Search Results (140)

This study develops and validates an artificial intelligence (AI)-assisted internship learning platform for automotive electronics based on the Llama 3 large language model, aiming to enhance pedagogical effectiveness within vocational training contexts. Addressing critical issues such as the persistent theory–practice gap and limited innovation capability prevalent in existing curricula, we leverage the natural language processing (NLP) capabilities of Llama 3 through fine-tuning based on transfer learning to establish a specialized knowledge base encompassing fundamental circuit principles and fault diagnosis protocols. The implementation employs the Hugging Face Transformers library with optimized hyperparameters, including a learning rate of 5 × 10⁻⁵ across five training epochs. Post-training evaluations revealed an accuracy of 89.7% on validation tasks (representing a 12.4% improvement over the baseline model), a semantic comprehension precision of 92.3% in technical question-and-answer assessments, a mathematical computation accuracy of 78.4% (highlighting this as a current limitation), and a latency of 6.3 s under peak operational workloads (indicating a system bottleneck). Although direct trials involving students were deliberately avoided, the platform’s technical feasibility was validated through multidimensional benchmarking against established models (BERT-base and GPT-2), confirming superior domain adaptability (F1 = 0.87) and enhanced error tolerance (σ² = 1.2). Notable limitations emerged in numerical reasoning tasks (Cohen’s d = 1.15 compared to human experts) and in real-time responsiveness deterioration when exceeding 50 concurrent users. The study concludes that Llama 3 demonstrates considerable promise for automotive electronics skills development. Proposed future enhancements include integrating symbolic AI modules to improve computational reliability, implementing Kubernetes-based load balancing to ensure latency below 2 s at scale, and conducting longitudinal pedagogical validation studies with trainees. This research provides a robust technical foundation for AI-driven vocational education, especially suited to mechatronics fields that require close integration between theoretical knowledge and practical troubleshooting skills. Full article

To address the limitations of the existing cotton seedling growth prediction methods in field environments, specifically, poor representation of spatiotemporal features and low visual fidelity in texture rendering, this paper proposes an algorithm for the prediction of cotton seedling growth from images based on FCA-STNet. The model leverages historical sequences of cotton seedling RGB images to generate an image of the predicted growth at time t + 1 and extracts 37 phenotypic traits from the predicted image. A novel STNet structure is designed to enhance the representation of spatiotemporal dependencies, while an Adaptive Fine-Grained Channel Attention (FCA) module is integrated to capture both global and local feature information. This attention mechanism focuses on individual cotton plants and their textural characteristics, effectively reducing the interference from common field-related challenges such as insufficient lighting, leaf fluttering, and wind disturbances. The experimental results demonstrate that the predicted images achieved an MSE of 0.0086, MAE of 0.0321, SSIM of 0.8339, and PSNR of 20.7011 on the test set, representing improvements of 2.27%, 0.31%, 4.73%, and 11.20%, respectively, over the baseline STNet. The method outperforms several mainstream spatiotemporal prediction models. Furthermore, the majority of the predicted phenotypic traits exhibited correlations with actual measurements with coefficients above 0.8, indicating high prediction accuracy. The proposed FCA-STNet model enables visually realistic prediction of cotton seedling growth in open-field conditions, offering a new perspective for research in growth prediction. Full article

To improve the identification of drought-affected areas in winter wheat, this paper proposes a lightweight network called MF-FusionNet based on multimodal fusion of RGB images and vegetation indices (NDVI and EVI). A multimodal dataset covering various drought levels in winter wheat was constructed. To enable deep fusion of modalities, a Lightweight Multimodal Fusion Block (LMFB) was designed, and a Dual-Coordinate Attention Feature Extraction module (DCAFE) was introduced to enhance semantic feature representation and improve drought region identification. To address differences in scale and semantics across network layers, a Cross-Stage Feature Fusion Strategy (CFFS) was proposed to integrate multi-level features and enhance overall performance. The effectiveness of each module was validated through ablation experiments. Compared to traditional single-modal methods, MF-FusionNet achieved higher accuracy, recall, and F1-score—improved by 1.35%, 1.43%, and 1.29%, respectively—reaching 96.71%, 96.71%, and 96.64%. A basis for real-time monitoring and precise irrigation management under winter wheat drought stress was provided by this study. Full article

