Search Results (1,836)

Pineapple peel cellulose nanofibrils (PCNFs) were facilely prepared by the ball milling method assisted by alkali solution (3 wt% NaOH) and a wet grinding medium, using various treated pineapple peels (hot water treatment (WT), bleaching treatment (BT), alkaline treatment (AT), and baleaching–alkaline treatment (ABT)) as raw materials. The structure of the obtained PCNFs (i.e., WT-PCNF, BT-PCNF, AT-PCNF, and ABT-PCNF) was characterized to analyze the influence of component intervention. The results indicated that NaOH-assisted ball milling did not change the crystal structure of cellulose, and the yield and thermal stability of the PCNFs was improved. The average diameters of WT-PCNF, BT-PCNF, AT-PCNF, and ABT-PCNF were 24.16, 21.53, 23.04, and 19.46 nm, respectively, in which BT-PCNF and ABT-PCNF exhibited a higher defibrillating degree and smaller diameter. Particularly, NaOH-assisted ball milling can promote the removal of non-cellulose components. The viscosity and modulus of BT-PCNF were relatively higher due to the presence of residual hemicellulose as a natural linker of fibers. The current research provides insights for simplifying the preparation and functionalization of nanocellulose. Full article

This study investigated the effects of Clostridium butyricum (C. butyricum) supplementation on the bacterial community composition and fermentation characteristics of the digestive system, assessed blood antioxidant capacity, and evaluated growth performance and digestibility in pre-weaning Angus calves. Sixteen male Angus calves were randomly divided into two groups (n = 8). The control group was fed a basal diet, while the other group was supplemented with 2 × 10⁷ colony-forming unit/d C. butyricum in the basal diet. After 8 weeks of feeding, body growth and nutrient intake were measured. Rumen fluid samples, blood samples, and fecal samples were collected and analyzed. Fecal scores were recorded daily, and the frequency of diarrhea treatments was documented. The results showed C. butyricum supplementation improved the digestibility of neutral detergent fiber, crude protein, and ether extract in calves, as well as the growth hormone content in the blood. Furthermore, supplementation elevated isovaleric acid concentrations in the rumen, modulated the microbiome, and increased pre-weaning body weight and average daily gain of calves. Meanwhile, C. butyricum supplementation reduced myeloperoxidase and superoxide dismutase levels while increasing total antioxidant capacity in the blood, alleviating oxidative stress, and enhancing the antioxidant capacity of the body. Intestinal health was also improved, as evidenced by an increase in butyrate content, a reduction in enterotoxigenic Escherichia coli, lower fecal scores, and a decreased need for diarrhea treatments. In summary, dietary C. butyricum supplementation may support digestive system development, enhance antioxidant capacity, and improve growth performance and health status in pre-weaning Angus calves. Full article

Rice wine is a beverage rich in flavor, but the flavor difference caused by rice raw materials has received little attention. To determine the key aroma compounds in rice wine, four types of samples were analyzed by gas chromatography–mass spectrometry (GC–MS), gas chromatography–olfactometry (GC-O), and sensory evaluation. Thirty-eight aroma compounds were detected in the experiment, thirteen of which were identified and quantified using the internal standard method. Additionally, multivariate statistical analyses such as partial least squares discriminant analysis (PLS-DA) effectively revealed three major differential aroma components in rice wine (VIP value ≥ 1). Furthermore, by correlation analysis, it was found that starch and fat in the raw material properties of glutinous rice were significantly and positively correlated with the main differential volatile aroma components in rice wine (p < 0.05). Combined with principal component analysis (PCA), the selection of glutinous rice varieties associated with starch and lipid characteristics during the rice wine brewing process is conducive to improving the overall quality of rice wine. Full article

