Public health activities under district health offices (DHOs) play a major role in Malaysia's fight against COVID-19. This article aims to describe and illustrate the public health activity pathway in combating the COVID-19 pandemic, and a team of public health workers who are familiar with DHO work settings was created in April 2020 for that purpose. Review of documents and the Ministry of Health's updates was carried out, followed by a series of discussions with stakeholders. Based on the steps in the outbreak investigation tasks, the flow of activities from January to May 2020 was listed in line with the phases of the country's National Movement Control Order 2020. Results show that the activities can be classified into three different sections-namely, the main action areas, category of cases, and level of care. The main process flow of activities comprised the case management and support activities. Case management flow was split into tasks for patients under investigation and persons under surveillance, while the support services existed throughout the phases. The pathways illustrate that the progression of the pandemic translated directly to changes in the pattern of activities, with additional subgroups of activities in accordance with all imposed guidelines.
Crumb rubber (CR) from scrap tires is used as a partial replacement of fine aggregates in cement paste. This promotes the sustainable development of the environment, economy, and society, as waste tires are non-biodegradable and flammable. They occupy large landfill areas and are breeding grounds for mosquitoes and rodents. Inclusion of CR in mortar leads to several improvements on the mixture properties such as ductility, toughness, and impact resistance. However, it exhibits lower strengths and Modulus of Elasticity (ME). Therefore, to promote the use of mortar containing CR, it is vital to improve its mechanical strength. Past studies proved that nano-silica (NS) improves the strength of concrete due to the physico-chemical effects of NS. This study aims to examine the mechanical properties of crumb rubber mortar containing nano-silica (NS-CRM) and to develop models to predict these properties using Response Surface Methodology (RSM). Two variables were considered, CR as partial replacement to sand by volume (0%, 7.5%, 15%), and NS as partial replacement to cement by weight (0%, 2.5%, 5%). The results demonstrated a significant improvement in the mechanical properties of CRM when incorporating NS, and the models developed using RSM were acceptable with a 2% to 3% variation.
In a number of circumstances, the Kachanov-Rabotnov isotropic creep damage constitutive model has been utilized to assess the creep deformation of high-temperature components. Secondary creep behavior is usually studied using analytical methods, whereas tertiary creep damage constants are determined by the combination of experiments and numerical optimization. To obtain the tertiary creep damage constants, these methods necessitate extensive computational effort and time to determine the tertiary creep damage constants. In this study, a curve-fitting technique was proposed for applying the Kachanov-Rabotnov model into the built-in Norton-Bailey model in Abaqus. It extrapolates the creep behaviour by fitting the Kachanov-Rabotnov model to the limited creep data obtained from the Omega-Norton-Bailey regression model and then simulates beyond the available data points. Through the Omega creep model, several creep strain rates for SS-316 were calculated using API-579/ASME FFS-1 standards. These are dependent on the type of the material, the flow stress, and the temperature. In the present work, FEA creep assessment was carried out on the SS-316 dog bone specimen, which was used as a material coupon to forecast time-dependent permanent plastic deformation as well as creep behavior at elevated temperatures and under uniform stress. The model was validated with the help of published experimental creep test data, and data optimization for sensitivity study was conducted by applying response surface methodology (RSM) and ANOVA techniques. The results showed that the specimen underwent secondary creep deformation for most of the analysis period. Hence, the method is useful in predicting the complete creep behavior of the material and in generating a creep curve.
The airframe structures of most fighter aircraft in the Royal Malaysian Airforce have been in service for 10 to 20 years. The effect of fatigue loading, operating conditions, and environmental degradation has led to the structural integrity of the airframe being assessed for its airworthiness. Various NDT methods were used to determine the current condition of the aircraft structure after operation of beyond 10 years, and their outcomes are summarized. In addition, although there are six critical locations, the wing root was chosen since it has the highest possibility of fatigue failure. It was further analyzed using simulation analysis for fatigue life. This contributes to the development of the maintenance task card and ultimately assists in extending the service life of the fighter aircraft. Using the concept of either safe life or damage tolerance as its fatigue design philosophy, the RMAF has adopted the Aircraft Structural Integrity Program (ASIP) to monitor the structural integrity of its fighter aircraft. With the current budget constraints and structural life extension requirements, the RMAF has embarked on the non-destructive testing method and engineering analysis. The research outcome will enhance the ASIP for other aircraft platforms in the RMAF fleet for its structure life assessment or service life extension program.