The Hetao irrigation area is one of the largest irrigation areas in the Yellow River Basin and a typical salinized agricultural area. Crop type shifts in this area can alter soil phosphorus (P) morphology and microbial functional diversity, thereby influencing soil P losses. However, few studies have elucidated the underlying mechanisms. In this study, soil samples were collected from four different crop planting areas: sunflower field (SF), corn field (CF), wheat land (WL), and vegetable and fruit land (VFL). Subsequently, the physicochemical properties, P fractions, and phosphate-solubilizing microorganisms (PSMs) were analyzed. The results indicated that when other lands shifted to SF, the soil pH increased significantly. Simultaneously, SOM, TN, and TP decreased significantly during the crop type conversion. Analysis of P fraction revealed that moderately active P, including NaOH-P_i, NaOH-P_o, and HCl-P_i, were the dominant fractions in the tested soils. Among them, HCl-P_i was the major component of moderately active P. The soil P leaching change point in the tested are was 6.25 mg Olsen-P kg⁻¹. The probabilities of P leaching in WL, VFL, CF, and SF were 91.7%, 83.8%, 83.8%, and 66.7%, respectively. Additionally, the sum of the relative abundances of the three PSMs in SF, VFL, WL, and CF were 8.81%, 11.88%, 8.03%, and 10.29%, respectively. The shift in crop type to SF exacerbated the soil degradation process. Both TP and residual P in the soil decreased. However, the NaHCO₃ slightly increased, which may have been due to the increased abundance of Thiobacillus and Escherichia. Full article

Photothermal conversion is a pivotal energy transformation mechanism in solar energy systems. Achieving high-efficiency and broadband photothermal conversion within the solar radiation spectrum holds strategic significance in driving the innovative development of renewable energy technologies. In this study, a transmission matrix method was employed to design an interference-type solar selective absorber based on multilayer Cr-SiO₂ planar films, successfully achieving an average absorption of 94% throughout the entire solar spectral range. Further analysis indicates that this newly designed absorber shows excellent absorption performance even at a relatively large incident angle (up to 60°). Additionally, the newly designed absorber demonstrates lower polarization sensitivity, enabling efficient operation under complicated incident conditions. With its simple fabrication process and ease of preparation, the proposed absorber holds substantial potential for applications in photothermal conversion fields such as solar thermal collectors. Full article

Hydnum, a well-defined genus in the family Hydnaceae (order Cantharellales), is characterized by its distinctive spine-bearing hymenophores. In this study, we performed a multi-locus phylogenetic analysis (ITS-nrLSU-tef1) of Hydnum species. Integrating morphological examinations and phylogenetic evidence, we identified and delineated five Hydnum species in China, which include four novel species (Hydnum crassipedum, H. albomarginatum, H. fulvostriatum, and H. bifurcatum) and the first record (H. orientalbidum) in Anhui Province. This study provides a comprehensive morphological description (including macroscopic morphology and microscopic structure), hand-drawn illustrations (encompassing basidiocarps, basidiospores, basidia, and pileipellis hyphae), morphological comparative analysis with similar species, and comparative phylogenetic analysis with related taxa. Furthermore, we developed a dichotomous key for identifying Hydnum species distributed in China. Full article

In the first part of this study, Y₂O₃-doped copper thiocyanate (CuSCN) with different x wt% (named CuSCN-xY, x = 0, 1, 2, and 3) films were synthesized onto ITO substrates using the spin coating method. UV-vis, SEM, AFM, EDS, and cyclic voltammetry were used to investigate the material properties of the proposed films. The conductivity and carrier mobility of the films increased with additional yttrium doping. It was found that the films with 2% Y₂O₃ (CuSCN-2Y) have the smallest valence band edges (5.28 eV). Meanwhile, CuSCN-2Y films demonstrated the densest surface morphology and the smallest surface roughness (22.8 nm), along with the highest conductivity value of 764 S cm⁻¹. Then, P-I-N self-powered UV photodetectors (PDs) were fabricated using the ITO substrate/ZnO seed layer/ZnO nanorod/CsPbBr₃/CuSCN-xY/Ag structure, and the characteristics of the devices were measured. In terms of response time, the rise time and fall time were reduced from 26 ms/22 ms to 9 ms/5 ms; the responsivity was increased from 243 mA/W to 534 mA/W, and the on/off ratio was increased to 2.47 × 10⁶. The results showed that Y₂O₃ doping also helped improve the P-I-N photodetector’s device performance, and the mechanisms were investigated. Compared with other published P-I-N self-powered photodetectors, our proposed devices show a fairly high on/off ratio, quick response times, and high responsivity and detectivity. Full article

