Blended system thinking approach to strengthen the education and training in university-industry research collaboration

University-industry research collaboration (UIRC) is a major source for research and innovations and economic growth. Despite the extensive evidence on the importance of such collaboration in developed and developing countries, the literature related to strengthen such collaboration along with its innovation performance is still scarce. Scholars believed that the impact of education and training on researchers haa a vigorous influence on research and innovations. Moreover, to enhance the competencies of education and training on researchers, it is mandatory to refurbish education and skills system in conjunction with technological infrastructure system along with their reinforcing factors i.e. knowledge sharing and research and development cooperation, respectively. In this paper, we evaluate the influence of education and skills and technological infrastructure system along with their corresponding reinforcing factors in the blended system thinking method to strengthen education and training. Evidence from UIRC in Malaysia provides empirical corroboration that the role of education and skills system and technological infrastructure system along with their reinforcing factors have a positive influence on education and training. Thus, the findings of this research suggest that intensifying the quality of education and skills system and technological infrastructure system with the reinforcing effect can enhance the effectiveness of education and training

A combined imaging, deformation and registration methodology for predicting respirator fitting

N95/FFP3 respirators have been critical to protect healthcare workers and their patients from the transmission of COVID-19. However, these respirators are characterised by a limited range of size and geometry, which are often associated with fitting issues in particular sub-groups of gender and ethnicities. This study describes a novel methodology which combines magnetic resonance imaging (MRI) of a cohort of individuals (n = 8), with and without a respirator in-situ, and 3D registration algorithm which predicted the goodness of fit of the respirator. Sensitivity analysis was used to optimise a deformation value for the respirator-face interactions and corroborate with the soft tissue displacements estimated from the MRI images. An association between predicted respirator fitting and facial anthropometrics was then assessed for the cohort

Factors Affecting E-Wallet Usage in Sarawak

E-wallet is a part of the fintech solution that provides customer convenience through fast speed of transaction using RFID technology, QR code and stable internet connection. The usage of e-wallet in Malaysia and Sarawak has risen in the past few years and accelerated during the Covid-19 pandemic crisis due to the needs of contactless payment to reduce the spread of the virus. However, the usage of e-wallet and factors that affect the usage of e-wallet in Sarawak has not been identified through research. Therefore, this research aims to identify the factors influencing e-wallet usage and evaluate the factors using Technology Acceptance Model (TAM) affecting Sarawakians perception towards e-wallet in general and towards Sarawak's own e-wallet app, SarawakPay. Data collection of online questionnaires have been distributed and 111 responses was analyzed using descriptive analysis and correlation analysis as well as multiple linear regression to obtain mean and standard deviation values. Five formulated hypotheses were tested using multiple linear regression showing a significant and positive relationship among the TAM variables. Descriptive analysis results have also shown that top factors affecting usage of SarawakPay is performance expectancy and perceived value. The result from this research indicates that the usage of e-wallet in Sarawak is still at medium level. The evaluation using TAM shows that there is significant relationship between the Technological and Social factors to the usage of e-wallet in Sarawak

Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review

Purpose. The study explored the clinical influence, effectiveness, limitations, and human comparison outcomes of machine learning in diagnosing (1) dental diseases, (2) periodontal diseases, (3) trauma and neuralgias, (4) cysts and tumors, (5) glandular disorders, and (6) bone and temporomandibular joint as possible causes of dental and orofacial pain. Method. Scopus, PubMed, and Web of Science (all databases) were searched by 2 reviewers until 29th October 2020. Articles were screened and narratively synthesized according to PRISMA-DTA guidelines based on predefined eligibility criteria. Articles that made direct reference test comparisons to human clinicians were evaluated using the MI-CLAIM checklist. The risk of bias was assessed by JBI-DTA critical appraisal, and certainty of the evidence was evaluated using the GRADE approach. Information regarding the quantification method of dental pain and disease, the conditional characteristics of both training and test data cohort in the machine learning, diagnostic outcomes, and diagnostic test comparisons with clinicians, where applicable, were extracted. Results. 34 eligible articles were found for data synthesis, of which 8 articles made direct reference comparisons to human clinicians. 7 papers scored over 13 (out of the evaluated 15 points) in the MI-CLAIM approach with all papers scoring 5+ (out of 7) in JBI-DTA appraisals. GRADE approach revealed serious risks of bias and inconsistencies with most studies containing more positive cases than their true prevalence in order to facilitate machine learning. Patient-perceived symptoms and clinical history were generally found to be less reliable than radiographs or histology for training accurate machine learning models. A low agreement level between clinicians training the models was suggested to have a negative impact on the prediction accuracy. Reference comparisons found nonspecialized clinicians with less than 3 years of experience to be disadvantaged against trained models. Conclusion. Machine learning in dental and orofacial healthcare has shown respectable results in diagnosing diseases with symptomatic pain and with improved future iterations and can be used as a diagnostic aid in the clinics. The current review did not internally analyze the machine learning models and their respective algorithms, nor consider the confounding variables and factors responsible for shaping the orofacial disorders responsible for eliciting pain

Impact of Elaeidobius kamerunicus faust introduction on oil palm fruit formation in Malaysia and factors affecting its pollination efficiency: a review

The oil palm pollinating weevil, Elaeidobius kamerunicus, has become the single most important insect pollinator of the commodity crop in Malaysia and Indonesia, 40 years after its introduction. However, in 2020, the average national oil extraction rate (OER) has decreased from 20.21% to 19.92%. The decline was attributed to the lower oil palm fruit bunch quality. This has raised concerns on the pollination efficiency of the pollinator. As such, the factors affecting the pollination efficiency of E. kamerunicus were thoroughly discussed in this review. Eight factors, which were categorised into intrinsic and extrinsic factors, were discussed in detail. Intrinsic factors discussed are the genetic make-up of E. kamerunicus and its population level. Meanwhile, factors such as planting materials, soil types, plant physiological condition, volatiles emission, diseases/predators threatening E. kamerunicus and climatic factors were discussed in the extrinsic factors. Methods for maintaining a healthy pollinator population were suggested, as well as an emphasis on future studies based on the shortlisted factors