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It is an indisputable fact that there exists a non-integrated phenomenon between educational theory and practice, and educational theory and practice have the correlation of both interdependence and mutual deviation, so how to make the two coordinate and harmonize the development of the two has become a hotspot of the current research. In this paper, under the perspective of evidence-based education, we explore the coordinated development of the two, construct the coordinated index system of theoretical and practical education, and utilize the entropy weighting method to assign weights to the index system. Subsequently, taking School A as an example, the data-driven web crawler technology was utilized to obtain the relevant data, and the internal coordination level and future development trend of its theoretical and practical education were measured by the linear weighting method, the coupled coordination degree model, and the GM(1,1) model, respectively. The results show that the coupling coordination degree of its theoretical and practical education increases from 0.1489 in 2013 to 0.5609 in 2022, and although it maintains an upward posture, the overall values are lower than 0.6, and the coupling coordination level is low. In addition, the level of theory and practice education coordination is showing a continuous upward trend, with an average growth rate of 15.19%, but has shown a slowing trend. The coordinated and healthy development of theory and practice education in higher vocational colleges and universities can be realized from three aspects: enriching the form of theory and practice education activities, strengthening the atmosphere of theory and practice education, and school-enterprise cooperation.

As digital technology advances, college aerobics instruction must evolve to enhance teaching efficiency and quality through tech-based solutions. Traditional aerobics evaluation faces challenges like subjectivity, delayed feedback, and inefficiency, hindering quality improvement and modern teaching management. This study introduces a novel, efficient, scientific evaluation platform for aerobics teaching, focusing on quantitative assessment and refined management. The platform demonstrates significant benefits by developing an evaluation index system, utilizing a genetic algorithm for index weight optimization, and employing a fuzzy comprehensive evaluation method for teacher performance. Results show enhanced student satisfaction, a notable 14.81% increase in assessment scores among the experimental group, and improved skill quality and application. This platform offers a fresh perspective and pathway for elevating aerobics teaching in higher education.

Objectives The motion attitude (sinkage and trim) of a semi-planing ship (at Froude numberFr > 0.4) have significant effects on its resistance performance during voyage. The sinkage and trim effects on the resistance components (frictional resistance and pressure resistance) is discussed.Methods A CFD study is carried out on the resistance performance of trimaran in calm water, and the k-ε turbulent model and overlapping grid are adopted. The free model and constrained model are computed. The computed results are compared to analyze the effects of sinkage and trim on the resistance components.Results The results show that the change of total resistance caused by sinkage and trim is more than 20% in semi-planing conditions. The sinkage and trim have a significant effect on the pressure resistance, up to nearly 50%; and a relatively small effect on frictional resistance, less than 8.5%. More than 90% of the resistance increase caused by motion attitude comes from the change of pressure resistance.Conclusions Predicting the motion attitudes and resulting pressure resistance changes accurately is extremely important for semi-planing trimaran.

Suppose that Γ is a graph of order n and A(Γ) = [ai,j] is its adjacency matrix such that ai,j is equal to 1 if vi is adjacent to vj and ai,j is zero otherwise, where 1 ≤ i, j ≤ n. In a family of graphs, a graph is called minimizing if the least eigenvalue of its adjacency matrix is minimum in the set of the least eigenvalues of all the graphs. Petrović et al. [On the least eigenvalue of cacti, Linear Algebra Appl., 2011, 435, 2357-2364] characterized a minimizing graph in the family of all cacti such that the complement of this minimizing graph is disconnected. In this paper, we characterize the minimizing graphs G ∈ Ωnc$\begin{array}{} {\it\Omega}^c_n \end{array}$, i.e.