The demand for composite materials in high-voltage electrical insulation is escalating over the last decades. In the power system, the composite glass-fiber-reinforced polymer has been used as an alternative to wood and steel crossarm structures due to its superior properties. As a composite, the material is susceptible to multi-aging factors, one of which is the electrical stress caused by continuous and temporary overvoltage. In order to achieve a better insulation performance and higher life expectancy, the distribution of the stresses should firstly be studied and understood. This paper focuses on the simulation work to better understand the stress distribution of the polyurethane foam-filled glass-fiber-reinforced polymer crossarm due to the lightning transient injection. A finite-element-based simulation was carried out to investigate the behavior of the electric field and voltage distribution across the sample using an Ansys Maxwell 3D. Electrical stresses at both outer and inner surfaces of the crossarm during the peak of lightning were analyzed. Analyses on the electric field and potential distribution were performed at different parts of the crossarm and correlated to the physical characteristics and common discharge location observed during the experiment. The results of the electric field on the crossarm indicate that both the outer and internal parts of the crossarm were prone to high field stress.
This study aims to develop a controlled release oil palm empty fruit bunch hemicellulose (EFB-H) inhibitor tablet for mild steel in 1 M HCl. As plant extracts tend to deteriorate at longer immersion time, limiting its industrial applicability, we attempted to lengthen the inhibition time by forming a controlled release inhibitor tablet. Electrochemical methods (potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS)) were employed to investigate the efficiency and mechanism of the inhibition. An optimum dosage and immersion time was determined via Response Surface Methodology (RSM). EFB-H tablet was formulated using D-optimal mixture design, and its anticorrosion action at extended immersion time was compared with EFB-H powder. PDP measurement revealed that EFB-H is a mixed type inhibitor. RSM optimization unveiled that the optimum point for a maximum inhibition efficiency (87.11%) was at 0.33 g of EFB-H and 120 h of immersion time. Tablet T3 with EFB-H to gum Arabic to hydroxypropyl methylcellulose ratio of 66:0:34 portrayed the best tensile strength (0.243 MPa), disintegration time (152 min) and dissolution behavior. EFB-H tablet exhibited a longer-lasting inhibition effect than powder, which was 360 h as compared to 120 h for powder. Overall, EFB-H tablet has been successfully developed, and its enhanced effective inhibition time has been experimentally proven.
The application of multiphysics models and soft computing techniques is gaining enormous attention in the construction sector due to the development of various types of concrete. In this research, an improved form of supervised machine learning, i.e., multigene expression programming (MEP), has been used to propose models for the compressive strength (fc'), splitting tensile strength (fSTS), and flexural strength (fFS) of sustainable bagasse ash concrete (BAC). The training and testing of the proposed models have been accomplished by developing a reliable and comprehensive database from published literature. Concrete specimens with varying proportions of sugarcane bagasse ash (BA), as a partial replacement of cement, were prepared, and the developed models were validated by utilizing the results obtained from the tested BAC. Different statistical tests evaluated the accurateness of the models, and the results were cross-validated employing a k-fold algorithm. The modeling results achieve correlation coefficient (R) and Nash-Sutcliffe efficiency (NSE) above 0.8 each with relative root mean squared error (RRMSE) and objective function (OF) less than 10 and 0.2, respectively. The MEP model leads in providing reliable mathematical expression for the estimation of fc', fSTS and fFS of BA concrete, which can reduce the experimental workload in assessing the strength properties. The study's findings indicated that MEP-based modeling integrated with experimental testing of BA concrete and further cross-validation is effective in predicting the strength parameters of BA concrete.
