Search Results (147)

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

Rotor–stator interaction and the coupling between the clearance flow and main flow amplify the flow complexity in small-scale, high-speed automotive pumps. This degrades the pressure fluctuations, compromising the operational stability of these pumps. To better understand the pressure fluctuation distribution characteristics within such a pump, the Reynolds-averaged Navier–Stokes equations and the shear stress transport k-ω turbulence model were applied to numerically compute the pump. The simulation results were compared with experimental data, and good agreement was achieved. The results show that pressure fluctuations in the main flow region are mainly dominated by the blade passing frequency, and the intensity of pressure fluctuations in the near-field area of the tongue reaches its peak value, showing significant fluctuation characteristics. Significant peak signals are captured in the low-frequency band of pressure fluctuations in the clearance region. The pressure fluctuation characteristics are also affected by the rotor–stator interaction between the impeller front shroud and the volute casing, while the dominant frequency is still the blade passing frequency. In addition, the dominant frequencies of pressure fluctuations in the main and clearance flows show a similar distribution to the flow rate, but the minimum amplitude corresponds to different flow rates. Full article

(1) Background: A safe and effective nucleic acid sample transportation method was developed that is suitable for underdeveloped areas which lack advanced sequencing capabilities, specifically for virus genomic sequencing and infectious disease monitoring. (2) Methods: This study evaluated the use of Flinders Technology Associates (FTA) cards for transporting amplified whole-genome DNA from 120 SARS-CoV-2-positive nasopharyngeal swab samples in Sierra Leone. Nucleic acid extraction and whole-genome amplification were conducted at a local laboratory. Amplified products were applied to FTA Elute cards for room temperature shipment to China CDC for elution and sequencing. (3) Results: The FTA card method achieved a 9.6% recovery rate for amplicons, sufficient for viral genome sequencing. In total, 86 (71.7%) high-quality SRAS-CoV-2 genomic sequences were obtained, with the majority reaching depths exceeding 100X. Sequence analysis revealed co-circulation of Delta, Omicron, and B.1 lineages. Higher Ct values in the original sample significantly reduced coverage and depth, with Ct ≤ 27; 73.6% of samples yielded effective sequences. (4) Conclusions: Transportation of amplified nucleic acid samples using FTA cards enables virus genomic sequencing in resource-limited areas. This approach can potentially improve local virus surveillance and outbreak response capabilities. Further optimizations could improve sequence recovery rate. Implementing this method could significantly enhance sequencing accessibility in underdeveloped regions. Full article

Under China’s dual land ownership system, the use rights of urban land (state-owned) and rural land (collective-owned) are not equal. Understanding the roles of ownership type and government decision-making behaviors in the formation of land prices is crucial for further reform to promote “equal rights and equal prices” for urban and rural land. This paper analyzed the impact of ownership type on construction land prices using micro-level land transaction data from Wujin District, Changzhou City, from 2015 to 2021 and investigated the role of government decision-making behaviors such as spatial planning and supply plan in this relationship. The results show that collective ownership has a negative impact on land prices, and the development of collective-owned construction land has a positive impact on the prices of adjacent land. In addition, the boundary of downtown areas determined by spatial planning enhances the negative impact of collective ownership on land prices, thus widening the price gap between state and collective-owned land within the downtown areas. Furthermore, the proportion of collective-owned construction land in the annual land supply determined by the land supply plan strengthens the negative impact of collective ownership on land prices, meaning that an increase in the supply of collective-owned construction land leads to further downward pressure on land prices. This study can provide insights for policy making aiming to achieve “equal rights and equal prices” for land with different ownership type in China and in other countries with a dual land ownership system. Full article

