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An important research topic is how teachers explore emotional intelligence in international Chinese language teaching and utilize emotional factors to cultivate students’ motivation to learn. Based on the theoretical model of emotional intelligence, this paper constructs a system of emotional intelligence factors for international Chinese teachers by combining cross-cultural communication theory. In terms of obtaining the relevant factors affecting teachers’ emotional intelligence, this paper introduces structural equation modeling to measure the pertinent aspects. It constructs the PLSSEM emotional intelligence model by estimating the model’s parameters through partial least squares. In terms of analyzing the level of emotional intelligence of international Chinese teachers, this paper chooses a total of 200 international Chinese teachers from various Chinese universities as the research sample. It uses SPSS software to analyze the data in terms of the parameter estimation of the emotional intelligence model, the test evaluation, and the differences in the level of emotional intelligence in multiple dimensions. The results show that the path coefficients of each latent variable of the PLS-SEM emotional intelligence model exceeded 0.3, and the value-added fitness index of the model exceeded 0.95. The emotional intelligence level scores of the 200 teachers averaged 4.557, and there were significant differences in their gender, teaching age, and education level within the 1% range. The emotional intelligence factor system of international Chinese teachers can clarify the specific reasons affecting their development. It can help teachers better develop emotional intelligence and help improve the quality of international Chinese education and teaching.

The rapid development of the new round of science and technology and the industrial revolution has put forward higher requirements on the quality of higher vocational students, especially the cultivation of interdisciplinary innovation ability. The study adopts statistical methods such as hierarchical regression analysis, chi-square test and gray correlation analysis, combined with questionnaire survey and empirical research, to deeply analyze the influencing factors of interdisciplinary innovation ability of higher vocational students. The evaluation system of innovation ability, which contained 4 first-level indicators and 12 second-level indicators, was tested, and SPSS software was utilized to process and analyze the data. The results show that there are significant differences in the critical thinking ability and knowledge integration ability of higher vocational students, with a mean difference value of 0.07191, a t-value of 7.4264, and a significance test of p=0.037<.05. The interdisciplinary task-driven-class has a more than 7 point advantage over the control class in terms of both the mean score and the innovative score. The results of the study provide strategies and methods for the cultivation of interdisciplinary, creative ability in higher vocational colleges and universities, which are of great practical significance for promoting the comprehensive development of higher vocational students and adapting to the industrial demands of the new era.

Several procedures that use summary data to test hypotheses about Pearson correlations and ordinary least squares regression coefficients have been described in various books and articles. To our knowledge, however, no single resource describes all of the most common tests. Furthermore, many of these tests have not yet been implemented in popular statistical software packages such as SPSS and SAS. In this article, we describe all of the most common tests and provide SPSS and SAS programs to perform them. When they are applicable, our code also computes 100 × (1 − α)% confidence intervals corresponding to the tests. For testing hypotheses about independent regression coefficients, we demonstrate one method that uses summary data and another that uses raw data (i.e., Potthoff analysis). When the raw data are available, the latter method is preferred, because use of summary data entails some loss of precision due to rounding.

The water quality index (WQI) is a globally accepted guideline to indicate the water quality standard of any groundwater resource. Water levels in existing groundwater sources are declining in several coastal zones. Therefore, for monitoring water quality and improving water management, the prediction and identification of groundwater status by an effective technique with higher accuracy is urgently needed. Therefore, this research aims to find an effective model for WQI prediction by comparing entropy and critic weight-based WQI (ENW-WQI and CRITIC-WQI) with multi-layer perceptron artificial neural network (MLP-ANN) technique and also to identify contaminated zones using GIS. Initially, 1000 water sampling datasets with concentrations of several water quality parameters of different coastal blocks of eastern India during 2018 to 2022 are considered for the estimation of ENW-WQI and CRITIC-WQI. It shows 65% and 67% of the samples are excellent to good for drinking. ENW-WQI and CRITIC-WQI-based MLP-ANN models have been established considering different data portioning and hidden neuron numbers. Input variables and appropriate dataset partitioning with hidden neurons for models obtained from correlation and trial–error analysis. Spatial distribution maps are also produced for calculated WQIs using inverse distance weighted interpolation approaches. Three fitting models are obtained: ENW-WQI-MLP-ANN, CRITIC-WQI-MLP-ANN-I and CRITIC-WQI-MLP-ANN-II. CRITIC-WQI-MLP-ANN-II model (data ratio 85:15, network structure 6–12-1, R 2  = 0.986, NSE = 0.98, and error rate 0.49%) provides the best accuracy in WQI prediction. The GIS-based WQI maps record several areas related to drinking water quality. The results of this research can help in planning the provision of safe drinking water in the future.

