Human umbilical vein endothelial cells (HUVECs) were cultured on microcarrier beads to accommodate different experiment apparatus such as rotating wall vessel. In this study, fluid operating apparatus (FPA) was used. However, the effect of inflammation and endothelial activation biomarkers in HUVECs cultured on different culture surface and containers are not well established. The effects of temperature changes on these biomarkers in HUVECs grown in FPA, a spaceflight hardware, are still unclear. The objective of this study was to compare the protein and gene expression of inflammation and endothelial activation biomarkers in (i) HUVECs cultured on microcarrier beads in conventional culture flask (CCFMC) vs. conventional culture flask (CCF) (ii) HUVECs cultured on microcarrier in FPA (FPAMC) vs. CCFMC and (iii) HUVEC cultured in FPAMC with ideal temperature (37°C) (FPAMC) vs. simulated space travel temperature(25-37°C), (FPAMC-ST). sICAM-1 and sVCAM-1protein expression in HUVECs grown in CCFMC were higher than CCF. FPAMC had higher IL-6, TNF-α, ICAM-1, VCAM-1, e-selectin, NFκB and eNOS gene expression than in CCFMC. FPAMC-ST had higher ICAM-1 and e-selectin protein expression than FPAMC- in ideal temperature. HUVECs are cultured onto microcarrier in simulated space flight temperature compared with ideal temperature had higher protein expression of sICAM-1 and e-selectin but the protein and gene expression of other biomarkers of inflammation and endothelial activation are comparable. This suggests that differences in culture surface and container are have an impact on the expression of inflammation and adhesion molecule by HUVECs.
Familial hypercholesterolaemia (FH) is an autosomal dominant genetic disorder characterised by severe hypercholesterolaemia leading to premature coronary artery disease (CAD). Oxidised low-density lipoprotein (ox-LDL), F2-isoprostanes (ISP) and malondialdehyde (MDA) are established oxidative stress biomarker, but the status of oxidative stress in FH is not well studied. The aim of this study is to investigate oxidative stress status among FH patients and normocholesterolaemic control (NC) subjects. Ninety-eight FH patients and 100 (age, gender and BMI matched) NC subjects were recruited in series of health screening programmes across the country. Fasting blood samples were analysed for serum ox-LDL, ISP and MDA. Ox-LDL, ISP and MDA concentrations were higher in FH groups compared to NC (mean±SEM: 63.0±6.5 vs 25.5±1.2 (U/l), p
Safe level of daylighting for artefact conservation in historic buildings is a difficult task to achieve. Previous studies indicated that lighting problems in historic museum galleries were mainly due to unshaded walls that allowed direct sun penetration over the display areas. Ceiling geometry can also affect the daylighting performance significantly, particularly on the interior distribution of light. Malaysia, with hot and humid climate, and tropical sky conditions receives plenty of natural light all year around. The fluxes in natural lighting exposures confirm the need for strategic daylight control programme in the exhibition gallery. The study aims to assess the ceiling geometry contribution for four orientations; North, East, South and West through computer simulations. The research approach was based on comparisons between pitched and flat ceiling simulation output data. Further comparisons were performed with the recommended lighting limits for conservation of artefacts. The comparisons allowed better understanding of light damage issues and highlight the control of daylighting distributions through realistic predictive images and ceiling geometry designs. The results showed that the types of exhibits materials and its placement are affected by the ceiling geometry and constant changes in natural lighting exposure. The study confirms that ceiling geometry can act as a control mechanism with the environment physical features as part of preventive conservation criteria in the exhibition gallery. Thus, a systematic light-monitoring programme in the exhibition gallery is necessary to control illuminance level and cumulative exposure limits, for artefact preservation.
This paper presents a study on the effect of Arenga Pinnata fibre volume fraction on the tensile and compressive properties of Arenga Pinnata fibre reinforced epoxy composite (APREC). The composites were produced using four different Arenga Pinnata fibre volume contents, which were 10vol%, 15vol%, 20vol%, and 25vol%, in unidirectional (UD) fibre alignment. Tensile and compression tests were performed on all APREC specimens in order to investigate the effect of fibre volume fraction on modulus of elasticity, strength and strain to failure. The morphological structure of fractured specimens was observed using scanning electron microscopy (SEM) in order to evaluate the fracture mechanisms involved when the specimens were subjected to tensile or compressive loading. The results indicated that the higher the amount of Arenga Pinnata fibres, the higher the stiffness of the composites. This is shown by the increment of tensile and compressive modulus of the specimens when the fibre volume content was increased. Tensile modulus increased up to 180% when 25vol% Arenga Pinnata fibre was used in APREC compared to Pure Epoxy specimen. It can also be observed that the tensile strength of the specimens increased 28% from 53.820 MPa (for Pure Epoxy) to 68.692 MPa (for Epoxy with 25vol% APREC addition). Meanwhile, compressive modulus and strength increased up to 3.24% and 9.17%, respectively. These results suggest that the addition of Arenga Pinnata fibres significantly improved the tensile and compressive properties of APREC.
