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In this paper, the project teaching reform is carried out in the course of “Production Logistics”, and it is found that in the project teaching activities, the personality composition of the group members has a great impact on the effect and achievement of the project activities. Therefore, this paper uses MyersBriggs Type Indicator(For short MBTl ) professional personality test to investigate the students, obtain the overall distribution of the vocational personality types of the students, and compare the achievement differences of different professional personalities. Analyze the influence of different professional personalities of team members on the achievement of project-based courses. By analyzing theinfluence of different professional personalities of group members on project-based courseactivities, the following conclusions are drawn: lNFP is the most common type of studentpersonality type, ESFl is the least. lNFl and ENFl students have better grades, ET type students havea stronger willingness to be group leaders, while EF type students have a better willingness to begroup leaders; in terms of the composition of group members, the more complex the type, whenthe proportion of certain personality type is about50%, the group performance isrelatively bette.

Hilly and mountainous areas are weak places for the development of agricultural mechanization in China. The way to improve the utilization rate of small agricultural machinery widely used in hilly and mountainous areas is of positive significance for optimizing resource allocation efficiency of agricultural production and ensuring food security supply. Taking microtillers as a representative tool, this study systematically analyzed the main factors affecting the utilization rate of small agricultural machines and its influencing mechanism. Then, based on the survey data of 4905 farmers in 100 counties in 10 hilly and mountainous provinces of China, empirical analysis was carried out by some econometric models, such as censored regression and the mediating effect model. Results show the following.: (1) Among farmers in hilly and mountainous areas, the average use time of each microtiller is 218.41 h per year. (2) Age, social identity, terrain conditions, crop types, land area, the number of microtillers, the number of large tractors, and the machinery purchase subsidy policy are the significant factors affecting the utilization rate of microtillers. (3) The increase of cultivated land area not only directly improves the utilization rate of microtillers, but also indirectly improves the utilization rate of microtillers due to the increase in quantity.

Natural gas is often described as the cleanest fossil fuel. The consumption of natural gas is increasing rapidly. Accurate prediction of natural gas spot prices would significantly benefit energy management, economic development, and environmental conservation. In this study, the least squares regression boosting (LSBoost) algorithm was used for forecasting natural gas spot prices. LSBoost can fit regression ensembles well by minimizing the mean squared error. Henry Hub natural gas spot prices were investigated, and a wide range of time series from January 2001 to December 2017 was selected. The LSBoost method is adopted to analyze data series at daily, weekly and monthly. An empirical study verified that the proposed prediction model has a high degree of fitting. Compared with some existing approaches such as linear regression, linear support vector machine (SVM), quadratic SVM, and cubic SVM, the proposed LSBoost-based model showed better performance such as a higher R-square and lower mean absolute error, mean square error, and root-mean-square error.

Antibiotic resistance and residues in aquaculture are a growing concern worldwide and consequently identifying favorable antibacterial compounds against aquatic pathogenic bacteria are gained more attention. Active compounds derived from marine microorganisms have shown great promise in this area. This review is aimed to make a comprehensive survey of anti-aquatic pathogenic bacterial compounds that were produced by marine microorganisms. A total of 79 compounds have been reported, covering literature from 1997 to 2021. The compounds are included in different structural classes such as polyketides, terpenoids, nitrogen compounds and others, and some of them present the potential to be developed into agents for the treatment of aquatic pathogenic bacteria.

Natural gas has been proposed as a solution to increase the security of energy supply and reduce environmental pollution around the world. Being able to forecast natural gas price benefits various stakeholders and has become a very valuable tool for all market participants in competitive natural gas markets. Machine learning algorithms have gradually become popular tools for natural gas price forecasting. In this paper, we investigate data-driven predictive models for natural gas price forecasting based on common machine learning tools, i.e., artificial neural networks (ANN), support vector machines (SVM), gradient boosting machines (GBM), and Gaussian process regression (GPR). We harness the method of cross-validation for model training and monthly Henry Hub natural gas spot price data from January 2001 to October 2018 for evaluation. Results show that these four machine learning methods have different performance in predicting natural gas prices. However, overall ANN reveals better prediction performance compared with SVM, GBM, and GPR.

Qingdao, a historically high-risk area for hemorrhagic fever with renal syndrome (HFRS) in China, is undergoing agricultural mechanization and urbanization. However, the specific risk factors for HFRS in this context remain unclear. This study sought to determine the risk factors for HFRS in Qingdao. Community-based, 1:2 case-control study. Each case was matched with two healthy neighborhood controls based on biological sex, age, and the same neighborhood or village. Univariate and multivariate conditional logistic regression analyses were performed. Furthermore, stratified analyses were performed to explore risk factor heterogeneity between the peak season for Hantaan virus (HTNV) type HFRS (October-January) and other months. 93 cases (73.2%, 93/127) reported from January 2022 to September 2023 and 186 controls completed this questionnaire. Farmers accounted for the highest proportion (68.8%, 64/93). In multivariate logistic regression analysis, there were three significant risk factors for HFRS: piles of firewood and/or grain in residential yards (odds ratio [OR]=3.75, 95% CI: 2.14-6.55), mite and/or flea bites (OR=1.83, 95% CI: 1.06-3.18) and contacting with rats and/or their excreta (OR=1.73, 95% CI: 1.09-2.74); three variables represented significant protective factors for HFRS: frequency of sun exposure for quilts and bedding (OR=0.41, 95% CI: 0.19-0.90), rodent control measures at home (OR=0.50, 95% CI: 0.30-0.81) and knowing the main sources of HFRS transmission (OR=0.58, 95% CI: 0.36-0.90). Stratified analysis revealed that the influence of these factors varied by season, with rodent contact and control measures being particularly salient during the HTNV peak season. This study provides the first comprehensive evidence of risk and protective factors for HFRS in Qingdao, highlighting the role of rodent control, promoting comprehensive health education, environmental management, and personal protection. However, the results should be interpreted considering the study's limitations, including a 73.2% response rate and the potential for recall bias.

