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Phylogeny and prevalence of filarial parasites (nematoda: onchocercidae) from the common treeshrew (Tupaia glis) in Peninsular Malaysia / Ahmad Syihan Mat Udin
No full textFilarial nematodes cause lymphatic filariosis and zoonotic diseases in humans. However, the origins and evolution of these group of parasites is still unclear because the ancestral species has not yet been discovered. This study reviews the taxonomic and phylogenetic relationship of filarial parasites from common treeshrews, Tupaia glis. The study also intends to determine the prevalence of filarial parasites in relation to the distribution of hosts in Peninsular Malaysia. Cage trapping techniques were used to capture host animals in selected sites throughout Peninsular Malaysia. A total of 98 common treeshrews were captured and examined. Two adult species of filarial parasites, Malayfilaria sofiani and Mansonella dunni were identified, and data on their distribution and prevalence was recorded. The parasites were subjected to details morphological and molecular analyses. Polymerase chain reaction (PCR) was performed using cox1 and 12S rRNA genes. Phylogenetic analysis indicated that M. dunni have a close affinity with Mansonella ozzardi which is a human parasite in South America, but their genetic distance was substantially large (p-distance 6.1-6.4%). M. sofiani appears to be closely related to Wuchereria spp. and Brugia spp. but differs based on several morphological characteristics. The Kimura 2-parameter distance between the cox1 gene sequences of M. sofiani and W. bancrofti was 11.8%. Molecular analyses indicated that M. sofiani differs from both W. bancrofti and Brugia spp. at the genus level. The total percentage of prevalence was 31% with 30 individuals of common treeshrews were infected. M. sofiani adult is only restricted to secondary forest of Jeram Pasu, Kelantan with 6.1% prevalence whereas M. dunni have a 23.4% prevalence and widely distributed in primary and secondary forest of Peninsular Malaysia. No adult Brugia tupaiae was found in this study, however one common treeshews from Gemas, Negeri Sembilan was infected with microfilaria of Brugia tupaiae. This study concluded that Malayfilaria sofiani appears to be a new genus and new species. while Mansonella dunni was closely related to human parasite, M. ozzardi and M. perstans. Only one common treeshews from Gemas, Negeri Sembilan was infected with B. tupaiae. Contrary to previous studies, there is a significant decrease in prevalence of B. tupaiae. Fortunately, there was no filariasis cases associated with filarial parasites of common treeshrews was reported recently. Filarial parasites are one of the agents of emerging zoonotic diseases. This study serves as a guide for medical practitioners and other authority to pinpoint the distribution of treeshrews filarial parasites. The present study provides molecular and morphological data that can be used to identify the filarial parasites, if zoonotic cases involving human occurs
An adaptive user interface model for enhancing usability and performance efficiency of diabetes mobile applications / Chew Sook Peng
No full textThere are many ways to promote patient empowerment and technology is one of them.
With the help of eHealth and mHealth, patient empowerment has become more
convenient and effective as patients will have the control of monitoring their own health
care right at their fingertips, especially for those with chronic diseases like diabetes. As
per the National Diabetes Registry Report published in 2020, there was an estimate of 3.9
million adults having raised blood glucose in 2019 and there is a huge number of diabetes
patients in our country. There are many diabetes mobile applications from the iOS App
Store and Google Playstore, but these apps are not usable enough to promote patient
empowerment. For example, a previous research in 2021 reported a few shortcomings of
two popular diabetes applications, namely mySugr and Glucose Buddies. The users found
the apps too time consuming and too complex to use. There were too many features in
the app. The participants also reported wanting customized notifications related to their
diabetes management goal. Usability is a key factor in influencing whether the user will
engage with the mHealth app. Previous research suggested that the user experience can
be enhanced with adaptive user interface (AUI). With AUI, users will be able to have
better control in self-care and self-management of their diseases. Context-based and rulebased
AUI has proven to improve user experience of a hypoglycemia emergency care
mobile applications but there was no research to ascertain whether AUI improves the user
experience and usability of a diabetes self-care mobile application. The objective of this
research is to propose and design a Diabetes Mellitus AUI Model with Role-Based UI
Simplification (RBUIS) that allows different diabetes mobile apps users to personalize the apps’ user interface based on their preference and specification. The model aims at
providing an efficient and usable application for better diabetes self-care and selfmanagement.
