Botnet phenomenon in smartphones is evolving with the proliferation in mobile phone technologies after leaving imperative impact on personal computers. It refers to the network of computers, laptops, mobile devices or tablets which is remotely controlled by the cybercriminals to initiate various distributed coordinated attacks including spam emails, ad-click fraud, Bitcoin mining, Distributed Denial of Service (DDoS), disseminating other malwares and much more. Likewise traditional PC based botnet, Mobile botnets have the same operational impact except the target audience is particular to smartphone users. Therefore, it is import to uncover this security issue prior to its widespread adaptation. We propose SMARTbot, a novel dynamic analysis framework augmented with machine learning techniques to automatically detect botnet binaries from malicious corpus. SMARTbot is a component based off-device behavioral analysis framework which can generate mobile botnet learning model by inducing Artificial Neural Networks' back-propagation method. Moreover, this framework can detect mobile botnet binaries with remarkable accuracy even in case of obfuscated program code. The results conclude that, a classifier model based on simple logistic regression outperform other machine learning classifier for botnet apps' detection, i.e 99.49% accuracy is achieved. Further, from manual inspection of botnet dataset we have extracted interesting trends in those applications. As an outcome of this research, a mobile botnet dataset is devised which will become the benchmark for future studies.
The unique characteristic of fast water permeation in laminated graphene oxide (GO) sheets has facilitated the development of ultrathin and ultrafast nanofiltration membranes. Here we report the application of fast water permeation property of immersed GO deposition for enhancing the performance of a GO/water nanofluid charged two-phase closed thermosyphon (TPCT). By benchmarking its performance against a silver oxide/water nanofluid charged TPCT, the enhancement of evaporation strength is found to be essentially attributed to the fast water permeation property of GO deposition instead of the enhanced surface wettability of the deposited layer. The expansion of interlayer distance between the graphitic planes of GO deposited layer enables intercalation of bilayer water for fast water permeation. The capillary force attributed to the frictionless interaction between the atomically smooth, hydrophobic carbon structures and the well-ordered hydrogen bonds of water molecules is sufficiently strong to overcome the gravitational force. As a result, a thin water film is formed on the GO deposited layers, inducing filmwise evaporation which is more effective than its interfacial counterpart, appreciably enhanced the overall performance of TPCT. This study paves the way for a promising start of employing the fast water permeation property of GO in thermal applications.
The Healthy Food Environment Policy Index (Food-EPI) aims to assess the extent of implementation of recommended food environment policies by governments compared with international best practices and prioritize actions to fill implementation gaps. The Food-EPI was applied in 11 countries across six regions (2015-2018). National public health nutrition panels (n = 11-101 experts) rated the extent of implementation of 47 policy and infrastructure support good practice indicators by their government(s) against best practices, using an evidence document verified by government officials. Experts identified and prioritized actions to address implementation gaps. The proportion of indicators at "very low if any," "low," "medium," and "high" implementation, overall Food-EPI scores, and priority action areas were compared across countries. Inter-rater reliability was good (GwetAC2 = 0.6-0.8). Chile had the highest proportion of policies (13%) rated at "high" implementation, while Guatemala had the highest proportion of policies (83%) rated at "very low if any" implementation. The overall Food-EPI score was "medium" for Australia, England, Chile, and Singapore, while "very low if any" for Guatemala. Policy areas most frequently prioritized included taxes on unhealthy foods, restricting unhealthy food promotion and front-of-pack labelling. The Food-EPI was found to be a robust tool and process to benchmark governments' progress to create healthy food environments.
Electricity is one of the most important resources and fundamental infrastructure for every nation. Its milestone shows a significant contribution to world development that brought forth new technological breakthroughs throughout the centuries. Electricity demand constantly fluctuates, which affects the supply. Suppliers need to generate more electrical energy when demand is high, and less when demand is low. It is a common practice in power markets to have a reserve margin for unexpected fluctuation of demand. This research paper investigates regression techniques: multiple linear regression (MLR) and vector autoregression (VAR) to forecast demand with predictors of economic growth, population growth, and climate change as well as the demand itself. Auto-Regressive Integrated Moving Average (Auto-ARIMA) was used in benchmarking the forecasting. The results from MLR and VAR (lag-values=20) and Auto-ARIMA are monitored for five months from June to October of 2019. Using the root mean square error (RMSE) as an indicator for accuracy, Auto-ARIMA has the lowest RMSE for four months except in June 2019. VAR (lag-values=20) shows good forecasting capabilities for all five months, considering it uses the same lag values (20) for each month. Three different techniques have been successfully examined in order to find the best model for the prediction of the demand.