In the vicinity of the coast, predominantly groundwater is the sole reliable resource for potable purposes as the surface water sources are highly saline and unfit for human consumption. However, the groundwater in Sagar Island is highly vulnerable to saltwater intrusion. The majority of drinking water comes from government-owned hand pump-equipped tube wells. But during the summer season, many of these tube wells yield significantly less water. Hence, in the current scenario, water quality assessment has become important to the quantity available. Total of 31 samples of deep tube wells (groundwater) are collected at variegated locations during pre-monsoon season throughout Sagar, and then, the physical and chemical quality parameters of these water samples are analysed. Furthermore, a multivariate statistical technique is executed with the aid of the SPSS program. The hydro-chemical parameters that are taken into account for the quality analysis are pH, salinity, electrical conductivity (EC), total dissolved solids (TDS), total hardness, aluminium, arsenic, bi-carbonate, cadmium, iron, chloride, copper, chromium, cobalt, lead, magnesium, manganese, nickel, potassium, sulphate, zinc, and sodium. Then, the analysed data evaluates the water quality index (WQI). Five components are identified through the principal component analysis (PCA) technique, and 82.642% total variance is found. The outcomes of the quality assessment study illustrate that about 54.84% of collected samples come in the “excellent” water quality class when calculated by the “weighted arithmetic WQI method,” and 90.32% of collected groundwater samples come in the “good” water quality class when computed using the “modified weighted arithmetic WQI method.” This study helps for the interpretation of WQI to assess groundwater quality.

Today, education, medicine, business, and all other fields rely heavily on computers. This reliance is increased much when both professionals and academic level students have to conduct research projects. This reliance is indicated by the availability and utility of the software, which is an integral part of computer technology. Hence, by keeping in view the importance of SPSS in research, we scrutinized the significant factors behind Statistical Package for Social Sciences (SPSS) adoption and acceptance. We executed an experimental approach and gathered data from n= 300 young researchers studying in the n= 4 public sector universities in Rawalpindi and Islamabad, Pakistan. By adopting the primary variables from the Technology Acceptance Model, we proposed and a study model and examined it by using Smart-PLS. Findings showed that perceived ease of use and usefulness are significantly associated with Quantitative Research. Here, perceived ease of use and usefulness also indicated their interrelationship to validate the technology acceptance further. As a result, we also found a significant relationship between perceived usefulness, perceived usefulness, and SPSS technology acceptance. In simple terms, ease of use and valuable outcomes are the primary reasons behind SPSS acceptance among Pakistani students. Thus, we conclude that today, when technology has facilitated all the fields of life, research and development is another major field that is availing enormous advantages from the technology acceptance, integration, and execution. We recommend that SPSS usage should be encouraged for research purposes. Educational institutions should introduce new courses regarding SPSS learning and use them to further increase quantitative research aptitude among students.

