Segmentation of the liver from Computed Tomography (CT) volumes plays an important role during the choice of treatment strategies for liver diseases. Despite lots of attention, liver segmentation remains a challenging task due to the lack of visible edges on most boundaries of the liver coupled with high variability of both intensity patterns and anatomical appearances with all these difficulties becoming more prominent in pathological livers. To achieve a more accurate segmentation, a random walker based framework is proposed that can segment contrast-enhanced livers CT images with great accuracy and speed. Based on the location of the right lung lobe, the liver dome is automatically detected thus eliminating the need for manual initialization. The computational requirements are further minimized utilizing rib-caged area segmentation, the liver is then extracted by utilizing random walker method. The proposed method was able to achieve one of the highest accuracies reported in the literature against a mixed healthy and pathological liver dataset compared to other segmentation methods with an overlap error of 4.47 % and dice similarity coefficient of 0.94 while it showed exceptional accuracy on segmenting the pathological livers with an overlap error of 5.95 % and dice similarity coefficient of 0.91.
The issue of classifying objects into groups when measured variables in an experiment are mixed has attracted the attention of statisticians. The Smoothed Location Model (SLM) appears to be a popular classification method to handle data containing both continuous and binary variables simultaneously. However, SLM is infeasible for a large number of binary variables due to the occurrence of numerous empty cells. Therefore, this study aims to construct new SLMs by integrating SLM with two variable extraction techniques, Principal Component Analysis (PCA) and two types of Multiple Correspondence Analysis (MCA) in order to reduce the large number of mixed variables, primarily the binary ones. The performance of the newly constructed models, namely the SLM+PCA+Indicator MCA and SLM+PCA+Burt MCA are examined based on misclassification rate. Results from simulation studies for a sample size of n=60 show that the SLM+PCA+Indicator MCA model provides perfect classification when the sizes of binary variables (b) are 5 and 10. For b=20, the SLM+PCA+Indicator MCA model produces misclassification rates of 0.3833, 0.6667 and 0.3221 for n=60, n=120 and n=180, respectively. Meanwhile, the SLM+PCA+Burt MCA model provides a perfect classification when the sizes of the binary variables are 5, 10, 15 and 20 and yields a small misclassification rate as 0.0167 when b=25. Investigations into real dataset demonstrate that both of the newly constructed models yield low misclassification rates with 0.3066 and 0.2336 respectively, in which the SLM+PCA+Burt MCA model performed the best among all the classification methods compared. The findings reveal that the two new models of SLM integrated with two variable extraction techniques can be good alternative methods for classification purposes in handling mixed variable problems, mainly when dealing with large binary variables.
Supercapacitors, based on fast ion transportation, are among the most promising energy storage solutions that can deliver fast charging-discharging within seconds and exhibit excellent cycling stability. The development of a good electrode material is one of the key factors in enhancing supercapacitor performance. Graphene (G), an allotrope of carbon that consists of a single layer of carbon atoms arranged in a hexagonal lattice, elicits research attention among scientists in the field of energy storage due to its remarkable properties, such as outstanding electrical conductivity, good chemical stability, and excellent mechanical behavior. Furthermore, numerous studies focus on 2D materials that are analogous to graphene as electrode supercapacitors, including transition metal dichalcogenides (TMDs). Recently, scientists and researchers are exploring TMDs because of the distinct features that make 2D TMDs highly attractive for capacitive energy storage. This study provides an overview of the structure, properties, synthesis methods, and electrochemical performance of G/TMD supercapacitors. Furthermore, the combination of G and TMDs to develop a hybrid structure may increase their energy density by introducing an asymmetric supercapacitor system. We will also discuss the future prospect of this system in the energy field.
