Search Results (130)

To enhance the effectiveness of sustainable preservation of modified corn wet distillers’ grains plus solubles (mcWDGS), various additives were tested under aerobic and anaerobic conditions. In Experiment I, the mcWDGS was stored under aerobic conditions for 5 days at 25 °C. Treatments included different organic acids applied at 0.3% or 0.6% of fresh matter (FM). In Experiment II, the mcWDGS was ensiled anaerobically for 8 weeks at 25 °C using organic acids, a commercial acid mixture, or a microbial inoculant at 0.2% FM. In aerobic conditions, the best preservability was achieved with propionic and formic acids at 0.6% FM, as indicated by the lowest temperature, pH, and microbial counts on days 3 and 5 (p ≤ 0.01). Under anaerobic storage, the highest lactic acid concentrations were recorded in the control, citric acid, and commercial acid mixture variants (p ≤ 0.01). Acetic acid levels were highest in the control (p ≤ 0.01). The highest NH3-N content was found in the formic acid variant and the lowest in the inoculant variant (p ≤ 0.01). Aerobic stability after ensiling was greatest in the control and propionic acid groups (p ≤ 0.01). Nutritional analysis showed that the citric acid group had the highest dry matter content (p ≤ 0.01), while the control group contained the most crude protein (p ≤ 0.01) and saturated fatty acids (p ≤ 0.05). The propionic acid and commercial acid mixture variants had the highest unsaturated fatty acids (p ≤ 0.05). Antioxidant capacity was also greatest in the control (p ≤ 0.01). In conclusion, mcWDGS can be effectively preserved aerobically with 0.6% FM of propionic or formic acid, and anaerobically via ensiling, even without additives. These findings support its potential as a stable and nutritious feed ingredient. Full article

Hyperspectral image change detection (HSI-CD) provides substantial value in environmental monitoring, urban planning and other fields. In recent years, deep-learning based HSI-CD methods have made remarkable progress due to their powerful nonlinear feature learning capabilities, yet they face several challenges: mixed-pixel phenomenon affecting pixel-level detection accuracy; heterogeneous spatial scales of change targets where coarse-grained features fail to preserve fine-grained details; and dependence on high-quality labels. To address these challenges, this paper introduces WSCDNet, a weakly supervised HSI-CD network employing coarse-to-fine feature learning, with key innovations including: (1) A dual-branch detection framework integrating binary and multiclass change detection at the sub-pixel level that enhances collaborative optimization through a cross-feature coupling module; (2) introduction of multi-granularity aggregation and difference feature enhancement module for detecting easily confused regions, which effectively improves the model’s detection accuracy; and (3) proposal of a weakly supervised learning strategy, reducing model sensitivity to noisy pseudo-labels through decision-level consistency measurement and sample filtering mechanisms. Experimental results demonstrate that WSCDNet effectively enhances the accuracy and robustness of HSI-CD tasks, exhibiting superior performance under complex scenarios and weakly supervised conditions. Full article

Hydraulic concrete is a critical infrastructure material, with its surface condition playing a vital role in quality assessments for water conservancy and hydropower projects. However, images taken in complex hydraulic environments often suffer from degraded quality due to low lighting, shadows, and noise, making it difficult to distinguish defects from the background and thereby hindering accurate defect detection and damage evaluation. In this study, following systematic analyses of hydraulic concrete color space characteristics, we propose a Dual-Branch Luminance–Chrominance Attention Network (DBLCANet-HCIE) specifically designed for low-light hydraulic concrete image enhancement. Inspired by human visual perception, the network simultaneously improves global contrast and preserves fine-grained defect textures, which are essential for structural analysis. The proposed architecture consists of a Luminance Adjustment Branch (LAB) and a Chroma Restoration Branch (CRB). The LAB incorporates a Luminance-Aware Hybrid Attention Block (LAHAB) to capture both the global luminance distribution and local texture details, enabling adaptive illumination correction through comprehensive scene understanding. The CRB integrates a Channel Denoiser Block (CDB) for channel-specific noise suppression and a Frequency-Domain Detail Enhancement Block (FDDEB) to refine chrominance information and enhance subtle defect textures. A feature fusion block is designed to fuse and learn the features of the outputs from the two branches, resulting in images with enhanced luminance, reduced noise, and preserved surface anomalies. To validate the proposed approach, we construct a dedicated low-light hydraulic concrete image dataset (LLHCID). Extensive experiments conducted on both LOLv1 and LLHCID benchmarks demonstrate that the proposed method significantly enhances the visual interpretability of hydraulic concrete surfaces while effectively addressing low-light degradation challenges. Full article

