Factors Influencing the Utilisation of High-Performance Computing (HPC) Systems in Higher Education Institutions

Utilizing crossbred fall-calving beef females over 2 years, relationships of uterine artery blood flow (UBF) with cow BW, calf birth BW, and placental characteristics were investigated. Transrectal color Doppler ultrasonography of both uterine arteries was conducted, and scans were grouped into 80, 60, 40, and 20 ± 5 d prior to calving (DPC; n = 28, 38, 36, and 24, respectively). Placentas were dissected post-expulsion (n = 21, 26, 26, and 19, respectively). Correlation coefficients were determined for UBF with BW and placental measures at each timepoint. Total UBF had a moderate positive (80, 40, and 20 DPC, P ≤ 0.009) or weak positive correlation (60 DPC, P = 0.02) with calf BW. Calf BW had a moderate positive correlation (P ≤ 0.04) with contralateral UBF (80, 60, and 40 DPC). Cow BW was not correlated (P ≥ 0.07) with UBF. Total and contralateral UBF had moderate positive correlations (P ≤ 0.03) with cotyledonary, intercotyledonary and total placental weights at 40 DPC and with intercotyledonary weight at 20 DPC. Contralateral UBF had a moderate positive correlation (P = 0.03) with total placental weight at 20 DPC. Ipsilateral UBF had no relationship (P ≥ 0.06) with calf or placental weights. Cotyledon number had a moderate negative correlation (P = 0.02) with ipsilateral UBF at 20 DPC, but a moderate (60 and 80 DPC) or strong (20 and 40 DPC) positive correlation (P ≤ 0.05) with contralateral UBF. Average cotyledonary size had a moderate positive correlation (P = 0.01) with total UBF at 20 DPC. Ipsilateral UBF had a moderate (80 and 40 DPC, P ≤ 0.04) or strong positive correlation (20 DPC, P = 0.003) with average cotyledonary size. These data suggest that late gestational UBF drives fetal and placental growth. Interestingly, contralateral UBF may have a disproportionately greater contribution.

Scientific applications are often large, complex, computationally-intensive, and irregular. Loops are often an abundant source of parallelism in scientific applications. Due to the ever-increasing computational needs of scientific applications, high performance computing (HPC) systems have become larger and more complex, offering increased parallelism at multiple hardware levels. Load imbalance, caused by irregular computational load per task and unpredictable computing system characteristics (system variability), often degrades the performance of applications. Besides, perturbations, such as reduced computing power, network latency availability, or failures, can severely impact the performance of the applications. System variability and perturbations are only expected to increase in future extreme-scale computing systems. Extrapolating the current failure rate to Exascale would result in a failure every 20 minutes. Such failure rate and perturbations would render the computing systems unusable. This doctoral thesis improves the performance of computationally-intensive scientific applications on HPC systems via robust load balancing. Robust scheduling ensures and maintains improved load balanced execution under unpredictable application and system characteristics. A number of dynamic loop self-scheduling (DLS) techniques have been introduced and successfully used in scientific applications between the 1980s and 2000s. These DLS techniques are not fault-tolerant as they were originally introduced. In this thesis, we identify three major research questions to achieve robust scheduling (1) How to ensure that the DLS techniques employed in scientific applications today adhere to their original design goals and specifications? (2) How to select a DLS technique that will achieve improved performance under perturbations? (3) How to tolerate perturbations during execution and maintain a load balanced execution on HPC systems? To answer the first question, we reproduced the original experiments that introduced the DLS techniques to verify their present implementation. Simulation is used to reproduce experiments on systems from the past. Realistic simulation induces a similar analysis and conclusions to the analysis of the native results. To this end, we devised an approach for bridging the native and simulative executions of parallel applications on HPC systems. This simulation approach is used to reproduce scheduling experiments on past and present systems to verify the implementation of DLS techniques. Given the multiple levels of parallelism offered by the present HPC systems, we analyzed the load imbalance in scientific applications, from computer vision, astrophysics, and mathematical kernels, at both thread and process levels. This analysis revealed a significant interplay between thread level and process level load balancing. We found that dynamic load balancing at the thread level propagates to the process level and vice versa. However, the best application performance is only achieved by two-level dynamic load balancing. Next, we examined the performance of applications under perturbations. We found that the most robust DLS technique does not deliver the best performance under various perturbations. The most efficient DLS technique changes by changing the application, the system, or perturbations during execution. This signifies the algorithm selection problem in the DLS. We leveraged realistic simulations to address the algorithm selection problem of scheduling under perturbations via a simulation assisted approach (SimAS), which answers the second question. SimAS dynamically selects DLS techniques that improve the performance depending on the application, system, and perturbations during the execution. To answer the third question, we introduced a robust dynamic load balancing (rDLB) approach for the robust self-scheduling of scientific applications under failures (question 3). rDLB proactively reschedules already allocated tasks and requires no detection of perturbations. rDLB tolerates up to P −1 processor failures (P is the number of processors allocated to the application) and boosts the flexibility of applications against nonfatal perturbations, such as reduced availability of resources. This thesis is the first to provide insights into the interplay between thread and process level dynamic load balancing in scientific applications. Verified DLS techniques, SimAS, and rDLB are integrated into an MPI-based dynamic load balancing library (DLS4LB), which supports thirteen DLS techniques, for robust dynamic load balancing of scientific applications on HPC systems. Using the methods devised in this thesis, we improved the performance of scientific applications by up to 21% via two-level dynamic load balancing. Under perturbations, we enhanced their performance by a factor of 7 and their flexibility by a factor of 30. This thesis opens up the horizons into understanding the interplay of load balancing between various levels of software parallelism and lays the ground for robust multilevel scheduling for the upcoming Exascale HPC systems and beyond.