Laser metal deposition (LMD) is one of the manufacturing processes in the industries, which is used to enhance the properties of components besides producing and repairing important engineering components. In this study, Stellite 6 was deposited on precipitation-hardened martensitic stainless steel (17-4 PH) by using the LMD process, which employed a pulsed Nd:YAG laser. To realize a favor deposited sample, the effects of three LMD parameters (focal length, scanning speed, and frequency) were investigated, as well as microstructure studies and the results of a microhardness test. Some cracks were observed in the deposited layers with a low scanning speed, which were eliminated by an augment of the scanning speed. Furthermore, some defects were found in the deposited layers with a high scanning speed and a low frequency, which can be related to the insufficient laser energy density and a low overlapping factor. Moreover, various morphologies were observed within the microstructure of the samples, which can be attributed to the differences in the stability criterion and cooling rate across the layer. In the long run, a defect-free sample (S-120-5.5-25) possessing suitable geometrical attributes (wetting angle of 57° and dilution of 25.1%) and a better microhardness property at the surface (≈335 Hv) has been introduced as a desirable LMDed sample.
SiNW (silicon nanowire) arrays consisting of 5- and 10-wires were fabricated by using an atomic force microscope-the local anodic oxidation (AFM-LAO) technique followed by wet chemical etching. Tetramethylammonium hydroxide (TMAH) and isopropyl alcohol (IPA) at various concentrations were used to etch SiNWs. The SiNWs produced were differed in dimension and surface roughness. The SiNWs were functionalized and used for the detection of deoxyribonucleic acid (DNA) dengue (DEN-1). SiNW-based biosensors show sensitive detection of dengue DNA due to certain factors. The physical properties of SiNWs, such as the number of wires, the dimensions of wires, and surface roughness, were found to influence the sensitivity of the biosensor device. The SiNW biosensor device with 10 wires, a larger surface-to-volume ratio, and a rough surface is the most sensitive device, with a 1.93 fM limit of detection (LOD).
At the 90-nm node, the rate of transistor miniaturization slows down due to challenges in overcoming the increased leakage current (Ioff). The invention of high-k/metal gate technology at the 45-nm technology node was an enormous step forward in extending Moore's Law. The need to satisfy performance requirements and to overcome the limitations of planar bulk transistor to scales below 22 nm led to the development of fully depleted silicon-on-insulator (FDSOI) and fin field-effect transistor (FinFET) technologies. The 28-nm wafer planar process is the most cost-effective, and scaling towards the sub-10 nm technology node involves the complex integration of new materials (Ge, III-V, graphene) and new device architectures. To date, planar transistors still command >50% of the transistor market and applications. This work aims to downscale a planar PMOS to a 14-nm gate length using La2O3 as the high-k dielectric material. The device was virtually fabricated and electrically characterized using SILVACO. Taguchi L9 and L27 were employed to study the process parameters' variability and interaction effects to optimize the process parameters to achieve the required output. The results obtained from simulation using the SILVACO tool show good agreement with the nominal values of PMOS threshold voltage (Vth) of -0.289 V ± 12.7% and Ioff of less than 10-7 A/µm, as projected by the International Technology Roadmap for Semiconductors (ITRS). Careful control of SiO2 formation at the Si interface and rapid annealing processing are required to achieve La2O3 thermal stability at the target equivalent oxide thickness (EOT). The effects of process variations on Vth, Ion and Ioff were investigated. The improved voltage scaling resulting from the lower Vth value is associated with the increased Ioff due to the improved drain-induced barrier lowering as the gate length decreases. The performance of the 14-nm planar bulk PMOS is comparable to the performance of the FDSOI and FinFET technologies at the same gate length. The comparisons made with ITRS, the International Roadmap for Devices and Systems (IRDS), and the simulated and experimental data show good agreement and thus prove the validity of the developed model for PMOSs. Based on the results demonstrated, planar PMOSs could be a feasible alternative to FDSOI and FinFET in balancing the trade-off between performance and cost in the 14-nm process.
