paper proposes a semantic segmentation network termed MGF-UNet. In the shallow layers, we integrate multi-scale feature sensing (MFS) and grouped efficient multi-scale attention (EMA) to sharpen anisotropic textures and boundary details under high-resolution representations. For the deeper layers, a Token-Selective Context Transformer (TSCT) is designed to perform selective global modeling on high-level semantic features, effectively capturing long-range dependencies while preserving the structural integrity of elongated fissures. Meanwhile, we employ feature-wise linear modulation (FiLM) to derive pixel-wise affine parameters from shallow structures, which pre-modulate deep features and strengthen cross-level interactions. In the decoder, a Fourier transform-based adaptive feature fusion (AFF) module suppresses background noise and enhances boundary contrast, followed by cross-scale aggregation for final prediction.Benchmark tests conducted on the mining-area fissure dataset (MFD) and road-based datasets demonstrate that MGF-UNet achieves an accuracy of 78.2%, a Dice score of 81.4%, and an IoU of 68.6%, outperforming existing mainstream networks. The results confirm that MGF-UNet provides an effective solution for automatic fissure extraction in deformation-prone environments, offering significant potential for geohazard monitoring and ecological restoration. Full article

Photovoltaic (PV) systems nowadays represent an essential component of renewable energy production. However, undetected faults often compromise their reliability, leading to significant energy losses and high maintenance costs. Existing deep learning approaches for PV fault diagnosis have achieved high accuracy, but they require massive, labeled datasets and high computational resources, which make them unsuitable for real-time applications. This paper proposes a lightweight, self-supervised hybrid learning framework for real-time PV fault diagnosis to address these limitations. First, the dataset is split into training, testing, and validation subsets. Thereafter, weighted class calculation steps are performed to overcome the issue of imbalance in the data. Then, a self-supervised pre-training phase is established to enable the encoder to produce effective internal representations prior to the implementation of a supervised fine-tuning classifier, characterized as a lightweight feed-forward network (Dense–Dropout–Dense Softmax), which will be trained using categorical cross-entropy and fault-type labels. Finally, a supervised fine-tuning stage is employed based on the pre-trained hybrid CNN–transformer encoder to perform PV fault classification. The experimental results indicate that the proposed approach outperforms existing models by achieving an overall accuracy of 99.8%, a recall of 99.6%, and an outstanding specificity of 100%. The confusion matrix demonstrates that classification is excellent on all operating types. Runtime analysis indicates that the model processes each sample in 2.78 ms and requires 0.07 MB to store weights of 19,429 parameters, confirming its suitability for real-time deployment. These findings highlight that using a hybrid CNN–Transformer encoder with self-supervised learning can improve fault detection and classification performance while significantly reducing inference time, making it an effective and efficient solution for intelligent PV system monitoring. Full article

Background/Objectives: Breast ultrasound is widely used for the diagnostic evaluation of breast lesions; however, reliable lesion characterization remains challenging due to substantial image heterogeneity and the limited size of most clinically available datasets. These constraints reduce the generalizability of end-to-end deep learning approaches in routine practice. The objective of this study was to evaluate a data-efficient diagnostic framework that integrates image-derived features with clinical sonographic descriptors to improve breast ultrasound lesion classification in small cohorts. Methods: Ultrasound images from the publicly available BrEaST-Lesions dataset were processed using a pretrained convolutional neural network to extract compact image feature representations from full images, lesion masks, and cropped tumor regions. These features were combined with manually recorded sonographic descriptors after label encoding to form a unified tabular dataset. Gradient-boosted tree models were trained using descriptor-only and fused feature sets with fivefold stratified cross-validation and evaluated on an independent external hold-out test set. Results: Using sonographic descriptors alone, the best-performing model (LightGBM) achieved an external validation accuracy of 0.88, with an area under the receiver operating characteristic curve (AUC) of 0.95. Incorporation of image-derived features improved diagnostic performance on the external test set, yielding an accuracy of 0.88, an AUC of 0.96, and a sensitivity of 1.00 for malignant lesion detection. The fused framework demonstrated more stable generalization than descriptor-only models, particularly for malignant cases. Conclusions: Combining image-derived features with clinical sonographic descriptors within a tabular learning framework provides a robust and data-efficient approach for breast ultrasound-based lesion classification. This strategy supports diagnostic decision-making in small ultrasound datasets and represents a clinically realistic alternative when large-scale deep learning models are impractical. Full article

