Search Results (101,363)

This study focuses on Siraitia grosvenorii, an economically important crop endemic to Guangxi Province, China, with significant value in both the pharmaceutical and food industries. However, its dioecious reproductive system results in low natural pollination efficiency, necessitating reliance on costly artificial pollination in commercial production, which severely constrains large-scale cultivation and the breeding of improved varieties. Herein, we systematically investigated the molecular mechanisms of silver nitrate (AgNO₃)-induced partial masculinization of female flowers, resulting in the formation of morphologically bisexual flowers with aborted stamens. Morphological observations revealed that untreated female flowers exhibited permanently arrested stamen primordia, whereas spraying treatments with 0–300 mg/L AgNO₃ triggered the development of morphologically complete bisexual flowers. Nevertheless, AgNO₃-induced stamens exhibited severely disrupted male reproductive development, as indicated by the absence of mature pollen grains at anthesis. Cytological evidence demonstrated that pollen abortion was initiated at the pollen mother cell (PMC) stage, accompanied by abnormal vacuolation and impaired nutrient metabolism in tapetal cells. The dominant abortive phenotype was that PMCs exhibited abnormal development and progressive degeneration during the meiotic developmental window. Methylation-sensitive amplification polymorphism (MSAP) assays further revealed markedly elevated CpG-type methylation levels in AgNO₃-induced bisexual flowers at early developmental stages. Expression analysis revealed that SgWIP1 exhibited distinct temporal expression patterns among female, male, and AgNO₃-induced bisexual flowers, with reduced expression during later stages of stamen development in induced bisexual flowers. Collectively, our findings confirm that AgNO₃ only partially activates the stamen developmental cascade in S. grosvenorii. Extensive epigenetic reprogramming triggered by AgNO₃, together with the inability to sustain the expression of core male-determining genes, is tightly linked to tapetal dysfunction and subsequent pollen sterility. This study aims to elucidate the unique mechanism by which AgNO₃-induced partial masculinization in S. grosvenorii from multiple perspectives—including tissue and cellular structure, DNA methylation epigenetic modifications, and gene transcription regulation—using techniques such as morphoanatomy, histochemistry, epigenetics (MSAP), and gene expression analysis. The findings of this study will contribute to molecular breeding efforts focused on sex regulation and promote industrial development. Full article

Soil saline–alkali stress has become a great risk, leading to significant decline in crop yields, especially in protected agriculture that is frequently threatened by excessive chemical fertilization. There is an urgent need to develop friendly strategies for improving the quality of saline–alkali soil. Microbial inoculants are promising agents in attenuating soil stress, but their colonization ability in the crop rhizosphere is often limited. To improve the efficiency of microbial inoculants, this study constructed the assembly of a salt-tolerant bacterium Halomonas (Homs) and a hydrogel composed of sodium alginate, chitosan and xanthine with the assistance of artificial Homs-binding protein (Schx). The effect of this Homs + Schx assembly on the growth of crops and the rhizosphere microecology under saline–alkali stress was systematically evaluated by pot experiments. The results indicate that the application of Homs + Schx enhanced the Shannon index of the rhizosphere bacterial community and increased the relative abundance of key bacterial genera related to biofilm formation and nitrogen cycling (e.g., Curvibacter and Nitrospira). Furthermore, Homs + Schx enhanced the activity of soil urease, peroxidase, and sucrase, reducing the sodium ion content, increasing the potassium ion content, and effectively lowering the soil pH and salt contents. Physiologically, this treatment induced a root osmotic regulatory response, resulting in an increase in the proline contents and a decrease in malondialdehyde contents. Consequently, Homs-Schx promoted the growth of tomatoes and wheat in saline–alkali soil. This study provides new ideas for enhancing the rhizosphere colonization of plant growth-promoting bacteria, regulating the structure of microbial communities, and enhancing crop stress tolerance with the aid of green material strategies. Full article