The literature has shown extensive interest in exploring the factors of subjective well-being (SWB). However, most research has conducted cross-sectional analysis of the built environment (BE), commute, and SWB, and little is known about gender differences in their connections. Based on two periods of survey data of 4297 respondents from China, the study performs a cross-sectional and longitudinal examination of whether the BE and commute have effects on SWB, and how the effects differ between men and women. The results reveal that BE features, including destination accessibility and residential density, significantly affect SWB, with stronger impacts observed among men. Men benefit more from greater accessibility and are more negatively affected by higher residential density than women. In contrast, commute mode and duration influence SWB in similar ways for both genders. A shift from nonactive to active commuting improves well-being for men and women alike. Furthermore, certain life events produce gender-specific effects. For instance, childbirth increases SWB for men but decreases it for women. These findings highlight the importance of gender-sensitive planning in building inclusive urban and transportation environments that enhance population well-being. Full article

Compression pants, as functional sportswear providing external pressure, are widely used to enhance athletic performance and accelerate recovery. However, systematic investigations into their effectiveness during anaerobic exercise and the impact of different pressure levels on performance and post-exercise recovery remain limited. This randomized crossover controlled trial recruited 20 healthy male university students to compare the effects of four garment conditions: non-compressive pants (NCP), moderate-pressure compression pants (MCP), high-pressure compression pants (HCP), and ultra-high-pressure compression pants (UHCP). Anaerobic performance was assessed through vertical jump, agility tests, and the Wingate anaerobic test, with indicators including time at peak power (TPP), peak power (PP), average power (AP), minimum power (MP), power drop (PD), and total energy produced (TEP). Post-exercise blood lactate concentrations and heart rate responses were also monitored. The results showed that both HCP and UHCP significantly improved vertical jump height (p < 0.01), while MCP outperformed all other conditions in agility performance (p < 0.05). In the Wingate test, MCP achieved a shorter TPP compared to NCP (p < 0.05), with significantly higher AP, lower PD, and greater TEP than all other groups (p < 0.05), whereas HCP showed an advantage only in PP over NCP (p < 0.05). Post-exercise, all compression pant groups recorded significantly higher peak blood lactate (Lamax) levels than NCP (p < 0.05), with MCP showing the fastest lactate clearance rate. Heart rate analysis revealed that HCP and UHCP induced higher maximum heart rates (HR_max) (p < 0.05), while MCP exhibited superior heart rate recovery at 3, 5, and 10 min post-exercise (p< 0.05). These findings suggest that compression pants with different pressure levels yield distinct effects on anaerobic performance and physiological recovery. Moderate-pressure compression pants demonstrated the most balanced and beneficial outcomes across multiple performance and recovery metrics, providing practical implications for the individualized design and application of compression garments in athletic training and rehabilitation. Full article

Anthracnose, a significant disease of aloe with characteristics of contact transmission, poses a considerable threat to the economic viability of aloe cultivation. To address the challenges of accurately detecting and classifying crop diseases in complex environments, this study proposes an enhanced algorithm, YOLOv11-seg-DEDB, based on the improved YOLOv11-seg model. This approach integrates multi-scale feature enhancement and a dynamic attention mechanism, aiming to achieve precise segmentation of aloe anthracnose lesions and effective disease level discrimination in complex scenarios. Specifically, a novel Disease Enhance attention mechanism is introduced, combining spatial attention and max pooling to improve the accuracy of lesion segmentation. Additionally, the DCNv2 is incorporated into the network neck to enhance the model’s ability to extract multi-scale features from targets in challenging environments. Furthermore, the Bidirectional Feature Pyramid Network structure, which includes an additional p2 detection head, replaces the original PANet network. A more lightweight detection head structure is designed, utilizing grouped convolutions and structural simplifications to reduce both the parameter count and computational load, thereby enhancing the model’s inference capability, particularly for small lesions. Experiments were conducted using a self-collected dataset of aloe anthracnose infected leaves. The results demonstrate that, compared to the original model, the improved YOLOv11-seg-DEDB model improves segmentation accuracy and mAP@50 for infected lesions by 5.3% and 3.4%, respectively. Moreover, the model size is reduced from 6.0 MB to 4.6 MB, and the number of parameters is decreased by 27.9%. YOLOv11-seg-DEDB outperforms other mainstream segmentation models, providing a more accurate solution for aloe disease segmentation and grading, thereby offering farmers and professionals more reliable disease detection outcomes. Full article