Environmental degradation remains a huge obstacle to sustainable development. Research on the factors that promote or degrade the environment has been extensively conducted. However, one important variable that has conspicuously received very limited attention is energy innovations. To address this gap in the literature, this study investigated the effects of energy innovations on environmental quality in the U.S. for the period 1974 to 2016. We have incorporated GDP and immigration as additional regressors. Three indices comprising of CO2 emissions, ecological footprint and carbon footprint were used to proxy environmental degradation. The cointegration tests established long-run relationships between the variables. Using a maximum likelihood approach with a break, the results showed evidence that energy innovations significantly improve environmental quality while GDP degrades the quality of the environment, and immigration has no significant effect on the environment. Policy implications of the results are discussed in the body of the manuscript.
CO2 separation from CH4 by using mixed matrix membranes has received great attention due to its higher separation performance compared to neat polymeric membrane. However, Robeson's trade-off between permeability and selectivity still remains a major challenge for mixed matrix membrane in CO2/CH4 separation. In this work, we report the preparation, characterization and CO2/CH4 gas separation properties of mixed matrix membranes containing 6FDA-durene polyimide and ZIF-8 particles functionalized with different types of amine groups. The purpose of introducing amino-functional groups into the filler is to improve the interaction between the filler and polymer, thus enhancing the CO2 /CH4 separation properties. ZIF-8 were functionalized with three differents amino-functional group including 3-(Trimethoxysilyl)propylamine (APTMS), N-[3-(Dimethoxymethylsilyl)propyl ethylenediamine (AAPTMS) and N1-(3-Trimethoxysilylpropyl) diethylenetriamine (AEPTMS). The structural and morphology properties of the resultant membranes were characterized by using different analytical tools. Subsequently, the permeability of CO2 and CH4 gases over the resultant membranes were measured. The results showed that the membrane containing 0.5 wt% AAPTMS-functionalized ZIF-8 in 6FDA- durene polymer matrix displayed highest CO2 permeability of 825 Barrer and CO2/CH4 ideal selectivity of 26.2, which successfully lies on Robeson upper bound limit.
Cancer is a devastating disease that has claimed many lives. Natural bioactive agents from plants are gaining wide attention for their anticancer activities. Several studies have found that natural plant-based bioactive compounds can enhance the efficacy of chemotherapy, and in some cases ameliorate some of the side-effects of drugs used as chemotherapeutic agents. In this paper, we have reviewed the literature on the anticancer effects of four plant-based bioactive compounds namely, curcumin, myricetin, geraniin and tocotrienols (T3) to provide an overview on some of the key findings that are related to this effect. The molecular mechanisms through which the active compounds may exert their anticancer properties in cell and animal-based studies also discussed.
Psychological well-being among students began to received attention and be seen as an important aspect in the life of an individual who are in primary, secondary or higher education institution. The purpose of this study was to examine the relationship between the three subfactors of perfectionism, three sub-factors in basic psychological needs and psychological well-being among university students. Methodology of the research is based on a survey among 468 university students using questionnaires of Scales of Psychological Well-Being (SPWB), Almost Perfect Scale Revised (APS-R) and Basic Psychological Needs Scale (BPNS). The subjects consisted of 468 undergraduate students in University Malaysia Sabah. Data were analysed by using IBM Statistical Package for the Social Sciences (SPSS) version 21. The results showed a significant relationship between perfectionism, basic psychological needs and psychological well-being among university students. Implications and suggestions for future research are also discussed.
Atomic force microscopes (AFM) as one of the scanning probe microscopy (spm) modes have become useful tools, not only for observing surface morphology and nanostructure topography but also for fabrication of various nanostructures itself. In this work, silicon oxide (SiOx) patterns were formed on Si(100) surface by means of AFM anodization, where a non-contact mode used to oxidize Si wafer at the nanoscale size. The oxide patterns could serve as masks for the chemical etching of Si surface in alkaline solution in order to create the Si nanodots. A special attention is paid to finding relations between the size of dots and operational parameters as tip bias voltage and tip writing speed Dot arrays with 10 nm high and less than 50 nm in diameter have been successfully fabricated. The ability to control oxidation and scanning speed can be utilized in fabrication of complex nanostructures and make scanning probe lithography (SPL) as a very promising lithographic technique in nanoelectronic devices, nanophotonics and other high-tech areas.
