Although wireless sensor networks (WSNs) have been the object of research focus for the past two decades, fault diagnosis in these networks has received little attention. This is an essential requirement for wireless networks, especially in WSNs, because of their ad-hoc nature, deployment requirements and resource limitations. Therefore, in this paper we survey fault diagnosis from the perspective of network operations. To the best of our knowledge, this is the first survey from such a perspective. We survey the proactive, active and passive fault diagnosis schemes that have appeared in the literature to date, accenting their advantages and limitations of each scheme. In addition to illuminating the details of past efforts, this survey also reveals new research challenges and strengthens our understanding of the field of fault diagnosis.
Nowadays, intelligent vehicles have received a considerable attention among the
researchers to reduce the number of collisions and road accidents. One of the
challenging tasks for these vehicles is road lane detection or road boundaries
detection. In this research, a lane detection algorithm was developed to detect the
right and left lane markers on the road by using two cameras which act as a stereo
vision for the system. It is based on edge detection by using Canny Edge Detection to
reduce unnecessary data on the images and to perform features recognition for the
lane. After the features has been extracted, the algorithm is followed by Hough
Transform method to generate the detected lines on the image obtained from the
stereo vision camera. The algorithm has to work in different environment to be used
in real world applications. The stereo vision algorithm is implemented to generate
disparity map of area. This helps to gain more information on environment, such as the
estimated distance of the lines, the distance of the vehicle to the turns. The experiment
result shows the detection of right and left lane on the road with disparity map to
determine an estimate of the distance of detected lanes from the stereo vision camera.
The attention on genetically modified (GM) food industry is increasing due to the flourishing
of biotechnology. However, there are some debates on the associated benefits and risks of
employing modification technology in food industry. This study strives to examine the causes
that determine consumers’ benefit and risk perceptions on GM foods. Besides, the influence of
perceived benefit and risk of GM food on consumers’ attitude is investigated. The empirical
results of this study showed that GM food knowledge, and GM food characteristics have been
acting as important predictors of both benefits and risks perceptions. Further, it is also found
that perceived benefits showed significant positive influence on attitude, and attitude affects
purchase intention towards GM food. Research implications to policy makers, scientists, and
market practitioners are covered, in which suggestions and recommendations are provided
to these parties. Lastly, research implications and recommendations to future research are
discussed.
This paper presents an evaluation of the alternative use of natural cellulose coir fibres for
soil reinforcement. Soil reinforcement is defined as a technique to improve the engineering properties
of soil. Inserting natural fibres into the soil has been proposed as a reinforcement method. Randomly
distributed coir fibre reinforced soils have recently attracted increasing attention in geotechnical
engineering due to their strength. Furthermore, there is a need to develop an innovative and
sustainable pre-treatment method to improve the effectiveness and performance of coir fibre as soil
reinforcement. The primary purpose of this paper is, therefore, to review the characteristics, benefits,
applications and weaknesses of coir fibre as soil reinforcement.
Road transport is the movement of passengers or goods on the road. To date, issues concerning the safety of students
and school institutions have continued to attract public attention. The recent spate of incidents inside and outside
the school compound has brought the issue of children safety into our attention. These include the escalating number
of road casualties. This study was conducted to observe road hazard outside the school compound and analyse the
safety risks faced by school children by using the risk matrix. The risks are calculated to include likelihood and
severity of hazard as identified in the Guideline of HIRARC 2008. The area selected was the East Coast state of
Peninsular Malaysia, with over 111 schools randomly selected in Kelantan. Results were then analysed and six
highlighted hazards were discussed. The results show that the main road posed the highest risk due to speeding
vehicles. These vehicles failed to slow down when approaching the school area. The school
administration must enhance the safety of the staff, students, and public while in the school area. In conclusion, road
safety awareness must be instilled among teachers, students, parents and road users alike.
Android devices have gained a lot of attention in the last few decades due to several reasons including ease of use, effectiveness, availability and games, among others. To take advantage of Android devices, mobile users have begun installing an increasingly substantial number of Android applications on their devices. Rapid growth in many Android devices and applications has led to security and privacy issues. It has, for instance, opened the way for malicious applications to be installed on the Android devices while downloading different applications for different purposes. This has caused malicious applications to execute illegal operations on the devices that result in malfunction outputs. Android botnets are one of these malfunctions. This paper presents Android botnets in various aspects including their security, architecture, infection vectors and techniques. This paper also evaluates Android botnets by categorising them according to behaviour. Furthermore, it investigates the Android botnets with respect to Android device threats. Finally, we investigate different Android botnet detection techniques in depth with respect to the existing solutions deployed to mitigate Android botnets.
