Factors Affecting Teachers Quality in Higher Vocational Colleges in Maoming City, China

Purpose: For learning environments like schools and colleges, predicting students' success is one of the most important issues since it aids in the creation of practical systems that, among other things, promote academic achievement and prevent dropout.   Theoretical framework: A rigorous analysis and processing of this data might provide us with knowledge about the students' knowledge and how it relates to academic assignments. In this study, classroom management, teacher qualification and in-service training with the effect so mediating variable of teacher quality have been used to measure students’ performance.   Design/methodology/approach: 364 respondents have been participated by using questionnaire are data collection methods and the data are analyses by using SMART-PLS.   Findings: The findings of the study revealed that the classroom management on students’ performance in-service training, and teacher qualification have significant relationship with teacher quality. The strength and the relationships of the variables might be re-examined by future researchers, including adding more variables from strategic, organizational, and environmental dimensions to determine the accuracy of the model.   Research, Practical & Social implications: This study contributes to the existing body of knowledge in understanding the school quality management as this is the first study with the set of such variables. In other words, this study attempts to increase the understanding of the relationship between school quality management strategies on students’ performance.   Originality/value: Education authorities should encourage all teachers to develop themselves through regular participation in seminars and conferences organized in their field of expertise in order to update their knowledge and acquire relevant teaching skills to help them impart appropriate knowledge to their students. In addition, the education department should provide schools with basic teaching equipment to make the teaching and learning process easier in schools

XML Data Retrieval Model Based on Two-dimensional Table Datasets

Retrieval problems of XML-based representation of data have been researched in this paper. In order to solve the large time and space overhead problem in building content index, this paper establish a data retrieval model advantageous to xml representation using the system automatically build two-dimensional table datasets. Take crop diseases and insect pests data for an example, this paper first gives the architecture of retrieval system based on XML crop diseases and insect pests' data; it also discusses about how to construct the two-dimensional table dataset and achieve the retrieval process; then it describes the text segmentation technique and the XSL style sheet conversion technology. Finally, under the VS.NET platform, using MVC design pattern develop and implement a prototype

Direct observation of high temperature superconductivity in one-unit-cell FeSe films

Heterostructure based interface engineering has been proved an effective method for finding new superconducting systems and raising superconductivity transition temperature (TC). In previous work on one unit-cell (UC) thick FeSe films on SrTiO3 (STO) substrate, a superconducting-like energy gap as large as 20 meV, was revealed by in situ scanning tunneling microscopy/spectroscopy (STM/STS). Angle resolved photoemission spectroscopy (ARPES) further revealed a nearly isotropic gap of above 15 meV, which closes at a temperature of ~ 65 K. If this transition is indeed the superconducting transition, then the 1-UC FeSe represents the thinnest high TC superconductor discovered so far. However, up to date direct transport measurement of the 1-UC FeSe films has not been reported, mainly because growth of large scale 1-UC FeSe films is challenging and the 1-UC FeSe films are too thin to survive in atmosphere. In this work, we successfully prepared 1-UC FeSe films on insulating STO substrates with non-superconducting FeTe protection layers. By direct transport and magnetic measurements, we provide definitive evidence for high temperature superconductivity in the 1-UC FeSe films with an onset TC above 40 K and a extremely large critical current density JC ~ 1.7*106 A/cm2 at 2 K. Our work may pave the way to enhancing and tailoring superconductivity by interface engineering

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Factors Affecting Students Performance Mediated by Teachers Quality in Higher Vocational Colleges in Maoming City, China

