We present an algorithm to reduce the number of slices from 2D contour cross sections. The main aim of the algorithm is to filter less significant slices while preserving an acceptable level of output quality and keeping the computational cost to reconstruct surface(s) at a minimal level. This research is motivated mainly by two factors; first 2D cross sections data is often huge in size and high in precisions – the computational cost to reconstruct surface(s) from them is closely related to the size and complexity of this data. Second, we can trades visual fidelity with speed of computations if we can remove visually insignificant data from the original dataset which may contains redundant information. In our algorithm we use the number of contour points on a pair of slices to calculate the distance between them. Selection to retain/reject a slice is based on the value of distance compared against a threshold value. Optimal threshold value is derived to produce set of slices that collectively represent the feature of the dataset. We tested our algorithm over six different set of data, varying in complexities and sizes. The results show slice reduction rate depends on the complexity of the dataset, where highest reduction percentage is achieved for objects with lots of constant local variations. Our derived optimal thresholds seem to be able to produce the right set of slices with the potential of creating surface(s) that traded off the accuracy and speed requirements.
To present several key factors that motivated Malaysian registered nurses to undertake a post-registration degree through an Australian university. The overall research study, from which this paper is drawn, looked at the professional learning of Malaysian registered nurses and the subsequent impact on their careers.
Stress has a negative effect on student nurses well-being and can impede learning or motivate them and is conducive to learning. This study examined the perceived stress and factors that influenced daily students’ life among both the Diploma and Bachelor of Nursing students. A total of 241 nursing students were involved in this research project. Findings of this study indicated that junior nursing students (
The nonlinear conjugate gradient (CG) methods have widely been used in solving unconstrained optimization problems. They are well-suited for large-scale optimization problems due to their low memory requirements and least computational costs. In this paper, a new diagonal preconditioned conjugate gradient (PRECG) algorithm is designed, and this is motivated by the fact that a pre-conditioner can greatly enhance the performance of the CG method. Under mild conditions, it is shown that the algorithm is globally convergent for strongly convex functions. Numerical results are presented to show that the new diagonal PRECG method works better than the standard CG method.
There are two main reasons that motivate people to detect outliers; the first is the researchers' intention; see the example of Mr Haldum's cases in Barnett and Lewis. The second is the effect of outliers on analyses. This article does not differentiate between the various justifications for outlier detection. The aim was to advise the analyst about observations that are isolated from the other observations in the data set. In this article, we introduce the eigenstructure based angle for outlier detection. This method is simple and effective in dealing with masking and swamping problems. The method proposed is illustrated and compared with Mahalanobis distance by using several data sets.
Collecting correlated scene images and camera poses is an essential step towards learning absolute camera pose regression models. While the acquisition of such data in living environments is relatively easy by following regular roads and paths, it is still a challenging task in constricted industrial environments. This is because industrial objects have varied sizes and inspections are usually carried out with non-constant motions. As a result, regression models are more sensitive to scene images with respect to viewpoints and distances. Motivated by this, we present a simple but efficient camera pose data collection method, WatchPose, to improve the generalization and robustness of camera pose regression models. Specifically, WatchPose tracks nested markers and visualizes viewpoints in an Augmented Reality- (AR) based manner to properly guide users to collect training data from broader camera-object distances and more diverse views around the objects. Experiments show that WatchPose can effectively improve the accuracy of existing camera pose regression models compared to the traditional data acquisition method. We also introduce a new dataset, Industrial10, to encourage the community to adapt camera pose regression methods for more complex environments.
