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Humanitarian disaster: mental health disorders at primary healthcare clinic
Get PDFINTRODUCTION: Little is known about the mental health of Rohingya refugees attending the Malaysian Field Hospital primary health clinic after arriving in Bangladesh. The objectives of this study were to assess the prevalence of mental health disorders, somatic symptoms and to ascertain the determinants of mental health disorders among the Rohingya refugee community attending the primary health clinic. MATERIAL AND METHODS: A cross-sectional, face-to-face interview using the DASS-21 Questionnaire was conducted among 180 random samples of patients from the Rohingya community. Symptoms of illnesses were recorded before giving the appropriate treatment. Data was collected to obtain the prevalence of mental health disorders, including anxiety, depression, somatic symptoms, and to study the association and predictors of mental health disorders. RESULTS: 70.6% of respondents reported having mental health disorders. 70% presented with anxiety and 51.6% had depression. Among the respondents with mental health disorders, 70.8% presented with somatic symptoms. Mental health disorders were associated with female gender, older age, formal education, unemployment, high number of households, being in Bangladesh one year or less, and presence of somatic symptoms. Being in Bangladesh one year or less (AOR, 11.73; 95% CI 3.38–40.71) and presence of somatic symptoms (AOR, 12.1; 95% CI: 4.02 to 36.44) were significant predictors of mental health disorders. CONCLUSIONS: The prevalence of mental health disorders among Rohingya refugees attending the primary health care clinic was high, and they presented with somatic symptoms
Embracing digital interactive platforms for rapid internationalization
No full textDigital interactive platforms (DIP) enhance communication in a growing online interaction and promote innovation in small business operations. However, there is still a lack of knowledge about the interactions between small and medium-sized enterprises (SMEs) and stakeholders via DIP and the factors contributing to accelerating internationalization. Therefore, this study aims to explore the DIP used by SMEs to interact with service providers to accelerate their internationalization. This study applied the case study methodology and used a dyadic process with selected service providers and Malaysian manufacturing SMEs. The findings of this study examine the interactions of SMEs and service providers, specifically through DIP. From the analysis, two factors contributing to SMEs' rapid internationalization can be deduced. The novelty of this study is that it contributes to the understanding of the DIP used by SMEs to interact with service providers and identifies internal resources and the external environment as DIP factors for SMEs' rapid internationalization. A framework is provided for SMEs to use DIP for rapid internationalization
A March 5n FSM-based memory built-in self-test (MBIST) architecture with diagnosis capabilities
No full textMBIST is a standard mechanism to test memory arrays and potentially detect all of the faults that may be present inside the memory cells using an effective collection of algorithms. However, a massive number of memory cells wrapped by BIST logic can result in substantial overhead in wiring, gate area and also be detrimental to memory performance. Therefore, new MBIST designs for advanced SoCs that address the challenges must be explored to reduce the overall cost of manufacturing tests. It is important to choose the appropriate BIST architecture and algorithmic coverage for a range of array sizes to get the products to market in the quickest fashion. March 5n algorithm in previous is proven to achieve shorter test time than conventional MATS++ algorithms without penalizing the fault coverage. Moreover, the algorithm is capable of covering inversion coupling faults. The fault coverage of the previous March 5n algorithm is extended and proved in this work. An improved March 5n architecture is proposed to extend its properties in terms of repair capabilities while incurring minimal area overhead expenses. The proposed work improved the March 5n MBIST architecture by nearly 8% of maximum operating frequency and repair capabilities with the trade-off of area overhead increment by about 4%
Thermal-aware directional and adaptive routing algorithm for 3d network-on-chip
Get PDFDue to the tier architecture of 3D network-on-chip (3D-NoC), reducing the thermal hotspot within the chip is challenging as a cooling mechanism that lies merely on the single side of a chip. High power density in 3D NoC is responsible for reliability degradation and thermal difficulties. Thermal-aware routing becomes substantial to handle thermal difficulties and diffusion of heat to the cooler regions. Thermal-aware routing focuses on bypassing hotspot areas by selecting cooler areas. Existing thermal-aware routing algorithms adopt slightly cooler but longer and extended paths, due to lack of ability to know the proximity of the destination's location, which aggravate thermal issues. This work presents a novel thermal-aware directional and adaptive routing algorithm. Objective of the proposed algorithm is to strive to find the best possible neighbour to reach closer to the proximity of the destination. The proposed algorithm can adaptively choose any suitable neighbour that can lead packets closer to the destination at each intermediate node. The performance of the proposed algorithm is evaluated and compared with existing thermal-aware routing algorithm in a simulator environment. Simulation results demonstrate that the proposed method outperformed its counterpart in terms of average delay with 11-26% improvement, total hop counts with 8-24% reduction under various traffic conditions and improvement in overall thermal profiling of the chip
Oil palm tree detection and counting for precision farming using deep learning CNN
No full textOil palm tree is a very important crop in Malaysia and other tropical areas. The number of oil palm trees in a plantation area is crucial as it could help to estimate the potential yield of palm oil, monitoring the growing situation of palm trees after plantation such as the age and the survival rate and also the amount of fertilizer and pesticides needed. In this paper, a deep learning-based oil palm tree detection and counting method is proposed and designed into a functioning app. Images of oil palm plantation are collected by using drones then they are pre-processed. The pre-processed images are used to train and optimize the convolutional neural network (CNN). After the CNN model is trained, it is used to predict the label for all the samples in an image dataset collected through the sliding window technique. Its performance is tested. The performance of the classifier is tested on three different tree conditions, from small number of properly separated trees to big number of crowded trees. Based on the result, accuracy ranging from 83.5% to 100% is obtained. Finally, the method is built into an application for a better user experience