Accurate identification of Alzheimer’s disease (AD)-related cellular characteristics from microscopy images is essential for understanding neurodegenerative mechanisms at the cellular level. While most computational approaches focus on macroscopic neuroimaging modalities, cell type classification from microscopy remains relatively underexplored. In this study, we propose a hybrid vision transformer–convolutional neural network (ViT–CNN) framework that integrates DeiT-Small and EfficientNet-B7 to classify three AD-related cell types—astrocytes, cortical neurons, and SH-SY5Y neuroblastoma cells—from phase-contrast microscopy images. We perform a comparative evaluation against conventional CNN architectures (DenseNet, ResNet, InceptionNet, and MobileNet) and prompt-based multimodal vision–language models (GPT-5, GPT-4o, and Gemini 2.5-Flash) using zero-shot, few-shot, and chain-of-thought prompting. Experiments conducted with stratified fivefold cross-validation show that the proposed hybrid model achieves a test accuracy of 61.03% and a macro F1 score of 61.85, outperforming standalone CNN baselines and prompt-only LLM approaches under data-limited conditions. These results suggest that combining convolutional inductive biases with transformer-based global context modeling can improve generalization for cellular microscopy classification. While constrained by dataset size and scope, this work serves as a proof of concept and highlights promising directions for future research in domain-specific pretraining, multimodal data integration, and explainable AI for AD-related cellular analysis. Full article

Reinforced concrete conical shell silo roofs continue to present construction challenges, despite the widespread adoption of slip-form technology for silo walls. This study introduces a novel combined temporary support system for cast-in-place conical shell silo roofs, validated through an engineering case in Suiping. The proposed system consists of (i) an umbrella-type conical shell combined support structure and (ii) a cross-type vertical core-tube support. Focusing on the umbrella subsystem, a shell–truss framework is developed based on the geometry of cylindrical–conical shell roofs. Special structural components, along with prestressed reinforcement techniques, are introduced following the principles of structural and elastic mechanics. The traditional inclined-beam shoring concept is incorporated into an arched load path: inclined members are circumferentially connected at nodes to form a shell–arch support mechanism, thereby improving force transfer efficiency and reducing flexural demands. Finite element analyses of representative construction stages are conducted to evaluate displacement and stress responses. The results show that the proposed combined support system meets strength and stiffness requirements during roof construction and provides an efficient and practical solution for large-span conical shell silo roofs. Full article

Secure and bandwidth-conscious transmission of model updates is a central bottleneck in distributed machine learning. Existing secure aggregation and homomorphic encryption pipelines either reveal more than the task requires or incur prohibitive computation and communication costs. We introduce a verifiable functional encryption (VFE) framework that releases only the intended linear functions of client gradients while providing end-to-end integrity and privacy guarantees under standard lattice assumptions. Our instantiation, FlowAgg-FE, combines two novel components. First, KS-IPFE, a key-splittable inner-product FE scheme, supports per-round weighted aggregation, vector packing, and on-the-fly function changes without client re-encryption; function keys are distributed across two non-colluding helpers, eliminating a single point of trust and enabling lightweight, homomorphically verifiable tags on decrypted outputs. Second, PaS-Stream is a rate-adaptive encryption-and-compression pipeline that couples sketch-based gradient compression with batched FE ciphertext streaming, ensuring unbiased aggregation in the presence of stragglers and dropouts. We further bind client-side clipping to zero-knowledge range proofs and offer an optional differentially private release layer that composes with FE to yield $(\epsilon ,\delta )$-privacy. A prototype based on LWE demonstrates practicality across cross-device and cross-silo training: client uplink is reduced by 1.9–3.4× and server CPU time by $1.6\times$ versus state-of-practice encrypted secure aggregation, with accuracy within $0.3\%$ of plaintext baselines and correctness preserved under up to $30\%$ client dropout. These results show that verifiable FE can make secure, communication-efficient gradient transmission viable, as appropriate for theme of security and privacy in distributed machine learning of the Special Issue. Full article