Entrepreneurship showed an increase in the popularity of business education, engineering education,
universities and educational institutions. All students who engage in entrepreneurial education has the
potential to develop their entrepreneurial skills and knowledge. However, the majority of
entrepreneurial education program focused on the exploitation of existing opportunities and assume
that these opportunities have been identified. Research on entrepreneurship also shows that efficiency
is often ignored or receive little attention while teaching entrepreneurship. This article was developed
to assist in improving the understanding of the concept of learning which supports entrepreneurship
and the development of entrepreneurial competence.
Due to their high ionic conductivity, solid polymer electrolyte (SPE) systems have attracted wide spread attention as the most appropriate choice to fabricate all-solid-state electrochemical devices, namely batteries, sensors and fuel cells. In this work, ion conductive polymer electrolyte membranes have been prepared for battery fabrication. However, fractals were found to grow in these polymer electrolyte membranes weeks after they were prepared. It was believed that the formation of fractal aggregates in these membranes were due to ionic movement. The discovery of fractal growth pattern can be used to understand the effects of such phenomenon in the polymer electrolyte membranes. Digital images of the fractal growth patterns were taken and a simulation model was developed based on the Brownian motion theory and a fractal dialect known as L-system. A computer coding has been designed to simulate and visualize the fractal growth.
Tool life of the cutting tools is considered as one of the factors which has effects on machining costs and the quality of machined parts. The topic of tool life prediction has been an interesting and important research topic attracting the attention of a wide number of researchers in this particular area. In terms of the suitable methods used in this research topic, it is stated that both statistical and artificial intelligence (AI) approaches can be employed to model tool life. For further justifying the capability of the ANN model in predicting tool life, the current study was based on conducting experimental work for collecting the experimental data. After carrying out the experiment, 17 data sets were collected and they were divided into two subsets; the first one for training and the second for testing. Since the data sets seemed to be lower than the number of data sets used in previous studies, we attempted to make verification of the ability of the ANN model in learning and adapting with low training and testing data. Diverse topologies accompanied with single and two hidden layers were created for modeling the tool life. For choosing the best and most effective network, the study adopted the mean square error function as criteria for the evaluation of the network selection. Thus, based on the data generated from the same experiment, a regression model (RM) was constructed employing the SPSS software. A comparison between the ANN model and RMs in terms of their accuracy was carried out and the findings revealed that the accuracy of the ANN was higher than that of the RM.
The new millennium witnessed increasing attention to the field of robotics, especially the development of humanoid bipedal robot. Attention is noticed from the increasing number of publications as a result of a multitude of humanoid projects for commercial and academic goals. This paper briefly visits the recent activities in this field, highlighting the importance and motivation behind adopting bipedal humanoid projects, particularly underlining biologically inspired design concept, bipedal locomotion and communication. Ultimately, emphasising on power-efficient design. The problem of endurance and effective duty cycle were presented. Finally, potential future application for the humanoid robot was briefly listed.
The recent designs of dynamic nanoassemblies exploiting the tumor-targeting properties have received increasing attention for tumor imaging and therapy due to their tumor-specific delivery and enhanced antitumor efficacy. However, these designs are mainly focused on the macroscopic tumor therapeutic effect, while the nano-bio interactions in the tumor microenvironment (TME) remain poorly understood. This review aims to provide an overview of the development of tumor-responsive nanoassemblies towards the imaging, therapy and TME modulation in the tumor site. The tumor biology leading to TME formation and the potential TME properties for the practicable design of tumor-targeting nanoassemblies has been outlined. Furthermore, the various approaches for TME modification and the realization via dynamic nanoassemblies for enhanced tumor therapy were reviewed. Lastly, the prospects of these methods were briefly discussed. These strategies may inspire the development of new combinational cancer therapeutics.
Environmental awareness and trends to develop sustainable resources have directed much research attention towards kenaf fibre as an alternative reinforcement in composite manufacturing. Numerous studies have been conducted on kenaf and its hybrid composites. Most studies were conducted on kenaf/glass hybrid composites compared to other kenaf/synthetic hybrid composites. Similar with other materials, mechanical properties were the fundamental knowledge identified by the researcher. Limited studies conducted on other properties have restricted the use of kenaf composites to non-structural applications. To extend the potential of kenaf composites to automotive exterior or other critical applications, studies on impact properties can be a valuable contribution in the material field. This review discusses the mechanical and low velocity impact properties of kenaf/glass hybrid composites reported previously. Percentage loading of fibre, the angle of orientation in woven fibres and the chemical treatment applied to the fibre before compounding are the three major parameters that affect the mechanical and impact properties of the composites. This review provides insights into the mechanical and impact properties of kenaf/glass hybrid composites for future research.