Massive waste rock wool was generated globally and it caused substantial environmental issues such as landfill and leaching. However, reviews on the recyclability of waste rock wool are scarce. Therefore, this study presents an in-depth review of the characterization and potential usability of waste rock wool. Waste rock wool can be characterized based on its physical properties, chemical composition, and types of contaminants. The review showed that waste rock wool from the manufacturing process is more workable to be recycled for further application than the post-consumer due to its high purity. It also revealed that the pre-treatment method-comminution is vital for achieving mixture homogeneity and enhancing the properties of recycled products. The potential application of waste rock wool is reviewed with key results emphasized to demonstrate the practicality and commercial viability of each option. With a high content of chemically inert compounds such as silicon dioxide (SiO2), calcium oxide (CaO), and aluminum oxide (Al2O3) that improve fire resistance properties, waste rock wool is mainly repurposed as fillers in composite material for construction and building materials. Furthermore, waste rock wool is potentially utilized as an oil, water pollutant, and gas absorbent. To sum up, waste rock wool could be feasibly recycled as a composite material enhancer and utilized as an absorbent for a greener environment.
Rice quality assessment is essential for meeting high-quality standards and consumer demands. However, challenges remain in developing cost-effective and rapid techniques to assess commercial rice grain quality traits. This paper presents the application of computer vision (CV) and machine learning (ML) to classify commercial rice samples based on dimensionless morphometric parameters and color parameters extracted using CV algorithms from digital images obtained from a smartphone camera. The artificial neural network (ANN) model was developed using nine morpho-colorimetric parameters to classify rice samples into 15 commercial rice types. Furthermore, the ANN models were deployed and evaluated on a different imaging system to simulate their practical applications under different conditions. Results showed that the best classification accuracy was obtained using the Bayesian Regularization (BR) algorithm of the ANN with ten hidden neurons at 91.6% (MSE = <0.01) and 88.5% (MSE = 0.01) for the training and testing stages, respectively, with an overall accuracy of 90.7% (Model 2). Deployment also showed high accuracy (93.9%) in the classification of the rice samples. The adoption by the industry of rapid, reliable, and accurate methods, such as those presented here, may allow the incorporation of different morpho-colorimetric traits in rice with consumer perception studies.
Optometrists, ophthalmologists, orthoptists, and other trained medical professionals use fundus photography to monitor the progression of certain eye conditions or diseases. Segmentation of the vessel tree is an essential process of retinal analysis. In this paper, an interactive blood vessel segmentation from retinal fundus image based on Canny edge detection is proposed. Semi-automated segmentation of specific vessels can be done by simply moving the cursor across a particular vessel. The pre-processing stage includes the green color channel extraction, applying Contrast Limited Adaptive Histogram Equalization (CLAHE), and retinal outline removal. After that, the edge detection techniques, which are based on the Canny algorithm, will be applied. The vessels will be selected interactively on the developed graphical user interface (GUI). The program will draw out the vessel edges. After that, those vessel edges will be segmented to bring focus on its details or detect the abnormal vessel. This proposed approach is useful because different edge detection parameter settings can be applied to the same image to highlight particular vessels for analysis or presentation.
Road traffic accidents have been listed in the top 10 global causes of death for many decades. Traditional measures such as education and legislation have contributed to limited improvements in terms of reducing accidents due to people driving in undesirable statuses, such as when suffering from stress or drowsiness. Attention is drawn to predicting drivers' future status so that precautions can be taken in advance as effective preventative measures. Common prediction algorithms include recurrent neural networks (RNNs), gated recurrent units (GRUs), and long short-term memory (LSTM) networks. To benefit from the advantages of each algorithm, nondominated sorting genetic algorithm-III (NSGA-III) can be applied to merge the three algorithms. This is named NSGA-III-optimized RNN-GRU-LSTM. An analysis can be made to compare the proposed prediction algorithm with the individual RNN, GRU, and LSTM algorithms. Our proposed model improves the overall accuracy by 11.2-13.6% and 10.2-12.2% in driver stress prediction and driver drowsiness prediction, respectively. Likewise, it improves the overall accuracy by 6.9-12.7% and 6.9-8.9%, respectively, compared with boosting learning with multiple RNNs, multiple GRUs, and multiple LSTMs algorithms. Compared with existing works, this proposal offers to enhance performance by taking some key factors into account-namely, using a real-world driving dataset, a greater sample size, hybrid algorithms, and cross-validation. Future research directions have been suggested for further exploration and performance enhancement.