The aim of this research is to apply the variance and conditional value at risk (CVaR) as risk measures in portfolio selection problem. Consequently, we are motivated to compare the behavior of two different type of risk measures (variance and CVaR) when the expected returns of a portfolio vary from a low return to a higher return. To obtain an optimum portfolio of the assets, we minimize the risks using mean variance and mean CVaR models. Dataset with stocks for FBMKLCI is used to generate our scenario returns. Both models and dataset are coded and implemented in AMPL software. We compared the performance of both optimized portfolios constructed from the models in term of risk measure and realized returns. The optimal portfolios are evaluated across three different target returns that represent the low risk low returns, medium risk medium returns and high risk high returns portfolios. Numerical results show that the composition of portfolios for mean variance are generally more diversified compared to mean CVaR portfolios. The in sample results show that the seven optimal mean CVaR0:05 portfolios have lower CVaR0:05 values as compared to their optimal mean variance counterparts. Consequently, the standard deviation for mean variance optimal portfolios are lower than the standard deviation of its mean CVaR0:05 counterparts. For the out of sample analysis, we can conclude that mean variance portfolio only minimizes standard deviation at low target return. While, mean CVaR portfolios are favorable in minimizing risks at high target return.
The focus of this study is to analyze the level of knowledge, awareness, and attitude toward plastic waste and to distinguish the key drivers that encourage the households in Kuala Lumpur, Malaysia, to participate in "No plastic campaign," This study used the logistic regression model to explain the factors that may affect the willingness to participate (WTP) of households in the campaign. In this study, it is found that 35 % of households are willing to participate in the campaign. The results of the study also indicate that people who are more informed and more convinced of their knowledge have a more positive attitude toward recycling than their counterparts do. Furthermore, this study provides additional evidence of the level and classification of importance of motivating factors for plastic recycling, using the modified average and coefficient of variation of the models. From the analysis, the factor "helps reduce landfill use" is found as the most important factor and the factor of "raising money for charity" is found as the least important factor that motivates households to participate in recycling. The determinations of the study suggest some strategies that could hold implications for government and households to boost them to participate in the campaign "No Plastic Bag."
This research predicted the effectiveness of variety game design elements in enhancing the intrinsic motivation of users on energy conservation behaviour prior to its actual implementation to ensure cost-effective. Face-to-face questionnaire surveys were conducted at the five recognized Malaysian research universities and obtained a total of 1500 valid survey data. The collected data was run with Structural Equation Modeling (SEM) analysis using SmartPLS 3 software. The results predicted the positive effect of gamification on intrinsically motivate the users based on Self-Determination Theory (SDT). The identified nine core game design elements were found to be useful in satisfying users' autonomy, competence and relatedness need satisfactions specified by SDT. This research is useful to guide the campaign organizer in designing a gamified design energy-saving campaign and provide understanding on the causal relationships between game design elements and users' intrinsic motivation to engage on energy conservation. A game-like campaign environment is believed to be created to users by implementing the game design elements in energy-saving campaign, and subsequently users' intrinsic motivation to engage on energy conservation behaviour can be enhanced.
Background: Falls are a significant incident among older adults affecting one in every three individuals aged 65 and over. Fall risk increases with age and other factors, namely instability. Recent studies on the use of fall detection devices in the Malaysian community are scarce, despite the necessity to use them. Therefore, this study aimed to investigate the association between the prevalence of falls with instability. This study also presents a survey that explores older adults' perceptions and expectations toward fall detection devices. Methods: A cross-sectional survey was conducted involving 336 community-dwelling older adults aged 50 years and older; based on randomly selected participants. Data were analyzed using quantitative descriptive analysis. Chi-square test was conducted to investigate the associations between self-reported falls with instability, demographic and walking characteristics. Additionally, older adults' perceptions and expectations concerning the use of fall detection devices in their daily lives were explored. Results: The prevalence of falls was 28.9%, where one-quarter of older adults fell at least once in the past 6 months. Participants aged 70 years and older have a higher fall percentage than other groups. The prevalence of falls was significantly associated with instability, age, and walking characteristics. Around 70% of the participants reported having instability issues, of which over half of them fell at least once within 6 months. Almost 65% of the participants have a definite interest in using a fall detection device. Survey results revealed that the most expected features for a fall detection device include: user-friendly, followed by affordably priced, and accurate. Conclusions: The prevalence of falls in community-dwelling older adults is significantly associated with instability. Positive perceptions and informative expectations will be used to develop an enhanced fall detection incorporating balance monitoring system. Our findings demonstrate the need to extend the fall detection device features aiming for fall prevention intervention.