Ischemic stroke is a serious disease that frequently occurs in the elderly and is characterized by a complex pathophysiology and a limited number of effective therapeutic agents. Salvianolic acid A (SAL-A) is a natural product derived from the rhizome of Salvia miltiorrhiza, which possesses diverse pharmacological activities. This study aims to investigate the effect and mechanisms of SAL-A in inhibiting ferroptosis to improve ischemic stroke. Brain injury, oxidative stress and ferroptosis-related analysis were performed to evaluate the effect of SAL-A on ischemic stroke in photochemical induction of stroke (PTS) in mice. Lipid peroxidation levels, antioxidant protein levels, tissue iron content, nuclear factor erythroid 2-related factor 2 (Nrf2), and mitochondrial morphology changes were detected to explore its mechanism. SAL-A significantly attenuated brain injury, reduced malondialdehyde (MDA) and long-chain acyl-CoA synthase 4 (ACSL4) levels. In addition, SAL-A also amplified the antioxidative properties of glutathione (GSH) when under glutathione peroxidase 4 (GPX4), and the reduction in ferrous ion levels. In vitro, brain microvascular endothelial cells (b.End.3) exposed to oxygen-glucose deprivation/reoxygenation (OGD/R) were used to investigate whether the anti-stroke mechanism of SAL-A is related to Nrf2. Following OGD/R, ML385 (Nrf2 inhibitor) prevents SAL-A from inhibiting oxidative stress, ferroptosis, and mitochondrial dysfunction in b.End.3 cells. In conclusion, SAL-A inhibits ferroptosis to ameliorate ischemic brain injury, and this effect is mediated through Nrf2. Full article

The southern Great Xing’an Range is located in the overlap zone of the Paleo-Asian Ocean metallogenic domain and the Circum-Pacific metallogenic domain. It hosts numerous Sn-polymetallic deposits, such as Weilasituo, Bianjiadayuan, Huanggang, and Dajing, and witnessed multiple episodes of magmatism during the Late Mesozoic. The study area is situated within the Huanggangliang-Ganzhuermiao metallogenic belt in the southern Great Xing’an Range. The region has witnessed extensive magmatism, with Mesozoic magmatic activities being particularly closely linked to regional mineralization. We present petrographic, zircon U-Pb chronological, lithogeochemical, and Lu-Hf isotopic analyses of the Yexilinhundi granites. The results indicate that the granite porphyry and granodiorite were emplaced during the Late Jurassic. Both rocks exhibit high SiO₂, K₂O + Na₂O, differentiation index (DI), and 10,000 Ga/Al ratios, coupled with low MgO contents. They show distinct fractionation between light and heavy rare earth elements (LREEs and HREEs), exhibit Eu anomalies, and have low whole-rock zircon saturation temperatures (T_zr), collectively demonstrating characteristics of highly fractionated I-type granites. The εHf(t) values of the granites range from 0.600 to 9.14, with young two-stage model ages (T_DM2 = 616.0~1158 Ma), indicating that the magmatic source originated from partial melting of Mesoproterozoic-Neoproterozoic juvenile crust. This study proposes that the granites formed in a post-collisional/post-orogenic extensional setting associated with the subduction of the Mongol-Okhotsk Ocean, providing a scientific basis for understanding the relationship between the formation of Sn-polymetallic deposits and granitic magmatic evolution in the study area. Full article