Aim/Purpose Technology adoption and utilization in educational institutions have increased since the pandemic. Recently, the learning management system has become crucial in the educational sector, enabling the efficient execution of online learning. In this study, we performed clustering on group learners to understand students’ concerns with the mobile Learning Management System (m-LMS) based on the clusters. Furthermore, we employed classification using the technology acceptance model to evaluate the correlation among factors necessitating the acceptance and use of m-LMS. Background Mobile learning (m-learning) is a prevalent method of education where educational content is accessed on the go via mobile devices such as smartphones and tablets. The acceptance and deployment of mobile learning in higher education institutions has become essential in Education 4.0, a reskilling approach associated with Industry 4.0. Since the pandemic, mobile learning management systems have seen increased usage among educational institutions. Methodology The study modified the standard data mining and knowledge discovery methodology. This study’s data set includes 446 students from the University of Education, Winneba, Ghana. The K-means algorithm was implemented to determine the number of clusters from the dataset according to the features. Subsequently, we employed a Pearson correlation coefficient heatmap to ascertain the predictability of perceived ease of use, perceived usefulness, attitude towards using, and actual system use of the features via the technology acceptance model. Then, a classifier was built by comparing five classification algorithms. Contribution A novel study on the application of a machine learning algorithm for a mobile learning management system dataset in Ghana. Implementing K-means, heatmap, and feature selection to understand learners’ concerns while using m-LMS in Ghana. Findings The findings indicate that Cluster 1 members disagree with the benefits of using m-LMS. The disagreement cuts across all the investigated variables: perceived usefulness, perceived ease of use, attitude toward using, and actual use. Cluster 2 members agree strongly with the benefits of using m-LMS across all the investigated variables. Cluster 0, with the highest number of members, moderately agrees with the benefits of using m-LMS. In addition, a strong correlation exists between perceived ease of use, perceived usefulness, and attitude towards using. Furthermore, the attitude towards using was predicted by perceived usefulness. However, there was an unreliable relationship between attitude towards using and actual system use. Recommendations for Practitioners Cluster segmentation of students using m-LMS facilitates the formulation of an implementation policy, enabling educational authorities to address the issues contributing to student dissatisfaction with m-LMS. Furthermore, the results imply that, even though students desire to use a mobile learning management system, they are not using it. It means there are challenges surrounding using the mobile learning management system at the University of Education, Winneba. Recommendations for Researchers The use of K-means, elbow function, and correlation heatmap in the technology acceptance model for variable correlation test reveals detailed predictability patterns that necessitate a new research direction using machine learning. Impact on Society Stakeholders in education should embrace and support machine learning implementation in the educational sector to reveal data patterns for improved teaching and learning. Future Research Subsequent research will broaden the data on mobile learning management systems to include the majority of tertiary institutions in Ghana. The data will be indicative, allowing for the generalisation of inferences regarding national policy directions on m-LMS.

