Search Results (618)

Liquid whey (LW) is a nutrient-rich dairy by-product and a promising resource for animal nutrition. However, data regarding its impact on intestinal morphology and endocrine signaling are limited. Therefore, the current study aims to dissect those aspects. An experiment was conducted on 14 crossbred pigs divided into control (fed 3% of their body weight pelleted feed) and LW (fed 3% of their body weight supplemented with 1.5 L of LW) groups. The results show a significantly increased body weight gain in LW pigs during the second half of the experiment. Moreover, an increased ileal villus height, deeper crypts, and a thicker muscularis externa in the duodenum and jejunum have been reported in LW-fed pigs. Goblet cell count revealed a significant abundance of these cells in duodenal villi and jejunal crypts of the LW group, suggesting enhanced mucosal defense in all segments of LW-fed pigs. While Cholecystokinin8 and Galanin showed the same expression pattern among both groups and SI segments, the leptin expression was significantly higher in LW swine. These findings indicate that LW promotes growth, gut mucosa remodeling, and neuroendocrine signaling, thus supporting LW use as a functional dietary strategy with attention to the adaptation period. Full article

Reconfigurable Intelligent Surface (RIS) has emerged as a promising enabling technology for wireless communications, which significantly enhances system performance through real-time manipulation of electromagnetic wave reflection characteristics. In RIS-assisted communication systems, existing deep learning-based channel state information (CSI) feedback methods often suffer from excessive parameter requirements and high computational complexity. To address this challenge, this paper proposes LwCSI-Net, a lightweight autoencoder network specifically designed for RIS-assisted multiple-input single-output (MISO) systems, aiming to achieve efficient and low-complexity CSI feedback. The core contribution of this work lies in an innovative lightweight feedback architecture that deeply integrates multi-layer convolutional neural networks (CNNs) with attention mechanisms. Specifically, the network employs 1D convolutional operations with unidirectional kernel sliding, which effectively reduces trainable parameters while maintaining robust feature-extraction capabilities. Furthermore, by incorporating an efficient channel attention (ECA) mechanism, the model dynamically allocates weights to different feature channels, thereby enhancing the capture of critical features. This approach not only improves network representational efficiency but also reduces redundant computations, leading to optimized computational complexity. Additionally, the proposed cross-channel residual block (CRBlock) establishes inter-channel information-exchange paths, strengthening feature fusion and ensuring outstanding stability and robustness under high compression ratio (CR) conditions. Our experimental results show that for CRs of 16, 32, and 64, LwCSI-Net significantly improves CSI reconstruction performance while maintaining fewer parameters and lower computational complexity, achieving an average complexity reduction of 35.63% compared to state-of-the-art (SOTA) CSI feedback autoencoder architectures. Full article

Fossil fuels, including coal, are a basis of energy systems in many countries worldwide. However, coal mining is associated with several difficulties, which include high temperatures within the coal mining area. It causes a need for cooling for safety reasons and also for the comfort of miners’ work. Typical cooling systems in mines are based on central systems, in which chilled water is generated in the compressor or absorption coolers on the ground and transported via pipelines to the air coolers in the areas of mining. The progressive mining operation causes a gradual increase in the distance between chilled water generators and air coolers, causing a decrease in the efficiency of the entire system and insufficient cooling capacity. As a result, it is necessary to increase the diameter of the chilled water pipelines and increase the cooling capacity of the chillers, which is associated with additional investment and technical problems. One solution to this problem may be the use of so-called ice slurry instead of chilled water in the existing mine cooling system. This article presents the cooling system, located in the mine LW Bogdanka S.A., based on ice slurry. The structure of the system and its key parameters are presented. The results show that switching from cooling water to ice slurry allowed the cooling capacity of the entire system to increase by 50% while maintaining the existing piping. This demonstrates the very high potential for the use of ice slurry, not only in mines, but wherever further increases in piping diameters to maintain the required cooling capacity are not possible or cost-effective. Full article