An AUI engine built upon the model enables processing data collected from
user profile and providing a personalized user experience based on their needs and goals.
An experiment done concluded that AUI performs better in user experience and usability
as compared to non-AUI. Results of the experiment showed that the application with AUI
integration showed a more positive result compared to non-AUI in terms of shorter time
to perform an action and easier data entry through suggestive data input; and
recommended function to be used in the home screen. The application of AUI has also
reduced the complexity of the app usage by only displaying the necessary functions to the
user. According to the System Usability Scale (SUS) analysis, the application when
integrated with AUI achieved the score of “Excellent” while when having AUI disabled
only achieved the score of “Good”
Public participation in the decision making of disposal facility sitings in Dhaka, Bangladesh / Sadia Afrin Proma
No full textIn recent years, the Bangladeshi public has shown concern on the expansion of solid waste landfills. Matuail and Amin Bazar landfills which serve the entire Dhaka city have been in operation for more than two decades. However, it has been reported that the two landfills were built without any environmental impact assessment and involving public participation. As the two landfills are approaching their full capacity, the government is currently planning for their expansion. This study aimed to find out mechanisms to improve the process and management of solid waste in Dhaka city. This study was carried out triangulation study and used a mixed methodologies approach (qualitative and quantitative). 31% of the respondents in this study area had been asked about the decision-making of present disposal facility siting. However, 42% of participants were unhappy regarding the present solid waste disposal facility location. It can be concluded that public participation in the decision-making for disposal facility siting in Dhaka city is minimal at present, the best approach to begin resolving the issue is for the city corporation’s authorities to demonstrate their value to the people by incorporating them in the early planning process
Particle track finding using hough transform in a central drift chamber detector / Khasmidatul Akma Mohamed Kamal Azmi
No full textWhen the method for representing the track of an electron was discovered in a cloud
chamber experiment in 1962, the track finding for the High Energy Physics (HEP) helped
discover a new dimension for evaluating the data for tracking and identification of
particles. The assessment of charged particle track findings have improved significantly.
The method of the Hough Transform became an iconic method to trace and identify the
pattern of the charged track in the HEP based on the evaluation of the track finding in the
cloud chamber experiment. This thesis investigates a potential algorithm for detecting a
mono-electron signal track after a muon stop for aluminium in the Cydet detector for the
Phase-1 COMET experiment. The Circular Hough Transform can be used to detect the
signature an electron curvature. The Phase-1 COMET experiment will look for the
neutrino less decay of a muon into an electron when the muon is electromagnetically
bound to an atomic nucleus. The Standard Model of particle physics forbids thi
Association of heart rate and blood pressure variability with psychological state and physical performance in elderly / Nur Husna Shahimi
No full textThe issue of falls among older individuals is one of the most common public
health problems which has become a global concern. In Malaysia, the prevalence of falls
was 14% among community-dwelling older adults aged 60 years and above in the past
year. Previous studies have established the associations between both autonomic function
indicators; heart rate variability (HRV) and blood pressure variability (BPV) with fall
recurrence, as well as physical inactivity and psychological disorders as risk factors for
falls, however, the influence of these fall risk factors on autonomic dysfunction among
older fallers has not been adequately investigated. Therefore, this thesis aims to: 1)
identify the differences between elderly fallers and non-fallers in terms of psychological
function, physical activity, physical performance and autonomic nervous system, 2)
investigate HRV or BPV indices that are most closely correlated to physical activity or
psychological disorder in elderly adults, 3) identify physical activity, physical
performance or psychological disorder measures that best predict autonomic function
indices.
This was a cross-sectional study with 92 adult participants aged ≥60 years
recruited from a tertiary hospital. Of those, forty-five older individuals who had at least
one fall incident in the past two months were labelled as the fall cohort, while forty-seven
older individuals with no history of fall in the past two months were labelled as the non fallers cohort. Continuous non-invasive blood pressure and heart rate was monitored over 5 minutes of supine rest and 3 minutes of standing upright. Subsequently, time domain and frequency domain analysis was performed on the continuous blood pressure signal to
derive autonomic function indices (i.e., HRV and BPV).