Enhancement of captured hand vein images is essential for a number of purposes, such as accurate biometric identification and ease of medical intravenous access. This paper presents an improved hand vein image enhancement technique based on weighted average fusion of contrast limited adaptive histogram equalization (CLAHE) and fuzzy adaptive gamma (FAG). The proposed technique is applied using three stages. Firstly, grey level intensities with CLAHE are locally applied to image pixels for contrast enhancement. Secondly, the grey level intensities are then globally transformed into membership planes and modified with FAG operator for the same purposes. Finally, the resultant images from CLAHE and FAG are fused using improved weighted averaging methods for clearer vein patterns. Then, matched filter with first-order derivative Gaussian (MF-FODG) is employed to segment vein patterns. The proposed technique was tested on self-acquired dorsal hand vein images as well as images from the SUAS databases. The performance of the proposed technique is compared with various other image enhancement techniques based on mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM). The proposed enhancement technique's impact on the segmentation process has also been evaluated using sensitivity, accuracy, and dice coefficient. The experimental results show that the proposed enhancement technique can significantly enhance the hand vein patterns and improve the detection of dorsal hand veins.
MeSH terms: Image Enhancement*; Normal Distribution; Signal-To-Noise Ratio
One of the most dangerous kinds of attacks affecting computers is a distributed denial of services (DDoS) attack. The main goal of this attack is to bring the targeted machine down and make their services unavailable to legal users. This can be accomplished mainly by directing many machines to send a very large number of packets toward the specified machine to consume its resources and stop it from working. We implemented a method using Java based on entropy and sequential probabilities ratio test (ESPRT) methods to identify malicious flows and their switch interfaces that aid them in passing through. Entropy (E) is the first technique, and the sequential probabilities ratio test (SPRT) is the second technique. The entropy method alone compares its results with a certain threshold in order to make a decision. The accuracy and F-scores for entropy results thus changed when the threshold values changed. Using both entropy and SPRT removed the uncertainty associated with the entropy threshold. The false positive rate was also reduced when combining both techniques. Entropy-based detection methods divide incoming traffic into groups of traffic that have the same size. The size of these groups is determined by a parameter called window size. The Defense Advanced Research Projects Agency (DARPA) 1998, DARPA2000, and Canadian Institute for Cybersecurity (CIC-DDoS2019) databases were used to evaluate the implementation of this method. The metric of a confusion matrix was used to compare the ESPRT results with the results of other methods. The accuracy and f-scores for the DARPA 1998 dataset were 0.995 and 0.997, respectively, for the ESPRT method when the window size was set at 50 and 75 packets. The detection rate of ESPRT for the same dataset was 0.995 when the window size was set to 10 packets. The average accuracy for the DARPA 2000 dataset for ESPRT was 0.905, and the detection rate was 0.929. Finally, ESPRT was scalable to a multiple domain topology application.