Purpose: For learning environments like schools and colleges, predicting students' success is one of the most important issues since it aids in the creation of practical systems that, among other things, promote academic achievement and prevent dropout. They gain from the automation of several student-related tasks that deal with large amounts of data gathered via software tools for technology-enhanced learning.   Theoretical framework: A rigorous analysis and processing of this data might provide us with knowledge about the students' knowledge and how it relates to academic assignments. In this study, classroom management, teacher qualification and in-service training with the effect so mediating variable of teacher quality have been used to measure students’ performance.   Design/methodology/approach: 364 respondents have been participated by using questionnaire are data collection methods and the data are analyses by using SMART-PLS.   Findings: The findings of the study revealed that the classroom management on students’ performance in-service training, and teacher qualification have significant relationship with teacher quality. The strength and the relationships of the variables might be re-examined by the future researchers, including adding more variables from strategic, organizational, and environmental dimensions to determine the accuracy of the model.   Research, Practical & Social implications: This study contributes to the existing body of knowledge in understanding the school quality management as this is the first study with the set of such variables. In other words, this study attempts to increase the understanding of the relationship between school quality management strategies on students’ performance.   Originality/value: Education authorities should encourage all teachers to develop themselves through regular participation in seminars and conferences organized in their field of expertise in order to update their knowledge and acquire relevant teaching skills to help them impart appropriate knowledge to their students. In addition, the education department should provide schools with basic teaching equipment to make the teaching and learning process easier in schools

Machine Learning-Based Estimation of Daily Cropland Evapotranspiration in Diverse Climate Zones

The accurate prediction of cropland evapotranspiration (ET) is of utmost importance for effective irrigation and optimal water resource management. To evaluate the feasibility and accuracy of ET estimation in various climatic conditions using machine learning models, three-, six-, and nine-factor combinations (V3, V6, and V9) were examined based on the data obtained from global cropland eddy flux sites and Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data. Four machine learning models, random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB), and backpropagation neural network (BP), were used for this purpose. The input factors included daily mean air temperature (Ta), net radiation (Rn), soil heat flux (G), evaporative fraction (EF), leaf area index (LAI), photosynthetic photon flux density (PPFD), vapor pressure deficit (VPD), wind speed (U), and atmospheric pressure (P). The four machine learning models exhibited significant simulation accuracy across various climate zones, reflected by their global performance indicator (GPI) values ranging from −3.504 to 0.670 for RF, −3.522 to 1.616 for SVM, −3.704 to 0.972 for XGB, and −3.654 to 1.831 for BP. The choice of suitable models and the different input factors varied across different climatic regions. Specifically, in the temperate–continental zone (TCCZ), subtropical–Mediterranean zone (SMCZ), and temperate zone (TCZ), the models of BPC-V9, SVMS-V6, and SVMT-V6 demonstrated the highest simulation accuracy, with average RMSE values of 0.259, 0.373, and 0.333 mm d−1, average MAE values of 0.177, 0.263, and 0.248 mm d−1, average R2 values of 0.949, 0.819, and 0.917, and average NSE values of 0.926, 0.778, and 0.899, respectively. In climate zones with a lower average LAI (TCCZ), there was a strong correlation between LAI and ET, making LAI more crucial for ET predictions. Conversely, in climate zones with a higher average LAI (TCZ, SMCZ), the significance of the LAI for ET prediction was reduced. This study recognizes the impact of climate zones on ET simulations and highlights the necessity for region-specific considerations when selecting machine learning models and input factor combinations

Transient voltage stability emergency control strategy for HVDC receiving end power grid based on global orthogonal collocation

When asynchronous motors, especially double-fed asynchronous motors in large capacity pump storage are the main loads in the high voltage direct current (HVDC) receiving end power grid, the increase of the equivalent slip of asynchronous motor load may cause transient voltage instability. In order to recover the voltage rapidly in the grid, the emergency reactive power support needs to be quick and accurate. A method for transient voltage stability emergency control by temporarily reducing DC current is proposed, the inverter station is used as emergency reactive power source for the HVDC receiving end power grid. In detail, firstly, aiming at the quantitative calculation of DC current, a nonlinear optimization model with the optimization variable of DC current and the objective of minimizing energy transmission reduction of HVDC is established. Further, in order to achieve fast solution and meet the accuracy requirements, global orthogonal collocation (GOC) is incorporated into the optimization model to transform the differential equations of both objective function and constraints into algebraic equations, thus the optimization is transformed into a nonlinear programming (NLP) problem, by which the emergency control strategy, in specific, the optimal DC current control scheme is obtained. Finally, the modified IEEE 14 benchmark is used to verify the effectiveness and superiority of the proposed strategy