Precise temperature measurement is essential in a wide range of applications in the medical environment, however the regarding the problem of temperature measurement inside a simple incubator, neither a simple nor a low cost solution have been proposed yet. Given that standard temperature sensors don't satisfy the necessary expectations, the problem is not measuring temperature, but rather achieving the desired sensitivity. In response, this paper introduces a novel hardware design as well as the implementation that increases measurement sensitivity in defined temperature intervals at low cost.
BACKGROUND: Test anxiety aggravates psychological distress and reduces the motivation among graduate students. This study aimed to identify psychological intervention for test anxiety, which reduces the level of psychological distress, amotivation and increases the intrinsic and extrinsic motivation among medical students.
MATERIALS AND METHODS: Westside test anxiety scale, Kessler Perceived Stress Scale and Academic Motivation Scale were used to measure test anxiety, psychological distress and motivation on 436 1(st) year medical students. Out of 436 students, 74 students who exhibited moderate to high test anxiety were randomly divided into either experimental or waiting list group. In this true randomized experimental study, 32 participants from the intervention group received five sessions of psychological intervention consist of psychoeducation, relaxation therapy and systematic desensitization. Thirty-three students from waiting list received one session of advice and suggestions.
RESULTS: After received psychological intervention participants from the intervention group experienced less anxiety, psychological distress, and amotivation (P < 0.01) and high intrinsic and extrinsic motivation (P < 0.01) in the postassessment compared with their preassessment scores.
CONCLUSION: Overall psychological intervention is effective to reduce anxiety scores and its related variables.
KEYWORDS: Anxiety; motivation; psychological distress
Event-related potentials (ERPs) time-locked to decision outcomes are reported. Participants engaged in a gambling task (see  for details) in which they decided between a risky and a safe option (presented as different coloured shapes) on each trial (416 in total). Each decision was associated with (fully randomised) feedback about the reward outcome (Win/Loss) and its magnitude (varying as a function of decision response; 5-9 points for Risky decisions and 1-4 points for Safe decisions). Here, we show data demonstrating: (a) the influence of Win feedback in the preceding outcome (Outcome t-1) on activity related to the current outcome (Outcome t ); (b) difference wave analysis for outcome expectancy- separating Expected Outcomes (consecutive Loss trials subtracted from consecutive reward) from Unexpected Outcomes (subtracting Loss t-1Win t trials from Win t-1Loss t trials); (c) difference waves separating Switch and Stay responses for Outcome Expectancy; (d) the effect of magnitude induced by decisions (Risk t vs. Safe t ) on Outcome Expectancy; and finally, (e) expectations reflected by response switch direction (Risk to Safe responses vs. Safe to Risk t ) on the FRN at Outcome t .
Bakery products become a regular food in most part of the world and are essential commodities today. There is a high potential for business growth in selling bakery products. In the business world, Small-Medium Enterprises (SMEs) are primarily engaged in the bakery business, but cannot compete with the branded bakery Industry. The SMEs in Malaysia are striving hard to achieve growth in the business of bakery products. The present case study deals with the issues faced by SMEs and provided some valid recommendations to resolve the existing problems in the bakery business. The case analysis and its findings reveal that SMEs have unstructured marketing strategies and needs enhancements in the areas of packaging, value add to the bakery products, focus on promotion and appropriate advertising strategies. Further, the analysis reveals that more coverage of selling points for bakery products, the increased number of distribution centers and proper incentives to the agents may definitely improve the marketing of bakery products.
The current issue of JUMMEC touches on many diverse topics and in many ways reflects the evolution of modern medicine from the practice of acupuncture to epidemics facilitated by modern travel to the subject of ethics including controversies surrounding financial incentives given in promoting organ donation.(Copied from article).
