Search Results (6,797)

Ultra-high heat input welding offers high efficiency for large-scale offshore engineering, but excessive heat input can degrade low-temperature toughness. This study investigates the microstructural evolution and impact toughness of EH36 ship steel under high heat inputs (300–500 kJ/cm) using Gleeble-3500 thermal simulation, Charpy impact tests, and multi-scale characterization (OM, SEM, EBSD). Results show that impact toughness peaks at 400 kJ/cm, with surface and core energies reaching 343.33 J and 215.18 J, respectively. The optimal toughness is attributed to the formation of acicular ferrite and a high fraction of high-angle grain boundaries (up to 48.7%), which effectively inhibit crack propagation. These findings provide a practical basis for selecting heat input to balance welding efficiency and mechanical performance in marine steel fabrication. Full article

Cephalotaxus hainanensis, a valuable medicinal and endangered conifer, requires scientific conservation and precision management to ensure the sustainable utilization of its genetic and ecological resources. Nitrogen (N) is a key nutrient that regulates plant growth and metabolism; rapid and accurate nitrogen diagnosis is vital for optimizing fertilization, reducing nutrient losses, and promoting healthy plant development. This study employed a combined approach integrating stepwise regression, correlation analysis, and Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify leaf color features strongly correlated with leaf nitrogen content (LNC). A support vector regression (SVR) model, suitable for small-sample datasets, was then employed to accurately estimate LNC across canopy layers. Nine color variables were found to be highly associated with LNC, among which the Green Minus Blue index (GMB) consistently appeared across all correlation methods, demonstrating strong robustness and generality. Color features effectively reflected LNC variations among nitrogen treatments—especially between N1 and N4—and across canopy layers, with the most pronounced contrasts observed between upper and lower leaves. The Spearman-based SVR model revealed that the middle canopy maintained the highest and most stable LNC. However, the lower leaves were most sensitive to nitrogen deficiency, while the upper leaves were more sensitive to nitrogen excess. Comprehensive analysis identified N2 as the optimal nitrogen treatment, representing a balanced nutrient state. Overall, this study confirms the reliability of color features for LNC estimation and highlights the importance of vertical canopy LNC distribution in nitrogen diagnostics, providing a theoretical and methodological foundation for color-based nitrogen diagnosis and precision nutrient management in evergreen conifers. Full article

Gastrodia elata is a fully mycoheterotrophic orchid whose tuber development depends on carbon delivered by Armillaria fungi. Its formal inclusion in China’s “medicine and food homology” catalog has intensified demand for cultivated tubers combining high yield with consistent bioactive quality. Here, we tested whether Armillaria mellea strains steer host carbon allocation between biomass accumulation and phenolic glycoside biosynthesis. Using a standardized EPS symbiotic cultivation system (AM1, AM2, AM3; n = 3 biological replicates per strain), we integrated agronomic traits with widely targeted metabolomics and RNA-seq transcriptomics, including weighted gene co-expression network analysis (WGCNA). AM3 produced the highest tuber yield and higher primary carbon status (PCAI), but lower gastrodin/parishin-type phenolic glycosides and lower allocation efficiency (BER), whereas AM1 showed a quality-dominant profile with significantly higher BER. WGCNA highlighted an AM3-associated module enriched in starch-biosynthetic genes, and PCAI was strongly negatively correlated with the weighted Parishin-Gastrodin Index (wPGI) across samples (n = 9), consistent with a carbohydrate-storage versus phenolic-glycoside trade-off. These results indicate that fungal strain identity functions as an external regulator of source–sink dynamics in G. elata, supporting “precision symbiosis” for food-grade versus medicinal-grade production. Full article

