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. Thus, they provide more active sites for trapping the dye molecules. On one hand, the photocatalytic performance of the COFs was attributed to their semiconducting properties, and when coupled with other functional semiconducting materials, they achieve good mechanical and thermal stabilities, positive light response, and narrow band gap, a typical characteristic of excellent photocatalysts. As such, COFs and their composites have demonstrated excellent potentialities for the elimination of the dyes.

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. The spatial analyses were conducted on a monthly basis at the state-level from March to September 2020. The results show that the most likely cluster of COVID-19 occurred in West Malaysia repeatedly from March to June, covering three counties (two federal territories and one neighbouring state) and moved to East Malaysia in July covering two other counties. The most likely cluster shows a tendency of having moved from the western part to the eastern part of the country. These results provide information that can be used for the evidence- based interventions to control the spread of COVID-19 in Malaysia.

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. ARIMA model is developed using the epidemiological data of dengue cases, whereas ensemble ARIMA incorporates the neighbouring regions' dengue models as the exogenous variable (X), into traditional ARIMA model. Ensemble ARIMA models have better model fit compared to the basic ARIMA models by incorporating neighbuoring effects of seven districts which made of state of Selangor. The AIC and BIC values of ensemble ARIMA models to be smaller compared to traditional ARIMA counterpart models. Thus, study concludes that pattern of dengue cases for a district is subject to spatial effects of its neighbouring districts and number of dengue cases in the surrounding areas.

Singular Value Thresholding Algorithm for Wireless Sensor Network Localization

Wireless Sensor Networks (WSN) are of great current interest in the proliferation of technologies. Since the location of the sensors is one of the most interesting issues in WSN, the process of node localization is crucial for any WSN-based applications. Subsequently, WSN’s node estimation deals with a low-rank matrix which gives rise to the application of the Nuclear Norm Minimization (NNM) method. This paper will focus on the localization of 2-dimensional WSN with objects (obstacles). Recent studies introduce Nuclear Norm Minimization (NNM) for node estimation instead of formulating the rank minimization problem. Common way to tackle this problem is by implementing the Semidefinite Programming (SDP). However, SDP can only handle matrices with a size of less than 100 × 100. Therefore, we introduce the method of Singular Value Thresholding (SVT) which is an iterative algorithm to solve the NNM problem that produces a sequence of matrices { X k , Y k } and executes a soft-thresholding operation on the singular value of the matrix Y k . This algorithm is a user-friendly algorithm which produces a low computational cost with low storage capacity required to give the lowest-rank minimum nuclear norm solution.

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. The spatial and space-time cluster analyses were conducted at the district-level based on the reported TB cases from January 2015 to April 2019 using space-time scan statistics (SaTScan). The most likely spatial and space-time clusters were detected in the northern rural part of the province. Additionally, two districts in the west were detected as the secondary space-time clusters. The most likely space-time cluster shows a tendency of spread toward the neighboring districts in the central part, and the most likely spatial cluster shows a tendency of spread toward the neighboring districts in the south. Most of the space-time clusters were detected at the start of the study period 2015-2016. The potential TB clusters in the remote rural part might be associated to the dry-cool climate and lack of access to the healthcare centers in the remote areas.

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. This is an important limitation, since, in reality, we may have multiple hotspots, sometimes at the same level of importance. We propose an extension of the EigenSpot algorithm, called Multi-EigenSpot that is able to handle multiple hotspots by iteratively removing previously detected hotspots and re-running the algorithm until no more hotspots are found. In addition, a visualization tool (heatmap) has been linked to the proposed algorithm to visualize multiple clusters with different colors. We evaluated the proposed method using the monthly data on measles cases in Khyber-Pakhtunkhwa, Pakistan (Jan 2016- Dec 2016), and the efficiency was compared with the state-of-the-art methods: EigenSpot and Space-time scan statistic (SaTScan). The results showed the effectiveness of the proposed method for detecting multiple clusters in a spatiotemporal space.

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. To address this problem, a new algorithm is proposed, which uses a co-clustering strategy to detect prospective and retrospective space-time disease clusters with no restriction on shape and size. The proposed method detects space-time disease clusters by tracking the changes in space-time occurrence structure instead of an in-depth search over space. This method was utilised to detect potential clusters in the annual and monthly malaria data in Khyber Pakhtunkhwa Province, Pakistan from 2012 to 2016 visualising the results on a heat map. The results of the annual data analysis showed that the most likely hotspot emerged in three sub-regions in the years 2013-2014. The most likely hotspots in monthly data appeared in the month of July to October in each year and showed a strong periodic trend. A Correction has been published: https://doi.org/10.4081/gh.2023.1232

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. The research indicates that confidence intervals can yield addition useful information about the estimated parameters.