The effects of hole size on open hole tensile properties of Kevlar-glass fibre hybrid composite laminates were thoroughly investigated in this work. Woven Kevlar/glass fibre epoxy composite laminates were fabricated using hand lay-up and vacuum bagging technique. Specimens of five different hole size (1 mm, 4 mm, 6 mm, 8 mm and 12 mm) were carefully prepared before the tensile test was performed according to ASTM D5766. Results indicated that hybridizing Kevlar to glass fibres improved tensile strength and failure strain of hybrid composite specimen. In addition, increasing the hole size reduced strength retention of the hybrid specimen from 96% for 1 mm hole size to 62% and 44% for 6 mm and 12 mm, respectively. Fractography analysis showed that several types of failure mechanisms were observed such as brittle failure, ductile failure, fibre breakage, delamination and fibre-matrix splitting. It is concluded that as hole size increased, failure behaviour changed from a matrix dominated failure mode to a fibre-dominated failure mode.
Sufficient knowledge on food safety and diligence during food handling are crucial to food safety and hygiene practices. A casual approach to handling food in the kitchen on a regular basis may link to foodborne pathogens, contaminations, and adverse health effects. The purpose of this study is to identify the right practices and behaviour among culinary students in terms of food hygiene practices and food safety perspectives. The methodology employed includes observations on 18 food culinary students in an actual kitchen setting. Effective food hygiene and food safety implementation are needed to improve the effectiveness of health education programmes for food handlers. The results suggest that transmission reduction of food pathogens, knowledge transfer and food safety training in selective industry criteria with proper guidelines should be introduced to produce a competent workforce.
This study focuses on the effect of boronizing medium on the boride layer thickness of pack boronized 304 stainless steel after surface modification. Pack boronizing treatment was conducted in temperature of 900oC for a duration of eight hours. The treatment was performed using two different boronizing mediums which are powder and paste inside a tight box in an induction furnace. The characteristics of the samples were then observed using optical microscopy and XRD analyser. The thickness of boride layer was then measured using MPS digital image analysis software. The results showed that boronizing medium significantly affected the thickness of boride layer as paste boronized samples exhibited thicker boride layer thickness. The enhancement was mainly due to the size of boron particle in the paste medium which was smaller than powder medium that enabled better diffusion. It is expected that the enhancement of the boride layer thickness would result in further improvement of the mechanical and wear properties of this material.
Weft density and draw in plan play an important role since they affect physical properties such as fabric weight, cloth cover factor as well as seam strength. Weft density refers to the amount of weft yarn in one inch. Meanwhile, draw in plan refers to the amount of heald shaft used and the order of warp yarn through the heald. In this study, plain woven fabrics were produced by using Sulzer Rapier Loom Machine. There were two different types of weft density used which were 15 and 20 weft per centimeter (wpcm) and four draws in plan: pointed, straight, broken and broken mirror. Seams were constructed by using plain seam of Ssa-1, four stitches of stitch density and 301 lockstitches for stitch type. Subsequently, the fabric samples were tested on seam strength by using Testometric tester. As a result of this study, it is proven that weft density and draw in plan of woven plain fabric are parameters that affect the seam strength and seam efficiency. The highest increase in percentage of seam strength was obtained from straight draw in plan which increases up to 17.19% from 15wpcm to 20wpcm. Meanwhile, broken draw in plan has the lowest increase percentage for seam strength which is 6.46%. Furthermore, seam efficiency also shows straight draw in plan gives good fabric durability compared to others. Lastly, it also shows broken draw in plan has no significant effect on fabric tensile strength and seam strength.