Grain drying technology is a core process for ensuring food quality, extending storage life, and improving processing adaptability. With the continuous growth of global food demand and the increasing requirements for food quality and energy efficiency, traditional drying technologies face multiple challenges. This paper reviews six major grain drying technologies, comprising hot air drying, microwave drying, infrared drying, freeze drying, vacuum drying, and solar drying. It provides an in-depth discussion of the working principles, advantages, and limitations of each technology, and analyzes their performance in practical applications. In response to challenges such as high energy consumption, uneven drying, and quality loss during the drying process, the paper also explores the research progress of several hybrid drying systems, such as microwave–hot air drying combined systems and solar–infrared drying systems. Although these emerging technologies show significant potential in improving drying efficiency, energy saving, and maintaining food quality, their high costs, scalability, and process stability still limit large-scale applications. Therefore, future research should focus on reducing energy consumption, improving drying precision, and optimizing drying system integration, particularly by introducing intelligent control systems. This would maximize the preservation of food quality while improving the system’s economic efficiency and sustainability, promoting innovation in food production and processing technologies, and further advancing global food security and sustainable agricultural development. Full article

Instruction tuning plays a pivotal role in aligning large language models with diverse tasks, yet its effectiveness hinges on the interplay of data quality, domain composition, and training strategies. This study moves beyond qualitative assessment to systematically quantify these factors through extensive experiments on data selection, data mixture, and training protocols. By quantifying performance trade-offs, we demonstrate that the implicit method SuperFiltering achieves an optimal balance, whereas explicit filters can induce capability conflicts. A fine-grained analysis of cross-domain interactions quantifies a near-linear competition between code and math, while showing that tool use data exhibits minimal interference. To mitigate these measured conflicts, we compare multi-task, sequential, and multi-stage training strategies, revealing that multi-stage training significantly reduces Conflict Rates while preserving domain expertise. Our findings culminate in a unified framework for optimizing instruction tuning, offering actionable, data-driven guidelines for balancing multi-domain performance and enhancing model generalization, thus advancing the field by providing a methodology to move from intuition to systematic optimization. Full article

Friction stir welding (FSW) is a novel welding technique that produces a solid-state weld by generating frictional heat and plastic deformation at the weld spot with a revolving, non-consumable welding tool. Despite processing a wide range of industrial materials, FSW has concentrated on welding aluminum and its alloys because of its high strength-to-weight ratio and uses in the shipbuilding, aerospace, and other fabrication industries. Important FSW process factors that determine the mechanical qualities of the weldment are the tool tilt angle, tool traverse feed, tool pin profile, tool rotational speed (TRS), tool traverse speed (TTS), tool pin profile (TPP), and shoulder plunge depth. Variations in the required process parameters cause defects, which lower the weld quality of FSWed aluminum alloys (AA). Therefore, keeping an eye on and managing the FSW process is crucial to preserving the caliber of the weld joints. The current study aims to investigate the changes in the mechanical characteristics and microstructure of the FSWed AA5052-H111 and AA6061-T6 joints. To perform the FSW experiments, we varied TRS, TTS, and TPP on plates that were 5 mm thick and had a butt joint structure. Following welding, the microstructure of the weld zones was examined to observe how the grains had changed. The joint’s tensile strength reached a maximum of 227 MPa for the square-shaped TPP, and the micro-Vickers hardness test results showed a maximum of 102 HV at the weld nugget zone (WNZ). Full article