In recent years, there has been an immense amount of research into fall event detection. Generally, a fall event is defined as a situation in which a person unintentionally drops down onto a lower surface. It is crucial to detect the occurrence of fall events as early as possible so that any severe fall consequences can be minimized. Nonetheless, a fall event is a sporadic incidence that occurs seldomly that is falsely detected due to a wide range of fall conditions and situations. Therefore, an automated fall frame detection system, which is referred to as the SmartConvFall is proposed to detect the exact fall frame in a video sequence. It is crucial to know the exact fall frame as it dictates the response time of the system to administer an early treatment to reduce the fall's negative consequences and related injuries. Henceforth, searching for the optimal training configurations is imperative to ensure the main goal of the SmartConvFall is achieved. The proposed SmartConvFall consists of two parts, which are object tracking and instantaneous fall frame detection modules that rely on deep learning representations. The first stage will track the object of interest using a fully convolutional neural network (CNN) tracker. Various training configurations such as optimizer, learning rate, mini-batch size, number of training samples, and region of interest are individually evaluated to determine the best configuration to produce the best tracker model. Meanwhile, the second module goal is to determine the exact instantaneous fall frame by modeling the continuous object trajectories using the Long Short-Term Memory (LSTM) network. Similarly, the LSTM model will undergo various training configurations that cover different types of features selection and the number of stacked layers. The exact instantaneous fall frame is determined using an assumption that a large movement difference with respect to the ground level along the vertical axis can be observed if a fall incident happened. The proposed SmartConvFall is a novel technique as most of the existing methods still relying on detection rather than the tracking module. The SmartConvFall outperforms the state-of-the-art trackers, namely TCNN and MDNET-N trackers, with the highest expected average overlap, robustness, and reliability metrics of 0.1619, 0.6323, and 0.7958, respectively. The SmartConvFall also managed to produce the lowest number of tracking failures with only 43 occasions. Moreover, a three-stack LSTM delivers the lowest mean error with approximately one second delay time in locating the exact instantaneous fall frame. Therefore, the proposed SmartConvFall has demonstrated its potential and suitability to be implemented for a real-time application that could help to avoid any crucial fall consequences such as death and internal bleeding if the early treatment can be administered.
MeSH terms: Humans; Movement*; Reproducibility of Results; Neural Networks (Computer)*
The aim of this work is to solve the case study singular model involving the Neumann-Robin, Dirichlet, and Neumann boundary conditions using a novel computing framework that is based on the artificial neural network (ANN), global search genetic algorithm (GA), and local search sequential quadratic programming method (SQPM), i.e., ANN-GA-SQPM. The inspiration to present this numerical framework comes through the objective of introducing a reliable structure that associates the operative ANNs features using the optimization procedures of soft computing to deal with such stimulating systems. Four different problems that are based on the singular equations involving Neumann-Robin, Dirichlet, and Neumann boundary conditions have been occupied to scrutinize the robustness, stability, and proficiency of the designed ANN-GA-SQPM. The proposed results through ANN-GA-SQPM have been compared with the exact results to check the efficiency of the scheme through the statistical performances for taking fifty independent trials. Moreover, the study of the neuron analysis based on three and 15 neurons is also performed to check the authenticity of the proposed ANN-GA-SQPM.
Traditional uncoordinated traffic flows in a roundabout can lead to severe traffic congestion, travel delay, and the increased fuel consumption of vehicles. An interesting way to mitigate this would be through cooperative control of connected and automated vehicles (CAVs). In this paper, we propose a novel solution, which is a roundabout control system (RCS), for CAVs to attain smooth and safe traffic flows. The RCS is essentially a bi-level framework, consisting of higher and lower levels of control, where in the higher level, vehicles in the entry lane approaching the roundabout will be made to form clusters based on traffic flow volume, and in the lower level, the vehicles' optimal sequences and roundabout merging times are calculated by solving a combinatorial optimization problem using a receding horizon control (RHC) approach. The proposed RCS aims to minimize the total time taken for all approaching vehicles to enter the roundabout, whilst minimally affecting the movement of circulating vehicles. Our developed strategy ensures fast optimization, and can be implemented in real-time. Using microscopic simulations, we demonstrate the effectiveness of the RCS, and compare it to the current traditional roundabout system (TRS) for various traffic flow scenarios. From the results, we can conclude that the proposed RCS produces significant improvement in traffic flow performance, in particular for the average velocity, average fuel consumption, and average travel time in the roundabout.
In this study, the numerical computation heuristic of the environmental and economic system using the artificial neural networks (ANNs) structure together with the capabilities of the heuristic global search genetic algorithm (GA) and the quick local search interior-point algorithm (IPA), i.e., ANN-GA-IPA. The environmental and economic system is dependent of three categories, execution cost of control standards and new technical diagnostics elimination costs of emergencies values and the competence of the system of industrial elements. These three elements form a nonlinear differential environmental and economic system. The optimization of an error-based objective function is performed using the differential environmental and economic system and its initial conditions. The optimization of an error-based objective function is performed using the differential environmental and economic system and its initial conditions.