The physiochemical and biological properties of animal manures are crucial factors in resource utilization. Herein, the physiochemical and biological characteristics of pig manure during anaerobic digestion and sheep manure during composting were investigated. The animal manures were rich in heavy metals. Zn was the most abundant heavy metal, in the range of 586.9–2069 mg/kg in the animal manures. After anaerobic digestion, the contents of cellulose, hemicellulose, and lignin increased by 59.97%, 6.90%, and 171.81%, respectively, while the contents of NH₄⁺-N, NO₃⁻-N, total nitrogen, total phosphorus, and K decreased by 5.50–48.27% in the pig manure. The contents of NH₄⁺-N, NO₃⁻-N, total phosphorus, and K increased by 20.56–61.82% in the sheep manure after composting. The contents of all heavy metals increased in the compost, especially the Zn content which increased by 145.6%. Potential pathogenic bacteria including Pseudomonas, Clostridium sensu stricto 1, Acholeplasma, Tissierella, and Halomonas were abundant in the animal manures. Composting could inactivate pathogenic bacteria in the animal manures well, while a large number of pathogenic bacteria still remained in the digestate if the solid retention time was short in anaerobic digestion. The findings would be helpful for understanding the characteristics of animal manures and developing effective treatment and resource utilization technologies. Full article

Phenotypic data of cotton can accurately reflect the physiological status of plants and their adaptability to environmental conditions, playing a significant role in the screening of germplasm resources and genetic improvement. Therefore, this study proposes a cotton phenotypic data extraction algorithm that integrates ResDGCNN with an improved region-growing method and constructs a 3D point cloud dataset of cotton covering the entire growth period under real growth conditions. To address the challenge of significant structural variations in cotton organs across different growth stages, we designed an innovative point cloud segmentation algorithm, ResDGCNN, which integrates residual learning with dynamic graph convolution to enhance organ segmentation performance throughout all developmental stages. In addition, to address the challenge of accurately segmenting overlapping regions between different cotton organs, we introduced an optimization strategy that combines point distance mapping with curvature-based normal vectors and developed an improved region-growing algorithm to achieve fine segmentation of multiple cotton organs, including leaves, stems, and flower buds. Experimental data show that, in the task of organ segmentation throughout the entire cotton growth cycle, the ResDGCNN model achieved a segmentation accuracy of 67.55%, with a 4.86% improvement in mIoU compared to the baseline model. In the fine-grained segmentation of overlapping leaves, the model achieved an R² of 0.962 and an RMSE of 2.0. The average relative error in stem length estimation was 0.973, providing a reliable solution for acquiring 3D phenotypic data of cotton. Full article

Mineral content is an important nutrient component in milk. At present, there is not much research and application on the ecological mineral profiles of milk, especially in the development and utilization of the dominant milk source in Xinjiang. This study uses a mid-infrared spectroscopy (MIRS) model to predict the mineral content of seven key minerals in milk, explores the secretion patterns and characteristics of mineral profiles in milk, provides production methods for the efficient utilization of high-calcium milk sources, and analyzes the possible economic benefits. The results indicate that the mineral content of milk in Xinjiang has advantages over that of other regions of China. Mineral profiles’ characteristics in milk are influenced by the parity, days of lactation, sampling season, calving season, and breast health status. Moreover, there are correlations between different minerals. Milk with higher calcium content also has elevated levels of other minerals and regular milk components (milk protein and milk fat). Therefore, such milk may serve as a reference for producing season-dependent high-calcium milk sources. If native calcium content above 1300 mg/kg, as identified in this study, was used to produce high-calcium pasteurized fresh milk and premium high-calcium pasteurized fresh milk, the dairy industry could see a significant increase in economic benefits. This study provides a foundation for the production of characteristic milk sources and diversified dairy products in Xinjiang. It also lays the groundwork for understanding the secretion patterns and mechanisms of minerals in milk. Full article