The image style transfer task aims to apply the style characteristics of a reference image to a content image, generating a new stylized result. While many existing methods focus on designing feature transfer modules and have achieved promising results, they often overlook the entanglement between content and style features after transfer, making effective separation challenging. To address this issue, we propose a Dual-Branch Decoupled Image Style Transfer Network (DBDST-Net) to better disentangle content and style representations. The network consists of two branches: a Content Feature Decoupling Branch, which captures fine-grained content structures for more precise content separation, and a Style Feature Decoupling Branch, which enhances sensitivity to style-specific attributes. To further improve the decoupling performance, we introduce a dense-regressive loss that minimizes the discrepancy between the original content image and the content reconstructed from the stylized output, thereby promoting the independence of content and style features while enhancing image quality. Additionally, to mitigate the limited availability of style data, we employ the Stable Diffusion model to generate stylized samples for data augmentation. Extensive experiments demonstrate that our method achieves a better balance between content preservation and style rendering compared to existing approaches. Full article

Drying agricultural crops is essential for preserving them and extending their shelf life. Incorporating drying technology in food production has improved product quality and helped meet increasing food demands. Corn (Zea mays) is a major crop grown in Southeast Asia, used for food and livestock. The preservation of crop grains, such as rice and corn, heavily relies on efficient drying processes. Common corn varieties like sweet corn, wild violet corn, waxy corn, white corn, purple corn, and young corn are cereal grains that are often dried for various food products. The study of drying kinetics of these crops is crucial, because drying parameters significantly impact the drying process. This review discusses various factors affecting drying, including airflow, temperature, relative humidity, sample size, and initial moisture content. Understanding these parameters helps optimize the drying process to achieve better quality and efficiency. The review also examines several mathematical models that are used to describe drying kinetics. Models such as the Weibull and Peleg models, Midilli Kucuk model, and the Page and Modified Page models are analyzed for their effectiveness in evaluating design parameters. These models provide a scientific basis for improving drying techniques and ensuring consistency in food production. By presenting a comprehensive review of these aspects, this review aims to enhance the understanding of how to utilize drying technology effectively in food manufacturing and preservation, which can be vital for developing better preservation methods, improving product quality, and ultimately meeting the growing food demands. Full article

Unmanned aerial vehicle (UAV) imagery detection faces challenges in preserving small object features during multi-level downsampling, handling angle and altitude-dependent variations in aerial scenes, achieving accurate localization in dense environments, and performing real-time detection. To address these limitations, we propose HSF-DETR, a lightweight transformer-based detector specifically designed for UAV imagery. First, we design a hybrid progressive fusion network (HPFNet) as the backbone, which adaptively modulates receptive fields to capture multi-scale information while preserving fine-grained details critical for small object detection. Second, building upon features extracted by HPFNet, we develop MultiScaleNet, which enhances feature representation through dual-layer optimization and cross-domain feature learning, significantly improving the model’s capability to handle complex aerial scenarios with diverse object orientations. Finally, to address spatial–semantic alignment challenges, we devise a position-aware align context and spatial tuning (PACST) module that ensures effective feature calibration through precise alignment and adaptive fusion across scales. This hierarchical architecture is complemented by our novel AdaptDist-IoU loss with dynamic weight allocation, which enhances localization accuracy, particularly in dense environments. Extensive experiments using standard detection metrics (mAP50 and mAP50:95) on the VisDrone2019 test dataset demonstrate that HSF-DETR achieves superior performance with 0.428 mAP50 (+5.4%) and 0.253 mAP50:95 (+4%) when compared with RT-DETR, while maintaining real-time inference (69.3 FPS) on an NVIDIA RTX 4090D GPU with only 15.24M parameters and 63.6 GFLOPs. Further validation across multiple public remote sensing datasets confirms the robust generalization capability of HSF-DETR in diverse aerial scenarios, offering a practical solution for resource-constrained UAV applications where both detection quality and processing speed are crucial. Full article