E-learning is being implemented in higher educational institutions as a method of teaching and learning. Several studies have been carried out on academic performance, particularly on a group of conventional students. However, only a few have been written on E-learning among PJJ students’ academic performance in E-learning technology. This study was conducted to determine and investigate the relationship between Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) towards Attitude (ATT) and Actual Use (AU) as a factor of Students’ Academic Performance towards e- learning technology among students of the business management faculty enrolled in BA232 Bachelor in Office Systems Management (Hons) at UiTM Puncak Alam. This study took a quantitative and stratified sampling approach with 155 respondents. Statistical methods analyses used were multiple regression and logistic regression. Actual Use (AU), Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Attitude (ATT), and Actual Use (AU) are significant as the p-values are smaller than = 0.05. E-learning use had a significant influence on students’ academic performance. Thus, the more the students use E-learning, the better their academic performance. This study would benefit online institutions, online / distance instructors, decision-makers at all higher education levels, and online students. The implications for practices, barriers to e-learning, ongoing support by the government, limitations of the study and recommendations for research were discussed.

The purpose of this study is to determine how well-suited BIM (Building Information Modeling) technology is for Sabah’s construction sector. Using a digital method called Building Information Modeling (BIM), users may design and oversee dynamic 3D models of construction projects that include details on the materials, budgets, and structures. This study looks at the external factors—technological, such as BIM system capabilities—organizational, such as organizational support for BIM use, and individual, such as knowledge and attitudes about BIM—that affect the adoption of BIM. Using quantitative approaches, thirty BIM practitioners from Sabah’s building industry participated. To evaluate the suggested hypothesis, use partial least squares-structural equation modeling (PLS-SEM) analysis. The study’s findings demonstrate that the impression of BIM’s utility (PU) is only positively impacted by its actual use (AU). On the other hand, BIM adoption is not significantly impacted by other criteria like personal knowledge, organizational support, or the competence of the BIM system. Furthermore, this study discovered that in Sabah’s construction sector, the perceived usefulness (PU) of BIM does not serve as a mediator between perceived ease of use (PEOU) and actual use (AU) of BIM. The study’s findings give JKR and the state government of Sabah a better knowledge of the variables influencing BIM use in Sabah. The results of this study can also serve as a roadmap for creating a more effective strategy for promoting the use of BIM in the construction industry.

