Current Issue : July-September Volume : 2026 Issue Number : 3 Articles : 5 Articles
This study investigated the relationship among university students’ attitudinal variables and their achievement in statistics. The study also examined the relative contribution that each variable makes to their academic achievement. The study sample comprised 96 university students. Data were collected using a self-designed questionnaire. The reliability coefficients of the various subscales of the piloted questionnaire exceeded 0.70 and were found good to use. The analysis of the data showed a significant positive correlation among three of the students’ attitudinal subscales, namely interest towards statistics and academic achievement (r=0.315, p=0.001), motivation to study the subject and academic achievement (r=0.375, p=0.000) and usefulness of statistics and academic achievement (r=0.268, p=0.004). However, there was a positive low, but insignificant, correlation between anxiety and academic achievement (r=0.218, p=0.084). Further analysis, using multiple regression, showed that all the students’ attitudinal variables, except anxiety, made a significant relative contribution to their academic achievement in statistics. It was recommended that lecturers should help to develop students’ interest and motivation, educate them on the usefulness of statistics, and also, consciously try and reduce university students’ anxiety towards statistics....
Additive manufacturing (AM) has revolutionized the production of aerodynamic components by enabling the rapid prototyping of complex geometries. However, the reliability of these components remains an active area of research. This study investigates: How do varying 3D printer parameters affect the reliability of additively manufactured aerospace components? Three objectives were defined: first, to identify optimal printing temperatures; second, to determine optimal thickness and velocity for the 3D printer settings; and third, to integrate the results using global optimization to determine the ideal set of parameters. Nozzle and bed temperatures were varied independently, and the results were compared using microscopic analysis. External perimeter speed and layer thickness were also tested independently to determine optimal settings. Systematic variations were applied to temperature, external perimeter speed, and layer thickness, following a statistical approach. Wing test sections were 3D printed to test the reliability of varying AM parameters. Microscopic analysis was then conducted to examine the reliability of the external surface of each print, capturing variations due to the different parameters. Finally, a machine learning algorithm and a global optimization were performed in order to determine the ideal set of AM printing parameters. This work bridged the gap between the reliability of AM and its applications in aerodynamics....
Objectives: Radiomics has enhanced quantitative ultrasound (QUS) imaging based on envelope statistics for liver fibrosis evaluation. However, early detection of liver fibrosis in patients with hepatic steatosis remains challenging. This study is to develop ultrasound scatteromics prediction models, utilizing simplified feature sets from multimodal QUS envelope statistics imaging, for detecting early-stage liver fibrosis (stage ≥ F1) and significant fibrosis (≥F2) in the presence of hepatic steatosis. Methods: The dataset in this prospective study included 252 subjects (n = 125 for training and validation; n = 127 subjects for independent testing), which underwent blood tests, liver biopsy, and ultrasound radiofrequency data acquisition. In scatteromics analysis, multimodal QUS envelope statistics imaging (Nakagami, homodyned K, and information entropy statistics) was employed. For each imaging, a predefined simplified feature set was calculated, followed by feature selection for machine learning using support vector machine (SVM), random forest (RF), and linear discriminant analysis (LDA). The scatteromics model was obtained using a repeated five-fold stratified cross-validation and then independently tested. The performance was evaluated by the area under the receiver operating characteristic curve (AUROC); scatteromics features were also compared with aspartate aminotransferase (AST) and alanine aminotransferase (ALT). Results: Scatteromics features showed no significant correlation with AST and ALT, with correlation coefficients ranging from 0.003 to 0.28. In patients with coexisting hepatic steatosis, scatteromics significantly outperformed QUS envelope statistics imaging in identifying early-stage liver fibrosis, achieving AUROC values of 0.85 to 0.87 for the training and validation datasets, and 0.78 to 0.81 for the testing dataset. In comparison, scatteromics demonstrated modest performance in detecting significant liver fibrosis (≥F2), with AUROC ranging from 0.66 to 0.71 in the training cohort and 0.64 to 0.76 in the testing cohort. Conclusions: The proposed scatteromics model streamlines the data analysis workflow of conventional QUS radiomics, enabling early detection of liver fibrosis with reduced dependence on inflammation and hepatic steatosis....
The dynamic development of Industry 4.0 technologies, referred to as smart manufacturing technologies (SMTs), is significantly changing both production systems and quality management practices. The aim of this article is to analyse the impact of smart manufacturing technologies on the seven principles of quality management (QMP). The research is based on a narrative, semi-systematic review of the literature from theWeb of Science and Scopus databases from the last seven years, using thematic analysis. Traditional interpretations of QMP principles were compared with new conditions resulting from the implementation of technologies such as the Internet of Things, big data, artificial intelligence, cloud computing, vision systems, virtual and augmented reality, and additive manufacturing. The results indicate that SMTs do not eliminate quality management principles, but significantly change the way they are implemented. There is a shift towards product personalisation, shorter product life cycles, decentralised decision-making, flexible and autonomous processes, digital surveillance, and intensive use of real-time data. The article argues that SMT and QMP are complementary approaches—technologies increase the effectiveness and efficiency of quality management, but do not replace it. The considerations presented here are a starting point for further empirical research on the new ‘Quality 4.0’ model in the intelligent manufacturing environment....
This study proposed a Kriging surrogate model incorporating active learning to overcome the high computational costs associated with conducting reliability and sensitivity analyses of industrial liquid storage tank structures. In the proposed method, the Kriging surrogate model efficiently captures the functional relationships between basic variables and structural responses. Two learning functions, i.e., the U learning function and the EFF learning function, are adopted to screen the training sample pool to identify and iteratively update the optimal next training sample point in the model. This strategy significantly reduces the number of limit state functions and finite element analysis calculations required, considerably decreasing the computational cost of analysis. Results from the liquid storage tank case study demonstrate that the adaptive learning Kriging method can achieve failure probability estimation at the order of 10−5 with only approximately 100 limit state function (LSF) evaluations. Additionally, it is found that the pressure exerted by the tank contents has the most significant impact on the tank’s structural reliability, followed by tank thickness and then tank radius....
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