Accurate forecasting of citywide electric vehicle (EV) charging load is critical for alleviating station-level congestion, improving energy dispatching, and supporting the stability of intelligent transportation systems. However, large-scale EV charging networks exhibit complex and heterogeneous spatio-temporal dependencies, and existing approaches often struggle to scale with increasing station density or long forecasting horizons. To address these challenges, we develop a modular spatio-temporal prediction framework that decouples temporal sequence modeling from spatial dependency learning under an encoder–decoder paradigm. For temporal representation, we introduce a global aggregation mechanism that compresses multi-station time-series signals into a shared latent context, enabling efficient modeling of long-range interactions while mitigating the computational burden of cross-channel correlation learning. For spatial representation, we design a dynamic multi-scale attention module that integrates graph topology with data-driven neighbor selection, allowing the model to adaptively capture both localized charging dynamics and broader regional propagation patterns. In addition, a cross-step transition bridge and a gated fusion unit are incorporated to improve stability in multi-horizon forecasting. The cross-step transition bridge maps historical information to future time steps, reducing error propagation. The gated fusion unit adaptively merges the temporal and spatial features, dynamically adjusting their contributions based on the forecast horizon, ensuring effective balance between the two and enhancing prediction accuracy across multiple time steps. Extensive experiments on a real-world dataset of 18,061 charging piles in Shenzhen demonstrate that the proposed framework achieves superior performance over state-of-the-art baselines in terms of MAE, RMSE, and MAPE. Ablation and sensitivity analyses verify the effectiveness of each module, while efficiency evaluations indicate significantly reduced computational overhead compared with existing attention-based spatio-temporal models. Full article

Gut microbiota influences colorectal cancer (CRC) development, tumor progression, and response to therapy. Fecal microbiota transplantation (FMT) has been proposed as a strategy to restore microbial balance and modulate treatment outcomes. We evaluated the effects of human fecal transplantation on gut microbiota composition, metabolites, tumor growth, and the efficacy of folinic acid, fluorouracil and oxaliplatin (FOLFOX) chemotherapy in four CRC patient-derived xenograft (CRC PDX) models in NSG mice. Gut microbiota was profiled by 16S rRNA sequencing; short-chain fatty acids (SCFAs) and amino acids (AAs) were analyzed by mass spectrometry. Prolonged FMT significantly altered gut microbiota structure, increasing α-diversity and modifying β-diversity, and induced distinct changes in bacterial genera. FMT alone did not affect tumor growth. FOLFOX inhibited tumor progression in all CRC PDXs, with FMT enhancing therapeutic efficacy in two models. Despite substantial microbiota shifts, FMT exerted minimal or no effect on fecal SCFAs and AAs. FMT induced robust microbiota remodeling but did not modify selected stool metabolites or intrinsic tumor growth. However, FMT enhanced FOLFOX responsiveness in selected CRC PDXs, supporting a microbiota-mediated modulation of chemotherapy outcomes. Full article

The rapid expansion of small and medium enterprise (SME) lending has intensified the need for accurate and interpretable credit risk forecasting. Financial institutions must anticipate potential business loan delinquency to maintain portfolio stability and meet regulatory standards. This study proposes an interpretable multi-model framework that integrates statistical (correlation screening and ordinary least squares regression), probabilistic (Gaussian Naïve Bayes), and classical time-series (SARIMA) methods to balance explanatory insight and predictive accuracy in delinquency forecasting. Ordinary least squares regression is used to quantify the direction and strength of each driver and yields statistically significant coefficients (β ≈ 1.336 for the overdue 15+ days bucket, p < 10⁻²²). The Naïve Bayes classifier provides a probabilistic early-warning signal with an out-of-sample accuracy of 55%, precision of 43%, recall of 75%, and ROC AUC of 0.371. Finally, a seasonal ARIMA model fitted on the selected regressors achieves a mean absolute percentage error (MAPE) of 7.6% and an out-of-sample R² of 0.49, demonstrating competitive forecasting performance while maintaining interpretability. The results show that the framework offers actionable insights for risk managers by identifying key risk drivers, providing probabilistic alarms, and generating calibrated point forecasts. The proposed approach contributes to the development of intelligent and explainable forecasting and control systems for modern financial institutions. Full article

Background: In biosimilar studies, assessing the switchability and interchangeability of biosimilars with their reference products is essential for ensuring reliable clinical evaluation. This study explores optimal trial design strategies incorporating balanced and uniform structures to enhance statistical efficiency in treatment effect under a carryover setting. Methods: Using a linear mixed-effect model for log-transformed responses, we conducted a theoretical variance-based evaluation of all possible two-treatment switching designs in three-period and four-period crossover trials, considering settings with and without carryover effects. A total of 247 distinct three-period designs and 65,519 distinct four-period designs were enumerated and classified according to structural properties, with particular attention to those incorporating a non-switching arm (NSA). Results: SBUwP-NSA (Strongly Balanced Uniform-within-Period designs with a Non-Switching Arm) consistently achieved the minimum variance for treatment effect estimation in both carryover and no-carryover settings. In the absence of carryover effects, UwP-NSA (Uniform-within-Period designs with a Non-Switching Arm) attained equivalent efficiency. In contrast, commonly used dedicated switching designs exhibited substantially lower relative efficiency, achieving as little as 50–55% of the efficiency of the optimal designs, depending on carryover assumptions. Conclusions: This comprehensive theoretical evaluation demonstrates that incorporating strong balance and uniformity properties can yield substantial efficiency gains in switching studies. The results provide quantitative guidance for selecting efficient crossover designs, enabling improved estimation precision while maintaining practical relevance for interchangeability and switching assessments in biosimilar research. Full article