Multimodal image fusion and semantic segmentation play pivotal roles in autonomous driving and robotic systems, yet their inherent interdependence remains underexplored. To address this gap and overcome performance bottlenecks, we propose SC-CoSF, a novel coupled framework that jointly optimizes these tasks through synergistic learning. Our approach replaces traditional duplex encoders with a weight-sharing CNN encoder, implicitly aligning multimodal features while reducing parameter overhead. The core innovation lies in our Self-correction and Collaboration Fusion Module (Sc-CFM), which integrates (1) a Self-correction Long-Range Relationship Branch (Sc-LRB) to strengthen global semantic modeling, (2) a Self-correction Fine-Grained Branch (Sc-FGB) for enhanced visual detail retention through local feature aggregation, and (3) a Dual-branch Collaborative Recalibration (DCR) mechanism for cross-task feature refinement. This design preserves critical edge textures and color contrasts for segmentation while leveraging segmentation-derived spatial priors to guide fusion. We further introduce the Interactive Context Recovery Mamba Decoder (ICRM) to restore lost long-range dependencies during the upsampling process; meanwhile, we propose the Region Adaptive Weighted Reconstruction Decoder (ReAW), which is mainly used to reduce feature redundancy in image fusion tasks. End-to-end joint training enables gradient propagation across all task branches via shared parameters, exploiting inter-task consistency for superior performance. Experiments demonstrate significant improvements over independently optimized baselines in both fusion quality and segmentation accuracy. Full article

Low-light images often contain noise due to the conditions under which they are taken. Fourier transform can reduce this noise in frequency while preserving the image detail embedded in the low-frequency components. Existing low-light image-enhancement methods based on CNN frameworks often fail to extract global feature information and introduce excessive noise, resulting in detail loss. To solve the above problems, we propose a low-light image-enhancement framework and achieve detail restoration and denoising by using Fourier transform. In addition, we design a dual-domain enhancement strategy, which cooperatively utilizes global frequency-domain feature extraction to improve the overall brightness of the image and the amplitude modulation of the spatial-domain convolution operation to perform local detail refinement to improve the quality of the image by suppressing noise, enhancing the contrast, and preserving the texture at the same time. Extensive experiments on low-light datasets show that our results outperform mainstream methods, especially in maintaining natural color distributions and recovering fine-grained details under extreme lighting conditions. We adopted two evaluation indicators, PSNR and SSIM. Our method improved the PSNR by 4.37% compared to the Restormer method and by 1.76% compared to the DRBN method. Full article

Fine-grained sedimentary rocks are ideal carriers for astronomical cycle analysis as they can record and preserve significant astronomical cycle signals. Spectral analysis using the Multi-taper Method (MTM) and Evolutionary Harmonic Analysis (EHA) using the Fast Fourier Transform (FFT) were conducted on natural gamma data from key wells in the Es₃l sub-member in the Bonan Sag, Bohai Bay Basin, China. Gaussian bandpass filtering was applied using a short eccentricity cycle of 100 ka, and a “floating” astronomical time scale for the Es₃l sub-member (Lower 3rd sub-member of Shahejie Formation in Eocene) was established using magnetic stratigraphic ages as boundaries. Stratigraphic divisions were made for single wells in the Es₃l of the Bonan Sag, and a stratigraphic framework was established based on correlations between key wells. The research results indicate the following: Firstly, the Es₃l of the Bonan Sag records significant astronomical cycle signals, with an optimal sedimentation rate of 8.39 cm/ka identified. Secondly, the cyclical thicknesses corresponding to long eccentricity, short eccentricity, obliquity, and precession cycles are 38.9 m, 9.7 m, 4.6–3.4 m, and 1.96–1.66 m, respectively. Thirdly, the Es₃l sub-member stably records 6 long eccentricity cycles and 26 short eccentricity cycles, and the short eccentricity curve is used as a basis for stratigraphic division for high-precision stratigraphic correlations. Fourthly, the quality of sandstone-interbedded mudrock is jointly controlled by the short eccentricity and precession. Eccentricity maximum values result in thicker sandstone interlayers, while minimum precession values promote the thickness of sandstone interlayers. Through astronomical cycle analysis, the depositional evolution mechanism of sandstone-interbedded mudrock is revealed. Combined with the results of high-precision stratigraphic division, this can provide a basis for fine evaluation and “sweet spot” prediction of lacustrine shale oil reservoirs. Full article