Background and Objectives: Identifying carriers of Pathogenic/Likely Pathogenic Variants in patients with dementia is crucial for risk stratification, particularly in individuals with a family history. This study developed and validated a clinical prediction model using whole-exome sequencing-confirmed cohorts. Methods: A total of 601 Chinese patients with dementia and a family history were enrolled at Peking Union Medical College Hospital, with 476 in a retrospective derivation cohort and 125 in a temporal validation cohort. Predictive factors included age at onset, APOE ε4 status, and family history characteristics. Model performance was assessed using discrimination and calibration metrics. Results: In the derivation cohort (median age at onset 66 years), 10.3% carried Pathogenic/Likely Pathogenic Variants. Among patients with dementia, those with age at onset < 55 years (OR 2.56, p = 0.0098), more than two affected relatives (OR 3.32, p = 0.0039), parental disease history (OR 4.72, p = 0.015), and early-onset cases in the family (OR 2.61, p = 0.0096) were positively associated with Pathogenic/Likely Pathogenic Variant carriage, whereas APOE ε4 carriage was inversely associated (OR 0.36, p = 0.0041). The model achieved an area under the curve of 0.776 (95% CI, 0.701–0.853) in the derivation cohort and 0.781 (95% CI, 0.647–0.914) in the validation cohort (median age at onset 58 years), with adequate calibration. Conclusions: This model demonstrated strong predictive performance for Pathogenic/Likely Pathogenic Variant carriage, supporting its clinical utility in guiding genetic testing. Further research is needed to refine the model. Full article

Automatic generation of Programmable Logic Controller (PLC) programs from system design models can reduce system development costs and shorten system development cycles. However, effective methods for ensuring the quality and performance of control logic automatically generated from system design models are still lacking. This paper proposed a model-driven PLC program automatic generation approach. Firstly, a clear formal specification for the system design model oriented to PLC programming was established. Secondly, the fundamental semantic correspondence between system model elements and PLC program elements was defined by devising a set of mapping rules. Thirdly, a novel mapping algorithm was proposed to generate a PLC program based on system design models. Finally, an example of the automatic generation of a PLC control program for a handling robot was used to verify the effectiveness of the method. This method can provide system-level design and analysis capabilities during the initial stages of model development, offering solutions to the challenges of complexity modeling and engineering efficiency. Additionally, it is expected to have wide-ranging applications in the industrial automation sector, thereby contributing to the innovation and advancement of automation systems. Full article

Referencing ion exchange applied in the extraction of ion-adsorption minerals, a mathematical model based on thermodynamic equilibrium has been researched and established to reveal and represent the chemical reaction between minerals and the leaching solution. Meanwhile, a Python-based numerical solver has been developed and programmed to solve the equations of the established model in order to achieve computational efficiency and obtain an accurate solution. Based on the simulation and computation with the established mathematical model, the effects of leaching solution concentration and mineral grade distribution on the extraction of ion-adsorption minerals can be evaluated, validating the set value of 20 g/L for the initial leaching solution concentration and the extraction rate of above 90% for the cut-off grade. Furthermore, the established mathematical model can be integrated into the simulation system for the overall multi-field extraction of ion-adsorption minerals, illustrating the relationship between extracted metal ion concentration in the aqueous phase over time and providing theoretical support for the engineering project. Full article

A parallel Fabry–Perot interferometer (FPI) optical fiber sensor, enhanced with UV glue, was proposed for environmental temperature detection. The UV glue is applied to the fiber’s sensing region using a coating method, forming an FP cavity through misalignment welding, allowing the FP to function as a temperature sensor. In parallel, a reference FPI with a similar free spectral range (FSR) is connected, generating a Vernier effect that amplifies small changes in the refractive index (RI) of the sensing region. The study demonstrates that UV glue enhances the temperature-sensing capabilities of the FP, and when combined with the Vernier effect, it significantly improves the sensitivity of a single interferometric sensor. The temperature sensitivity of the parallel-connected FPI is −2.80219 nm/°C, which is 7.768 times greater than that of a single FPI (−0.36075 nm/°C). The sensor shows high sensitivity, stability, and reversibility, making it promising for temperature-monitoring applications in various fields, including everyday use, industrial production, and the advancement of optical fiber temperature-sensing technologies. Full article

This study investigated a suspected Feline calicivirus (FCV) outbreak at a veterinary facility in Zhengzhou, Henan Province, China. RT-PCR analysis confirmed the FCV presence, with subsequent CRFK cell culture propagation leading to the isolation and characterization of strain ZZ202306. Immunofluorescence and Western blot analyses validated the specificity of monoclonal antibodies targeting the FCV VP1 capsid protein. Transmission electron microscopy revealed non-enveloped virions of ~40 nm in diameter, exhibiting typical caliciviral architecture. Viral replication kinetics demonstrated exponential growth between 6 and 18 h post-inoculation, reaching a peak titer of 10^7.96 TCID₅₀/0.1 mL. Genomic sequencing coupled with phylogenetic reconstruction of the VP1 gene revealed a close genetic relation to domestic Chinese strains and international variants, while maintaining distinct evolutionary divergence from other calicivirus genera. Full article