Lipid metabolism is critical for the physiological activities of signal transduction, metabolic regulation, and energy provision, and Wuzhishan (WZS) pigs are a promising animal model for studying human diseases. However, lipid metabolites in the heart and liver of WZS pigs are indistinct. In this study, we detected gene expression, blood biochemical parameters, and metabolic profiles of hearts and livers of WZS and Large White (LW) pigs, and analyzed correlations between metabolites. The results showed that the fatty acid metabolic process was present in both the heart and liver, and was more dominant in the liver. Although the expression of lipid absorption-related genes of CYP7A1 increased in the liver, CEBPB levels increased in both the liver and heart; the fatty acid beta-oxidation genes RXRA and ACSS2 also showed increased expression. The quantity of metabolites related to lipid synthesis decreased in the liver, heart, and blood for WZS pigs compared to that of LW pigs, indicating a balance of lipid synthesis and breakdown for WZS pigs. Moreover, the lipid metabolites in the liver and heart exhibited strong correlations with each other and showed similar correlations to blood biochemical parameters, respectively. This study declared the balance of lipid metabolism in both the heart and liver, and identified their connections for WZS pigs. Full article

Clouds modulate the net radiative flux that interacts with both shortwave (SW) and longwave (LW) radiation, but the uncertainties regarding their effect in polar regions are especially high because ground observations are lacking and evaluation through satellites is made difficult by high surface reflectance. In this work, sky conditions for six different polar stations, two in the Arctic (Ny-Ålesund and Utqiagvik [formerly Barrow]) and four in Antarctica (Neumayer, Syowa, South Pole, and Dome C) will be presented, considering the decade between 2010 and 2020. Measurements of broadband SW and LW radiation components (both downwelling and upwelling) are collected within the frame of the Baseline Surface Radiation Network (BSRN). Sky conditions—categorized as clear sky, cloudy, or overcast—were determined using cloud fraction estimates obtained through the RADFLUX method, which integrates shortwave (SW) and longwave (LW) radiative fluxes. RADFLUX was applied with daily fitting for all BSRN stations, producing two cloud fraction values: one derived from shortwave downward (SWD) measurements and the other from longwave downward (LWD) measurements. The variation in cloud fraction used to classify conditions from clear sky to overcast appeared consistent and reasonable when compared to seasonal changes in shortwave downward (SWD) and diffuse radiation (DIF), as well as longwave downward (LWD) and longwave upward (LWU) fluxes. These classifications served as labels for a machine learning-based classification task. Three algorithms were evaluated: Random Forest, K-Nearest Neighbors (KNN), and XGBoost. Input features include downward LW radiation, solar zenith angle, surface air temperature ($T_a$), relative humidity, and the ratio of water vapor pressure to $T_a$. Among these models, XGBoost achieved the highest balanced accuracy, with the best scores of 0.78 at Ny-Ålesund (Arctic) and 0.78 at Syowa (Antarctica). The evaluation employed a leave-one-year-out approach to ensure robust temporal validation. Finally, the results from cross-station models highlighted the need for deeper investigation, particularly through clustering stations with similar environmental and climatic characteristics to improve generalization and transferability across locations. Additionally, the use of feature normalization strategies proved effective in reducing inter-station variability and promoting more stable model performance across diverse settings. Full article

Crash boxes play a crucial role in automotive safety by absorbing impact energy during collisions. The advancement of additive manufacturing (AM), particularly Fused Deposition Modeling (FDM), has enabled the fabrication of geometrically complex and lightweight crash boxes. This study presents a comparative evaluation of the crashworthiness performance of five FDM materials, namely, PLA+, PLA-ST, PLA-LW, PLA-CF, and PETG, across four structural configurations: Single-Cell Circle (SCC), Multi-Cell Circle (MCC), Single-Cell Square (SCS), and Multi-Cell Square (MCS). Quasi-static axial compression tests are conducted to assess the specific energy absorption $\left(SEA\right)$ and crush force efficiency $\left(CFE\right)$ of each material–geometry combination. Among the materials, PLA-CF demonstrates superior performance, with the MCC configuration achieving an $SEA$ of 22.378 ± 0.570 J/g and a $CFE$ of 0.732 ± 0.016. Multi-cell configurations consistently outperformed single-cell designs across all materials. To statistically quantify the influence of material and geometry on crash performance, a two-factor ANOVA was performed, highlighting geometry as the most significant factor across all evaluated metrics. Additionally, a comparative test with aluminum 6063-T5 demonstrates that PLA-CF offers comparable crashworthiness, with advantages in mass reduction, reduced $PCF$, and enhanced design flexibility inherent in AM. These findings provide valuable guidance for material selection and structural optimization in FDM-based crash boxes. Full article