Cotton, as an important cash crop and strategic material, is widely planted in Xinjiang, China. In the traditional way, the management of the cotton field is extensive and the cost is huge. This paper analyzed the economic benefits and the related influence factors of cotton field management digitalization by collecting costs from 2020 of four major tasks in field management in Xinjiang, China. These four main tasks included field scouting, plant protection, topping and irrigation. By analyzing the intersection of the average cost curves of each major task in field management, we obtained the critical size of digital agriculture replacing traditional agriculture. Then, we used sensitivity analysis to find the main factors affecting the promotion and application of digital agricultural equipment. The results show: (1) at a certain critical size, the use of digital agricultural equipment can reduce the cost of production compared to traditional agriculture. However, the critical size varies for different management segments. (2) Fixed equipment costs, labor costs, water costs and energy costs have a large impact on the critical size. On large-scale cotton farms, digital agriculture tends to be more economical than traditional agriculture. In the future, as the cost of fixed equipment decreases, and labor costs and water costs rise, the critical size of digital agriculture replacing traditional agriculture will get smaller, and the scope of the economic benefits of digital cotton field management will increase further.

Agricultural machinery maintenance skill training is conducive to improving the fault diagnosis and maintenance levels of agricultural machinery for agricultural socialized service providers and plays an important role in providing stable and reliable agricultural machinery operation services. This paper aims to study whether maintenance skill training gives agricultural socialized service providers more advantages than untrained providers, exploring the relationship between maintenance skill training and agricultural machinery service area. Based on a survey of 4905 farmers from 10 provinces in China, an empirical analysis was carried out using a fixed effect model and a propensity score matching method. The results showed the following: First, maintenance skill training had a significant positive impact on agricultural machinery operation service area, including 10.426 ha of machinery tilling service area and 8.524 ha of machinery harvesting service area. Second, since maintenance skill training gave agricultural socialized service providers more advantages in agricultural machinery operation services and enabled them to obtain more orders, it had an indirect positive impact on the quantity of demand for large- and middle-sized agricultural machinery.

The pathogenicity of foodborne Vibrio parahaemolyticus is a major concern for global public health. This study aimed to optimize the liquid–solid extraction of Wu Wei Zi extracts (WWZE) against Vibrio parahaemolyticus, identify its main components, and investigate the anti-biofilm action. The extraction conditions optimized by the single-factor test and response surface methodology were ethanol concentration of 69%, temperature at 91 °C, time of 143 min, and liquid–solid ratio of 20:1 mL/g. After high performance liquid chromatography (HPLC) analysis, it was found that the main active ingredients of WWZE were schisandrol A, schisandrol B, schisantherin A, schisanhenol, and schisandrin A–C. The minimum inhibitory concentration (MIC) of WWZE, schisantherin A, and schisandrol B measured by broth microdilution assay was 1.25, 0.625, and 1.25 mg/mL, respectively, while the MIC of the other five compounds was higher than 2.5 mg/mL, indicating that schisantherin A and schizandrol B were the main antibacterial components of WWZE. Crystal violet, Coomassie brilliant blue, Congo red plate, spectrophotometry, and Cell Counting Kit-8 (CCK-8) assays were used to evaluate the effect of WWZE on the biofilm of V. parahaemolyticus. The results showed that WWZE could exert its dose-dependent potential to effectively inhibit the formation of V. parahaemolyticus biofilm and clear mature biofilm by significantly destroying the cell membrane integrity of V. parahaemolyticus, inhibiting the synthesis of intercellular polysaccharide adhesin (PIA), extracellular DNA secretion, and reducing the metabolic activity of biofilm. This study reported for the first time the favorable anti-biofilm effect of WWZE against V. parahaemolyticus, which provides a basis for deepening the application of WWZE in the preservation of aquatic products.

A water hydraulic axial piston pump has become the preferred power component of environmentally friendly water hydraulic transmission systems, due to its advantages of a compact structure, high power density, and so on. The poor friction and wear performance in the water medium, especially under extreme conditions of high speed and high pressure, limit the engineering application of the water hydraulic axial piston pump. In this review, the research progress for key friction pair materials (such as special corrosion-resistant alloys, engineering plastics, and engineering ceramics) for water hydraulic axial piston pumps is, firstly, summarized. Secondly, inspired by nature, the processing methods, lubrication drag-reduction mechanism, and tribological properties of the biomimetic surface textures are discussed. The effects of the surface texture shape, equivalent diameter, depth, and arrangement on the pump’s tribological properties are reviewed in detail. Finally, the application status of, and problems with, surface texture technology in water hydraulic axial piston pumps are summarized. It is suggested that future studies should focus on the multi-field coupling lubrication anti-friction mechanism of the multi-type composite texture under extreme conditions and mixed lubrication; and the anti-wear performance of the texture coupled with a coating modification, to further promote the surface texture in the field of lubrication antifriction engineering applications.