Student Information Management System (SIMS) is a computerized system for education that can be used to manage student information and data. PASTI An-Nur is chosen as a case study in developing the system. Thus, several problems are identified that PASTI An-Nur faces due to the
implementation of a manual system in the admission process. The first problem is the paper-based registration form that is prone to lose, misplaced and less secure. As for the payment process, arise a problem in term of higher error rate when checking and calculating the payments. The biggest downfall for PASTI An-Nur is the amount of space used to store all the students' files.
These problems bring inefficiency since the world is changing to computerized, where data management become one of the most significant issues nowadays. So, the aim of developing the Preschool Management System (PRESIMS) is for helping the staffs and teachers in managing the
students' information. The Adapter Waterfall model was used in developing this system. Additionally, usability heuristics was used also as a theory to guide the development of this system. The system has been tested with the four (4) users and two (2) experts. The testing method is the ISO/IEC 9126- 4 approach to measure usability metrics, including efficiency, effectiveness, and satisfaction. Whereas, for the experts, heuristic evaluation is used to bring six (6) usability principles into implementation for testing. The result of the testing is very satisfying, which shows 75.5% of efficiency, 83.33% of effectiveness and three (3) out of four (4) users very satisfied with the system. The result of heuristic evaluation also shows a successful implementation of the system. The details of the result are discussed in this paper and expected to meet the users' specification and it is ready to go live.
This study investigated the relationships between internal and external training load metrics across a 2-week 'in-season' microcycle in squash. 134 on-court and 32 off-court 'conditioning' sessions were completed by fifteen elite squash players with an average (±SD) of 11 ± 3 per player. During every session, external load was captured using a tri-axial accelerometer to calculate Playerload; i.e., the instantaneous rate of change of acceleration across 3-dimensional planes. Internal load was measured using heart rate (HR), global (sRPE) and differential RPE (dRPE-Legs, dRPE-Breathing). Additionally, HR was used to calculate Banister's, Edward's and TEAM TRIMPs. Across 166 training sessions, Playerload was moderately correlated with TRIMP-Banister (r = 0.43 [95% CI: 0.29-0.55], p < 0.001) and TRIMP-Edwards (r = 0.50 [0.37-0.61], p < 0.001). Association of Playerload with TRIMP-TEAM (r = 0.24 [0.09-0.38], p = 0.001) was small. There was a moderate correlation between sRPE and Playerload (r = 0.46 [0.33-0.57], p < 0.001). Association of sRPE was large with TRIMP-Banister (r = 0.68 [0.59-0.76], p = 0.001), very large with TRIMP-Edwards (r = 0.79 [0.72-0.84], p < 0.001) and moderate with TRIMP-TEAM (r = 0.44 [0.31-0.56], p < 0.001). Both dRPE-Legs (r = 0.95 [0.93-0.96], p < 0.001) and dRPE-Breathing (r = 0.92 [0.89-0.94], p < 0.001) demonstrated nearly perfect correlations with sRPE and with each other (r = 0.91 [0.88-0.93], p < 0.001). Collection of both internal and external training load data is recommended to fully appreciate the physical demands of squash training. During a training microcycle containing a variety of training sessions, interpreting internal or external metrics in isolation may underestimate or overestimate the training stress a player is experiencing.
communication, full compliance and good behavior) as among the crucial elements required to be possessed by the principal employers in three sectors, namely manufacturing, public services and construction. In relation to this, this paper will describe the development and validation of instruments prior to the measurement of principal employers’ roles and responsibilities in the implementation of OSH. Three assessment tools were developed, namely the Benchmarking Interview, Questionnaire and Workplace Inspection. Fifteen companies were selected for the benchmarking interview, 50 employers conveniently selected for the survey interview (covering three sectors) and 90 employers selected for the workplace inspection (30 respondents for each sector). The development of benchmarking interview and workplace inspection scores are briefly discussed while the main focus is on the validation of the survey constructs (or items). The reliability check on 53 items representing four elements (i.e., Commitment, Communication, Compliance, Behaviour) of employers’ roles and responsibilities in the implementation of OSH showed that the Cronbach’s Alpha coefficient is more than 0.90 which indicates that the internal consistency is extremely reliable. It also indicates that the set of items in each element are closely related and well understood by the respondents. Validity check on the items based on the Rasch measurement infit and outfit mean square statistics and standardized z-score found that nine items had misfitting values and finally corrected for further analysis. This study had shown that a valid and reliable instruments are important in ensuring that accurate and precise findings are obtained in measuring the roles and responsibilities of principal employer in the implementation of OSH.