Individual recognition of Holstein cows is the basis for realizing precision dairy farming. Current machine vision individual recognition systems usually rely on fixed vertical illumination and top-view camera perspectives or require complex camera systems, and these requirements limit their promotion in practical applications. To solve this problem, a lightweight Holstein cow individual recognition feature extraction network named CowBackNet is designed in this paper. This network is not affected by camera angle and lighting changes and is suitable for farm environments. Secondly, a fusion multi-attention mechanism approach was adopted to integrate the attention mechanism, inverse residual structure, and depth-separable convolution technique to design a new feature extraction module, LightCBAM. This module was placed in the corresponding layer of CowBackNet to enhance the model’s ability to extract the key features of the cow’s back image from different viewpoints. In addition, the CowBack dataset was constructed in this study to verify the model’s ability to be applied in real scenarios, containing Holstein cowback images in real production environments under different viewpoints. The experimental results show that when using CowBackNet as a feature extraction network, the recognition accuracy reaches 88.30%, FLOPs are 0.727 G, and the model size is only 6.096 MB. Compared with the classical EfficientNetV2, the accuracy of CowBackNet is improved by 11.69%, the FLOPs are reduced by 0.001 G, and the number of parameters is also reduced by 14.6%. Therefore, the model developed in this paper shows good robustness in shooting angle, light change, and real production data, which not only improves the recognition accuracy but also optimizes the computational efficiency of the model, which is of great practical application value for realizing precision farming. Full article

Intelligent swarm is a powerful tool for targeting high-value objectives. Within the Anti-Access/Area Denial (A2/AD) context, an unmanned aerial vehicle (UAV) swarm must leverage its autonomous decision-making capability to execute tasks with independence. This paper focuses on the Suppression of Enemy Air Defenses (SEAD) mission for intelligent stealth UAV swarms. The current research field mainly faces challenges in fully simulating the complexity of real-world scenarios and in insufficient autonomous task planning capabilities. To address these issues, this paper develops a representative problem model, establishes a six-tier standardized simulation environment, and selects the Deep Deterministic Policy Gradient (DDPG) algorithm as the core intelligent algorithm to enhance the autonomous task planning capabilities of UAV swarms. At the algorithm level, this paper designs reward functions corresponding to UAV swarm behaviors, aiming to motivate UAV swarms to adopt more effective action strategies, thereby achieving autonomous task planning. Simulation results demonstrate that the scenario and architectural design are feasible and that artificial intelligence algorithms can enable the UAV swarm to show a higher level of intelligence. Full article

Accurate, rapid, and intelligent stored-grain insect detection and counting are important for integrated pest management (IPM). Existing stored-grain insect pest detection models are often not suitable for detecting tiny insects on the surface of grain bulks and often require high computing resources and computational memory. Therefore, this study presents a YOLO-SGInsects model based on YOLOv8s for tiny stored-grain insect detection on the surface of grain bulk by adding a tiny object detection layer (TODL), adjusting the neck network with an asymptotic feature pyramid network (AFPN), and incorporating a hybrid attention transformer (HAT) module into the backbone network. The YOLO-SGInsects model was trained and tested using a GrainInsects dataset with images captured from granaries and laboratory. Experiments on the test set of the GrainInsects dataset showed that the YOLO-SGInsects achieved a stored-grain insect pest detection mean average precision (mAP) of 94.2%, with a counting root mean squared error (RMSE) of 0.7913, representing 2.0% and 0.3067 improvement over the YOLOv8s, respectively. Compared to other mainstream approaches, the YOLO-SGInsects model achieves better detection and counting performance and is capable of effectively handling tiny stored-grain insect pest detection in grain bulk surfaces. This study provides a technical basis for detecting and counting common stored-grain insect pests on the surface of grain bulk. Full article

Background: The fatality rate of hemorrhagic fever with renal syndrome (HFRS), due to hantavirus transmitted by rodents, ranges from 1% to 12%. This study aims to delineate the clinical and laboratory characteristics of HFRS, identify factors associated with disease severity, and construct and validate a nomogram for prognosis prediction of HFRS in the central part of China. Methods: Out of 598 HFRS patients diagnosed via serology tests from four hospitals in Hubei Province, 551 were included. Clinical data were gathered and analyzed, followed by logistic univariate and multivariate analyses to identify independent prognostic factors. A nomogram was developed and validated to forecast the patient’s prognosis. Results: Vaccination led to a notable drop in HFRS incidence from 2018 to 2019, and seasonal trends exhibited bimodal changes with peaks from May to July and November to January. The 30-day mortality rate was 4.17% (23/551). Red blood cell count (RBC), age, two-stage overlap, qSOFA ≥ 2, aspartate aminotransferase (AST), and three-stage overlap were identified as independent prognostic factors. A predictive risk classification system using a nomogram chart was developed, and Kaplan–Meier curves indicated that the new system accurately distinguished 30-day mortality among the three risk groups. Conclusions: The risk score (EASTAR) system demonstrated good predictive performance for prognostic prediction, and it can be applied to quickly screen patients who require ICU admission. Full article