The intensifying global demand for food presents significant challenges for sustainable pig production, particularly in the context of escalating input costs, environmental degradation, and resource scarcity. Life cycle assessment provides a comprehensive framework for quantifying environmental impacts and identifying production hotspots within pig production systems. Feed production and manure management are consistently identified as major contributors, emphasising the need for targeted interventions. Although soybean meal remains a key protein source, its association with deforestation and biodiversity loss is driving an interest in more sustainable alternatives. In temperate climates, faba beans offer a promising, locally sourced option, though their wider adoption is limited by amino acid imbalances and anti-nutritional factors. Grain preservation is another critical consideration, as post-harvest losses and fungal contamination compromise feed quality and animal health. Organic acid preservation has emerged as an energy-efficient, cost-effective alternative to industrial drying, improving storage stability and reducing fossil fuel dependence. Additional nutritional strategies, including dietary crude protein reduction, carbohydrate source modification, feed additive inclusion, and maternal nutritional interventions, can enhance nutrient utilisation, intestinal health, and herd resilience while mitigating environmental impact. This review explores practical feed-based strategies to support sustainable, resilient, and resource-efficient pig production and contribute to global food security. Full article

The Hetao Plain Irrigation District of Inner Mongolia faces critical agricultural sustainability challenges due to its arid climate, exacerbated by tightening Yellow River water allocations and pervasive water inefficiencies in the current wheat cultivation practices. This study addresses water scarcity by evaluating the impact of regulated deficit irrigation strategies on spring wheat production, with the dual objectives of enhancing water conservation and optimizing yield–quality synergies. Through a two-year field experiment (2020~2021), four irrigation regimes were implemented: rain-fed control (W0), single irrigation at the tillering–jointing stage (W1), dual irrigation at the tillering–jointing and heading–flowering stages (W2), and triple irrigation incorporating the grain-filling stage (W3). A comprehensive analysis revealed that an incremental irrigation frequency progressively enhanced plant morphological traits (height, upper three-leaf area), population dynamics (leaf area index, dry matter accumulation), and physiological performance (flag leaf SPAD, net photosynthetic rate), all peaking under the W2 and W3 treatments. While yield components and total water consumption exhibited linear increases with irrigation inputs, grain yield demonstrated a parabolic response, reaching maxima under W2 (29.3% increase over W0) and W3 (29.1%), whereas water use efficiency (WUE) displayed a distinct inverse trend, with W2 achieving the optimal balance (4.6% reduction vs. W0). The grain quality parameters exhibited divergent responses: the starch content increased proportionally with irrigation, while protein-associated indices (wet gluten, sedimentation value) and dough rheological properties (stability time, extensibility) peaked under W2. Notably, protein content and its subcomponents followed a unimodal pattern, with the W0, W1, and W2 treatments surpassing W3 by 3.4, 11.6, and 11.3%, respectively. Strong correlations emerged between protein composition and processing quality, while regression modeling identified an optimal water consumption threshold (3250~3500 m³ ha⁻¹) that concurrently maximized grain yield, protein output, and WUE. The W2 regime achieved the synchronization of water conservation, yield preservation, and quality enhancement through strategic irrigation timing during critical growth phases. These findings establish a scientifically validated framework for sustainable, intensive wheat production in arid irrigation districts, resolving the tripartite challenge of water scarcity mitigation, food security assurance, and processing quality optimization through precision water management. Full article

Stored product pests are controlled primarily through applying pyrethroid and organophosphate insecticides or through fumigation with phosphine (PH₃). However, several populations of weevils are resistant to these insecticides. Essential oils appear to be safe alternatives for both humans and the environment. The objective was to investigate the toxicity of Piper aduncum essential oil (PAEO) to Sitophilus zeamais and evaluate its effects on corn grain quality during the four-month storage period. This study was conducted in two stages. In the first stage, the toxicity of PAEO at concentrations lethal to 50 and 95% of insects (LC₅₀ and LC₉₅) was estimated. The second step evaluated the degree of infestation, water content, apparent specific mass, loss of mass, electrical conductivity, and percentage of germination of grains at 0, 30, 60, 90, and 120 days after exposure to PAEO, deltamethrin (pyrethroid), and the control treatment. PAEO presents toxicity to S. zeamais. The LC₅₀ and LC₉₅ values are 298.50 µL kg⁻¹ and 585.20 µL kg⁻¹, respectively. The increases in infestation degree, water content, electric conductivity, and mass loss, as well as reductions in apparent specific mass and germination, show the loss of corn quality during the 120-day storage period, being more significant when no product is applied. PAEO delays the loss of quality of the grains, presenting a greater capacity to preserve the grains for a longer period. Full article