Classification of human emotions based on electroencephalography (EEG) is a very popular topic nowadays in the provision of human health care and well-being. Fast and effective emotion recognition can play an important role in understanding a patient's emotions and in monitoring stress levels in real-time. Due to the noisy and non-linear nature of the EEG signal, it is still difficult to understand emotions and can generate large feature vectors. In this article, we have proposed an efficient spatial feature extraction and feature selection method with a short processing time. The raw EEG signal is first divided into a smaller set of eigenmode functions called (IMF) using the empirical model-based decomposition proposed in our work, known as intensive multivariate empirical mode decomposition (iMEMD). The Spatio-temporal analysis is performed with Complex Continuous Wavelet Transform (CCWT) to collect all the information in the time and frequency domains. The multiple model extraction method uses three deep neural networks (DNNs) to extract features and dissect them together to have a combined feature vector. To overcome the computational curse, we propose a method of differential entropy and mutual information, which further reduces feature size by selecting high-quality features and pooling the k-means results to produce less dimensional qualitative feature vectors. The system seems complex, but once the network is trained with this model, real-time application testing and validation with good classification performance is fast. The proposed method for selecting attributes for benchmarking is validated with two publicly available data sets, SEED, and DEAP. This method is less expensive to calculate than more modern sentiment recognition methods, provides real-time sentiment analysis, and offers good classification accuracy.
Hydroxylammonium nitrate (HAN) is a promising green propellant because of its low toxicity, high volumetric specific impulse, and reduced development cost. Electrolytic decomposition of HAN is an efficient approach to prepare it for further ignition and combustion. This paper describes the investigation of a co-electrolysis effect on electrolytic decomposition of HAN-fuel mixtures using stainless steel-platinum (SS-Pt) electrodes. For the first time, different materials were utilized as electrodes to alter the cathodic reaction, which eliminated the inhibition effect and achieved a repeatable and consistent electrolytic decomposition of HAN solution. Urea and methanol were added as fuel components in the HAN-fuel mixtures. When the mass ratio of added urea ≥20%, the electrolytic decomposition of a HAN-urea ternary mixture achieved 67% increment in maximum gas temperature (Tgmax) and 185% increment in overall temperature increasing rate over the benchmark case of HAN solution. The co-electrolysis of urea released additional electrons into the mixtures and enhanced the overall electrolytic decomposition of HAN. In contrast, the addition of methanol did not improve the Tgmax but only increased the overall temperature increasing rate. This work has important implications in the development of an efficient and reliable electrolytic decomposition system of HAN and its mixtures for propulsion applications.
Background: As hepatitis C elimination efforts are launched, national strategies for screening and treatment scale-up in countries, such as Malaysia, must be designed and implemented. Strategic information, including estimates of the total number of patients chronically-infected with hepatitis C virus (HCV) and the size of key populations, such as people who inject drugs (PWID), is critical to informing these efforts. For Malaysia, the estimate of the PWID population size most frequently reported in global systematic reviews is for the year 2009. Objectives: To support ongoing national HCV planning efforts, we aimed to estimate the national population size of active PWID in Malaysia, for the years 2014 and 2017. Methods: To estimate the PWID population size, we applied standard benchmark-multiplier methodology, frequently used for PWID population size estimation, and extended it by adjusting for cessation of injecting drug use within the benchmark and calculating statistical uncertainty intervals. Results: The estimated active PWID population size was 153,000 (95% uncertainty interval (UI): 136,000-172,000) for 2014 and 156,000 (95% UI: 137,000-188,000) for 2017. Conclusions/importance: This updated estimate of the active PWID population size in Malaysia will help inform effective planning for the scale-up of HCV screening and treatment services. The proposed methodology is applicable to other countries that maintain national HIV registries and have conducted Integrated Biological and Behavioral Surveys among active PWID.