The adsorption of coalbed methane alters the pore structure of reservoirs, subsequently affecting the coal seam’s gas adsorption capacity. However, traditional gas adsorption models often neglect this crucial aspect. In this article, we introduce a fractal capillary bundle model that accounts for the expansion of coal seam adsorption. We utilize curvature fractal dimension and capillary fractal dimension to characterize the complexity of the coal seam’s pore structure. By incorporating the concept of fictitious stress, we have described the relationship between gas adsorption, matrix porosity, and permeability changes. We have developed a model that describes the changes in matrix porosity and permeability during the gas adsorption process. After fitting this model to experimental data, it demonstrated high accuracy in predictions. Furthermore, our investigation into how factors such as curvature fractal dimension, capillary fractal dimension, and fictitious stress influence gas adsorption capacity reveals several key findings. Firstly, the specific surface area within the pore structure of coal seams is the primary factor controlling gas adsorption capacity. Secondly, the virtual stress generated during the gas adsorption process alters the coal seam’s maximum gas adsorption capacity, a factor that cannot be overlooked. Lastly, we found that gas adsorption primarily affects the gas migration process, while under high-pressure conditions, gas desorption does not cause significant changes in the matrix porosity and permeability. Full article

To enhance the physicochemical quality, drying efficiency, and nutrient retention of dried Licorice products, this study investigated the effects of ultrasonic pretreatment on the radio frequency vacuum (RFV) drying characteristics, microstructure, and retention of natural active substances in Licorice slices. The ultrasonic time, power, and frequency were considered as experimental factors. The results showed that, compared with conventional RFV drying, ultrasonic pretreatment reduced the drying time of Licorice slices by 20–60 min. The Weibull model accurately described the moisture ratio changes under different pretreatment conditions (R² > 0.9984, χ² < 2.381 × 10⁻⁵). The optimal retention of polysaccharides, total phenols, total flavonoids, and antioxidants was achieved under pretreatment conditions of 30 min of ultrasonic time, 180 W of ultrasonic power, and 40 kHz of ultrasonic frequency. Furthermore, ultrasonic pretreatment preserved the internal cellular structure of Licorice slices, maintaining intact tissue cells and well-defined microchannels. However, a slight reduction in sample color was observed following ultrasound application. In conclusion, ultrasonic pretreatment significantly improved the RFV drying process for Licorice slices by enhancing drying efficiency, preserving active ingredients, and optimizing the physicochemical quality of the dried product. This study provides novel insights and methods for optimizing the drying process of Licorice, offering a foundation for further research and industrial applications. Full article

Understanding the relationships between nutrient content in plant roots and ecological stoichiometry is crucial for elucidating nutrient utilization strategies and material cycling in alpine plant communities. However, data characterizing the stoichiometric characteristics of plant roots in this region remain limited. In this study, we collected fine-root and soil samples from five common alpine shrub species—Salix gilashanica, Potentilla fruticosa, Caragana jubata, Caragana tangutica, and Berberis diaphana—to investigate the carbon (C), nitrogen (N), and phosphorus (P) stoichiometric characteristics of their fine roots and examine the potential nutrient control strategies based on the soil properties. Our analysis revealed that the mean C (541.38 g kg⁻¹) and P (1.10 g kg⁻¹) contents in the shrub fine roots exceeded the average levels of the plant roots in China. However, the mean N content (8.61 g kg⁻¹) was lower than the global average. Notably, the mean C:N ratio (71.3) in these fine roots was significantly higher than the global average, whereas both the mean C:P ratio (527.61) and N:P ratio (8.11) were considerably lower. The N:P ratios in the fine roots of the five shrub species were below 14, indicating nitrogen limitation for growth in the degraded alpine shrub communities. Our findings indicate that soil available phosphorus (33.2%) and pH (20.5%) are the primary factors influencing the eco-stoichiometric characteristics of shrub fine roots in the Qilian Mountains. These findings provide valuable data and theoretical support for a better understanding of the role of shrub roots in nutrient cycling within alpine ecosystems. Full article