Mineral imbalances in livestock can critically impair growth, immunity, and productivity. Yaks inhabiting the Qinghai–Tibetan Plateau face unique environmental challenges, including high-altitude-induced nutrient variability. This study investigated the status of mineral elements and their correlations with altitude in healthy yak calves across five regions in Ganzi Prefecture, located at elevations ranging from 3100 to 4100 m. Hair and serum samples from 35 calves were analyzed for 11 essential elements (Na, K, Ca, Mg, S, Cu, Fe, Mn, Zn, Co, and Se). The results revealed widespread deficiencies. Key deficiencies were identified: hair Na and Co were significantly below references value (p < 0.05), and Se was consistently deficient across all regions, with deficiency rates ranging from 35.73% to 56.57%. Serum Mg and Cu were generally deficient (Mg deficiency > 26% above 3800 m). S, Mn (low detection), and Co were also suboptimal. Serum selenium deficiency was notably severe in lower-altitude areas (≤59.07%). Significant correlations with altitude were observed: hair sodium levels decreased with increasing altitude (r = −0.72), while hair manganese (r = 0.88) and cobalt (r = 0.65) levels increased. Serum magnesium deficiency became more pronounced at higher elevations (r = 0.58), whereas selenium deficiency in serum was more severe at lower altitudes (r = −0.61). These findings indicate prevalent multi-element deficiencies in yak calves that are closely linked to altitude and are potentially influenced by soil mineral composition and feeding practices, as suggested by previous studies. The study underscores the urgent need for region-specific nutritional standards and altitude-adapted mineral supplementation strategies to support optimal yak health and development. Full article

Hydrogen energy is a pivotal carrier for achieving carbon neutrality, requiring green and efficient production via water electrolysis. However, the anodic oxygen evolution reaction (OER) involves a sluggish four-electron transfer process, resulting in high overpotentials, while the prohibitive cost and complex preparation of precious metal catalysts impede large-scale commercialization. In this study, we develop a FeCo-based bimetallic deep eutectic solvent (FeCo-DES) as a multifunctional reaction medium for engineering a three-dimensional (3D) coral-like FeOOH-Co₂(OH)₃Cl/NF composite via a mild one-step impregnation approach (70 °C, ambient pressure). The FeCo-DES simultaneously serves as the solvent, metal source, and redox agent, driving the controlled in situ assembly of FeOOH-Co₂(OH)₃Cl hybrids on Ni(OH)₂/NiOOH-coated nickel foam (NF). This hierarchical architecture induces synergistic enhancement through geometric structural effects combined with multi-component electronic interactions. Consequently, the FeOOH-Co₂(OH)₃Cl/NF catalyst achieves a remarkably low overpotential of 197 mV at 100 mA cm⁻² and a Tafel slope of 65.9 mV dec⁻¹, along with 98% current retention over 24 h chronopotentiometry. This study pioneers a DES-mediated strategy for designing robust composite catalysts, establishing a scalable blueprint for high-performance and low-cost OER systems. Full article

The Generalized Hough Transform (GHT) is a powerful method for rigid shape detection under rotation, scaling, translation, and partial occlusion conditions, but its four-dimensional accumulator incurs prohibitive computational and memory demands that prevent real-time deployment. To address this, we propose a framework that compresses the 4-D search space into a concise 2-D voting scheme by combining two-level sparse point-pair screening with an accelerated lookup. In the offline stage, template edges are extracted using an adaptive Canny operator with Otsu-determined thresholds, and gradient-direction differences for all point pairs are quantized to retain only those in the dominant bin, yielding rotation- and scale-invariant descriptors that populate a compact 2-D reference table. During the online stage, an adaptive grid selects only the highest-gradient pixels per cell as a base points, while a precomputed gradient-direction bucket table enables constant-time retrieval of compatible subpoints. Each valid base–subpoint pair is mapped to indices in the lookup table, and “fuzzy” votes are cast over a 3 × 3 neighborhood in the 2-D accumulator, whose global peak determines the object center. Evaluation on 200 real industrial parts—augmented to 1000 samples with noise, blur, occlusion, and nonlinear illumination—demonstrates that our method maintains over 90% localization accuracy, matches the classical GHT, and achieves a ten-fold speedup, outperforming IGHT and LI-GHT variants by 2–3×, thereby delivering a robust, real-time solution for industrial rigid object localization. Full article