A symmetrical azomethine ligand L was synthesised from a reaction of m-phenylenediamine and o-vanillin in 1:2 molar ratio. Dinuclear complexes of Zn2L2 and Co2L2 have been successfully isolated and characterised through 1H NMR, IR and magnetic moment. The x-ray crystal structure of Zn2L2 showed that the two Zn(II) nuclei were coordinated to two L moieties through the phenolic oxygen and imine nitrogen atoms, forming a slightly distorted tetrahedral geometry around the Zn(II) centres. When coordinated to metal centres, the signature n(C=N) of Lat 1616 cm-1 experienced a shift towards lower wave numbers of 1573-1613 cm-1. The Zn(II) complex was diamagnetic whereas the Co2L2 complex was paramagnetic with 3 unpaired electrons having µeff = 4.07 B.M. An antibacterial screening against methicillin resistant Staphylococcus aureus (MRSA) revealed that the activity of the complexes was more pronounced than that of the uncoordinated L. The complex Zn2L2 revealed the lowest MIC value of 0.56 µg/µl MIC, indicating that it was a better bactericides than Co2L2 and L, in that order.
Landfill leachate imposes a huge problem to the environment and human beings. This work focused on bioconversion of leachate to acetic and butyric acids by Clostridium butyricum NCIMB 7423. A continuous stirred tank reactor (CSTR) was applied and connected to fabricate membrane module. The leachate was collected from Pulau Burung Landfill Site (PBLS), Nibong Tebal, Penang. Prior to fermentation, leachate was treated to remove volatile fatty acid and adjusted to meet the minimum requirement of nutrients for anaerobic fermentation. Synthetic medium fermentation acts as a benchmark to the leachate fermentation. The outcomes indicated that the yield of acetic acid and butyric acid in synthetic medium fermentation was 0.70 g/L and 0.71 g/L, respectively. Meanwhile, leachate fermentation showed that the yield of acetic and butyric acid was 0.93 g/L and 1.86 g/L, respectively. High production of acetic and butyric acid showed that leachate fermentation is a green alternative to produce a cleaner product.
Police patrol routing problem (PPRP) attracts researchers’ attention especially on artifitial inteligence. The challenge here is that a limited number of patrols cover a wide range of area that includes several hotspots. In this study, a new model for PPRP is proposed simulating the Solomon’s benchmark for vehicle routing problem with time windows. This model can solve this problem by maximising the coverage of hotspots with frequencies of high priority locations while ensuring the feasibility of routes. Two constructive greedy heuristics are developed to generate the initial solution of the PPRP: highest priority greedy heuristic (HPGH) and nearest neighbour greedy heuristic (NNGH). Experimental results show that the simulated Solomon’s benchmark is suitable to represent PPRP. In addition, results illustrate that NNGH is more efficient to construct feasible solution than HPGH.
Simulating Lotka-Volterra model using a numerical method requires the researcher to apply tiny mesh sizes to come up with an accurate solution. This approach will increase the complexity and burden of computer memory and consume long computational time. To overcome these issues, a new solver is used that could simulate Lotka-Volterra model using bigger mesh size. In this paper, prey and predator behaviour is simulated via Lotka-Volterra model. We approximate the nonlinear terms in the model via weighted average approach and differential equation via nonstandard denominators. We provide three new schemes for one step method and simulate four sets of parameters to examine the performance of these new schemes. Results show that these new schemes simulate better for large mesh sizes.
Feature descriptor for image retrieval has emerged as an important part of computer vision and image analysis application. In the last decades, researchers have used algorithms to generate effective, efficient and steady methods in image processing, particularly shape representation, matching and leaf retrieval. Existing leaf retrieval methods are insufficient to achieve an adequate retrieval rate due to the inherent difficulties related to available shape descriptors of different leaf images. Shape analysis and comparison for plant leaf retrieval are investigated in this study. Different image features may result in different significance interpretation of images, even though they come from almost similarly shaped of images. A new image transform, known as harmonic mean projection transform (HMPT), is proposed in this study as a feature descriptor method to extract leaf features. By using harmonic mean function, the signal carries information of greater importance is considered in signal acquisition. The selected image is extracted from the whole region where all the pixels are considered to get a set of features. Results indicate better classification rates when compared with other classification methods.
Simulating Lotka-Volterra model using numerical method require researchers to apply tiny mesh sizes to obtain an accurate result. This approach nevertheless increases the complexity and burden of computer memory and consume long computational time. To overcome these issues, we investigate and construct new two-step solver that could simulate Lotka-Volterra model using bigger mesh size. This paper proposes three new two-step schemes to simulate Lotka-Volterra model. A non-standard approximation scheme with trimean approach was adopted. The nonlinear terms in the model is approximated via trimean approach and differential equation via non-standard denominators. Four sets of parameters were examined to analyse the performance of these new schemes. Results show that these new schemes provide better simulation for large mesh size.