Multimodal multiobjective optimization problems (MMOPs) are widely encountered in real-world applications. While numerous evolutionary algorithms have been developed to locate equivalent Pareto-optimal solutions, existing Multimodal Multiobjective Evolutionary Algorithms (MMOEAs) often struggle to handle large-scale decision variables and sparse Pareto sets due to the curse of dimensionality and unknown sparsity. To address these challenges, this paper proposes a novel approach named MASR-MMEA, which stands for Large-scale Sparse Multimodal Multiobjective Optimization via Multi-stage Search and Reinforcement Learning (RL)-assisted Environmental Selection. Specifically, to enhance search efficiency, a multi-stage framework is established incorporating three key innovations. First, a dual-strategy genetic operator based on improved hybrid encoding is designed, employing sparse-sensing dynamic redistribution for binary vectors and a sparse fuzzy decision framework for real vectors. Second, an affinity-based elite strategy utilizing Mahalanobis distance is introduced to pair real vectors with compatible binary vectors, increasing the probability of generating superior offspring. Finally, an adaptive sparse environmental selection strategy assisted by Multilayer Perceptron (MLP) reinforcement learning is developed. By utilizing the MLP-generated Guiding Vector (GDV) to direct the evolutionary search toward efficient regions and employing an iteration-based adaptive mechanism to regulate genetic operators, this strategy accelerates convergence. Furthermore, it dynamically quantifies population-level sparsity and adjusts selection pressure through a modified crowding distance mechanism to filter structural redundancy, thereby effectively balancing convergence and multimodal diversity. Comparative studies against six state-of-the-art methods demonstrate that MASR-MMEA significantly outperforms existing approaches in terms of both solution quality and convergence speed on large-scale sparse MMOPs. Full article

Improving fruit quality is one of the most critical core tasks in fruit tree breeding. However, the complexity of the constituent factors of fruit quality and their interrelationships, the significant influence of environmental factors on quality, and the diversity of consumer demands, among other factors, make quality breeding a more challenging endeavor than other breeding objectives. Essentially, fruit quality is defined by the delicate balance of sugar, acid, and aromas, which collectively influence the fruit’s flavor, consumer satisfaction, and economic value. While substantial progress has been made in the depiction of the metabolic pathways underlying these traits, the molecular mechanism coordinating carbon partitioning and competition between sugars, acids, and volatiles remains unknown. This review focuses on recent advances in understanding stone fruit metabolism and identifies key gaps in knowledge. We emphasize the need for integrated approaches combining spatial metabolomics, transcriptomics, genetics, and genomics to reveal the regulatory networks underlying metabolomic variation during fruit development and ripening. We also discuss the application of molecular tools, such as marker-assisted selection and metabolite-associated markers, to accelerate the breeding of flavor-balanced stone fruit cultivars. By adapting these advances in breeding practices, we can achieve coordinated improvement and precise regulation of various components of fruit quality, thereby developing elite stone fruit cultivars with improved flavor that meet prevailing consumer demands. Full article

Underwater imaging suffers from significant degradation due to scattering by suspended particles, selective absorption by the medium, and depth-dependent noise, leading to issues such as contrast reduction, color distortion, and blurring. Existing enhancement methods typically address only one aspect of these problems, relying on unrealistic assumptions of uniform noise, and fail to jointly handle the spatially heterogeneous noise and spectral channel attenuation. To address these challenges, we propose the variational-based spatial–spectral joint enhancement method (VSJE). This method is based on the physical principles of underwater optical imaging and constructs a depth-aware noise heterogeneity model to accurately capture the differences in noise intensity between near and far regions. Additionally, we propose a channel-sensitive adaptive regularization mechanism based on multidimensional statistics to accommodate the spectral attenuation characteristics of the red, green, and blue channels. A unified variational energy function is then formulated to integrate noise suppression, data fidelity, and color consistency constraints within a collaborative optimization framework, where the depth-aware noise model and channel-sensitive regularization serve as the core adaptive components of the variational formulation. This design enables the joint restoration of multidimensional degradation in underwater images by leveraging the variational framework’s capability to balance multiple enhancement objectives in a mathematically rigorous manner. Experimental results using the UIEBD-VAL dataset demonstrate that VSJE achieves a URanker score of 2.4651 and a UICM score of 9.0740, representing a 30.9% improvement over the state-of-the-art method GDCP in the URanker metric—a key indicator for evaluating the overall visual quality of underwater images. VSJE exhibits superior performance in metrics related to color uniformity (UICM), perceptual quality (CNNIQA, PAQ2PIQ), and overall visual ranking (URanker). Full article

