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Associations between physical activity and academic competence : a cross-sectional study among Slovenian primary school students
Joca Zurc, Jurij Planinšec, 2022, original scientific article

Abstract: Physical activity has beneficial effects on overall academic performance in children. However, there is a lack of evidence regarding how the individual characteristics of physical activity interact with other confounding variables of academic competence. Leisure-time physical activity with potential confounders—such as developmental, behavioral, family, and school factors, predicting overall, mathematical, and reading academic competence—was studied in a random sample of 1520 Slovenian primary school students in grades 4–6 (51.9% female; mean age = 10.4 years; SD = 0.93). A structured self-reported questionnaire was used to gather data on the children’s leisuretime physical activity and social-demographic variables, while academic competence was measured by teachers using the SSRS Academic Competence Evaluation Scale. The findings showed that children engage in physical activity most days a week, with moderate-intensity and unorganized activities. It was predicted that engaging in physical activity would lead to an increase in academic performance by 4.2% in males (p = 0.002) and 3.2% in females (p = 0.024), but after fully adjusting the model for controlling confounding variables, the prediction increased to 81.1% in females and 84.1% in males (p < 0.001). The frequency and intensity of physical activity, the absence of digital games, and attending sports clubs seem to have the most beneficial effects in terms of academic competence in school children, among other relevant confounders mediating in this complex relationship.
Keywords: physical activity, leisure time, sports, academic competences, late childhood, multiple regression analyses
Published in DKUM: 12.07.2024; Views: 9; Downloads: 0
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Materials for HybridNeuro webinar titled "Validation of results: statistical models and MU identification accuracy"
Aleš Holobar, Nina Murks, 2024, complete scientific database of research data

Abstract: This dataset contains a collection of teaching materials that were used in the HybridNeuro project webinar titled "Validation of results: statistical models and MU identification accuracy". The webinar was presented by Aleš Holobar and covered the complexities of motor unit (MU) identification accuracy, regression analysis and Bayesian models. The primary aim of the webinar was to spark a robust discussion within the scientific community, particularly focusing on the application and implications of linear mixed models and Bayesian regression in the realm of MU identification. The teaching materials include Matlab and R source code for statistical analysis of the included data, as well as three examples of MU identification results in CSV format (from both synthetic and experimental HDEMG signals). The presentation slides in PDF format are also included. The dataset is approximately 9 MB in size.
Keywords: HybridNeuro, webinar, teaching materials, statistical models, regression analysis, motor unit identification, matlab, rstudio, statistics, surface high density electromyogram (HDEMG), tibialis anterior, dataset
Published in DKUM: 30.05.2024; Views: 155; Downloads: 12
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Directions for the sustainability of innovative clustering in a country
Vito Bobek, Vladislav Streltsov, Tatjana Horvat, 2023, original scientific article

Abstract: This paper presents potential improvements through utilizing the cyclical nature of clusters by human capital, technology, policies, and management. A historical review of the formation and sustainable development of clusters in the US, Europe, Japan, China, and other regions is carried out to achieve this. The aim was to identify and assess the prominent occurrence cases, the central institutional actors, the indicators of their innovative activity, and the schematics of successful cluster management. The theory section covers current classification methods and typology of innovation-territorial economic associations. Consequently, a regression analysis model is produced to identify the potential dominant success factors in implementing the innovation policy of the most successful innovative clusters. Comments on the influence of these predictors on the competitiveness and level of innovative development of the 50 inspected countries follow. As a result of qualitative and quantitative analysis, an overview of the best world practice, the new vision, and its priorities are proposed to improve the efficiency at the level of management structures of innovation clusters.
Keywords: cluster, cluster policy, state policy, regression analysis, institutions, innovation, R&D
Published in DKUM: 09.04.2024; Views: 245; Downloads: 121
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Exploring factors afecting elementary school teachers' adoption of 3D printers in teaching
Branko Anđić, Andrej Šorgo, Christoph Helm, Robert Weinhandl, Vida Lang, 2023, original scientific article

Abstract: Owing to its advantages such as producing durable models and easy accessibility, 3D modeling and printing (3DMP) has become increasingly popular in educational practice and research. However, the results of many studies have shown that the adoption rate of 3DMP among teachers is still low, especially in elementary schools. Therefore, research is needed to expand current knowledge about what influences teachers' decisions to start and continue using 3DMP in elementary schools. To investigate factors that affect elementary school teachers' decisions to use 3D printing in teaching, this study uses a mixed methods research approach combining binary logistic regression with a qualitative thematic analysis approach. Both approaches assembled predictive constructs from a range of theories on (1) technology acceptance and (2) intentions to continue or abandon 3DMP use. Using a sample of 225 elementary teachers from Montenegro, this study empirically concluded that intentions to discontinue 3DMP was slightly more strongly correlated with the predictors (i.e., performance expectancy, effort expectancy, perceived pedagogical impact, personal innovativeness, management support, user interface quality, technology compatibility, social influence, student expectations) than was intentions to continue using 3DMP. Performance expectancy was a significant determinant of teachers’ continued use of a 3DMP approach. The remaining seven factors (constructs) were found to be insignificant predictors. Perceived pedagogical impact and technology compatibility were significantly associated with teachers' decisions to discontinue using 3DMP. Our results also suggest that the time required to use 3DMP during instruction, the impact of 3DMP on the implementation of curriculum activities, and the availability of 3D printers are all relevant factors that influence teachers’' decision to use or abandon 3DMP.
Keywords: elementary school, binary logistic regression, continuance intentions, thematic analysis approach, 3D modelling and printing
Published in DKUM: 26.03.2024; Views: 227; Downloads: 191
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Reduction of surface defects by optimization of casting speed using genetic programming : an industrial case study
Miha Kovačič, Uroš Župerl, Leo Gusel, Miran Brezočnik, 2023, original scientific article