Indoor environmental quality (IEQ) has a high-level of impact on one’s health and productivity. It is widely accepted that IEQ is composed of four categories: thermal comfort, indoor air quality (IAQ), visual comfort, and acoustic comfort. The main physical parameters that primarily represent these comfort categories can be monitored using sensors. To this purpose, the article proposes a wireless indoor environmental quality logger. In the literature, global comfort indices are often assessed objectively (using sensors) or subjectively (through surveys). This study adopts an integrated approach that calculates a predicted indoor global comfort index (P-IGCI) using sensor data and estimates a real perceived indoor global comfort index (RP-IGCI) based on questionnaires. Among the 19 different tested algorithms, the stepwise multiple linear regression model minimized the distance between the two comfort indices. In the case study involving a university classroom setting—thermal comfort and indoor air quality were identified as the most relevant IEQ elements from a subjective point of view. The model also confirms this findings from an objective perspective since temperature and CO2 merge as the measured physical parameters with the most impacts on overall comfort.

This research comprehensively analyzes the spatial morphology of 177 traditional villages within Linxia Hui Autonomous Prefecture, Gansu Province. The study delineates these characteristics utilizing a combination of five quantitative measured indices—ratio, boundary, saturation, building density, and dispersion coefficients. Leveraging sophisticated analytical techniques facilitated by “SPSS + GIS” integration, the investigation systematically explores the intricate details of village spatial form. Their overarching distribution patterns, and the determinant factors influencing them, provide insights across both granular and broad-scale dimensions. The aim is to establish a robust quantitative data analysis framework, facilitating a precise description of traditional villages’ spatial dynamics. The findings categorize the spatial morphology of Linxia’s traditional villages into three distinct types: linear multi-point concentration, dense clustering, and irregular dispersion. Common traits among these categories include widespread dispersal, small settlements, and a mix of dwellings. Spatial distribution patterns vary, with dense clusters forming an “olive-shaped” trend in the southeast–northwest direction, while irregularly dispersed villages develop along mountains and valleys, exhibiting multi-core structures. Additionally, linear multi-point concentrated villages display a random, multi-point distribution interspersed with dense clusters. The survival strategies of these commercial, subsistence, and resource-based villages are shaped by a confluence of factors such as elevation, river proximity, ancient road networks, and the interplay between Han Chinese and Tibetan cultural influences. The implications of this study are significant for understanding traditional village dynamics, promoting sustainable development, and refining quantitative methods for rural studies.

In this paper, we take big data, the GDP of the United States from 1992 to 2020 as the research object. Through SPSS software analysis and descriptive statistics, we can compare the GDP of Biden and Trump during their tenure. Using big data’s analytical technology, the line chart we get visually shows that the election of different candidates will shape different strategic patterns of global economic and financial development. A new comprehensive factor W is obtained from the data that directly reflect the overall GDP of the United States. Finally, by establishing a forecasting model, we can find out the comprehensive event factors that will occur due to different political positions and policy plans between Biden and Trump in the future. In the end, we use the results of big data’s analysis to show that Biden’s economic plan may increase US GDP growth by 1.2 percentage points in 2021, push GDP growth to 4.9%, and help the US economy recover the economic losses caused by the epidemic in mid-2021.

Speaking is a very important part of English learning, which reflects the students’ ability to express themselves and their comprehensive use of language. The research object of this paper is the students of two parallel classes in the first year of a high school. Test method, questionnaire survey method and classroom observation method were mainly utilized in the experiment for the study. Before and after the experiment, two speaking tests were conducted to verify whether the task-based teaching method could improve students’ oral English. The classroom observation method was used throughout the experiment, based on the lexical and syntactic complexity analyzer in terms of both learning motivation and oral proficiency improvement. To verify whether the task-based teaching method can stimulate students’ interest in oral learning from an objective point of view. After the experiment, a questionnaire survey was conducted for students to find out their attitudes towards speaking learning and task-based teaching method. Finally, and SPSS software was used to analyze the data to draw conclusions. The study shows that the mean value of students’ oral English scores in the experimental class after the teaching experiment (M=50.384) is significantly higher than that of the control class (M=37.612), and there is a significant difference (p=0.001<0.05). It can be seen that the task-based teaching method is more effective in improving students’ oral English achievement.