Loess is prone to brittle failure and structural collapse, motivating binder-free strategies that improve geotechnical performance while limiting material consumption. This study evaluates fibre effects on compaction, unconfined compressive strength (UCS), and stiffness of loess. Flexible polypropylene (PP) and stiff E-glass fibres, both 6 mm long, were added at 0–1.2% of the dry soil mass. Standard Proctor and unconfined compression tests, supplemented by stereo-microscopic observations, were performed. Fibre addition caused moderate changes in optimum moisture content and maximum dry density, whereas mechanical response depended strongly on fibre type. PP fibres increased UCS by up to 126.9% at 1.2%; however, the gain from 1.0% to 1.2% was comparatively small. E-glass fibres reduced UCS at all dosages, with a maximum decrease of 22.1% at 1.0%. Both fibre systems reduced the secant stiffness modulus (E₅₀) relative to unreinforced loess. A fibre-to-soil stiffness ratio was used as an interpretative parameter, while the Fibre Reinforcement Efficiency Index (FREI) quantified relative UCS change per unit fibre dosage. For PP, maximum FREI occurred at 1.0%, showing that maximum absolute strength did not coincide with maximum dosage-normalised response. The findings support evaluating mechanical benefit relative to fibre dosage when selecting reinforcement for binder-free loess improvement. Full article

Underwater object detection faces significant challenges including uneven lighting, low contrast, and scattering-induced distortions. Existing underwater datasets are limited in scale, class diversity, and annotation consistency, which hinders robust model development. This work addresses these limitations by creating the UWOD dataset through the integration of seven publicly available underwater datasets, comprising over 107 K images with approximately 374 K annotations across 39 classes. We employ a semi-automatic annotation pipeline that combines manual labeling with iterative model-in-the-loop training to ensure high-quality ground truth, As a final verification step, all auto-generated labels were manually inspected and corrected as needed. We benchmark state-of-the-art object detectors—including YOLOv8, YOLOv7, YOLOv5, FCOS, EfficientDet, YOLOX, RT-DETR, SSD, and Faster R-CNN—establishing comprehensive performance baselines; YOLOv8 and YOLOv7 achieve the best accuracy–efficiency trade-off. Our transfer learning analysis shows that domain-specific pretraining substantially often outperforms pretraining on general-purpose datasets, yielding up to more than 50% improvement in low-data regimes versus training from scratch, with markedly lower seed-to-seed variance than scratch training. Sequential pretraining on COCO followed by UWOD achieves the strongest results on our most challenging dataset. Due to upstream licensing constraints, we release trained model weights and an automated annotation pipeline that encapsulate the learned underwater-domain knowledge, enabling immediate application to new imagery while respecting intellectual property. Full article

Axial blood pumps serve as vital auxiliary therapeutic devices for patients with end-stage heart failure, and parameter optimization of the controller is critical to improve system performance. Existing optimization methods cannot satisfy the parameter optimization requirements of variable-speed axial blood pump controllers in terms of optimization accuracy. Therefore, this paper investigates a many-objective optimization method adapted to the operating characteristics of blood pumps. Firstly, a many-objective optimization model is established for the controller. To efficiently solve the proposed model, an optimization algorithm integrating deep reinforcement learning, named DQN-NSGA-CT, is developed. On the basis of the population evolution state, the optimal strategy learned by DQN dynamically adjusts the crossover probability and mutation probability, which adaptively balances population diversity in the early iteration stage and convergence speed in the later iteration stage. Meanwhile, a novel environmental selection strategy is used to reconcile population convergence and diversity. To select the best compromise solution, an entropy weight–Copula–TOPSIS comprehensive evaluation method is proposed, which realizes objective weight assignment, objective correlation correction and multi-attribute ranking. Experimental results verify the efficiency of the DQN-NSGA-CT algorithm in solving the many-objective optimization model of the controller. The research addresses the many-objective optimization problem of variable-speed axial blood pump control. Full article

As urban mining plays an increasingly important role in resource security and sustainable development, insufficient coordination among reverse supply chain participants has become a major constraint on the efficient recovery of urban mineral resources. From the perspective of interfirm cooperative innovation, this study takes waste mobile phones as an application context and develops a four-party evolutionary game model involving the government, recyclers, remanufacturers, and consumers. The model incorporates opportunistic behavior in cooperative innovation, government subsidy intensity, and product pricing into a unified analytical framework. Replicator dynamics, Jacobian-based stability analysis, and numerical simulations are employed to examine the evolutionary mechanisms and stability conditions of multi-actor collaborative governance. The results identify four stable equilibrium configurations and show that: (1) excessive innovation spillovers and asymmetric interdependence between recyclers and remanufacturers may weaken incentive compatibility and induce opportunistic behavior; (2) the effects of government subsidies vary across evolutionary stages and depend on the strategic responses of other actors; and (3) interfirm transfer prices and final market prices promote stable cooperation only within specific ranges. These findings provide a theoretical basis and policy implications for improving collaborative governance in reverse supply chains for urban mineral resource recovery and supporting sustainable urban mining. Full article