The perennial selenium (Se) hyperaccumulator Cardamine hupingshanensis (Brassicaceae) thrives in aquatic and subaquatic Se-rich environments along the Wuling Mountains, China. Using bright-field and epifluorescence microscopy, the present study determined the anatomical structures and histochemical features that allow this species to survive in Se-rich aquatic environments. The roots of C. hupingshanensis have an endodermis with Casparian walls, suberin lamellae, and lignified secondary cell walls; the cortex and hypodermal walls have phi (Φ) thickenings; and the mature taproots have a secondary structure with a periderm. The stems possess a lignified sclerenchymal ring and an endodermis, and the pith and cortex walls have polysaccharide-rich collenchyma. Air spaces are present in the intercellular spaces and aerenchyma in the cortex and pith of the roots and shoots. The dense fine roots with lignified Φ thickenings and polysaccharide-rich collenchyma in the shoots may allow C. hupingshanensis to hyperaccumulate Se. Overall, our study elucidated the anatomical features that permit C. hupingshanensis to thrive in Se-rich aquatic environments.

The air environment in the subway car has a great impact on the comfort and health of passengers. In order to know the real air quality in the car when the train is running, this paper takes CO2 as an indicator and conducts a field test on Line 11 of Shanghai Metro to study and analyse the dynamic CO2 concentration and the fresh air rate in the subway car. The results show that the CO2 concentration increases from the head car to the tail car, and the fresh air volume decreases from the head car to the tail car is the reason for the large difference of CO2 concentration in different cars. The CO2 concentration in the car is greatly affected by the passenger load, and the maximum value of CO2 concentration in the morning peak can reach 2.3 times that of the normal hours. The background CO2 concentration has a certain influence on the CO2 concentration in the car, and the CO2 concentration in the underground line is higher than that in the elevated line in the same carriage. The CO2 concentration in subway cars of the underground line in the morning peak is significantly higher than 1500 ppm, which indicates that the dilution effects of the ventilation can’t meet the fresh air requirements in the morning peak. The research in this paper can provide a reference for the design of the ventilation system of subway trains and the environmental control in the car.

In order to analyze the thermal environment and its influence on workers’ thermal adaptation, a field study was conducted in a rubber factory during winter. Indoor and outdoor environmental parameters were measured and subjective questionnaire surveys were collected. The study was conducted from November to January the next year. During the survey, indoor temperature was in the range of 12.6 to 21.7°C and indoor air velocity ranged from 0.15 to 0.3 m/s. The clothing insulation during heating period was between 0.85-0.9 clo, while it was between 1.21-1.33 clo during pre-heating period. The estimated metabolic rate was around 2-2.2 met. Analysis showed that the thermal sensation vote (TSV) was higher than that predicted by PMV. The neutral temperature difference between TSV and PMV was 4.8°C. The thermal comfort and thermal adaptation of workers in winter were systematically analyzed and a thermal adaptive model was proposed. Basis on the adaptive model, the comfort temperature range of workers was suggested.

The human cognitive performance under hot and humid exposure is a critical issue for people working under hot and humid environments, for the considerations of both their health and safety. In order to find an indicator of the cognitive performance, the electrocardiogram (ECG) data were recorded continuously for 2.5 hours with portable Holter, when participants were exposed to 4 hot-humid environmental conditions and conducting different cognitive tests. The ECG data then were analysed to obtain the HRV indices that were used to establish a relationship with the cognitive test results. Temporal index pNN50 of HRV time domain indices was selected as a biomarker of the cognitive performance in this study. Results showed that the pNN50 responded significantly to the air temperature, while the influences of the cognitive tasks and the exposure time were found to be less significant. Meanwhile, the mean accuracy of the cognitive tasks was found to be positively related to the changes of mean pNN50, but a negatively related relationship between the mean reaction time and the mean pNN50 was observed. Furthermore, a positive relationship between the relative cognitive performance and the pNN50 was obtained, which meant that higher pNN50 responded to the higher cognitive performance. The potential reason could be ascribed to the changes in temperature-related allocation of the mental resources. And the low cognitive performance could be attributed to the rise of the mental fatigue due to the high sympathetic activities that corresponded to the low pNN50. The results revealed that the pNN50 could act as a biomarker of the cognitive performance under hot-humid exposure. The findings gave some implications to the real-time evaluation of the working performance and health of the people working under hot-humid environments.