Sentiment analysis classification has been typically performed by combining features that represent the dataset at hand. Existing works have employed various features individually such as the syntactical, lexical and machine learning, and some have hybridized to reach optimistic results. Since the debate on the best combination is still unresolved this paper addresses the empirical investigation of the combination of features for product review classification. Results indicate the Support Vector Machine classification model combined with any of the observed lexicon namely MPQA, BingLiu and General Inquirer and either the unigram or inte-gration of unigram and bigram features is the top performer.
There has been an increase of content related to Quran and Hadith on the internet over the past few years. Diacritical Digital Quran is very sensitive to tampering. Diacritics are the symbols used beneath/above Quranic verses for reading purposes of the Quran. Minor change in diacritics can alter the meaning of a particular Quranic verse. Hence, there is a need for an authentication system to differentiate between fake and original verses. In this work, a model is proposed related to automatic authentication of Digital Quran. Authentication model is divided into two phases: tokenisation and authentication. For tokenisation, regular expressions are used to split input Quranic verse into single characters. In case of authentication, existing and standard exact matching algorithm i.e. Quick search (QS) is used. On testing the proposed model by comparing popular search engines and other related existing works, our approach is 100 % accurate in terms of full verse detection.
Over the last few years, the Android smartphone had faced attacks from malware and malware variants, as there is no effective commercial Android security framework in the market. Thus, using machine learning algorithms to detect Android malware applications that can fit with the smartphone resources limitations became popular. This paper used state of the art Deep Belief Network in Android malware detection. The Lasso is one of the best interpretable ℓ1-regularisation techniques which proved to be an efficient feature selection embedded in learning algorithm. The selected features subset of Restricted Boltzmann Machines tuned by Harmony Search feature reduction with Deep Belief Network classifier was used, achieving 85.22% Android malware detection accuracy.
One of the major problems in today’s economy is the phenomenon of tax evasion. The linear regression method is a solution to find a formula to investigate the effect of each variable in the final tax evasion rate. Since the tax evasion data in this study has a great degree of uncertainty and the relationship between variables is nonlinear, Bayesian method is used to address the uncertainty along with 6 nonlinear basis functions to tackle the nonlinearity problem. Furthermore, variational method is applied on Bayesian linear regression in tax evasion data to approximate the model evidence in Bayesian method. The dataset is collected from tax evasion in Malaysia in period from 1963 to 2013 with 8 input variables. Results from variational method are compared with Maximum Likelihood Estimation technique on Bayeisan linear regression and variational method provides more accurate prediction. This study suggests that, in order to reduce the tax evasion, Malaysian government should decrease direct tax and taxpayer income and increase indirect tax and government regulation variables by 5% in the small amount of changes (10%-30%) and reduce direct tax and income on taxpayer and increment indirect tax and government regulation variables by 90% in the large amount of changes (70%-90%) with respect to the current situation to reduce the final tax evasion rate.
Face detection and analysis is an important area in computer vision. Furthermore, face detection has been an active research field in the recent years following the advancement in digital image processing. The visualisation of visual entities or sub-pattern composition may become complex to visualise due to the high frequency of noise and light effect during examination. This study focuses on evaluating the ability of Haar classifier in detecting faces from three paired Min-Max values used on histogram stretching. Min-Max histogram stretching was the selected method for implementation given that it appears to be the appropriate technique from the observation carried out. Experimental results show that, 60-240 MinMax values, Haar classifier can accurately detect faces compared to the two values.
Resident’s vehicles in some institutions have to be registered to maintain traffic safety. Penalties should be imposed if residents break traffic rules. Most of the time, the vehicle owner’s information is difficult to access making the penalty registration process complicated. An effective penalty registration process is required to make the process easier for security officers to give notice to the residents who have committed traffic offenses. A mobile application is proposed to recognize vehicle owner information. The proposed application uses optical-character-recognition (OCR) technologies that can facilitate the process of recognizing vehicle’s registration number in order to obtain owner information and use the information to enrol the penalty. The proposed application recognizes the vehicle registration number or sticker serial number to access the owner information. For evaluation of the proposed application, a user study was conducted by asking the users to use the application and answer the questiuonnaire. The findings revealed that average score of 77 of the respondents agree in terms of satisfaction and adoption of the application to be utilized in some institutions. The proposed application reduces the paper work of security officers and makes them more efficient.