Background Personal Health Records (PHRs) empirical research has overwhelmingly focused on patient and family adoption across care setting, severity of illness, dealing with privacy concerns and overall facilitators and barriers to adoption. PHRs Technology Acceptance is defined as three types of PHR usage.1 These usage types are behavioral intent to use, actual use and proficient use. Providers are the focus of this research, which are less researched for PHR barriers and facilitators, acceptance and adoption. Pediatric primary care providers (PCP) are key in terms of the health and well-being of their patients and families, and results in, the often close relationship developed with parents and children during their formative years and the heavily surrogate population that exists in the pediatric population. Wolcott, et al. determines that a supportive provider relationship has been associated with increased patient compliance, decreased pain, and shortened recovery periods.2 Objectives The objectives of this dissertation study were to investigate the existing literature regarding personal health care records and provider use, utilization and practice. Another objective was to evaluate a newly created PHR Organizational Pediatric PCP PHR Acceptance survey and model (POP3) to identify pathways and mechanisms responsible for pediatric PCP PHR behavioral intent to use and actual use. Finally, a targeted network intervention was investigated to determine what, if any, impact it had on increasing pediatric PCP PHR actual use and utilization and practice. Methods The POP3 survey and model was offered to pediatric providers practicing at 30 urban and suburban primary care practices affiliated with an academic medical center. The POP3 survey and model was based on existing technology acceptance and adoption theory. The survey scored external variables impact on both predictor and outcome variables based on a Likert scale. Demographic variables included age, gender, years of healthcare experience including residency and provider type. Predictor variables were individual, technology, patient and family experience, network and organizational level. The median scores were utilized to analyze any pathways and impact on perceived ease of use (PEOU), perceived benefit of use (PBOU), behavioral intent to use (BITU), and actual use (AU). A network intervention was used to further validate impacts of network level interventions on actual and proficient use. Results The POP3 survey response rate was 45%, with 94 providers attempting to complete or completing the survey. Proficient use (UP) had the highest self-reported median score of all outcome variables. Age and years of experience had a statistically significant impact on BITU. The POP3 model resulted in positive impacts of the four of five external variables on both PEOU and PBOU. The highest results were for PHR patient family experience on PEOU and individual PHR technology use, ([beta]= 0.6, P< 0.001), on PBOU. PEOU has an impact, ([beta]= 0.5, P< 0.001), on PBOU which has been demonstrated in past empirical studies. BITU ([beta]= 0.6, P< 0.001), of all predictor variables had the highest impact of all, meaning if the provider intended to use the PHR, there was a higher impact or potential increase on actual use (AU) in comparison to other predictor variables such as PEOU, PBOU and BITU. The self-scheduling network intervention had a 2.0% absolute and 4.0% relative increase in PHR actual provider use in comparison with the control group. For providers deemed as proficient users, a 38% increase in PHR actual use in the intervention group. Finally, PCP providers identified both patient family and provider PHR interventions that could increase PHR use. Conclusion The POP3 model and the impact of a network intervention were both positively validated by this dissertation research. Individual and network level data were integrated to analysis provider PHR use. Additionally, both objective and subjective data are analyzed in this study. This study allows for comparison of both survey and PHR log data to determine provider PHR actual and proficient use. Future research opportunities include updated the POP3 survey and model to include top interventions identified by participants. Further testing of these new interventions, after policy development is completed and implements by care network and organizational leadership. The research should also be extended to include other ambulatory specialty care network locations and providers as well as including other PCP network office roles such as nurses and patient access staff. A further evaluation of proficient usage post intervention should be conducted with the new interventions identified. Finally, a review and analysis of the implications and effects of both patients and families and providers interventions on provider PHR use.

This research aims was to measure and analyze user acceptance of Sistem Informasi Koperasi (Siskop) in Koperasi Karyawan Budi Setia Jakarta, using Technology Acceptance Model (TAM). This research is quantitative research. Meanwhile, according to the the characteristics of the problem, this research included in exploratory research, descriptive research, and explanatory research. The data used in this research are primary data and secondary data, collected using several data collection techniques, such as observation, questionnaires, document review, and literature review. The variables in this research are independent variables; perceived ease of use - PEOU, intervening variables; perceived of usefullness - PU, attitude toward using - ATU, and behavioral intention to use - BIU, and the dependent variable; actual use - AU. Processing and analysis of data in this research was using descriptive statistics analysis and simple linear regression analysis, which done by the support of the Application SPSS version 21 for Windows. The results of this research are as follows 1) perceived ease of use have given positive and significant influence to perceived usefulness and attitude toward using Siskop; 2) perceived usefulness Siskop given positive and significant influence to attitude toward using Siskop, behavioral intention to use, and actual Siskop use; 3) attitude towards using Siskop positive and significant influence to behavioral intention to use Siskop. However 4) behavioral intention to use Siskop did not has a signicant influences to actual Siskop use.

PurposeThere is a significant decrease in students' attendance in Zoom classes compared to traditional classes. This paper investigates the factors that affect students' attitudes, behavioral intentions and actual use of Zoom for online classes at higher educational institutions (HEIs) in Sri Lanka.Design/methodology/approachThis research uses the technology acceptance model (TAM) as a theoretical model. The data are collected from HEI students via an online survey form. The hypotheses between constructs in the model are tested using partial least squared–structural equation model.FindingsThe analysis shows that computer self-efficacy (CSE) affects perceived usefulness (PU) and perceived ease of use (PEU), which affects attitude (ATT) and behavioral intention (BI) and actual use (AU) of Zoom in a chain reaction. Further, PEU affects PU, which, in turn, affects BI. Furthermore, the effect size of PU to BI is larger than ATT to BI.Practical implicationsStudents' attendance for Zoom classes mainly depends on CSE, PU and PEU. Therefore, HEIs should promote Zoom with interactive training before teaching online. Further, officials should revise the curriculum in schools to upsurge the CSE of students.Originality/valueDuring coronavirus-19, no research was published on students' use of Zoom for online classes in the Sri Lankan context. Moreover, the TAM model has been modified by including CSE as an external variable.