Local draft experiments were conducted to establish the overall thermal sensation prediction model under low pressure environment. Head, chest, hand, and ankle were exposed to local airflow under three pressure environments: 1 atm, 0.87 atm, and 0.74 atm, respectively. The skin temperature was measured and the subjective questionnaires were collected. The results showed that in cooler environment, the mean skin temperature decreased with the decrease of pressure, and people maybe more sensitive. When the environmental pressure decreased, the overall thermal sensation decreased correspondingly. Compared with the normal pressure condition, as the pressure decreased, the weight of local thermal sensation on overall thermal sensation changed a little. The weight of chest decreased while the weight of hands increased. When exposed to local draft, the four body parts showed similar influence on the overall thermal sensation under lower pressure conditions. Based on influence of pressure, temperature, and local draft speed, the overall thermal sensation prediction model for each body part was established respectively. The results indicated that the predicted values were highly correlated with the measured values. The models could be used to predict thermal sensation of people who are exposed to local draft in low pressure environment.

Protein-protein interactions (PPI) play a key role in many biological systems. Over the past few years, an explosion in availability of functional biological data obtained from high-throughput technologies to infer PPI has been observed. However, results obtained from such experiments show high rates of false positives and false negatives predictions as well as systematic predictive bias. Recent research has revealed that several machine and statistical learning methods applied to integrate relatively weak, diverse sources of large-scale functional data may provide improved predictive accuracy and coverage of PPI. In this paper we describe the effects of applying different computational, integrative methods to predict PPI in Saccharomyces cerevisiae. We investigated the predictive ability of combining different sets of relatively strong and weak predictive datasets. We analysed several genomic datasets ranging from mRNA co-expression to marginal essentiality. Moreover, we expanded an existing multi-source dataset from S. cerevisiae by constructing a new set of putative interactions extracted from Gene Ontology (GO)- driven annotations in the Saccharomyces Genome Database. Different classification techniques: Simple Naive Bayesian (SNB), Multilayer Perceptron (MLP) and K-Nearest Neighbors (KNN) were evaluated. Relatively simple classification methods (i.e. less computing intensive and mathematically complex), such as SNB, have been proven to be proficient at predicting PPI. SNB produced the “highest” predictive quality obtaining an area under Receiver Operating Characteristic (ROC) curve (AUC) value of 0.99. The lowest AUC value of 0.90 was obtained by the KNN classifier. This assessment also demonstrates the strong predictive power of GO-driven models, which offered predictive performance above 0.90 using the different machine learning and statistical techniques. As the predictive power of single-source datasets became weaker MLP and SNB performed better than KNN. Moreover, predictive performance saturation may be reached independently of the classification models applied, which may be explained by predictive bias and incompleteness of existing “Gold Standards”. More comprehensive and accurate PPI maps will be produced for S. cerevisiae and beyond with the emergence of largescale datasets of better predictive quality and the integration of intelligent classification methods.

With the rapid development of computer vision, techniques of machine vision and visual inspection have been applied into the inspection of catenary on high-speed railways. Visual inspection systems have been developed and super-high-resolution images are captured to check the status of catenary components. Automatic recognition of defects becomes very important since the number of images is too huge to be manually checked one by one. However, it is not easy for the development of recognition algorithms on catenary components. There are many types of defects to be checked on different kinds of catenary components, but the number of defect images is too small in real world. In this paper, a solution was proposed and implemented. An on-site data acquisition system was designed and developed, and different types of defects were manually made on different catenary components beforehand. Finally, a visual inspection database was successfully constructed, including plenty of different kinds of catenary components, different types of defects, in different inspection conditions. The visual inspection database will be of great use in the development and test of recognition algorithms for catenary.