Markov chain quasi-Monte Carlo method for forecasting fire hotspots in Sarawak, Malaysia.

Stochastic modeling approaches have attracted many researchers to the field. However, fire hotspot detection suffers from not using a Markov chain quasi-Monte Carlo (MCQMC) as a forecasting methodology. This paper proposes improvements to the computational time by combining the strengths of the Markov chain Monte Carlo (MCMC) and quasi-Monte Carlo (QMC) methods.

Applications of covalent organic frameworks for the elimination of dyes from wastewater: A state-of-the-arts review.

Covalent organic frameworks (COFs) are class of porous coordination polymers made up of organic building blocks joined together by covalent bonding through thermodynamic and controlled reversible polymerization reactions. This review discussed versatile applications of COFs for remediation of wastewater containing dyes, emphasizing the advantages of both pristine and modified materials in adsorption, membrane separation, and advanced oxidations processes. The excellent performance of COFs towards adsorption and membrane filtration has been centered to their higher crystallinity and porosity, exhibiting exceptionally high surface area, pore size and pore volumes.

Spatial cluster analysis of COVID-19 in Malaysia (Mar-Sep, 2020).

Coronavirus disease 2019 (COVID-19) is the current worldwide pandemic as declared by the World Health Organization (WHO) in March 2020. Being part of the ongoing global pandemic, Malaysia has recorded a total of 8639 COVID-19 cases and 121 deaths as of 30th June 2020. This study aims to detect spatial clusters of COVID-19 in Malaysia using the Spatial Scan Statistic (SaTScan™) to guide control authorities on prioritizing locations for targeted interventions.

Determine neighboring region spatial effect on dengue cases using ensemble ARIMA models.

The state of Selangor, in Malaysia consist of urban and peri-urban centres with good transportation system, and suitable temperature levels with high precipitations and humidity which make the state ideal for high number of dengue cases, annually. This study investigates if districts within the Selangor state do influence each other in determining pattern of dengue cases. Study compares two different models; the Autoregressive Integrated Moving Average (ARIMA) and Ensemble ARIMA models, using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) measurement to gauge their performance tools.

Space-Time Clustering Characteristics of Tuberculosis in Khyber Pakhtunkhwa Province, Pakistan, 2015-2019.

The number of tuberculosis (TB) cases in Pakistan ranks fifth in the world. The National TB Control Program (NTP) has recently reported more than 462,920 TB patients in Khyber Pakhtunkhwa province, Pakistan from 2002 to 2017. This study aims to identify spatial and space-time clusters of TB cases in Khyber Pakhtunkhwa province Pakistan during 2015-2019 to design effective interventions.

An Eigenspace approach for detecting multiple space-time disease clusters: Application to measles hotspots detection in Khyber-Pakhtunkhwa, Pakistan.

Identifying the abnormally high-risk regions in a spatiotemporal space that contains an unexpected disease count is helpful to conduct surveillance and implement control strategies. The EigenSpot algorithm has been recently proposed for detecting space-time disease clusters of arbitrary shapes with no restriction on the distribution and quality of the data, and has shown some promising advantages over the state-of-the-art methods. However, the main problem with the EigenSpot method is that it cannot be adapted to detect more than one spatiotemporal hotspot.

Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach.

Ability to detect potential space-time clusters in spatio-temporal data on disease occurrences is necessary for conducting surveillance and implementing disease prevention policies. Most existing techniques use geometrically shaped (circular, elliptical or square) scanning windows to discover disease clusters. In certain situations, where the disease occurrences tend to cluster in very irregularly shaped areas, these algorithms are not feasible in practise for the detection of space-time clusters.

The computation of confidence intervals for the state parameters of power systems.

Background: In the past few decades, a significant volume of work has been carried out on various aspects of the state estimation problem to estimate an optimum state vector of the power system. This problem has been focused on, in previous studies regarding the computational efficiency and numerical robustness in view to find point estimates for system state parameters. This current investigation, constructed confidence intervals for the unknown state parameters of the system.

Observation of a cubical-like microstructure of strontium iron garnet and yttrium iron garnet prepared via sol-gel technique.

This is our initial response towards preparation of nano-inductors garnet for high operating frequencies strontium iron garnet (Sr3Fe5O12) denoted as SrIG and yttrium iron garnet (Y3Fe5O12) denoted as YIG. The garnet nano crystals were prepared by novel sol-gel technique. The phase and crystal structure of the prepared samples were identified by using X-ray diffraction analysis.