Comparison of 3D reconstruction of mandible for preoperative planning using commercial and open-source software

3D printing of mandible is important for pre-operative planning, diagnostic purposes, as well as for education and training. Currently, the processing of CT data is routinely performed with commercial software which increases the cost of operation and patient management for a small clinical setting. Usage of open-source software as an alternative to commercial software for 3D reconstruction of the mandible from CT data is scarce. The aim of this study is to compare two methods of 3D reconstruction of the mandible using commercial Materialise Mimics software and open-source Medical Imaging Interaction Toolkit (MITK) software. Head CT images with a slice thickness of 1 mm and a matrix of 512x512 pixels each were retrieved from the server located at the Radiology Department of Hospital Universiti Sains Malaysia. The CT data were analysed and the 3D models of mandible were reconstructed using both commercial Materialise Mimics and open-source MITK software. Both virtual 3D models were saved in STL format and exported to 3matic and MeshLab software for morphometric and image analyses. Both models were compared using Wilcoxon Signed Rank Test and Hausdorff Distance. No significant differences were obtained between the 3D models of the mandible produced using Mimics and MITK software. The 3D model of the mandible produced using MITK open-source software is comparable to the commercial MIMICS software. Therefore, open-source software could be used in clinical setting for pre-operative planning to minimise the operational cost

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

Performance evaluation of AODV in MASNETs: Study on different simulators

Nowadays, most of researchers working on Wireless Sensor Networks (WSNs) focus on Mobile Ad-hoc and Sensor Networks (MASNETs) due to their wide range of potential applications ranging from underwater monitoring to search and rescue mobile robotics applications. Most of these applications are deployed in remote and unattended areas. Since MASNETs are energy-constrained networks which have a low radio frequency coverage, their network topologies are frequently change due to mobile sensor nodes. In this paper, through extensive simulation of two different simulators namely Avrora and Castalia, we evaluated the capability of Ad-hoc On Demand Distance Vector (AODV) routing protocol on how far it can react to different speed and density of mobile nodes in MASNETs. We investigated the performance of AODV in terms of the average percentage of packet loss with the various speed and density of mobile nodes. Our performance study demonstrates that both simulators show the performance of AODV is significantly decreased in mobile environment due to the frequent topology change in MASNET

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

The study of AODV routing protocol in Vehicular Ad-hoc Sensor Networks

In VASNETs, each vehicle carries an intelligent node, which sense the real phenomena and collects the required data. There are many routing protocols that have been proposed and assessed the efficiency of VANETs. However, there are only several research about performance evaluation of routing protocol in VASNETs. In order to fill that gap, in this research we have undergo the performance evaluation of AODV routing protocol in VASNETs. The experiment was setup in order to investigate the effect of different simulation area towards increasing velocity and number of mobile nodes. The tools that been used for this purpose is AVRORA simulation tool. Based on the simulation results obtained, the performance of AODV is analyzed and compared in different sizes of simulation area. The simulation results show the significant different in the variation of simulation area in term of the percentage of packet loss in VASNE