This study aimed to develop and validate an inventory that measures the whole range of basic and integrated science process skills as stipulated in the Malaysian science curricula and that is suitable for Malaysian lower secondary school students. The validated inventory was subsequently utilised to determine the differential acquisition of science process skills by gender, location, and ethnicity. In the instrument development phase which involves item generation and field testing, a reliable 60-item Malaysian-Based Basic and Integrated Science Process Skills Inventory (MB-BISPSI) that has a KR-20 reliability of 0.88, difficulty indices of items that range between 0.25-0.75 and discrimination indices which are above 0.4 was produced. In the main causal-comparative study using a sample of 1021 Form 2 students (548 girls and 473 boys) from seven (four rural and three interior) out of the eight secondary schools in Kapit Division, Sarawak, the findings indicate that the students achieved a mastery level which fell short of the two-third benchmark (e.g., 66.7%) for the overall science process skills, basic and integrated science process skills, and also for each of the 12 science process skills. Additionally, it was found that, while female students generally achieved a markedly higher mean percentage score in the overall Science Process Skills than did the male students, such phenomenon was only observed amongst the Kenyah ethnicity. There were no significant differences in science process skills acquisition between rural and interior students. Implications for a more thoughtful inculcation of science process skills are proffered alongside recommendations for future research using a more nationally representative sample to examine the validity of such generalisation.
Background: The occupationally acquired accident and injuries in Malaysian medical laboratories are still largely unexplored prior to this survey. Some of these questions are attempted in this survey and act as source of reference for the number and accident injuries in medical laboratories in the area of Klang Valley and also in Malaysia.
Methods : This survey was carried out based on recordable cases throughout the calendar year of 2001 to 2005 from 3 main medical laboratories of Hospital Kuala Lumpur (HKL), Hospital Universiti Kebangsaan Malaysia (HUKM) and Pusat Perubatan Universiti Malaya (PPUM).
Results : The average annual incident rate for this three medical laboratories is 2.05/100 full time equivalent (FTE) employees. The annual incident rate in individual medical laboratory is 2.04/100 FTE (HKL), 2.07/100 FTE (HUKM) and 2.04/100 FTE (PPUM) employees, respectively. The most common injury that is 25.3% of the total cases reported was due to cuts by sharp objects and the second most common injury was exposure to biohazard and chemical substances which constitutes 19.9% of the total cases. . Needle prick injury (16.8%), fire (8.4%), fall/slip (6.3%) and gases leak and locked in cold room were reported as one case each.
Conclusion : The average incident rate from this study is remarkably similar compared with the incident injury rate reported by BLS (2006) which is 2.1/100 FTE in the average size of medical laboratory and diagnostic. Besides this incident rate of injury and illness as a comparison, it also can be used as a benchmark to evaluate the safety performance among medical laboratories in Malaysia.
Evidence on rising global temperature, melting of ice caps, and withdrawal of glaciers
brings attentions to the enhancement of energy efficiency in energy intensive industries. Having a
realistic comparison between one plant and the best practice technology (BPT) in operation in the field
helps significantly to distinguish and diagnose the potentials where measures towards energy efficiency
improvement would be applicable. In this regard, for manufacturing industries, one of the most widely
used energy benchmarking tools is the Energy Benchmark Curve. An energy benchmark curve plots the
efficiency of plants as a function of the total production volume from all similar plants or as a function
of the total number of plants that operate at that level of efficiency or worse. This paper reviews the
methodology through which the benchmark curve is obtained for a specific industry followed by a
comparison of energy intensity for the iron and steel industry among China and the US. According to
the international energy benchmark curve for the iron and steel industry, the savings potentials per ton
of crude steel for the US. and China have been respectively 4.1 and 7.1 gigajoule comparing with the
BPT in the field. Finally, an overview over certain measures to enhance efficiency of such plants is
presented.
The classical procedures of comparing two groups, such as t-test are, usually restricted with the assumptions of normality and equal variances. When these assumptions are violated, the rates of the Type I errors of the independent samples t-test are affected, particularly when the sample sizes are small. In this situation, the bootstrap procedure has an advantage over the parametric t-test. In this study, the performances of the bootstrap procedure and the independent sample t-test were investigated. The investigation focused on the power of both the test procedures to compare the two groups under different design specifications for normal and chi-square distributions. The results showed that the bootstrap procedure has a slight edge over the conventional t-test in term of the rate of achieving the benchmark level for both the distributions. In fact, the bootstrap procedure consistently outperformed the conventional t-test across all the combinations of the test conditions.
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.
Occupational safety and health (OSH) in Small and Medium Enterprises (SMEs) have not received the proper
attention not only in Malaysia, but in most of the countries globally, in terms of research or support for implementation.
This research focuses on the implementation of the occupational safety and health in small and medium industries in
the southern region of peninsular of Malaysia. The objective of this research is to determine the level of awareness of
SME owners and also their employers towards the importance of implementing OSH in their daily tasks. This study
will be based on conducting a survey to 200 SMEs owners and workers throughout the southern region of Malaysia.