After publication of the article [1], it has been brought to our attention that the methodology outlined in the original article was not able to be fully carried out. The article planned a two armed randomized control trial. However, due to a lower response than expected and one housing complex dropping out from the study, the method was changed to pre- and post-intervention with no control group. All other methods were conducted as outlined in the original article.
In recent years, closed-cell porous Aluminum (Al) has drawn increasing attention, particularly in the applications requiring reduced weight and energy absorption capability such as in the automotive and aerospace industries. In the present work, porous Al with closed-cell structure was successfully fabricated by powder metallurgy technique using PMMA as a space holder. The effects of the amount of PMMA powder on the porosity, density, microstructure and compressive behaviors of the porous specimens were systematically evaluated. The results showed that closed-cell porous Al having different porosities (12%-32%) and densities (1.6478 g/cm³, 1.5125 g/cm³ and 1.305 g/cm³) could be produced by varying the amount of PMMA (20-30 wt %). Meanwhile, the compressive behavior results demonstrated that the plateau stress decreased and the energy absorption capacity increased with increasing amount of PMMA. However, the maximum energy absorption capacity was achieved in the closed-cell porous Al with the addition of 25 wt % PMMA. Therefore, fabrication of closed-cell porous Al using 25 wt % PMMA is considered as the optimal condition in the present study since the resultant closed-cell porous Al possessed good combinations of porosity, density and plateau stress, as well as energy absorption capacity.
Most neuroscience cognitive experiments involve repeated presentations of various stimuli across several minutes or a few hours. It has been observed that brain responses, even to the same stimulus, evolve over the course of the experiment. These changes in brain activation and connectivity are believed to be associated with learning and/or habituation. In this paper, we present two general approaches to modeling dynamic brain connectivity using electroencephalograms (EEGs) recorded across replicated trials in an experiment. The first approach is the Markovian regime-switching vector autoregressive model (MS-VAR) which treats EEGs as realizations of an underlying brain process that switches between different states both within a trial and across trials in the entire experiment. The second is the slowly evolutionary locally stationary process (SEv-LSP) which characterizes the observed EEGs as a mixture of oscillatory activities at various frequency bands. The SEv-LSP model captures the dynamic nature of the amplitudes of the band-oscillations and cross-correlations between them. The MS-VAR model is able to capture abrupt changes in the dynamics while the SEv-LSP directly gives interpretable results. Moreover, it is nonparametric and hence does not suffer from model misspecification. For both of these models, time-evolving connectivity metrics in the frequency domain are derived from the model parameters for both functional and effective connectivity. We illustrate these two models for estimating cross-trial connectivity in selective attention using EEG data from an oddball paradigm auditory experiment where the goal is to characterize the evolution of brain responses to target stimuli and to standard tones presented randomly throughout the entire experiment. The results suggest dynamic changes in connectivity patterns over trials with inter-subject variability.
Parameter estimation in Generalized Autoregressive Conditional Heteroscedastic (GARCH) model has received much attention in the literature. Commonly used quasi maximum likelihood estimator (QMLE) may not be suitable if the model is misspecified. Alternatively, we can consider using variance targeting estimator (VTE) as it seems to be a better fit for misspecified initial parameters. This paper extends the application to see how both QMLE and VTE perform under error distribution misspecifications. Data are simulated under two error distribution conditions: one is to have a true normal error distribution and the other is to have a true student-t error distribution with degree of freedom equals to 3. The error distribution assumption that has been selected for this study are: normal distribution, student-t distribution, skewed normal distribution and skewed student-t. In addition, this study also includes the effect of initial parameter specification.
Neurotoxicants present in water supply may affect human functions in terms of attention, response speed and perceptual motor speed. Neurobehavioural performance can be influenced by gender, age and education levels. This study aims to assess the neurobehavioral performance of palm oil estate residents with private water supply in southern Peninsular of Malaysia.
Previous studies have revealed that self-related tasks (items) receive more attention than non-self-related, and that they elicit event-related potential (ERP) components with larger amplitudes. Since personality has been reported as one of the biological correlates influencing these components, as well as our behavioural differences, it is important to examine how it affects our self-consciousness in relation to tasks of varied relevance and the neurological basis.
Human cognition involves many mental processes that are highly interrelated, such as perception, attention, memory, and thinking. An important and core cognitive process is memory, which is commonly associated with the storing and remembering of environmental information. An interesting issue in memory research is on ways to enhance memory performance, and thus, remembering of information. Can colour result in improved memory abilities? The present paper highlights the relationship between colours, attention, and memory performance. The significance of colour in different settings is presented first, followed by a description on the nature of human memory. The role of attention and emotional arousal on memory performance is discussed next. The review of several studies on colours and memory are meant to explain some empirical works done in the area and related issues that arise from such studies.