Methods

The feasible interval estimates for the system state parameters have been modelled in this study by considering the random uncertainty in the processing measurements. The statistical assumptions of the measurement errors have been utilized to characterize the probabilistic behavior of the estimated parameters in terms of confidence intervals. The Gauss–Newton algorithm has been adopted for maximizing the likelihood function of the processing measurements and obtaining the confidence intervals.

Results

The usage of the confidence intervals was demonstrated through Monte Carlo experiments on a real dataset of the 6-bus and IEEE 14-bus power systems for both small and large sample sizes. The confidence intervals were constructed for the test networks for the sample of measurements 18, 28, 44 and 68 based on the redundancy ratio R. The proposed interval estimates outperformed for the sample sizes of 28 in the 6 bus and 68 in the IEEE 14-bus systems, respectively. The poor performance for the constructed interval estimates have been reported even for the large sample sizes in the existence of contaminated measurements.

Conclusions

The results of the study show that the method is effective and practically applicable in the state estimation of a power system. The constructed confidence intervals for the system state parameters adequately perform for the lager sample size. However, the existence of the gross errors in the processing measurements had severe effect on the performance of the proposed interval estimates.

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. SEM images were done to reveal the surface morphology of the samples. Raman spectra was taken for yttrium iron garnet (Y3Fe5O12). The magnetic properties of the samples namely initial permeability (micro), relative loss factor (RLF) and quality factor (Q-Factor) were done by using LCR meter. From the XRD profile, both of the Y3Fe5O12 and Sr3Fe5O12 samples showed single phase garnet and crystallization had completely occurred at 900 degrees C for the SrIG and 950 degrees C for the YIG samples. The YIG sample showed extremely low RLF value (0.0082) and high density 4.623 g/cm3. Interesting however is the high Q factor (20-60) shown by the Sr3Fe5O12 sample from 20-100 MHz. This high performance magnetic property is attributed to the homogenous and cubical-like microstructure. The YIG particles were used as magnetic feeder for EM transmitter. It was observed that YIG magnetic feeder with the EM transmitter gave 39% higher magnetic field than without YIG magnetic feeder.

The extraction of zinc(II) from chloride solutions by methyltrioctylammonium and methyltridecylammonium chlorides dissolved in chloroform and other diluents and a comparison with Aliquat 336

Equilibrium constants for the extraction of zinc(II) from 1.0 and 6.0 M HCl and from 6.0 M LiCl solutions by methyltrioctylammonium and methyltridecylammonium chlorides in chloroform are reported. Vapour phase osmometry studies show that methyltrioctylammonium and methyltridecylammonium chlorides form weak dimers in chloroform and isobutyl methyl ketone. The presence of water in the diluent decreases the extent of dimerization. The aggregation in o-xylene and cyclohexane is more extensive with larger aggregates being formed. The extraction of zinc(II) from 1.0 M HCl by methyltridecylammonium chloride in different diluents follows the order isobutyl methyl ketone > cyclohexane > o-xylene > chloroform. At higher reagent concentrations the effect of aggregation is observed in reversing the order for cyclo- hexane and o-xylene. Anomalous extraction behaviour is observed with isobutyl methyl ketone which may be due to the extraction of a different species in this diluent.

The mechanism of extraction of zinc(II) from aqueous chloride solutions into chloroform solutions of methyltrioctylammonium chloride

The mechanism of the transfer of zinc(11) between 1.0 M HCl, 6.0 M HCl and 6.0 M LiCl and methyltrioctylammonium chloride in chloroform has been studied by using the single-drop technique. It is suggested that interfacial complexes are formed by fast ion-exchange reactions and that the rate-determining step, in the case of 1.0 M HCl and 6.0 M LiCl, involves the replacement at the interface of the complex R2ZnCl4 by two molecules of reagent from the bulk organic phase. For 6.0 M HCl the rate-determining step is the replacement of the interfacial complex RHZnCl4 by one molecule of reagent from the bulk organic phase. Support for this mechanism is obtained by comparing the extraction equilibrium constants for the complex R2ZnCl4 for 1.0 M HCl and 6.0 M LiCl from previous with the values calculated from the rate data. Interfacial tension measurements have shown that the interface is saturated with reagent over the full range of concentrations studied.