Employee deviance has received increasing attention in the past decade. Past research have reported that work environment related factors such as organizational support, supervisory support, role conflict, and job demand were associated with deviant behavior The purpose of this paper is to examine the relationship between job demand (psychological job demand), and job resources (social support), and employee workplace deviant behavior. This study adopts a cross-sectional correlation study design. A total of 315 employees were selected using cluster sampling technique participated in this study. Data were collected using a self-administered questionnaire using the drop and collect method. Data were analyzed using descriptive analysis (mean, standard deviation, frequency distribution) to describe the demographic profile and study variables. Correlation and regression analysis was performed to test the relationship between psychological job demand, and social support, and employee workplace deviant behaviors. The result revealed that lack of social support has significant positive influence on employee workplace deviant behavior. The findings suggest that lack of job resources such as social support may drive employees to engage in deviant work behavior. However, high job demand experienced by employees does not drive them towards engaging in deviant work behavior.
This qualitative study has been done to 24 teachers and 72 students from various secondary schools in Penang, Malaysia, related to the effect of between class ability grouping (BCAG). Studies reported that BCAG triggered correspondence bias among teachers, which eventually affect them to show different perception and expectations towards high achiever classes (HAC) and low achiever classes (LAC) students. However, even teachers tend to expect HAC students not to be significantly involved in disciplinary problems; they still do, such as distrusting schoolteachers, paying less attention to in the classroom, doing external work during classes at school, and being blatantly arrogant to the teachers. Semi-structured interview have been utilized in order to collect the data, and two-cycled analyses method, namely In-Vivo and Thematic Analyses has been operated in order to analyze the massive amount of qualitative data. Findings of this study showed that the disciplinary problems among HAC are related to their self-esteem types due to locus of control difference, as well as bigger issues apart from the competition among themselves. School management system, BCAG itself, and reciprocal envy between HAC and LAC students, as well as their inclination towards tuition centers contributed to disciplinary problems among HAC students.
Previous studies have found that luminance contrast may enhance attention and attention is positively correlated with memory. However, little attention has been given to understand the impact of luminance contrast on memory. The present study attempts to address this gap by examining the effect of luminance contrast on attention and memory. A total of 159 undergraduates were randomly assigned to three luminance contrast conditions (high vs. moderate vs. low) and were administered a modified d2 test and modified words memory test. Multivariate analysis of variance showed significant effect of luminance contrast on memory performance. Participants in the high and moderate luminance contrast groups recalled more words than counterparts in the low contrast group. However, the effect of luminance contrast on attention was not significant, though planned comparison found that high contrast group scored higher than low contrast group. The findings not only shed light on improvement of memory but also have implication for design and marketing and consumer behaviours study.
The rapid urban expansion in East-Asian cities has increased the need for comfortable public spaces. This study presents field measurements and parametric simulations to evaluate the microclimatic characteristics in a university campus in the tropical climate of Kuala Lumpur, Malaysia. The study attempts to identify the thermally uncomfortable areas and their physical and design characteristics while debating on the circumstances of enhancing the outdoor comfort conditions for the campus users. Simulations in Envi-met and IES-VE are used to investigate the current outdoor thermal conditions, using classic thermal metric indices. Findings show high levels of thermal discomfort in most of the studied spaces. As a result, suggestions to improve the design quality of outdoor areas optimizing their thermal comfort conditions are proposed. The study concludes that effective re-design of outdoor spaces in the tropics, through adequate attention to the significant impacts of shading and vegetation, can result in achieving outdoor spaces with high frequency of use and improved comfort level.
Purpose: The object of this study was to identify patients with diagnosed dengue infection, who were positive
for both dengue-specific NS1 antigen and IgM antibody.