Purpose: To investigate the correlation of symptoms of Diabetes with the severity of Diabetes Mellitus (DM) in Middle-aged subjects with diabetes. Participants: 68 subjects with diagnosed diabetes mellitus between the ages of 40 and 60 with/without symptoms of neuropathy were included. Participants with neurological disease, recent surgical procedures or injuries to feet, vascular disease, and cognitive impairment were excluded. Method: In a cross-sectional observational study, a total of 68 middle-aged diabetic subjects were interviewed in terms of demographic data, Fasting Blood Sugar (FBS), Post-Prandial Blood Sugar (PPBS), HBA1c, duration of diabetes, and associated medical conditions. All participants were examined by the Michigan Neuropathy Screening Instrument (MNSI). MNSI consists of 2 parts. 1) questionnaire 2) clinical examination. Analysis: Statistical analysis was done by SPSS 20 & Spearman correlation test was applied. Results: In mild diabetes, hba1c has a weak positive correlation (r=0.232) with MNSI-1 and a very weak positive (0.016) correlation with MNSI-2. In moderate diabetes, hba1c has significant(p=0.003) strong positive correlation (r=0.703) with MNSI-1 and significant (p=0.019) moderate positive correlation (r=0.0596) with MNSI-2. In severe diabetes, hba1c has significant (p=0.028) moderate positive correlation (r=0.532) with MNSI-1 and significant (p=0.034) moderate positive (0.515) correlation with MNSI-2 Conclusion: Diabetic symptoms are moderately correlated with moderate & severe levels of Diabetes based on hba1c in the middle-aged population. Keywords: Diabetes Mellitus, Middle age, Diabetic symptoms, Michigan Neuropathy Screening Instrument.

Purpose – The purposes of this research were to study factors affecting intention to use National Single Window (NSW) through perceived ease of use and perceived usefulness in import, export, and logistics enterprises.Methodology – The research methodology was quantitative research with survey method by using questionnaires for data collection. The respondents were 400 participants, who are importers, exporters, and logistics service providers which are located in the Bangkok Metropolitan Region. Descriptive statistics used for data analysis included frequency, percentage, mean, and standard deviation. Due to hypothesis testing, inferential statistics were used, specifically Pearson’s Product Moment Correlation Coefficient and Partial Least Squares-Structural Equation Modeling (PLS-SEM).Results – The results of hypothesis testing revealed that acceptance and use of technology had a positive and significant effect on perceived ease of use and perceived usefulness whereas organizational support had a positive and significant effect on perceived ease of use, and technology experience had a positive and significant effect on perceived ease of use and perceived usefulness at a significance level of 0.001. In addition, perceived ease of use had a positive and significant effect on perceived usefulness and intention to use, and perceived usefulness had a positive and significant effect on intention to use at a significance level of 0.001. Nevertheless, the results showed that organizational support did not have a significant effect on perceived ease of use. Finally, there were significant indirect effects on the relationships among acceptance and use of technology, perceived ease of use, perceived usefulness, and intention to use. Implications –The findings present several implications for both academia and management practitioners, providing insights into crafting effective strategies to enhance users’ or employees’ technology adoption once new technologies and/or systems are introduced in the workplace.Originality/Value – This research sought to enhance comprehensive comprehension of the intricate interplay among the UTAUT Model, perceived ease of use, perceived usefulness, and intention to use, interpreting their collective influence on potential users’ decisions toward the use of systems or IT. Also, the research can fill existing gaps in the literature and provide valuable insights for refining technological capability and competencies, particularly focusing on international entrepreneurship.