Findings based on comparison between fallers and non-fallers showed that fallers
were significantly older, requiring longer time to complete Time-Up and Go test, having
weaker hand grip strength and lower physical activity scale (PASE), as well as reduced
functional dependency (Lawton IADL). The HRV and BPV results from this study
indicated potential deterioration in the autonomic function in older fallers, as compared
to non-fallers.
Lawton Instrumental Activities of Daily Living (IADL) Scale and Physical
Activity Scale for the Elderly (PASE) that indicate physical dependency and daily
lifestyle of an individual were found to be the best independent predictors for autonomic
function, as measured by the HRV and BPV indices. On the other hand, the stress and
depression scores in the DASS-21 questionnaire best predict autonomic function (as
measured by the HRV and BPV indices) during supine and standing, respectively. By
knowing the association between these modifiable risk factors and autonomic function,
early identification of patients at risk of recurrent falls and cardiovascular diseases can be
performed to allow timely intervention
Structural crack detection using deep convolutional neural network / Raza Ali
No full textConvolutional Neural Networks (CNN) have immense potential to solve a broad range of computer vision (CV) problems. It has achieved encouraging results in numerous applications in engineering, medical, and other research fields. Thanks to the advancement in hardware, data collection procedures, and efficient algorithms. These innovations have changed the way how specific problems are solved as compared to conventional methods. In this work, CNN is implemented for civil structural crack detection. Cracks are significant indicators for the evaluation of the structural health and monitoring process. However, manual crack detection is a time-consuming and challenging task due to large areas, complex structures, and safety risks. Deep learning (DL) has emerged as an effective technique to automate the crack detection and identification process. For balanced data, existing DL models attempt to segment both crack pixels and non-crack pixels equally. However, due to the highly imbalanced ratio between crack pixels and non-crack pixels, the pixel-wise loss is dominantly guided by the non-crack region and has relatively little influence from the crack region. This leads to the low segmentation accuracy for crack pixels. To address the imbalance problem, this work proposes a local weighting factor with a difference transform map to remove the network biasness and accurately predict the sensitive pixels. Further, a deep fully CNN called crack segmentation network (CSN) is implemented for crack pixel segmentation. The CSN is an encoder-decoder architecture with four convolutional blocks in each section. Each convolutional block has residual connections with a different number of filters in each convolutional operation that segments the crack pixels and non-crack pixels with unbiased probabilities. Furthermore, the crack indicators are assessed through the implementation of a pixel connection technique that measures the crack characteristics (length, width, and area) and determines the crack orientation (vertical, horizontal, and diagonal). For performance evaluation, a new Multi Structure Crack Image (MSCI) dataset is built to train the proposed method which achieved 98.60% crack pixel accuracy, 98.35% non-crack pixel accuracy, and 98.48% average accuracy, respectively. In addition, the training time for 10 epochs has dramatically decreased and the experimental results show that the proposed CSN architecture has better crack pixel segmentation accuracy than FCN, U-Net, SegNet, and DeepLabv3+ architectures. Similarly, the proposed local weighting factor and difference transform map (LWF-DTM) has significantly reduced the wrong predictions, minimized the effect of an imbalanced pixel ratio, and outperformed the Cross-Entropy, Weighted Cross-Entropy, Dice, Tversky, and Focal loss function
Design of A unmanned aerial vehicles assisted search and rescue collaboration architecture for emergency communication systems / Abdu Ahmed Saif Ahmed
No full textDuring natural disasters, it is highly likely that the established wireless communication
infrastructure. For effective disaster management, it is crucial to replace ground base
stations in order to avoid network failure and carry out life-saving activities and recovery
operations. The current wireless technologies used for public safety coordination do not
provide flexibility, low-latency services, and swift adaptation to the environment during
natural disasters. This thesis addresses these issues by studying a theoretical framework
for designing and analyzing emergency communication system (ECS) algorithms for
post-disaster recovery. UAVs are increasingly valuable to assist ECS and search and rescue
(SAR) teams in multiple disaster management operations to increase disaster response
effectiveness. However, the UAVs have limited battery lifetime and transmission distance
of coverage area and intermittent connectivity on the edge of UAV coverage for the search
and rescue operation. Moreover, the interference effect ECS performance while trying to
achieve optimal solutions. The ECS design is based on the collaboration of multiple UAVs
and SAR teams in order to provide reliable connectivity of wireless coverage service and
save people during disasters. The ECS minimizes outage probability and extends the UAV
coverage area through clustering and D2D communication based on the proposed selection