Maritime security is vital to national security. The application of unmanned aerial vehicle (UAV) clusters in marine anti-submarine warfare (ASW) presents a new and significant challenge worthy of in-depth study. Based on the anti-submarine principle of geomagnetic anomaly detection and the Find-Fix-Track-Target-Engage-Assess (F2T2EA) framework, this paper divides UAV cluster ASW operations into two stages: regional coverage and cooperative convergence. The regional coverage stage enables the UAV cluster to perform a broad search for submarines, while the cooperative convergence stage facilitates the precise positioning of detected submarines. In simulation, the combat scenario of five UAV clusters against a submarine is carried out. In the given area, the UAV can locate and stalk the target submarine in limited time. Simulation results demonstrate the feasibility of the proposed approach, providing reference for advancing marine ASW capabilities and related research. Full article

In this study, we analyzed the effects of three pretreatment methods—microwave, steam, and blanching—on the quality of Honeysuckle to determine the optimal pretreatment method; we then investigated the influence of different drying temperatures, vacuum levels, and rotation speeds on the drying characteristics, color, and active ingredient content of the Honeysuckle that was pretreated by the optimal pretreatment method during rotary microwave vacuum drying. The results indicated that a microwave pretreatment for 75 s was the optimal pretreatment method, which enhanced the retention of active ingredients and effectively improved the browning of the material. During the process of rotary microwave vacuum drying, as the temperature increased, the vacuum level rose, and the rotation speed increased, the drying rate gradually increased. However, excessively high vacuum levels and rapid rotation speeds could actually decrease the drying rate. In addition, the total phenols, total flavonoids, antioxidant activity, and various active ingredients of Honeysuckle dried by rotary microwave vacuum were effectively preserved. Furthermore, its rehydration properties and color were significantly superior to those dried through sun drying. The TIOPSIS method analysis showed that the optimal process parameters were a temperature of 50 °C, a vacuum level of −0.070 MPa, and a rotation speed of 35 Hz, which exhibited the highest relative closeness (0.76). The comprehensive analysis indicated that microwave pretreatment followed by rotary microwave vacuum drying was a promising drying method with potential applications in the dehydration of agricultural products and medicinal plants. Full article

Identifying changes in driver’s vigilance under combined manual and automated driving conditions, as well as during prolonged driving, is crucial for reducing car crashes. Existing studies have not adequately considered the similarities and differences in physiological signals between different driving modes or the cumulative effects during extended driving periods. To address this gap, our study focuses on enhancing the feature selection method for driver’s vigilance recognition. A long-duration simulated car-following driving experiment was designed and conducted to simultaneously collect EEG, eye movement, EOG, and driving data. Similarities and differences in the physiological signals of vigilance between manual and automated driving are analyzed in terms of correlation and significance. The cumulative effects of physiological signals are investigated using time series analysis. The proposed feature selection method was validated using an LSTM-based driver’s vigilance recognition model. Results showed a recognition accuracy of 86.32% for manual driving, with a fluctuation rate of 1.18% over prolonged periods. For automated driving, the accuracy was 87.12%, with a fluctuation rate of 0.66%. By considering the similarities and differences in physiological signals between manual and automated driving modes and the cumulative effects, our study enhances the applicability and stability of the driver’s vigilance recognition model across different driving conditions. The validation results demonstrate that the proposed method improves the applicability and stability of the driver’s vigilance recognition model across different driving modes during extended driving periods. Full article