Abstract: Štore Steel Ltd. produces more than 200 different types of steel with a continuous caster installed in 2016. Several defects, mostly related to thermomechanical behaviour in the mould, originate from the continuous casting process. The same casting speed of 1.6 m/min was used for all steel grades. In May 2023, a project was launched to adjust the casting speed according to the casting temperature. This adjustment included the steel grades with the highest number of surface defects and different carbon content: 16MnCrS5, C22, 30MnVS5, and 46MnVS5. For every 10 °C deviation from the prescribed casting temperature, the speed was changed by 0.02 m/min. During the 2-month period, the ratio of rolled bars with detected surface defects (inspected by an automatic control line) decreased for the mentioned steel grades. The decreases were from 11.27 % to 7.93 %, from 12.73 % to 4.11 %, from 16.28 % to 13.40 %, and from 25.52 % to 16.99 % for 16MnCrS5, C22, 30MnVS5, and 46MnVS5, respectively. Based on the collected chemical composition and casting parameters from these two months, models were obtained using linear regression and genetic programming. These models predict the ratio of rolled bars with detected surface defects and the length of detected surface defects. According to the modelling results, the ratio of rolled bars with detected surface defects and the length of detected surface defects could be minimally reduced by 14 % and 189 %, respectively, using casting speed adjustments. A similar result was achieved from July to November 2023 by adjusting the casting speed for the other 27 types of steel. The same was predicted with the already obtained models. Genetic programming outperformed linear regression.
Keywords: continuous casting of steel, surface defects, automatic control, machine learning, modelling, optimisation, prediction, linear regression, genetic programming
Published in DKUM: 25.03.2024; Views: 220; Downloads: 10
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Evaluation of Machine Learning Algorithms for Predicting the Processing Time of Order Picking in a Warehouse
Tilen Škrinjar, 2019, master's thesis

Abstract: Optimization of warehouse processes increases efficiency and lowers the cost of managing a warehouse. The most expensive and time-consuming activity is picking. Knowing picking process time is an important factor for proper organization of material and information flow. Orders delivered to a packing station too early or too late can cause delays in a warehouse. The purpose of this study is to evaluate machine learning pipeline for processing time prediction of order picking. This includes data gathering, data preprocessing and the evaluation of machine learning algorithms, which are the most important aspects of this research.
Keywords: warehouse, order picking, machine learning, regression analysis
Published in DKUM: 25.02.2019; Views: 1791; Downloads: 41
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Statistical analysis of the development indicators' impacts on e-commerce of individuals in selected European countries
Ksenija Dumičić, Ivana Skoko Bonić, Berislav Žmuk, 2018, original scientific article

Abstract: The aim of this paper is to analyse the influence of the development level indicators on the e-commerce, i.e. on the online purchase by individuals, in selected European countries in 2013. In the analysis, the main variable under study and all the independent variables are included as standardised. Based on nine variables, the principal component analysis with varimax rotation was performed and the two extracted factors were used as the regressors in the multiple regression analysis. In the regression model both components, Factor 1, which includes seven variables, called Prosperity, Investing in Education and IT Infrastructure, and Awareness, and Factor 2, comprised of two variables, called IT Skills, are statistically significant at the significance level of 1%. Both factors show a positive correlation with the online purchase of individuals. Inclusion and analysis of distributions and impacts of even nine independent variables, which make up two distinct factors affecting the e-commerce, make a new contribution of this work.
Keywords: e-commerce, broadband access to the Internet, factor analysis, multiple regression analysis
Published in DKUM: 10.10.2018; Views: 1260; Downloads: 370
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Prediction of California Bearing Ratio (CBR) and Compaction Characteristics of granular soil
Attique ul Rehman, Khalid Farooq, Hassan Mujtaba, 2017, original scientific article

Abstract: This research is an effort to correlate the index properties of granular soils with the California Bearing Ratio (CBR) and the compaction characteristics. Soil classification, modified proctor and CBR tests conforming to the relevant ASTM methods were performed on natural as well as composite sand samples. The laboratory test results indicated that samples used in this research lie in SW, SP and SP-SM categories based on Unified Soil Classification System and in groups A-1-b and A-3 based on the AASHTO classification system. Multiple linear regression analysis was performed on experimental data and correlations were developed to predict the CBR, maximum dry density (MDD) and optimum moisture content (OMC) in terms of the index properties of the samples. Among the various parameters, the coefficient of uniformity (Cu), the grain size corresponding to 30% passing (D30) and the mean grain size (D50) were found to be the most effective predictors. The proposed prediction models were duly validated using an independent dataset of CBR tests on sandy soils. The comparative results showed that the variation between the experimental and predicted results for CBR falls within ±4% confidence interval and that of the maximum dry density and the optimum moisture content are within ±2%. Based on the correlations developed for CBR, MDD and OMC, predictive curves are proposed for a quick estimation based on Cu , D30 and D50. The proposed models and the predictive curves for the estimation of the CBR value and the compaction characteristics would be very useful in geotechnical & pavement engineering without performing the laboratory compaction and CBR tests.
Keywords: CBR, regression, model, prediction, compaction characteristics
Published in DKUM: 18.06.2018; Views: 1389; Downloads: 220
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