Multi-view clustering has emerged as a significant research direction in the information age, as multiple feature representations become increasingly available. However, traditional multi-view clustering methods are often unsupervised and fail to exploit available label information. In practice, fully labeled data are scarce, while partially labeled data are more common and can significantly improve clustering performance. Moreover, real-world data are frequently corrupted by noise or outliers. To address these challenges, this paper proposes a Correntropy-guided Matrix Factorization and Tensor Graph Learning (CMTGL) framework for robust semi-supervised multi-view clustering. Specifically, CMTGL incorporates partial label information into a shared low-dimensional representation through constrained low-rank matrix factorization and employs the maximum correntropy criterion (MCC) to reduce the influence of noisy samples and outliers. In addition, view-specific graphs are stacked into a third-order tensor and regularized by the tensor Schatten p-norm to exploit high-order correlations across multiple views. A consensus graph is jointly learned to capture the common structural information shared among different views. The resulting optimization problem is efficiently solved by a block coordinate descent algorithm with an extrapolation accelerated block coordinate update (BCU) scheme. Extensive experiments on six public benchmark datasets demonstrate that the proposed method achieves superior clustering performance compared to state-of-the-art approaches. Full article

Compact deep models are attractive for medical image classification, but conventional knowledge distillation mainly transfers class-level predictions and may not adequately preserve lesion-relevant spatial cues. To address this limitation, we propose LaHKD, a lesion-aware hierarchical knowledge distillation framework for parameter-efficient medical image classification. LaHKD enables a lightweight student to learn hierarchical semantic representations together with lesion-focused guidance from a stronger teacher. We evaluate LaHKD on HAM10000 dermoscopic lesion classification and a brain tumor MRI classification benchmark. Across both datasets, LaHKD improves compact-student classification performance, with the clearest lesion-focused spatial benefits observed on HAM10000, where lesion morphology is central to diagnosis and direct lesion supervision is available. On the magnetic resonance imaging (MRI) benchmark, localization analysis is limited to an auxiliary recovered-mask subset and is therefore interpreted as exploratory; under this setting, consistent localization gains are not observed. Overall, LaHKD provides an effective framework for compact medical image classification, with spatial benefits most clearly supported in tasks with reliable lesion supervision. Full article

Accurate short-term traffic forecasting and efficient dynamic route guidance are pivotal for mitigating urban congestion. However, accurately capturing complex temporal dependencies and nonlinear variations in traffic flow remains challenging, while conventional path-planning algorithms may also suffer from local-optimum problems. To address these challenges, this study proposes a hybrid framework integrating an optimized prediction model with an enhanced ant colony optimization algorithm. First, a GA-BiGRU-ATT model is developed for traffic state prediction. By combining a bidirectional gated recurrent unit (BiGRU), a temporal attention mechanism, and genetic algorithm-based hyperparameter optimization, the model captures contextual temporal dependencies within the observed historical input window and emphasizes critical time-step features. Under the evaluated model configurations, the proposed approach achieved traffic-state classification accuracies of 90.28% and 87.50% on Segments 1 and 2, respectively. Second, based on the predicted traffic states, an Improved Ant Colony Algorithm (IACA) incorporating traffic-state feedback is proposed to mitigate slow convergence and local-optimum entrapment in conventional ACO. Within the ant-colony-based comparison, the IACA reduced the average computational time by 49.57% relative to the conventional ACA. Furthermore, under the evaluated SUMO evening-peak scenario, periodic dynamic guidance reduced the average travel time of the selected guided vehicles by 12.06% and increased their average speed by 27.07%. These results demonstrate the potential of prediction-guided dynamic rerouting under the evaluated simulation conditions. Full article

