Magnetic Field Enhanced Superconductivity in Epitaxial Thin Film WTe2.

In conventional superconductors an external magnetic field generally suppresses superconductivity. This results from a simple thermodynamic competition of the superconducting and magnetic free energies. In this study, we report the unconventional features in the superconducting epitaxial thin film tungsten telluride (WTe2). Measuring the electrical transport properties of Molecular Beam Epitaxy (MBE) grown WTe2 thin films with a high precision rotation stage, we map the upper critical field Hc2 at different temperatures T. We observe the superconducting transition temperature T c is enhanced by in-plane magnetic fields. The upper critical field Hc2 is observed to establish an unconventional non-monotonic dependence on temperature. We suggest that this unconventional feature is due to the lifting of inversion symmetry, which leads to the enhancement of Hc2 in Ising superconductors

Performance analysis of two typical greenhouse lettuce production systems: Commercial hydroponic production and traditional soil cultivation

Introduction: Due to the shortage of land and water resource, optimization of systems for production in commercial greenhouses is essential for sustainable vegetable supply. The performance of lettuce productivity and the economic benefit in greenhouses using a soil-based system (SBS) and a hydroponic production system (HPS) were compared in this study. Methods: Experiments were conducted in two identical greenhouses over two growth cycles (G1 and G2). Three treatments of irrigation volumes (S1, S2, and S3) were evaluated for SBS while three treatments of nutrient solution concentration (H1, H2, and H3) were evaluated for HPS; the optimal levels from each system were then compared. Results and discussion: HPS was more sensitive to the effects of environmental temperature than SBS because of higher soil buffer capacity. Compared with SBS, higher yield (more than 134%) and higher water productivity (more than 50%) were observed in HPS. We detected significant increases in ascorbic acid by 28.31% and 16.67% and in soluble sugar by 57.84% and 32.23% during G1 and G2, respectively, compared with SBS. However, nitrate accumulated in HPS-grown lettuce. When the nutrient solution was replaced with fresh water 3 days before harvest, the excess nitrate content of harvested lettuce in HPS was removed. The initial investment and total operating cost in HPS were 21.76 times and 47.09% higher than those in SBS, respectively. Consideration of agronomic, quality, and economic indicators showed an overall optimal performance of the H2 treatment. These findings indicated that, in spite of its higher initial investment and requirement of advanced technology and management, HPS was more profitable than SBS for commercial lettuce production

High-throughput sequencing and characterization of potentially pathogenic fungi from the vaginal mycobiome of giant panda (Ailuropoda melanoleuca) in estrus and non-estrus

IntroductionThe giant panda (Ailuropoda melanoleuca) reproduction is of worldwide attention, and the vaginal microbiome is one of the most important factors affecting the reproductive rate of giant pandas. The aim of this study is to investigate the diversity of vaginal mycobiota structure, and potential pathogenic fungi in female giant pandas during estrus and non-estrus.MethodsThis study combined with high-throughput sequencing and laboratory testing to compare the diversity of the vaginal mycobiota in giant pandas during estrus and non-estrus, and to investigate the presence of potentially pathogenic fungi. Potentially pathogenic fungi were studied in mice to explore their pathogenicity.Results and discussionThe results revealed that during estrus, the vaginal secretions of giant pandas play a crucial role in fungal colonization. Moreover, the diversity of the vaginal mycobiota is reduced and specificity is enhanced. The abundance of Trichosporon and Cutaneotrichosporon in the vaginal mycobiota of giant pandas during estrus was significantly higher than that during non-estrus periods. Apiotrichum and Cutaneotrichosporon were considered the most important genera, and they primarily originate from the environment owing to marking behavior exhibited during the estrous period of giant pandas. Trichosporon is considered a resident mycobiota of the vagina and is an important pathogen that causes infection when immune system is suppressed. Potentially pathogenic fungi were further isolated and identified from the vaginal secretions of giant pandas during estrus, and seven strains of Apiotrichum (A. brassicae), one strain of Cutaneotrichosporon (C. moniliiforme), and nine strains of Trichosporon (two strains of T. asteroides, one strain of T. inkin, one strain of T. insectorum, and five strains of T. japonicum) were identified. Pathogenicity results showed that T. asteroides was the most pathogenic strain, as it is associated with extensive connective tissue replacement and inflammatory cell infiltration in both liver and kidney tissues. The results of this study improve our understanding of the diversity of the vaginal fungi present in giant pandas and will significantly contribute to improving the reproductive health of giant pandas in the future

Statistical Model of College Ideological and Political Learning Based on Fractional Differential Equations

Based on fractional differential equations, this paper focuses on the internal mechanism of college ideological and political learning. We also elaborate on its microstructure, channel characteristics, and competitive field. We put forward Gauss’s theorem and the loop theorem of the related field of ideological and political learning in colleges and universities. At the same time, the convexity theorem of the information entropy of the competitive field and the principle of maximum entropy are proved. Research shows that college students can change the relationship between student learning and society by adapting, functioning, and coordinating. We need to help students develop more effective political learning strategies