In recent years, the food processing industry in the Gulf Cooperation Council (GCC) has faced increasing pressures to improve operational efficiency while improving its environmental performance. This research examines whether Lean Six Sigma (LSS) methodologies can be used as tools to incorporate sustainability into current operational processes at a date processing facility in Saudi Arabia. In addition to illustrating the ways in which production was improved, this research developed and preliminarily validated a Sustainability Integration Index (SII) framework to measure the contributions of improvement projects to sustainable practices in terms of their impact on the environment, society, and economy. Furthermore, this research examined the role of organizational culture as a moderator of the effectiveness of integrated LSS–sustainability approaches using a Cultural Readiness Assessment Model (CRAM). This research addressed production bottlenecks and aligned production with selected United Nation Sustainable Development Goals (SDGs) using the Define–Measure–Analyze–Improve–Control (DMAIC) methodology. Production bottlenecked in packaging operations resulted in schedule overruns and excessive overtime; therefore, the intervention focused on improving the production process in these areas. There were three distinct improvement streams: demand-based resource leveling, advanced production planning to allow for pull-based flow, and targeted maintenance to raise Overall Equipment Effectiveness (OEE) from 48.2% to 74.6%. Results indicated a 23% increase in daily processing capacity, a 38 min decrease in the average length of time of production closures, and estimated annual cost savings of 940,000 SAR (approximately USD 250,000). The SII framework showed a 21.2% improvement in sustainability scores, with a total composite score improvement from 0.66 to 0.80. Social sustainability had the greatest relative increase (+24.2%). Exploratory correlation analysis found that improvements in cultural maturity and cross-functional collaboration are possible predictors of successful sustainability integration; however, the limitations of the single case study limit the ability to draw causal inferences. The results provide both empirical evidence and possible measurement tools to an under-explored area: the use of LSS in Middle Eastern food processing industries with specific sustainability goals. Validation of the frameworks across different industries will be necessary to establish generalizability. Full article

Lassa virus (LASV), a member of the Arenaviridae family, is the causative agent of Lassa fever (LF), an acute zoonotic hemorrhagic disease transmitted by rodents, characterized by high infectivity and mortality rates. Due to the nonspecific nature of early clinical symptoms, the development of rapid, sensitive, and specific diagnostic methods is critical for effective epidemic control. In this study, the Lassa virus glycoprotein complex (LASV-G) was selected as the target antigen. High-affinity rabbit monoclonal antibodies were generated using a single B-cell cloning approach, and an AlphaLISA (Amplified Luminescent Proximity Homogeneous Assay)-based homogeneous, no-wash detection system was established. Sixteen LASV-G-specific monoclonal antibodies were isolated through flow cytometric sorting, and the optimal antibody pair (56–24) was identified by AlphaLISA pairing and performance screening. The established AlphaLISA system exhibited a limit of detection (LOD) of 0.025 ng/mL, representing approximately a 30-fold increase in sensitivity compared with conventional Enzyme Linked Immunosorbent Assay (ELISA), while reducing the total assay time to less than 30 min. The coefficient of variation (CV) was below 8%, and no cross-reactivity was observed with Ebola, dengue, yellow fever, Zika, or influenza virus antigens. These findings demonstrate that the developed AlphaLISA assay possesses high sensitivity, rapid detection, and good tolerance to matrix effects, significantly improving the efficiency of early LASV antigen detection. This work provides a potential platform for the rapid on-site screening and epidemiological surveillance of highly pathogenic viruses. Full article