Pesticide residues in fruits and vegetables pose a serious threat to food safety. Traditional detection methods have defects such as complex operation, high cost, and long detection time. Therefore, it is of great significance to develop rapid, non-destructive, and efficient detection technologies and equipment. In recent years, the combination of spectroscopic techniques and imaging technologies with machine learning algorithms has developed rapidly, providing a new attempt to solve this problem. This review focuses on the research progress of the combination of spectroscopic techniques (near-infrared spectroscopy (NIRS), hyperspectral imaging technology (HSI), surface-enhanced Raman scattering (SERS), laser-induced breakdown spectroscopy (LIBS), and imaging techniques (visible light (VIS) imaging, NIRS imaging, HSI technology, terahertz imaging) with machine learning algorithms in the detection of pesticide residues in fruits and vegetables. It also explores the huge challenges faced by the application of spectroscopic and imaging technologies combined with machine learning algorithms in the intelligent perception of pesticide residues in fruits and vegetables: the performance of machine learning models requires further enhancement, the fusion of imaging and spectral data presents technical difficulties, and the commercialization of hardware devices remains underdeveloped. This review has proposed an innovative method that integrates spectral and image data, enhancing the accuracy of pesticide residue detection through the construction of interpretable machine learning algorithms, and providing support for the intelligent sensing and analysis of agricultural and food products. Full article

Real-time quality monitoring using molten pool images is a critical focus in researching high-quality, intelligent automated welding. To address interference problems in molten pool images under complex welding scenarios (e.g., reflected laser spots from spatter misclassified as porosity defects) and the limited interpretability of deep learning models, this paper proposes a multi-granularity spatiotemporal representation learning algorithm based on the hybrid enhancement of handcrafted and deep learning features. A MobileNetV2 backbone network integrated with a Temporal Shift Module (TSM) is designed to progressively capture the short-term dynamic features of the molten pool and integrate temporal information across both low-level and high-level features. A multi-granularity attention-based feature aggregation module is developed to select key interference-free frames using cross-frame attention, generate multi-granularity features via grouped pooling, and apply the Convolutional Block Attention Module (CBAM) at each granularity level. Finally, these multi-granularity spatiotemporal features are adaptively fused. Meanwhile, an independent branch utilizes the Histogram of Oriented Gradient (HOG) and Scale-Invariant Feature Transform (SIFT) features to extract long-term spatial structural information from historical edge images, enhancing the model’s interpretability. The proposed method achieves an accuracy of 99.187% on a self-constructed dataset. Additionally, it attains a real-time inference speed of 20.983 ms per sample on a hardware platform equipped with an Intel i9-12900H CPU and an RTX 3060 GPU, thus effectively balancing accuracy, speed, and interpretability. Full article

In recent years, cross-domain few-shot learning (CDFSL) has demonstrated remarkable performance in hyperspectral image classification (HSIC), partially alleviating the distribution shift problem. However, most domain adaptation methods rely on similarity metrics to establish cross-domain class matching, making it difficult to simultaneously account for intra-class sample size variations and inherent inter-class differences. To address this problem, existing studies have introduced a class weighting mechanism within the prototype network framework, determining class weights by calculating inter-sample similarity through distance metrics. However, this method suffers from a dual limitation: susceptibility to noise interference and insufficient capacity to capture global class variations, which may lead to distorted weight allocation and consequently result in alignment bias. To solve these issues, we propose a novel class-discrepancy dynamic weighting-based cross-domain FSL (CDDW-CFSL) framework. It integrates three key components: (1) the class-weighted domain adaptation (CWDA) method dynamically measures cross-domain distribution shifts using global class mean discrepancies. It employs discrepancy-sensitive weighting to strengthen the alignment of critical categories, enabling accurate domain adaptation while maintaining feature topology; (2) the class mean refinement (CMR) method incorporates class covariance distance to compute distribution discrepancies between support set samples and class prototypes, enabling the precise capture of cross-domain feature internal structures; (3) a novel multi-dimensional feature extractor that captures both local spatial details and continuous spectral characteristics simultaneously, facilitating deep cross-dimensional feature fusion. The results in three publicly available HSIC datasets show the effectiveness of the CDDW-CFSL. Full article