The gut microbiota critically influences lipid metabolism and fat deposition in pigs, processes that underpin pork quality preferences and differentiate the meat traits of Chinese indigenous breeds (fat-type) from those of Western commercial breeds (lean-type). To explore the mechanisms underlying breed-specific fatty acid absorption, we compared the rectal and colonic microbiota and metabolite profiles of Huainan and Large White pigs using 16S rRNA sequencing and untargeted metabolomics. HN pigs exhibited enriched Lactobacillus johnsonii and Lactobacillus amylovorus, along with a significantly higher Firmicutes/Bacteroidetes ratio. Functional predictions further revealed elevated microbial pathways related to glycolysis, pyruvate metabolism, and ABC transporters in HN pigs. Conversely, LW pigs showed increased abundance of potentially pro-inflammatory bacteria and enriched pathways for lipopolysaccharide (LPS) biosynthesis. Metabolites such as 4-ethyl-2-heptylthiazole and picolinic acid were significantly upregulated in HN pigs and served as robust biomarkers (Area Under the Curve, AUC = 1.0),with perfect discrimination observed in both rectal and colonic samples. Integrative analysis identified 52 co-enriched microbial and metabolic pathways in HN pigs, including short-chain fatty acid (SCFA) production, lipid biosynthesis and transport, amino acid metabolism, ABC transporter activity, and the PPAR signaling pathway, supporting a microbiota–metabolite axis that enhances fatty acid absorption and gut immune balance. These findings provide mechanistic insight into breed-specific fat deposition and offer candidate biomarkers for improving pork quality via precision nutrition and breeding. Full article

The transition to passenger car electrification is a crucial step in China’s strategic efforts to achieve carbon peak and carbon neutrality. However, previous research has not considered the variances in vehicle models. Hence, this study aims to fill this gap by comparing the carbon emission reduction and economic feasibility of battery electric vehicles (BEVs) in the Chinese market, taking into account different powertrains, vehicle segments, classes, and driving ranges. Next, the study identifies the most cost-effective BEV within each market segment, employing life-cycle assessment and life cycle cost analysis methods. Moreover, at different levels of technological development, we construct three low-carbon measures, including electricity decarbonization (ED), energy efficiency improvement (EEI), and vehicle lightweight (LW), to quantify the emission mitigation potentials from different carbon reduction pathways. The findings indicate that BEVs achieve an average carbon reduction of about 31.85% compared to internal combustion engine vehicles (ICEVs), demonstrating a significant advantage in carbon reduction. However, BEVs are not economically competitive. The total life cycle cost of BEVs is 1.04–1.68 times higher than that of ICEVs, with infrastructure costs accounting for 18.8–57.8% of the vehicle’ s life cycle costs. In terms of cost-effectiveness, different models yield different results, with sedans generally outperforming sport utility vehicles (SUVs). Among sedans, both A-class and B-class sedans have already reached a point of cost-effectiveness, with the BEV400 emerging as the optimal choice. In low-carbon emission reduction scenarios, BEVs could achieve carbon reduction potentials of up to 45.3%, 14.9%, and 9.0% in the ED, EEI, and LW scenarios, respectively. Thus, electricity decarbonization exhibits the highest potential for mitigating carbon emissions, followed by energy efficiency improvement and vehicle lightweight. There are obvious differences in the stages of impact among different measures. The ED measure primarily impacts the waste treatment process (WTP) stage, followed by the vehicle cycle, while the EEI measure only affects the WTP stage. The LW measure has a complex impact on emission reductions, as the carbon reductions achieved in the WTP stage are partially offset by the increased carbon emissions in the vehicle cycle. Full article