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ALODOKTER is one quickly evolving application in the healthcare services sector. The purpose of this application is to help medical professionals carry out their jobs more effectively by giving the community rapid and easy access to healthcare services. This study aims to measure user acceptance of the ALODOKTER application using the Technology Acceptance Model (TAM) approach to improve the use and quality of health services. A survey method with a quantitative approach was employed to analyze perceived ease of use (PEU), perceived usefulness (PU), attitude towards use (ATU), behavioral intention to use (BIU), and actual use (AU) of the application. The study involved 41 respondents from various demographic backgrounds. Results show significant relationships between user perception variables, attitudes, and actual use. Correlation analysis revealed strong relationships between PEU, PU, and ATU, with a very strong correlation between ATU and BIU. Linear regression analysis indicated that BIU was the strongest predictor of actual use of the app (β = 1.066, p < 0.01), followed by PU (β = 0.628, p < 0.01). The regression model explained 38.7% of the variance in actual use. Cronbach's Alpha coefficients for all scales exceeded 0.9, indicating high reliability of the instruments used. This research suggests that ALODOKTER developers should focus on enhancing the perceived usefulness and ease of use of the application to increase acceptance and use. The study's limitations include a small sample size and reliance on self-reporting, suggesting the need for further research with larger samples and more diverse methods.

This research provides valuable insights into the impact of digital entrepreneurship on the digitalization of small and medium-sized enterprises (SMEs) in Yemen. By integrating the Technology Acceptance Model (TAM) and the Technology-Organization-Environment (TOE) framework, the research examines the role of various factors-technological, organizational, environmental, and individual, affecting technology adoption and utilization in SMEs. The study primarily explores the direct and indirect influences of these factors on actual system use, with a particular focus on the mediating roles of perceived usefulness and perceived ease of use in shaping attitudes toward technology adoption. Data were collected through a survey of 392 respondents, and the results were analyzed using partial least squares structural equation modelling (PLS-SEM). The measurement model was validated for reliability and accuracy, followed by an evaluation of the structural model to test the proposed hypotheses. The findings highlight the critical role of information and communication technology (ICT) adoption in enhancing both perceived ease of use and perceived usefulness. Additionally, the knowledge of IT entrepreneurs and government support were found to uniquely influence perceived ease of use and perceived usefulness, respectively. Both of these perceptions, in turn, positively impacted attitudes toward and the actual use of digital tools within SMEs. To address the challenges faced by Yemeni SMEs, it is recommended to enhance digitalization through ICT adoption and promote a pro-technology culture. Policymakers should create a supportive environment via targeted policies, subsidies, and public-private partnerships to foster sustainable growth and competitiveness.

This study aims to examine the effect of perceived usefulness, perceived ease of use, and behavioral intention to actually use accounting software. Therefore, the variables of this study consist of 4 (four) independent variables, they are perceived usefulness and perceived ease of use, intervening variable is behavioral intention and dependent variable is actual system use. The population of this study is the owner of the MSMEs food and beverages in Mataram city. Samples were taken using the solvin formula. Data obtained by distributing questionnaires to 50 respondents. Questionnaires are returned and can be used as many is 46 questionnaries. Data were analyzed using Partial Least Square (PLS) with SmartPLS 3.0 software. The results indicate that perceived usefulness and perceived ease of use doesn't affect significantly to the actual system use, instead of perceived usefulness and perceived ease of use has a significant effect on the behavioral intention. Behavioral intention has a significant effect on the actual system use, perceived usefulness has a significant effect on the actual system use through behavioral intention, perceived ease of use doesn't affect significantly to the actual system use through behavioral intention.

The use of a learning management system (LMS) is believed to be significant for students’ academic performance, but students’ attitudes regarding its use are divided. Therefore, the purpose of this study is to apply a modified version of the technology acceptance model to determine the motivators for University of Professional Studies Accra students’ behavioural intentions (BIs) and actual use of LMSs using a convenient sampling technique to elicit data from first-year students in the faculty of information technology and communication studies. The investigation included a total of 188 samples. The study used structured equation modelling partial least squares to analyse the data. Specifically, the study employed the importance-performance map analysis to establish the factors that are important to students’ actual use of an LMS. The study’s findings suggest that REL and output quality significantly impact the perceived usefulness (PU). Also, perceived enjoyment and subjective norm exert a significant impact on the perceived ease of use (PEOU). Additionally, PEOU influences the PU, which in turn influences the BI. Furthermore, the findings reveal a strong link between BI and the actual use of the LMS. Finally, the study suggests that students’ BI to use the LMS is the most important factor for students’ actual use. PU is also an important determinant factor of students’ actual use. Following the study’s conclusion that BI and PU are important for students’ actual use of an LMS, higher education administrators must develop rules that increase the students’ PU of LMSs, while also ensuring that current measures that awaken students’ BIs are maintained or improved so that students can use the LMS for academic work.