Background: Crop fungal diseases cause huge yield losses globally, and the abuse of chemical fungicides has led to severe resistance and environmental risks. Antimicrobial peptides (AMPs) represent ideal green alternatives, but poor salt tolerance and high production costs limit their field application. Results: In this study, we developed a His-tag engineering strategy to enhance salt tolerance of antifungal peptides, and identified a novel peptide Cla-H derived from Styela clava. Cla-H was efficiently expressed via secretory fermentation in Pichia pastoris, and its crude fermentation supernatant could be directly applied to plants. Cla-H exhibited strong and rapid fungicidal activity against Fusarium graminearum and Botrytis cinerea, accompanied by irreversible inhibition of spore germination and observable membrane permeabilization phenotypes. While SYTOX green and PI staining indicated membrane-damaging events, adequate control experiments are still required to firmly establish membrane permeabilization as its primary mode of action. Meanwhile, it showed outstanding thermal and storage stability. Most importantly, His-tag modification significantly improved its salt tolerance, enabling stable activity under high-salt conditions that completely inactivated the parental peptide. In planta assays demonstrated that crude Cla-H fermentation supernatant effectively controlled wheat scab and tomato gray mold. Conclusions: This study provides a promising salt-tolerant antifungal peptide for crop protection and establishes a simple and low-cost technical route for developing next-generation green biofungicides. Full article

The recovery of bioactive compounds from industrial wastewaters is a key strategy for improving resource efficiency and reducing the environmental impact of high-load effluents. In this work, two protein-rich tannery wastewater streams with different origins and compositions, unhairing–liming wastewater and lime-fleshing wastewater, were comparatively evaluated as secondary raw materials for the production of amino acid-based protein hydrolysates with potential biostimulant activity. Both streams were characterised in terms of physicochemical composition, mineral content and amino acid profile, and subsequently subjected to a comparative sequential enzymatic hydrolysis approach using endo- and exo-proteolytic enzymes. Alcalase, followed by Pancreatin, was selected as the most effective enzymatic system for both streams, although different optimal enzyme loadings were required depending on the wastewater composition. For unhairing–liming wastewater, the optimal conditions were 1.5% Alcalase and 1.0% Pancreatin, yielding 70.76 ± 1.95% hydrolysate, 80.87 ± 1.98% protein recovery and 11.42 ± 1.03% free amino acids. For lime-fleshing wastewater, 0.7% Alcalase and 1.0% Pancreatin provided 98.42 ± 1.97% yield, 92.19 ± 1.92% protein recovery and 16.97 ± 0.98% free amino acids. The two hydrolysates showed differentiated amino acid profiles, reflecting the keratin- and collagen-derived nature of the original streams. Germination assays indicated a growth-stimulating effect of the hydrolysates, increasing seed growth by up to 20.7% for the unhairing–liming wastewater hydrolysate at 0.10% (w/v) and by up to 27.1% for the lime-fleshing wastewater hydrolysate at 0.07% (w/v). These results demonstrate that enzymatic hydrolysis can be an effective and environmentally friendly route for converting tannery wastewaters into value-added bio-based products for agricultural applications, while also highlighting the industrial relevance of adapting the enzymatic process to the origin, composition and protein accessibility of each wastewater stream. Full article

Maritime transportation carries more than 80% of global cargo, making efficient port operations essential for international trade. Accurate prediction of ship arrival time is important for berth allocation, resource scheduling, and operational management. However, prediction accuracy is often affected by complex sea conditions, delayed vessel information, and reliance on human experience. To address these challenges, this study proposes a deep learning method that combines unidirectional (UniLSTM) and bidirectional long short-term memory (BiLSTM) networks with an attention mechanism to predict ship estimated time of arrival (ETA). The proposed models are evaluated using Automatic Identification System (AIS) data from the Port of New York, USA. Shapley additive explanations (SHAP) are also employed to analyze the contribution of different variables to the prediction results. The results show that BiLSTM performs better than the UniLSTM, and the attention mechanism further improves prediction accuracy. In particular, the root mean square error (RMSE) of the attention-based BiLSTM is reduced by an average of 5.7 compared with the traditional recurrent neural network (RNN), while the deviation between predicted and actual arrival times remains below 5%. These findings can support port operators and shipping companies in berth allocation, resource scheduling, and operational decision-making. Full article