African swine fever virus (ASFV) causes a highly contagious and lethal hemorrhagic disease and significantly threatens the pig industry. There is no commercially effective vaccine available currently, making the detection of ASFV critical for control and prevention. Previously, we established the CRISPR-LbCas12a and LwCRSIRP-Cas13a visual detections of ASFV, separately, targeting the structural p17 gene D117L. In this study, we performed the parallel detections of ASFV based on the conserved viral protease gene S273R using CRISPR-LbCas12a and CRISPR-LbuCas13a systems. Our results showed that both systems are able to specifically detect ASFV as low as two copies of the S273R gene, and effectively detect clinical samples with minimal DNA purification. The work promotes CRISPR-Cas systems for the application of on-site detection in the field. Full article

Quantifying methane (CH₄) emissions is essential for climate change mitigation; however, current estimation methods often suffer from substantial uncertainties, particularly at the site level. This study introduces a drone-based approach for measuring CH₄ emissions using an open-path Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor mounted parallel to the ground, rather than in the traditional nadir-pointing configuration. Controlled CH₄ release experiments were conducted to evaluate the method’s accuracy, employing a modified mass-balance technique to estimate emission rates. Two wind data processing strategies were compared: a logarithmic wind profile (LW) and a constant scalar wind speed (SW). The LW approach yielded highly accurate results, with an average recovery rate of 98%, while the SW approach showed greater variability with increasing distance from the source, although it remained reliable in close proximity. The method demonstrated the ability to quantify emissions as low as 0.08 g s⁻¹ with approximately 4% error, given sufficient sampling. These findings suggest that the proposed UAV-based system is a promising, cost-effective tool for accurate CH₄ emission quantification in sectors, such as agriculture, energy, and waste management, where traditional monitoring techniques may be impractical or limited. Full article

This study investigates the potential of low-density polymeric materials to enhance the deformation energy absorption of drone fuselage components manufactured using fused filament fabrication (FFF). Two materials—PLA (polylactic acid) and LW-PLA (lightweight polylactic acid)—were selected based on their accessibility, printability, and prior mechanical characterizations. While PLA is widely used in additive manufacturing, its brittleness limits its suitability for components subjected to accidental or impact loads. In contrast, LW-PLA exhibits greater ductility and energy absorption, making it a promising alternative where weight reduction is critical and structural redundancy is available. To evaluate the structural efficiency, a simplified analysis scenario was developed using a theoretical 300 J collision energy, not as a design condition, but as a comparative benchmark for assessing the performance of various metastructural configurations. The experimental results demonstrate that a stiffening core of the LW-PLA metastructure can reduce the component weight by over 60% while maintaining or improving the deformation energy absorption. Modified prototypes with hybrid internal structures demonstrated stable performances under repeated loading; however, the tests also revealed a buckling-like failure of the internal core in specific configurations, highlighting the need for core stabilization within metastructures to ensure reliable energy dissipation. Full article

Advances in spinning technology have increased the demand for upland cotton (Gossypium hirsutum L.) cultivars with superior fiber quality. However, progress in breeding for traits such as fiber length is constrained by limited phenotypic and genetic diversity within upland cotton. Introgression from Gossypium barbadense, a closely related species known for its superior fiber traits, offers a promising strategy. Sealand 883 is an obsolete upland germplasm developed through G. barbadense introgression and is known for its long and fine fibers. Previous studies have identified a fiber length quantitative trait locus (QTL) on Chromosome 25, designated qFL-Chr.25, in Sealand 883, conferred by an allele introgressed from G. barbadense. This study evaluated the effect of qFL-Chr.25 in near-isogenic introgression lines (NIILs) using Advanced Fiber Information System (AFIS) measurements. Across four genetic backgrounds, NIILs carrying qFL-Chr.25 consistently exhibited longer fibers, as reflected in multiple length parameters, including UHML, L(n), L(w), UQL(w), and L5%. Newly developed TaqMan SNP diagnostic markers flanking the QTL enable automated, reproducible, and scalable screening of large populations typical in commercial breeding programs. These markers will facilitate the incorporation of qFL-Chr.25 into commercial breeding pipelines, accelerating fiber quality improvement and enhancing the competitiveness of cotton against synthetic fibers. Full article