of the optimal cluster head. Energy harvesting is employed to power communication
devices and prolong the wireless communications network lifetime during a disaster to deal
with these challenges. An optimal cluster head technique has been proposed to improve
energy transfer efficiency and establish sustainable ECS connectivity. Simulation results indicate that the proposed algorithms can significantly reduce the outage probability and
energy consumption. The multi-UAV and SAR collaboration have been evaluated based
on average capacity, energy efficiency, line-of-sight probability, path loss, throughput
performance, coverage probability analysis and outage probability performance. Moreover,
the proposed approach has effectively extended the coverage areas and speed up the
response to disaster recovery. Furthermore, the proposed EH method maximizes the UAV
direct link scenario by around 50% for D2D communication. The optimal cluster head
selection algorithm also gives a lower outage probability of approximately 40% compared
to nonoptimal cluster selection in UAV to cluster head links and cluster head to cluster
member links to improve the network stability. The outage probability of the proposed
solution is approximately 10% better than that of related work. This will guarantee the
communication link quality between the optimal cluster head and cluster members as
D2D communication pairs. It can eliminate the battery power barriers and interference of
UAVs and user devices through a combination of EH and PC. The lower computational
complexity is evaluated to reduce interference and increase the convergence rate compared
with related work
Online consumer website commitment: The role of online consumer experience, digital consumer experience and online consumer review / Nina Farisha Isa
No full textThis thesis is motivated by the need to establish a model covering online consumer experience and buying behaviour. Studies on buying behaviour related to consumer experience are increasing attention to e-commerce environments. Most prior studies, however, have still relied on models developed from studies of pre adoptive and post adoptive of information systems (IS). The applicability to online consumer buying behaviour of findings and models developed from pre and post adoptive of IS is not without question as consumers are not miniature versions of information systems. Consumers face different challenges and opportunities from the usage of information system (IS).
The thesis empirically issues on the shopping cart abandonment of decision making in e-commerce process. In line with these issues, three research questions were established. First, what type of experience can consumers have when they interact with the brand product and services in e-commerce website using laptop/PCs and application? Second, how does online consumer experience (OCX) and digital consumer experience (DCX) impact online consumer website commitment (OCWC)? Third, how does OCX and DCX affect the outcome of OCWC with the influence of OCR. The conceptualization of online consumer experience (OCX) and digital consumer experience (DCX) is introduced. This thesis postulates that appropriate online consumer experience (OCX) and digital consumer experience (DCX) to be enabled for making decision in e-commerce environment. A model incorporating aspects of the theory underlying the flow theory Csikszentmihalyi, (1975; 1990) and the decision-making process in e-commerce (Kim and Srivastava, 2007) was developed to describe the causal linkages between the determinants that activate and stimulate online consumer experience (OCX), digital consumer experience (DCX) and online consumer review (OCR) buying behaviour at the initial stage of decision-making process in e-commerce (i.e., information search and evaluation). The first three hypotheses proposes that online consumer experience (OCX) (i., usability, interactivity and aesthetic) have a positive effect on online consumer website commitment (OCWC). The second hypotheses propose that digital consumer experience (DCX) have a positive effect on online consumer website commitment (OCWC). The last hypotheses propose that online consumer review have a positive relationship between online consumer consumer experience (OCX) and digital consumer experience (DCX) on online consumer website commitment (OCWC).
The results indicate that online consumer experience (OCX), digital consumer experience (DCX) and online consumer review (OCR) posses by the decision-making process in e-commerce at the initial stages of consumer buying behavior (i.e., information search and evaluation). More significantly, online consumer experience (OCX) and digital consumer experience (DCX) dominate the effect on online consumer website commitment (OCWC). Aesthetic experience dominates the effects on online consumer website commitment at the initial stage of the decision-making process in e-commerce. Usability experience have a negative effect on online consumer website commitment (OCWC). The results also demonstrate online consumer review to be contributing factors to the online consumer website commitment (OCWC). Results indicate that when online consumers have a positive online consumer experience (OCX) and digital consumer experience (DCX) they are more likely to have a positive of attitude towards the website commitment. Another finding is the influence of online consumer review (OCR) at the initial stage of information searh and evaluation. However, online consumer review at the initial stage do not contributing to the relationship between online consumer experience (OCX) and digital consumer experience (DCX).