Rolling Prediction of Emergency Supplies Based on Postdisaster Multisource Time-Varying Information

Accurate prediction of material demands is key to ensuring the overall efficiency of emergency rescue operations. From the perspective of the prediction method, the single-material static prediction method based on the overall data has limitations. This method cannot flexibly adjust multiperiod material demands. Considering data sources, acquiring data regarding material demands in historical disasters is more difficult and has more uncertainty compared with statistical data on deaths. This study investigates a rolling prediction method for emergency supplies based on postdisaster multisource time-varying information to ensure prediction accuracy. First, the proposed method uses historical cases, real-time disasters, and time-sharing simulation data as the source data. The method implements attribute reduction of original data samples based on rough set theory and predicts cumulative death tolls in each rolling period by using the rolling time-domain as the basic framework and combining a support vector machine (SVM). Second, the proposed method estimates the material demands in the corresponding period by using the material demand model according to the prediction results in a single period. Finally, the proposed method is verified by an experiment with a general mean prediction error of 10.96%. However, the general mean prediction error of SVM reaches 17.77% in the static multistep prediction. Moreover, the general mean prediction error of the methods in the references is 14.13%. Overall, the method has high accuracy and strong timeliness. Prediction results can not only be used as a basis for material estimation, but also provide several scientific and effective references for the allocation and scheduling of emergency supplies

Study on the Detection Method for Daylily Based on YOLOv5 under Complex Field Environments

Intelligent detection is vital for achieving the intelligent picking operation of daylily, but complex field environments pose challenges due to branch occlusion, overlapping plants, and uneven lighting. To address these challenges, this study selected an intelligent detection model based on YOLOv5s for daylily, the depth and width parameters of the YOLOv5s network were optimized, with Ghost, Transformer, and MobileNetv3 lightweight networks used to optimize the CSPDarknet backbone network of YOLOv5s, continuously improving the model’s performance. The experimental results show that the original YOLOv5s model increased mean average precision (mAP) by 49%, 44%, and 24.9% compared to YOLOv4, SSD, and Faster R-CNN models, optimizing the depth and width parameters of the network increased the mAP of the original YOLOv5s model by 7.7%, and the YOLOv5s model with Transformer as the backbone network increased the mAP by 0.2% and the inference speed by 69% compared to the model after network parameter optimization. The optimized YOLOv5s model provided precision, recall rate, mAP, and inference speed of 81.4%, 74.4%, 78.1%, and 93 frames per second (FPS), which can achieve accurate and fast detection of daylily in complex field environments. The research results can provide data and experimental references for developing intelligent picking equipment for daylily

Study on the Influence of PCA Pre-Treatment on Pig Face Identification with Random Forest

To explore the application of a traditional machine learning model in the intelligent management of pigs, in this paper, the influence of PCA pre-treatment on pig face identification with RF is studied. By this testing method, the parameters of two testing schemes, one adopting RF alone and the other adopting RF + PCA, were determined to be 65 and 70, respectively. With individual identification tests carried out on 10 pigs, accuracy, recall, and f1-score were increased by 2.66, 2.76, and 2.81 percentage points, respectively. Except for the slight increase in training time, the test time was reduced to 75% of the old scheme, and the efficiency of the optimized scheme was greatly improved. It indicates that PCA pre-treatment positively improved the efficiency of individual pig identification with RF. Furthermore, it provides experimental support for the mobile terminals and the embedded application of RF classifiers

Experimental study of thermal performance in a rectangular finned-tube latent heat storage device with composite polyethylene wax/expanded graphite

Phase change materials and geometric structure of latent heat storage (LHS) devices constitute crucial factors affecting the performance of phase change thermal energy storage systems. In this study, focusing on the LHS system with a heat source temperature of 150 °C, a rectangular finned-tube latent heat storage device was designed and fabricated. Polyethylene wax (PEW) with a 10% mass fraction of expanded graphite was adopted to prepare the composite phase change material. A series of experiments was conducted to evaluate the thermodynamic performance of the device with varying inlet temperatures and mass flow rates. The experimental results showed that the process of heat storage and release was predominantly governed by thermal conduction due to the high viscosity of PEW. Although the temperature variations in various directions within the LHS device remain nearly consistent, the heat transfer in the non-fin regions on the rectangular LHS device walls and corners was comparatively inferior due to the absence of natural convective heat transfer. In comparison to the mass flow rate, the inlet temperature exerts a more significant impact on accelerating the heat storage/release processes. While an increase in the mass flow rate enhanced the heat transfer capacity, its slightly accelerates the heat transfer duration. Moreover, the heat release ratio of the device exhibits an initial increase followed by a decrease with an increasing mass flow rate. In practical applications, prioritizing an increase in inlet temperature difference proves to be more effective than adjusting mass flow rate to enhance the heat storage performance