Distinctions between plant perianths are often defined by structural variations, which makes it critical to understand species evolution through the lens of morphological differentiation. Additionally, the size of the perianth is often closely related to the successful reproduction of plants, and the perianth area is generally considered one of the indicators of perianth size. The Montgomery equation (ME) hypothesizes that the individual leaf area is proportional to the product of leaf length and width, with the proportionality coefficient referred to as the Montgomery parameter (MP). To test the validity of the ME for calculating the tepal area, a total of 541 tepals (including petaloid and sepaloid tepals, which have similar shapes but different colors) from 60 Liriodendron × sinoamericanum P.C. Yieh ex C.B. Shang & Z.R. Wang flowers were used to fit the relationship between the tepal area (A) and the product of the tepal length (L) and width (W). Furthermore, this study compared whether there were significant differences in MPs between the two types of tepals, as well as differences in the fitting performance of the ME for each type. The root-mean-square error (RMSE) and mean absolute percentage error (MAPE) were used to assess the goodness of fit. The results revealed that the ME had low RMSE values (<0.05) and MAPE values (<5%), along with a high correlation coefficient (>0.95), when fitting the relationship between A and LW for either of the two different types of tepals. These findings indicate that the ME is effective in predicting the tepal area. Furthermore, there was a difference between the MPs of the two types of tepals. However, since the ME fitting of the data for each tepal type individually, as well as the combined data, all yielded a good fitting performance, the difference between the two types of tepals can be considered negligible in terms of its impact on the fitting results. Therefore, based on the combined morphology and ME fitting results of the two types of tepals, the tepals in L. × sinoamericanum do not show obvious differentiation. This study provides new insights into the understanding of the differentiation of similar organs during the evolution of angiosperms. Full article

Lightning-generated whistlers (LW) play a crucial role in understanding magnetosphere–ionosphere coupling mechanisms and, perhaps, identifying precursor signals of natural disasters, such as volcanic eruptions and earthquakes. Traditional frequency–time image recognition techniques require approximately 40 years to analyze seven years of observational data from the China Seismo-Electromagnetic Satellite (CSES), which fails to meet the requirements for practical implementation. To address this issue, a novel and highly efficient model for LW recognition is proposed, integrating speech processing technology with a parallel bidirectional Simple Recurrent Unit (SRU) neural network. The proposed model significantly outperforms traditional methods in computational efficiency, reducing the parameter count by 99% to 0.1 M and enhancing processing speed by 99%, achieving 20 ms per sample. Despite these improvements, the model maintains excellent performance metrics, including 93% precision, 88.7% recall, and 90.7% F1-score, which is a measure of predictive performance. As a result, the model can process seven years of data in just 33 days, marking a 442-fold increase in processing speed compared to conventional approaches. Full article

Knowledge graph-enhanced recommendation has gained increasing attention for its ability to provide structured semantic context. However, most existing approaches struggle with two critical challenges: the sparsity of long-tail relations in knowledge graphs and the lack of adaptability to users’ dynamic preferences. In this paper, we propose GLARA, a novel recommendation framework that combines semantic abstraction and behavioral adaptation through a two-stage modeling process. First, a Virtual Relational Knowledge Graph (VRKG) is constructed by clustering semantically similar relations into higher-level virtual groups, which alleviates relation sparsity and enhances generalization. Then, a global Local Weighted Smoothing (LWS) module and a local Graph Attention Network (GAT) are integrated to jointly refine item and user representations: LWS propagates information within each virtual relation subgraph to improve semantic consistency, while GAT dynamically adjusts neighbor importance based on recent interaction signals. Extensive experiments on Last.FM and MovieLens-1M demonstrate that GLARA outperforms state-of-the-art methods, achieving up to 5.8% improvements in NDCG@20, especially in long-tail and cold-start scenarios. Additionally, case studies confirm the model’s interpretability by tracing recommendation paths through clustered semantic relations. This work offers a flexible and interpretable solution for robust recommendation under sparse and dynamic conditions. Full article