Visualizing a control strategy for estimating electricity consumption

This paper investigates the potential of applying different control measures on low power and high power appliances with the goal of evolving efficiency in electricity consumption. The research involves carrying out simulations on their power consumption readings to set up a control system. The study discovers savings on all appliances under study to be 12.8% Kw, not minding occupancy rate of the building. Air-conditioners have the greatest impact of a 6% Kw contribution on savings. This would lead to a substantial contribution when converted to pricing rates. The results from the study indicate that control measures should be extended to peak periods and power saving measures extended to more appliances

Model Simulation for the Spread of Rabies in Sarawak, Malaysia

There is a growing concern over the ongoing rabies epidemic in Sarawak that has remain unresolved ever since the outbreak began in July 2017. As of today, there has been 18 positive human rabies cases reported, which includes 17 fatalities, and one survivor who is now on life support after a severe neurological complications. Subsequently, the death rate now stands at approximately 94%. This paper is a preliminary report on the simulation of rabies transmission dynamics in Sarawak. At present, research is still lacking on the disease dynamics of rabies in Malaysia particularly in the state of Sarawak. We propose here a deterministic, compartmental model with SEIRS framework to fit actual data on the number of human infected rabies cases in Sarawak from June 2017 to January 2019. The simulation predicts that rabies in Sarawak will persist even with the current outbreak management and control efforts. Further, sensitivity analysis showed that dog vaccination rate is the most influential parameter and the basic reproduction number is estimated to be higher than 1. Henceforth, there is a need to increase the access to dog vaccines especially in remote rural areas with lack of health facilities. Our findings also suggest that controlling dog births could prevent the spread of  rabies from perpetuating in the state. Neutering or using other fertility control methods would reduce the input of new susceptible domestic dogs into the population while Trap-Neuter-Vaccinate-Release (TNVR) method can be implemented to control new births of free-roaming strays. In summary, increasing the coverage of dog vaccination and reducing the number newborn dogs would be the more effective strategies to manage the current rabies outbreak in Sarawak

Mathematical Modeling of the Arterial Blood Flow

Blood flow is a study of measuring the blood pressure and finding the flow through the blood vessel. Blood flow problem has been studied for centuries where one of the motivations was to understand the conditions that contribute to high blood pressure. This occurs when the blood vessel became narrowed from its normal size. This paper presents a mathematical modeling of the arterial blood flow which was derived from the Navier-Stokes equations and some assumptions. A system of nonlinear partial differential equations for blood flow and the cross-sectional area of the artery was obtained. Finite difference method was adopted to solve the equations numerically. The result obtained is very sensitive to the values of the initial conditions and this helps to explain the condition of hypertension

Applying Bipartite Network Approach to Scarce Data: Validation of the Habitat Suitability Model of a Marine Mammal Species

This paper presents the validation of the bipartite habitat suitability network (BiHSN) model formulated for a marine mammal. The model formulation published earlier resulted in the ranking of location nodes of the concerned area of possible habitats. Thus, the validation of the model is achieved by comparing the result produced by the BiHSN Model with the result acquired i) using another sample of actual data; and ii) from an ecological survey conducted by another researcher. Spearman’s Rank Correlation Coefficient (SRCC) is used to quantify the similarity of the comparison where a threshold value of at least 0.70 is set in order to signify an acceptable validation analysis. In the former validation analysis, this study reports an SRCC of 0.976 whereas the later validation analysis reports an SRCC of 0.914. Due to the high values of SRCC obtained, we conclude that the BiHSN Model is thus validated

Ensemble model of Artificial Neural Networks with randomized number of hidden neurons

Conventional artificial intelligence techniques and their hybrid models are incapable of handling several hypotheses at a time. The limitation in the performance of certain techniques has made the ensemble learning paradigm a desirable alternative for better predictions. The petroleum industry stands to gain immensely from this learning methodology due to the persistent quest for better prediction accuracies of reservoir properties for improved hydrocarbon exploration, production, and management activities. Artificial Neural Networks (ANN) has been applied in petroleum engineering but widely reported to be lacking in global optima caused mainly by the great challenge involved in the determination of optimal number of hidden neurons. This paper presents a novel ensemble model of ANN that uses a randomized algorithm to generate the number of hidden neurons in the prediction of petroleum reservoir properties. Ten base learners of the ANN model were created with each using a randomly generated number of hidden neurons. Each learner contributed in solving the problem and a single ensemble solution was evolved. The performance of the ensemble model was evaluated using standard evaluation criteria. The results showed that the performance of the proposed ensemble model is better than the average performance of the individual base learners. This study is a successful proof of concept of randomization of the number of hidden neurons and demonstrated the great potential for the application of this learning paradigm in petroleum reservoir characterization

Validation of bipartite network model of dengue hotspot detection in Sarawak

This paper presents the verification and validation processes in producing a realistic bipartite network model to detect dengue hotspot in Sarawak. Based on the result of previous published work, ranking of location nodes of possible dengue hotspot at Sarawak are used to illustrate the validation by comparing the Spearman rank correlation coefficients (SRCC) between the network models. UCINET 6 is used to generate a benchmark ranking result for model verification. A centrality measure analysis feature available in UCINET is used to determine the node centrality of a network model. The validation results show strong ranking similarity for all three groups of network models with good Spearman rank correlation coefficients values of 1.000, 0.8000 and 0.8824 (ρ>0.80; p<0.001) respectively. The top-ranked locations are seen as dengue hotspots and this study demonstrate a new approach to model dengue transmission at district-level by locating the hotspots and prioritizing the locations according to vector density. © Springer Nature Singapore Pte Ltd. 2019

Feature Selection with Mutual Information for Regression Problems

Selecting relevant features for machine learning modeling improves the performance of the learning methods. Mutual information (MI) is known to be used as relevant criterion for selecting feature subsets from input dataset with a nonlinear relationship to the predicting attribute. However, mutual information estimator suffers the following limitation; it depends on smoothing parameters, the feature selection greedy methods lack theoretically justified stopping criteria and in theory it can be used for both classification and regression problems, however in practice more often it formulation is limited to classification problems. This paper investigates a proposed improvement on the three limitations of the Mutual Information estimator (as mentioned above), through the use of resampling techniques and formulation of mutual information based on differential entropic for regression problems

Reaction-diffusion generic model for mosquito-borne diseases

Diseases which are transmitted by vector mosquitoes are major health problems in many countries. Although many mathematical models for diseases had been formulated, they are customized. As these diseases are spread by a common vector, similarities in the disease transmission are notable hence it will be beneficial to construct a general model which encompasses the epidemiology aspects and transmission of mosquito-borne diseases. In this paper, a SI (Susceptible-Infectious) generic model for mosquito borne diseases is formulated. The model is made up of partial differential reaction-diffusion equations which incorporate both the human and mosquito populations. Numerical simulation of this model is presented