Under conditions of gradual depletion of gas-condensate field reserves and declining well production rates, one of the key challenges in operating offshore production facilities is improving the energy efficiency and reliability of process equipment, particularly compressor stations used for gas gathering, treatment, and transportation. Resource conservation and the adaptation of operating modes to changing production conditions require flexible compression systems capable of operating efficiently over a wide range of inlet flow rates and pressures. This article examines examples from three key regions where compressor stations have been adapted to declining production rates and reservoir pressures, and analyses the challenges encountered under actual operating conditions. A compressor model is proposed for the late stage of field development, during which the production rate of gas-condensate wells decreases from 5.0 million m³/day to 0.5 million m³/day over a ten-year period. The wellhead pressure and compressor suction pressure decline from 5.0 MPa to 0.1 MPa, while the discharge pressure remains at 5.5 MPa. Calculations were performed for the principal operating parameters, including gas production rate, wellhead and compressor suction pressures, the capacity of a single compressor under different configurations, the required number of compressors, and the total annual capacity of all operating compressors. The proposed compressor-unit reconfiguration scheme makes it possible to compress the produced gas for at least nine years using a single unit size, without replacing cylinders and without changing the type of compressor; over this period the number of units operating in parallel increases from three to six. Unlike a single oversized centrifugal machine, whose efficiency falls sharply once anti-surge recycling becomes necessary at part load, the proposed configuration is estimated to remain operable down to about 10% of the rated capacity of one unit. This lower limit is obtained by extrapolation of the model presented here and requires confirmation by test or manufacturer data. Full article

The free fibula flap (FFF) is a reference reconstructive option, particularly for extensive mandibular defects. This narrative review examines the evolution of implant–prosthetic rehabilitation from conventional free-hand reconstruction to prosthetically driven digital workflows. A structured, non-systematic search of PubMed/MEDLINE, Scopus, and Web of Science included English-language publications from 1989 to May 2026. Early approaches were limited by anatomical discrepancies, unfavorable implant positioning, excessive prosthetic space, and difficult peri-implant maintenance. Surgical refinements, including double-barrel reconstruction and soft-tissue procedures, improved rehabilitative conditions in selected cases. Virtual surgical planning, CAD/CAM guides, patient-specific fixation, and guided implant placement subsequently enabled closer coordination of reconstruction, implant positioning, and prosthetic design, supporting immediate protocols such as Jaw-in-a-Day. However, the available evidence remains heterogeneous and predominantly retrospective. Although digital workflows may improve accuracy and operative efficiency, their superiority in terms of long-term biological, prosthetic, functional, and patient-reported outcomes has not been conclusively demonstrated. Emerging technologies, including augmented reality, robotics, artificial intelligence, and digital twins, remain largely preliminary. Full article

Background/Objectives: Class imbalance and probabilistic prediction quality are important considerations in lightweight retinal classification. We evaluated whether knowledge distillation (KD) from a ConvNeXtV2-Base teacher improved an EfficientNet-B0 student in nine-class MultiEYE fundus classification. Methods: Exact-content screening quarantined 131 of 58,036 records because of exact-content duplication or label conflicts. Six prespecified seeds were evaluated in no-KD, label-smoothing, and KD arms (18 runs) under matched training conditions. Within the controlled experiment, TEST data were not used for training, hyperparameter tuning, checkpoint or model selection, or protocol modification. The primary endpoint was the KD−no-KD macro-F1 difference on decontaminated TEST (n = 11,573), assessed by paired image-level bootstrap (10,000 replicates). Grad-CAM and Grad-CAM++ were examined qualitatively using one deterministically selected case per class and six-seed consensus maps. Results: Mean macro-F1 was 0.593, 0.597, and 0.633 for no-KD, label smoothing, and KD. KD exceeded no-KD by 0.039 (95% CI, 0.025–0.053; p < 0.001), with positive paired differences in all six seeds, and label smoothing by 0.036 (Holm-adjusted p < 0.001); label smoothing did not differ from no-KD (Holm-adjusted p = 0.554). Class-wise F1 differences were non-negative across all nine classes, with six remaining significant after correction for multiple comparisons. The Brier score difference was −0.077 (95% CI, −0.081 to −0.072; Holm-adjusted p < 0.001), while ECE decreased descriptively from 0.171 to 0.084. Nine-class Grad-CAM and direct Grad-CAM++ consensus maps enabled qualitative no-KD versus KD comparison without establishing lesion-localization superiority. Conclusions: KD improved macro-F1 and lowered the Brier score while retaining the same EfficientNet-B0 student at inference. The evaluated label-smoothing configuration did not reproduce the macro-F1 gain; clinical superiority and external generalizability remain unestablished. Full article