In the context of Industry 4.0 and intelligent manufacturing, conventional serial manipulators face limitations in dynamic task environments due to fixed structural parameters and the traditional decoupling of mechanism design from motion planning. To address this issue, this study proposes SAC-SC (Soft Actor–Critic-based Structure–Control Co-Design), a reinforcement learning framework for the co-design of manipulator link lengths and motion planning policies. The approach is implemented on a custom four-degree-of-freedom PRRR manipulator with manually adjustable link lengths, where a hybrid action space integrates configuration selection at the beginning of each episode with subsequent continuous joint-level control, guided by a multi-objective reward function that balances task accuracy, execution efficiency, and obstacle avoidance. Evaluated in both a simplified kinematic simulator and the high-fidelity MuJoCo physics engine, SAC-SC achieves 100% task success rate in obstacle-free scenarios and 85% in cluttered environments, with a planning time of only 0.145 s per task, over 15 times faster than the two-stage baseline. The learned policy also demonstrates zero-shot transfer between simulation environments. These results indicate that integrating structural parameter optimization and motion planning within a unified reinforcement learning framework enables more adaptive and efficient robotic operation in unstructured environments, offering a promising alternative to conventional decoupled design paradigms. Full article

Aquatic animal products are vital to global food security and nutrition, necessitating accurate, scalable, and non-destructive methods for quality assessment in aquaculture. Conventional techniques such as dissection and biochemical analysis are invasive, labor-intensive, and unsuitable for real-time or high-throughput decision-making. This review synthesizes six major categories of non-destructive technologies—electrical, spectroscopic, natural sensory, acoustic, radiographic, and infrared and microwave—classified by their underlying sensing mechanisms and therefore differing measurement capabilities and deployment feasibilities. To support objective technology selection, an Analytic Hierarchy Process (AHP) framework was developed using general performance criteria (cost, accuracy, speed, usability) and one decision-critical application-specific criterion (non-invasiveness), and was demonstrated for ovarian maturation staging in mud crabs by ranking 19 candidate techniques. Accuracy had the highest weight (0.416), but non-invasiveness (0.224) and usability (0.197) substantially influenced the final ranking, illustrating how operational and welfare constraints could shift preferred solutions despite differences in analytical accuracy. Based on the global priority weights (GA), computer vision (CV) was identified as the most suitable option (GA = 0.076), balancing affordability, throughput, ease of deployment, and animal welfare compatibility, whereas high-end modalities such as nuclear magnetic resonance (NMR; GA = 0.073) and computed tomography (CT; GA = 0.070) were constrained by cost and operational complexity. Overall, this review–AHP–case study pipeline provides a transparent and reproducible decision-support basis for selecting non-destructive technologies across aquaculture species and quality targets. Full article

This study aimed to develop a quantitative Cultivation Suitability Index (CSI) and identify growth-limiting environmental factors for the stable cultivation of Ainsliaea acerifolia, an understory perennial native to the southern and south-central mountainous regions of Korea. Climatic conditions, site topography, microenvironment, soil physicochemical properties, vegetation structure, and plant growth indices were investigated at six representative natural habitats. The soils were generally acidic and nutrient-limited, with low available phosphorus and low exchangeable Ca and Mg. Community diversity indices indicated stable understory assemblages across sites. Thirteen environmental indicators were normalized and weighted to construct the CSI, and suitability classes were defined as highly suitable (≥0.75), suitable (0.5–0.75), potential (0.25–0.5), and unsuitable (<0.25). The Maximum Limiting Factor Method (MLFM) was applied to identify site constraints, yielding a Limiting Factor Index (LFI) of 0.29–0.42, with light, humidity, temperature, EC, Ca and Mg emerging as dominant limiting factors. CSI and LFI exhibited a negative linear relationship (R² = 0.6353), demonstrating that alleviation of limiting conditions directly improves site suitability. Optimal cultivation environments were characterized by moderately acidic soils, adequate Ca and Mg availability, moderate shade, and improved moisture balance. From a management perspective, maintaining soil pH around 4.5–5.0, supplementing Ca and Mg, enhancing drainage, and applying organic mulching or clay amendments to coarse textured soils are recommended. The CSI–MLFM framework provides a practical and transferable tool for selecting suitable cultivation sites and establishing sustainable understory and ecological mountain cultivation systems for A. acerifolia. Full article