Method: From January 2013 to December 2016, in Central Kolkata in West Bengal in India, patients with
symptoms of dengue infection, were sent to the laboratory by the physicians for confirmatory diagnosis of
dengue infection. A total of 4762 patients were seen, and serum samples tested and distributed into seven
panels, according to the investigations requested. 1436 patients were tested positive.
Results: 1053 cases were tested for both NS1 and Ig M antibody, 835 for dengue-specific NS1 antigen, IgM and
IgG antibodies and 218 for NS1 dengue-specific antigen and IgM antibody. Of these, dengue was confirmed in
34.3 %, with 16.6% positive for both NS1 antigen and IgM antibody. Eleven were diagnosed, with late dengue
infection, thirty-nine with late primary infections and ten with late secondary dengue infection.
Conclusions: Many of the patients were reactive for both NS1 antigen and IgM antibody, and they required
proper attention and strict vigilance with effective monitoring and treatment, not of early dengue infection,
but of late dengue infection. Unless the serological tests for Ig M and IgG antibodies, and the dengue specific
viral antigen NS1 are performed simultaneously, these types of cases would not all be detected.
There is a growing attention toward personalized medicine. This is led by a fundamental shift from the 'one size fits all' paradigm for treatment of patients with conditions or predisposition to diseases, to one that embraces novel approaches, such as tailored target therapies, to achieve the best possible outcomes. Driven by these, several national and international genome projects have been initiated to reap the benefits of personalized medicine. Exome and targeted sequencing provide a balance between cost and benefit, in contrast to whole genome sequencing (WGS). Whole exome sequencing (WES) targets approximately 3% of the whole genome, which is the basis for protein-coding genes. Nonetheless, it has the characteristics of big data in large deployment. Herein, the application of WES and its relevance in advancing personalized medicine is reviewed. WES is mapped to Big Data "10 Vs" and the resulting challenges discussed. Application of existing biological databases and bioinformatics tools to address the bottleneck in data processing and analysis are presented, including the need for new generation big data analytics for the multi-omics challenges of personalized medicine. This includes the incorporation of artificial intelligence (AI) in the clinical utility landscape of genomic information, and future consideration to create a new frontier toward advancing the field of personalized medicine.
The application of nanoparticles (NPs) has attracted considerable attention as targeted delivery systems. CaCO3 has become the focus due to its advantages including affordability, low toxicity, biocompatibility, cytocompatibility, pH sensitivity and sedate biodegradability and environment friendly materials. In this article, we will discuss the po- tential roles of CaCO3-NPs in three major therapeutic applications; as antimicrobial, for drug delivery, and as gene delivery nanocarrier.
Perovskite solar cells (PSCs) have appeared as a promising design for next-generation thin-film photovoltaics because of their cost-efficient fabrication processes and excellent optoelectronic properties. However, PSCs containing a metal oxide compact layer (CL) suffer from poor long-term stability and performance. The quality of the underlying substrate strongly influences the growth of the perovskite layer. In turn, the perovskite film quality directly affects the efficiency and stability of the resultant PSCs. Thus, substrate modification with metal oxide CLs to produce highly efficient and stable PSCs has drawn attention. In this review, metal oxide-based electron transport layers (ETLs) used in PSCs and their systemic modification are reviewed. The roles of ETLs in the design and fabrication of efficient and stable PSCs are also discussed. This review will guide the further development of perovskite films with larger grains, higher crystallinity, and more homogeneous morphology, which correlate to higher stable PSC performance. The challenges and future research directions for PSCs containing compact ETLs are also described with the goal of improving their sustainability to reach new heights of clean energy production.