In conclusion, online consumer experience (OCX) and digital consumer experience (DCX) display an important role as antecedents to online consumer website commitment (OCWC). Online consumer experience (OCX) dimensions of aesthetic also display important roles as stimulators at the initial stage of buying behaviour. Thus, apart from requiring a good brand of products and services, online consumers also require appropriate of online consumer experience when searching the information and evaluation of the brand products and services from the e-commerce website
Perception of health care services utilization among foreign labourers in Malaysia / Maryam Sohrabi
No full textIntroduction: Population diversity can create different health profiles, creating new challenges for national health systems. Even in countries where access to health care is widely and freely available, immigrants are pretty often unable to take advantage of such services, for some reasons including differences in lifestyle or culture, language barriers, financial barriers, or lack of awareness. The lack of access to or low uptake of health care services among this group of people can negatively impact overall public health outcomes.
Objectives: This study aims to investigate the associations between socio-demographic factors and barriers in getting healthcare services. It examines the influence of socio-demographic characteristics on the extent of exposure to barriers (personal, structural, and financial) in getting healthcare services. It further investigates the impact of barriers (personal, structural, and financial) on the tendency of obtaining medical treatment for different levels of illness (mild and severe) among unskilled foreign labourer in Malaysia.
Methodology: This study had been based on the primary data gathered through the usage of a structured questionnaire. Data were collected through quota sampling and had involved respondents who are legal foreign workers in Klang Valley, Malaysia. These respondents were approached by enumerator at their residential areas, embassies, and workplaces.
Analysis: All of the data derived from the survey were analysed by using SPSS Statistics version 20. The demographic profiles and variables representing barriers were examined using descriptive analysis and cross tabulation. To find answer for the first research question, cross tabulation and chi-square independence test was used to examine associations between socio-demographic factors and barriers in getting healthcare services. For the second research question, answer was obtained using non-parametric tests, namely Mann-Whitney and Kruskal-Wallis tests. The two tests were used to determine the influence of socio-demographic characteristics on the extent of exposure to barriers (personal, structural, and financial) in getting healthcare services. Finally, answer for the third research question was determined based on the results of chi-square independence test and logistic regression analysis. These statistical techniques were used to examine the impact of barriers on the tendency of getting medical treatment for different levels of sickness.
Finding: Results of the study suggest that association exists between certain types of socio-demographic factors and personal, structural, and financial barriers in getting healthcare services. Similarly, the results have shown that certain socio-demographic factors influence the extent of exposure to personal, structural, and financial barriers in getting healthcare services. Finally, the findings of this study indicate that the impact of barriers (personal, structural, and financial) on the tendency of obtaining medical treatment were not the same for different levels of sickness
Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde
No full textSentiment Analysis is a field that deals with the problem of identifying and extracting sentiment (or opinion) from data (particularly textual data). Studies have shown how user perception can have a strong influence on policies and decision-making processes in a place, society, and nation. This thesis is based on the application of sentiment classification algorithm to tweet data with the goal of classifying messages based on the polarity of sentiment towards a particular topic (or subject matter). Political analysts often communicate with the public and exchange information through the social media platform. Their activities (otherwise termed cyber-trooping) could have either positive, negative, or neutral feedbacks (perceptions) in the public space. Thus, there is a need to automate the process of identifying and predicting (positive, negative, or neutral class) these cyber-trooping data. This work employed the use of machine learning approach. Four conventional classification algorithms: naïve bayes (NB), support vector machines (SVM), nearest neighbor (k-NN), and decision trees (J48) classifiers are implemented in identifying and categorizing tweet data of three political figures in Malaysia: Dato Seri Anwar, Dato Hadi Awang, and Lim Guang Eng, as either positive, negative, or neutral perceptions. The method was implemented using Java and the results of the simulation were evaluated using five standard performance metrics: accuracy, AUC, precision, recall, and f-Measure. The support vector machines (SVM) algorithm obtained the overall best results of 94.5% accuracy, 91.8% precision, 91.7% recall, and 91.1% f-Measure while the naïve bayes (NB) algorithm obtained the best AUC score of 0.944 with the tweet data of Dato Seri Anwar