Coal gangue sorting is crucial for improving coal quality and reducing environmental pollution; however, traditional methods suffer from resource wastage, high cost, and intensive labor demands. To address these challenges, this paper investigates an image recognition-based coal gangue sorting technique and proposes an improved YOLOv7-tiny detection model tailored for edge GPU devices with limited computational power and memory. YOLOv7-tiny is selected as the baseline due to its balanced performance in detection accuracy, architectural maturity, and deployment stability on edge GPUs. Compared to newer lightweight detectors such as YOLOv8-N and YOLOv6-N, YOLOv7-tiny adopts an ELAN-based modular design, which facilitates structural optimization without relying on anchor-free reconstruction or complex post-training strategies, making it particularly suitable for engineering enhancements in real-time industrial sorting under resource constraints. To tackle the limitations in computing and storage, we first introduce an ELAN-PC feature extraction module based on partial convolution and ELAN. Secondly, a GhostCSP module is proposed by integrating cross-stage aggregation and Ghost bottleneck concepts. These modules replace the original ELAN structures in the backbone and neck networks, significantly reducing floating-point operations (FLOPs) and the number of parameters. Furthermore, the SIoU loss function is adopted to replace the original bounding box loss, enhancing detection accuracy. Experimental results demonstrate that compared with the baseline YOLOv7-tiny, the improved model increases mAP0.5 from 86.9% to 88.7% (a gain of 1.8%), reduces FLOPs from 13.2 G to 9.2 G (a decrease of 30%), and cuts parameters from 6.0 M to 4.3 M (a reduction of 28%). In dynamic sorting tests, the model achieves a coal gangue sorting rate of 82.2% with a misclassification rate of 8.1%, indicating promising practical applicability. Full article

The growing threat posed by climate change and extreme weather events necessitates the adoption of advanced solutions for crop protection, such as agrotextile nets. The use of anti-rain (AR) and anti-insect (AI) nets is essential to safeguard production, but their effectiveness varies significantly. AR nets offer rain protection but can compromise ventilation, while AI nets ensure a better microclimate but offer poor resistance to precipitation. Given the lack of a standardized index, this study aims to use the rainwater permeability index (Φ_rw) to provide an objective parameter for evaluating and comparing the performance of different agrotextiles. Laboratory tests were conducted on eight different nets (three AR and five AI) using a rainfall simulator. The Φ_rw index, defined as the ratio between the mass of water passing through the net and the total mass of water applied, was evaluated as a function of rainfall intensity (39, 80, and 170 mm/h), net inclination (10°, 20°, and 30°), and the orientation of the warp relative to the slope. The results confirmed that AR nets are most suitable in protecting crops from extreme rainfall, because it becomes clear that AI nets are much more permeable than AR nets. In this sense, the plots show that AI nets usually have a higher permeability than AR nets, between 15% and 25%, depending on rainfall intensity and net inclination. In fact, the AR1 net showed the best performance, with Φ_rw values stabilizing between 40% and 50% under the most common installation conditions. Conversely, AI nets generally exceed 60% permeability, with the AI1 net reaching Φ_rw above 90%, confirming their inadequacy for rain protection alone. In general, AR nets show Φ_rw between 33% and 92%, while Φ_rw for AI nets ranges from 45% and 98%. The research allowed for the comparison of eight agricultural nets with different characteristics and the identification of those that perform best in terms of protection against three different levels of rainfall intensity. The introduction of the Φ_rw index constitutes a significant contribution, providing a quantifiable standard for the selection of agrotextiles in terms of protection from rainfall, regardless of manufacturers’ claims. The data obtained underscore the need to develop future hybrid and multifunctional nets capable of balancing the low water permeability of AR nets with the high ventilation and insect protection of AI nets, thereby ensuring an optimal microclimate and comprehensive crop protection. Full article