The accelerating transition toward carbon neutrality necessitates the synergistic integration of power and hydrogen systems to mitigate renewable intermittency; however, coordinating regulatory policies with the operational flexibility of these coupled systems remains a critical challenge under deep uncertainty. Motivated by this gap, this study develops a bilevel carbon price-coupled optimization framework for integrated power–hydrogen systems, aiming to coordinate environmental policy design with operational scheduling under deep uncertainty. The upper-level model represents the decision-making of a market regulator that determines the optimal carbon price and emission allowances to maximize overall social welfare, while the lower-level model captures the coordinated operation of electricity and hydrogen subsystems that minimize total dispatch cost, including renewable utilization, electrolyzer conversion, and fuel-cell recovery.To address stochastic variations in renewable generation and load demand, a Distributionally Robust Optimization (DRO) formulation is introduced using Wasserstein ambiguity sets, ensuring decision feasibility against worst-case probability distributions. The bilevel structure is efficiently solved via a Benders–Column-and-Constraint Generation (CCG) algorithm, which decomposes policy and operation layers into tractable subproblems with provable convergence. Case studies on a 33-bus integrated power–hydrogen network demonstrate that the proposed framework effectively balances economic efficiency and carbon reduction. Results show that the optimal carbon price of approximately 45 $/tCO₂ achieves a 27% emission reduction with only a 9% cost increase, revealing a near-optimal social welfare equilibrium. Hydrogen subsystems operate flexibly, with electrolyzer utilization increasing by 30% and storage cycling deepening by 15%, enabling enhanced renewable absorption. Sensitivity analyses confirm that the DRO layer reduces operational risk by 4% compared with stochastic optimization, validating robustness against distributional shifts. The study provides a rigorous and computationally efficient paradigm for policy-coordinated decarbonization, highlighting the synergistic role of carbon pricing and cross-energy scheduling in the next generation of resilient low-carbon energy systems. Full article

Instance segmentation of 3D point clouds is a fundamental task for scene understanding in applications such as autonomous driving, robotics, and augmented reality. The inherent irregularity and sparsity of point clouds, compounded by scale variations and instance adhesion, pose significant challenges to accurate segmentation. Existing grouping-based methods are often limited by the loss of geometric details in single-path backbones and by error propagation near complex boundaries. To address these issues, a Multi-grained Dual-aware Grouping algorithm (MDGroup) is proposed, which explicitly integrates multi-grained feature representation with dual awareness of class and boundary. The algorithm features a Dual-Resolution 3D U-Net (DRNet) that preserves local geometric details while aggregating global semantics through adaptive alignment. A four-branch prediction scheme enhances semantic and offset estimation with boundary and directional cues, enabling fine-grained boundary modeling. Furthermore, a Hierarchical Adaptive Multi-grained Feature fusion framework (HAMF) achieves efficient cross-scale alignment by combining Class-Aware Dynamic Voxelization and Class-Aware Pyramid Scaling. Finally, a Boundary-Aware Weighted Aggregation mechanism (BAWA) refines instance grouping by dynamically weighting semantic confidence, geometric distance, boundary probability, and directional consistency. To extend the model to dynamic scenes, a Temporal Adaptive Gating (TAG) module is introduced to leverage historical frame correlations. Extensive experiments on the ScanNet v2, S3DIS, STPLS3D, SemanticKITTI, LiDAR-Net, and OCID benchmarks demonstrate that MDGroup achieves state-of-the-art performance among grouping-based methods, particularly on small objects, complex boundaries, and dynamic environments. Full article

Short bowel syndrome (SBS) develops when the remaining intestine is unable to sustain adequate nutrient and electrolyte absorption following extensive bowel resection. The condition is characterized by malabsorption and significant fluid losses which lead to dehydration and progressive weight loss, thus promoting patient dependence on parenteral fluids or nutrition. After an initial acute phase marked by accelerated intestinal transit and gastric hypersecretion, long-term clinical outcomes are largely determined by the capacity of the remaining bowel for intestinal—a sustained process of structural, functional, and molecular remodeling that enhances absorptive efficiency and restores fluid and nutrient homeostasis. This review summarizes the key histological and cellular features of the adaptive response, including crypt and villus remodeling, mucosal hyperplasia, and smooth muscle hypertrophy, and integrates emerging concepts in crypt biology that define the dynamic cross-talk between intestinal stem cells and the mesenchymal niche, together with their upstream regulatory pathways. Full article