Clustering a set of objects into homogeneous groups is a fundamental operation in data mining. Recently, many attentions have been put on categorical data clustering, where data objects are made up of non-numerical attributes. For categorical data clustering the rough set based approaches such as Maximum Dependency Attribute (MDA) and Maximum Significance Attribute (MSA) has outperformed their predecessor approaches like Bi-Clustering (BC), Total Roughness (TR) and Min-Min Roughness(MMR). This paper presents the limitations and issues of MDA and MSA techniques on special type of data sets where both techniques fails to select or faces difficulty in selecting their best clustering attribute. Therefore, this analysis motivates the need to come up with better and more generalize rough set theory approach that can cope the issues with MDA and MSA. Hence, an alternative technique named Maximum Indiscernible Attribute (MIA) for clustering categorical data using rough set indiscernible relations is proposed. The novelty of the proposed approach is that, unlike other rough set theory techniques, it uses the domain knowledge of the data set. It is based on the concept of indiscernibility relation combined with a number of clusters. To show the significance of proposed approach, the effect of number of clusters on rough accuracy, purity and entropy are described in the form of propositions. Moreover, ten different data sets from previously utilized research cases and UCI repository are used for experiments. The results produced in tabular and graphical forms shows that the proposed MIA technique provides better performance in selecting the clustering attribute in terms of purity, entropy, iterations, time, accuracy and rough accuracy.
As the debate on accepting financial incentives persists, more and more findings linked to its success as well as to its foreseeable backlash continue to unravel. Specifically out to enhance perceptions on financial incentives, this paper reviews important aspects of the financial incentives and provides a diverse range of empirical findings at a glance. Through a review of several empirical findings and literature, this paper argues that several basic practices of the financial incentives are indeed instrumental to enhancing organ donation. However, more experimentation is necessary to unearth the best mode that is best responsive to a society and subsequently, rejects the overly generalization that labels it as unethical.
Background: In Malaysia the percentage of diploma registered nurses outnumber the percentage of degree registered nurses. Internationally, most registered nurses earn associate degrees or bachelor’s degrees in nursing. Malaysia is in the pipeline of ensuring that its registered nurses are professionally qualified with nursing degree by year 2020. Registered nurses with diploma qualification are feeling the pressure to upgrade their qualification to degree. There are concerns as to why these nurses are not pursuing their post registration nursing degree. Objective: To determine factors that are deterring the registered nurses of a private hospital in Penang from pursuing the post registered nursing degree. Methods: This descriptive study utilised a convenient sample of 150 registered nurses from Lam Wah Ee Hospital in Penang. The instrument of this study was developed based on literature search and the conceptual framework of Force Fields Analysis developed by Kurt Lewin in 1952. Results: The deterring factors for registered nurses not pursuing post registration nursing degree from this hospital were determined through negative mean score, which was valued at less than 2.5. The top 3 deterring factors identified were: high educational cost, with a score of 1.92; financial commitment, with a score of 2.22 and time constraints and high workload, with a score of 2.27. Conclusions: High educational cost, financial commitment, time constraint and high workload were the main factors deterring the registered nurses from this hospital from pursuing their post registration nursing degree. Thus it is timely for the organisational management to consider workable measures to assist and motivate their nurses to upgrade themselves with nursing degree in line with Malaysia’s vision to meet the increasing challenges and complex needs in the care of clients in health services.
The radiotracer injector is meant for transferring liquid radiotracer in the system for industrial radiotracer application with minimal radiation exposure to the operator. The motivation of its invention is coming from the experience of the workers who are very concern about the radiation safety while handling with the radioactive source. The idea ensuring the operation while handling the radioactive source is fast and safe without interrupting the efficiency and efficacy of the process. Thus, semi automated device assisting with pneumatic technology is applied for its invention.
Integrated approach of naqli and aqli knowledge is applied in most educational
activities conducted in Faculty of Dentistry, Universiti Sains Islam Malaysia (USIM). Naqli is
knowledge from al-Quran and as-Sunnah while aqli is rational knowledge or knowledge based on
scientific evidence. Currently the integration of naqli and aqli knowledge in periodontology subject
is at the initial stage of implementation. Motivational session for periodontitis patients is identified
to be one of the educational activities that integrates the naqli and aqli knowledge. Therefore, this
study is aimed to identify elements of naqli knowledge that can be included during motivational
session in periodontal clinic. (Copied from article).