Performance analysis of parallel virtual machine in solving large-scale multi-dimensional problems
No full textMATLAB Distributed Computing Server (MDCS) and Parallel Virtual Machine (PVM) software are two types of distributed computing environments. MDCS is recently used in selecting the best network training algorithm and assessing the effect of parallelization. Since it is practical and user friendly, it gives PVM the opportunity to become the communication paradigm of choice. The PVM provides a powerful set of process control and dynamic resource management features. In the distributed parallel computing (DPC), however, both solutions have different strengths and limitations. Based on these concerns, this paper compares the numerical analysis for mathematical modeling of large sparse 2D and 3D of second order parabolic partial differential equations (PDE) on MDCS and PVM based on parallel performance indicators (PPI). The PDE geometry is discretized into a sparse grid structure using the FDM method. In using a method with the highest accuracy, Parallel Alternating Group Explicit (PAGE) scheme was chosen. The parallel strategies focus on the PAGE's convergence speed and various domain de-composition techniques, as well as a block iterative scheme and load balance using fine granular techniques. Furthermore, comparison of distributed computing environments also relies on multiple processors running on Unix-like operating systems with Fedora installed to support large-scale simulations. The analysis and validation of PPI for both communication software is also investigated in this paper. For a sequential algorithm, accuracy, estimation of error, and stability are employed as indicators. As a conclusion, based on the PPI and numerical analysis, the tables and graphs show that compared to MDCS, PVM is a better environment for solving multidimensional parabolic PDE modeling
Metamodel for enterprise architecture: A systematic literature review
No full textEnterprise Architecture (EA) models can be used to support IT/business alignment. Frequent architecture changes and the challenge of collecting EA data from various stakeholders in large organizations can be problematic due to the size and complexity of EA models. A tighter synchronization between EA models and what they represent in the real world is discussed in this paper, resulting in increased model actuality and consistency. To support these processes, the EA model implementation tool must be able to work with contextual data that is not typically stored alongside EA models. The purpose of this paper is to present a systematic review of the criteria for developing an EA metamodel. The criteria for developing an EA metamodel were assessed in research studies published between March 2019 and December 2019. An established method of the systematic literature review was used to extract and synthesize data in this research
Variable selection in high dimensional data with interactions
Get PDFA common research area in statistical machine learning has been variable selection in high dimensional settings. In recent years, numerous effective approaches have been created to deal with these challenges. In order to improve the prediction accuracy of the model for the given dataset, this study sought to present a double approach variable selection method when pairwise interactions between the explanatory variables exist and to choose the smallest explanatory variable set (considering interactions among them). In this study, a double step method consolidating Random Forest and Adaptive Elastic Net was further examined to mimic potential health effects of environmental contamination. When there were existing interactions in the data or none at all, the double step approach was compared to the single-step adaptive elastic net method and two-step CART paired with the adaptive elastic net method. Using significant statistical tests like RMSE, R2, and the quantity of the variable chosen for the final model, the success of the strategies was measured. The double step RF+AENET approach produces a simple, constrained model. Despite the complex association between exposure variables, it has the lowest false detection rate for null interactions. A set of variables that have correlation with the result are effectively retained by the screening and variable reduction processes in the RF step of the RF+AENET approach. The double step RF+AENET performs prediction better than a single technique and chooses a sparse model that is close to the true model. Thus, it can be said that when there are pairwise interactions between variables in the simulated biological dataset, the double step technique is a better method for model prediction and parameter estimation
Quadratic convective nanofluid flow at a three-dimensional stagnation point with the g-jitter effect
Get PDFThe nonlinear density variation with temperature happens in many thermal applications like solar collectors, energy production, heat exchangers, and combustions, and it gives a significant impact on heat transfer and fluid flows. Thus, a nonlinear convective of an unsteady stagnation point flow under the influence of gravity modulation in the presence of water-based nanoparticles alumina (Al2O3) is studied here. Suitable variables are utilized to reduce the highly coupled nonlinear governing equations into a system of dimensionless simple partial differential equations. The Keller-box method is then applied to solve the consequent governing equations. Velocity and temperature profiles for various values of pertinent parameters are displayed graphically and discussed. The results indicate that the quadratic convection has enhanced the fluid flow and heat transport. Furthermore, the nonlinear convection parameter and the nanoparticles volume fractions have delivered a positive effect on the skin friction and the rate of heat transfer
Understanding power transformer frequency response based on model simulation
No full textFrequency Response Analysis (FRA) has been approved and there is an increased interest in performing electric power transformers tests. The drawback of using the FRA method is that there is no available recognized standard to interpret the obtained results, which depend on personal expertise. For further understanding of the FRA signature of power transformers faults, it was recommended to use lumped or distributed circuit approaches, therefore understanding the fault effect on the FRA signature. This study presents a power transformer model and simulates its FRA. Also, the effect of reducing the model electrical circuit RLC parameters was investigated. The effect of each parameter was determined in the FRA spectrum. The results show a significant change in a specific region for each parameter