The results from this research can be use as a benchmark for other researchers to further enhance the research in this
area.
Linear time series models are not able to capture the behaviour of many financial time series, as in the cases of exchange rates and stock market data. Some phenomena, such as volatility and structural breaks in time series data, cannot be modelled implicitly using linear time series models. Therefore, nonlinear time series models are typically designed to accommodate for such nonlinear features. In the present study, a nonlinearity test and a structural change test are used to detect the nonlinearity and the break date in three ASEAN currencies, namely the Indonesian Rupiah (IDR), the Malaysian Ringgit (MYR) and the Thai Baht (THB). The study finds that the null hypothesis of linearity is rejected and evidence of structural breaks exist in the exchange rates series. Therefore, the decision to use the self-exciting threshold autoregressive (SETAR) model in the present study is justified. The results showed that the SETAR model, as a regime switching model, can explain abrupt changes in a time series. To evaluate the prediction performance of SETAR model, an Autoregressive Integrated Moving Average (ARIMA) model used as a benchmark. In order to increase the accuracy of prediction, both models are combined with an exponential generalised autoregressive conditional heteroscedasticity (EGARCH) model. The prediction results showed that the construct model of SETAR-EGARCH performs better than that of the ARIMA model and the combined ARIMA and EGARCH model. The results indicated that nonlinear models give better fitting than linear models.
Feedstock preparation, as well as its characterization, is crucial in the production of highly sintered parts with minimal defect. The hard metal powder - particularly, cemented carbide (wc-co) used in this study was investigated both physically and thermally to determine its properties before the mixing and injection molding stage. Several analyses were conducted, such as scanning electron microscopy, energy dispersive X-ray diffraction, pycnometer density, critical powder volume percentage (cPvP), as well as thermal tests, such as thermogravimetric analysis and differential scanning calorimetry. On the basis of the CPVP value, the feedstock, consisting of wc-co powder, was mixed with 60% palm stearin and 40% polyethylene at an optimal powder loading, within 2 to 5% lower than the CPVP value. The CPVP spotted value was 65%. The feedstock optimal value at 61% showed good rheological properties (pseudoplastic behavior) with an n value lower than 1, considerably low activation energy and high moldability index. These preliminary properties of the feedstock serve as a benchmark in designing the schedule for the next whole steps (i.e. injection, debinding and sintering processes).
Open-cut coal mining can seriously disturb and reshape natural landscapes which results in a range of impacts on local ecosystems and the services they provide. To address the negative impacts of disturbance, progressive rehabilitation is commonly advocated. However, there is little research focusing on how these impacts affect ecosystem services within mine sites and changes over time. The aim of this study was to assess the cumulative impacts of mining disturbance and rehabilitation on ecosystem services through mapping and quantifying changes at multiple spatial and temporal scales. Four ecosystem services including carbon sequestration, air quality regulation, soil conservation and water yield were assessed in 1989, 1997, 2005 and 2013. Disturbance and rehabilitation was mapped using LandTrendr algorithm with Landsat. We mapped spatial patterns and pixel values for each ecosystem service with corresponding model and the landscape changes were analyzed with landscape metrics. In addition, we assessed synergies and trade-offs using Spearman's correlation coefficient for different landscape classes and scales. The results showed that carbon sequestration, air quality regulation and water yield services were both positively and negatively affected by vegetation cover changes due to mined land disturbance and rehabilitation, while soil conservation service were mainly influenced by topographic changes. There were strong interactions between carbon sequestration, air quality regulation and water yield, which were steady among different spatial scales and landscape types. Soil conservation correlations were weak and changed substantially due to differences of spatial scales and landscape types. Although there are limitations associated with data accessibility, this study provides a new research method for mapping impacts of mining on ecosystem services, which offer spatially explicit information for decision-makers and environmental regulators to carry out feasible policies, balancing mining development with ecosystem services provision.
In this paper, a support vector regression (SVR) using radial basis function (RBF) kernel is proposed using an integrated
parallel linear-and-nonlinear model framework for empirical modeling of nonlinear chemical process systems. Utilizing
linear orthonormal basis filters (OBF) model to represent the linear structure, the developed empirical parallel model
is tested for its performance under open-loop conditions in a nonlinear continuous stirred-tank reactor simulation case
study as well as a highly nonlinear cascaded tank benchmark system. A comparative study between SVR and the parallel
OBF-SVR models is then investigated. The results showed that the proposed parallel OBF-SVR model retained the same
modelling efficiency as that of the SVR, whilst enhancing the generalization properties to out-of-sample data.