The deaf-mutes population always feels helpless when they are not understood by others and vice versa. This is a big humanitarian problem and needs localised solution. To solve this problem, this study implements a convolutional neural network (CNN), convolutional-based attention module (CBAM) to recognise Malaysian Sign Language (MSL) from images. Two different experiments were conducted for MSL signs, using CBAM-2DResNet (2-Dimensional Residual Network) implementing "Within Blocks" and "Before Classifier" methods. Various metrics such as the accuracy, loss, precision, recall, F1-score, confusion matrix, and training time are recorded to evaluate the models' efficiency. The experimental results showed that CBAM-ResNet models achieved a good performance in MSL signs recognition tasks, with accuracy rates of over 90% through a little of variations. The CBAM-ResNet "Before Classifier" models are more efficient than "Within Blocks" CBAM-ResNet models. Thus, the best trained model of CBAM-2DResNet is chosen to develop a real-time sign recognition system for translating from sign language to text and from text to sign language in an easy way of communication between deaf-mutes and other people. All experiment results indicated that the "Before Classifier" of CBAMResNet models is more efficient in recognising MSL and it is worth for future research.
Exploring and monitoring the underwater world using underwater sensors is drawing a lot of attention these days. In this field cooperation between acoustic sensor nodes has been a critical problem due to the challenging features such as acoustic channel failure (sound signal), long propagation delay of acoustic signal, limited bandwidth and loss of connectivity. There are several proposed methods to improve cooperation between the nodes by incorporating information/game theory in the node's cooperation. However, there is a need to classify the existing works and demonstrate their performance in addressing the cooperation issue. In this paper, we have conducted a review to investigate various factors affecting cooperation in underwater acoustic sensor networks. We study various cooperation techniques used for underwater acoustic sensor networks from different perspectives, with a concentration on communication reliability, energy consumption, and security and present a taxonomy for underwater cooperation. Moreover, we further review how the game theory can be applied to make the nodes cooperate with each other. We further analyze different cooperative game methods, where their performance on different metrics is compared. Finally, open issues and future research direction in underwater acoustic sensor networks are highlighted.
Cancer mortality and morbidity is projected to increase significantly over the next few decades. Current chemotherapeutic strategies have significant limitations, and there is great interest in seeking novel therapies which are capable of specifically targeting cancer cells. Given that fundamental differences exist between the cellular membranes of healthy cells and tumor cells, novel therapies based on targeting membrane lipids in cancer cells is a promising approach that deserves attention in the field of anticancer drug development. Phosphatidylethanolamine (PE), a lipid membrane component which exists only in the inner leaflet of cell membrane under normal circumstances, has increased surface representation on the outer membrane of tumor cells with disrupted membrane asymmetry. PE thus represents a potential chemotherapeutic target as the higher exposure of PE on the membrane surface of cancer cells. This feature as well as a high degree of expression of PE on endothelial cells in tumor vasculature, makes PE an attractive molecular target for future cancer interventions. There have already been several small molecules and membrane-active peptides identified which bind specifically to the PE molecules on the cancer cell membrane, subsequently inducing membrane disruption leading to cell lysis. This approach opens up a new front in the battle against cancer, and is of particular interest as it may be a strategy that may be prove effective against tumors that respond poorly to current chemotherapeutic agents. We aim to highlight the evidence suggesting that PE is a strong candidate to be explored as a potential molecular target for membrane targeted novel anticancer therapy.
Bacterial foodborne pathogens are a significant health burden and the recent emergence of pathogenic resistant strains due to the excessive use of antibiotics makes it more difficult to effectively treat infections as a result of contaminated food. Awareness of this impending health crisis has spurred the search for alternative antimicrobials with natural plant antimicrobials being among the more promising candidates as these substances have good acceptability and likely low toxicity levels as they have long been used in traditional medicines. Resveratrol (3,5,4'-trihydroxystilbene) is a naturally occurring stilbenoid which has been gaining considerable attention in medical field due to its diverse biological activities - it has been reported to exhibit antioxidant, cardioprotective, anti-diabetic, anticancer, and antiaging properties. Given that resveratrol is phytoalexin, with increased synthesis in response to infection by phytopathogens, there has been interest in exploring its antimicrobial activity. This review aims to provide an overview of the published data on the antibacterial activity of resveratrol against foodborne pathogens, its mechanisms of action as well as its possible applications in food packing and processing; in addition we also summarize the current data on its potential synergism with known antibacterials and future research and applications.