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Fulltext A machine learning approach of predicting high potential archers by means of physical fitness indicators

Muazu Musa R, P P Abdul Majeed A, Taha Z, Chang SW, Ab Nasir AF, Abdullah MR

PLoS One, 2019;14(1):e0209638.
PMID: 30605456 DOI: 10.1371/journal.pone.0209638

k-nearest neighbour (k-NN) has been shown to be an effective learning algorithm for classification and prediction. However, the application of k-NN for prediction and classification in specific sport is still in its infancy. The present study classified and predicted high and low potential archers from a set of physical fitness variables trained on a variation of k-NN algorithms and logistic regression. 50 youth archers with the mean age and standard deviation of (17.0 ± 0.56) years drawn from various archery programmes completed a one end archery shooting score test. Standard fitness measurements of the handgrip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle strength were conducted. Multiple linear regression was utilised to ascertain the significant variables that affect the shooting score. It was demonstrated from the analysis that core muscle strength and vertical jump were statistically significant. Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the significant variables identified. k-NN model variations, i.e., fine, medium, coarse, cosine, cubic and weighted functions as well as logistic regression, were trained based on the significant performance variables. The HACA clustered the archers into high potential archers (HPA) and low potential archers (LPA). The weighted k-NN outperformed all the tested models at itdemonstrated reasonably good classification on the evaluated indicators with an accuracy of 82.5 ± 4.75% for the prediction of the HPA and the LPA. Moreover, the performance of the classifiers was further investigated against fresh data, which also indicates the efficacy of the weighted k-NN model. These findings could be valuable to coaches and sports managers to recognise high potential archers from a combination of the selected few physical fitness performance indicators identified which would subsequently save cost, time and energy for a talent identification programme.
Fulltext A measles IgM rapid diagnostic test to address challenges with national measles surveillance and response in Malaysia

Senin A, Noordin NM, Sani JAM, Mahat D, Donadel M, Scobie HM, et al.

PLoS One, 2024;19(3):e0298730.
PMID: 38483868 DOI: 10.1371/journal.pone.0298730

INTRODUCTION: A lateral flow rapid diagnostic test (RDT) enables detection of measles specific immunoglobulin M (IgM) antibody in serum, capillary blood, and oral fluid with accuracy consistent with enzyme immunoassay (EIA). The objectives of the study were: 1) to assess measles RDT inter-reader agreement between two clinic staff; 2) to assess the sensitivity and specificity of the measles RDT relative to standard surveillance testing in a low transmission setting; 3) to evaluate the knowledge, attitudes, and practices of staff in clinics using the RDT; and 4) to assess the impact of RDT testing on the measles public health response in Malaysia.
MATERIALS AND METHODS: The clinic-based prospective evaluation included all suspected measles cases captured by routine measles surveillance at 34 purposely selected clinics in 15 health districts in Malaysia between September 2019 and June 2020, following day-long regional trainings on RDT use. Following informed consent, four specimens were collected from each suspected case, including those routinely collected for standard surveillance [serum for EIA and throat swabs for quantitative reverse transcriptase polymerase chain reaction (RT-qPCR)] together with capillary blood and oral fluid tested with RDTs during the study. RDT impact was evaluated by comparing the rapidity of measles public health response between the pre-RDT implementation (December 2018 to August 2019) and RDT implementation periods (September 2019 to June 2020). To assess knowledge, attitudes, and practices of RDT use, staff involved in the public health management of measles at the selected sites were surveyed.
RESULTS: Among the 436 suspect cases, agreement of direct visual readings of measles RDT devices between two health clinic staff was 99% for capillary blood (k = 0.94) and 97% for oral fluid (k = 0.90) specimens. Of the total, 45 (10%) were positive by measles IgM EIA (n = 44, including five also positive by RT-qPCR) or RT-qPCR only (n = 1), and 38 were positive by RDT (using either capillary blood or oral fluid). Using measles IgM EIA or RT-qPCR as reference, RDT sensitivity using capillary blood was 43% (95% CI: 30%-58%) and specificity was 98% (95% CI: 96%-99%); using oral fluid, sensitivity (26%, 95% CI: 15%-40%) and specificity (97%, 95% CI: 94%-98%) were lower. Nine months after training, RDT knowledge was high among staff involved with the public health management of measles (average quiz score of 80%) and was highest among those who received formal training (88%), followed by those trained during supervisory visits (83%). During the RDT implementation period, the number of days from case confirmation until initiation of public response decreased by about 5 days.
CONCLUSION: The measles IgM RDT shows >95% inter-reader agreement, high retention of RDT knowledge, and a more rapid public health response. However, despite ≥95% RDT specificity using capillary blood or oral fluid, RDT sensitivity was <45%. Higher-powered studies using highly specific IgM assays and systematic RT-qPCR for case confirmation are needed to establish the role of RDT in measles elimination settings.
Fulltext A metamodel for mobile forensics investigation domain

Ali A, Abd Razak S, Othman SH, Mohammed A, Saeed F

PLoS One, 2017;12(4):e0176223.
PMID: 28445486 DOI: 10.1371/journal.pone.0176223

With the rapid development of technology, mobile phones have become an essential tool in terms of crime fighting and criminal investigation. However, many mobile forensics investigators face difficulties with the investigation process in their domain. These difficulties are due to the heavy reliance of the forensics field on knowledge which, although a valuable resource, is scattered and widely dispersed. The wide dispersion of mobile forensics knowledge not only makes investigation difficult for new investigators, resulting in substantial waste of time, but also leads to ambiguity in the concepts and terminologies of the mobile forensics domain. This paper developed an approach for mobile forensics domain based on metamodeling. The developed approach contributes to identify common concepts of mobile forensics through a development of the Mobile Forensics Metamodel (MFM). In addion, it contributes to simplifying the investigation process and enables investigation teams to capture and reuse specialized forensic knowledge, thereby supporting the training and knowledge management activities. Furthermore, it reduces the difficulty and ambiguity in the mobile forensics domain. A validation process was performed to ensure the completeness and correctness of the MFM. The validation was conducted using two techniques for improvements and adjustments to the metamodel. The last version of the adjusted metamodel was named MFM 1.2.
Fulltext A new Multi Sine-Cosine algorithm for unconstrained optimization problems

Rehman MZ, Khan A, Ghazali R, Aamir M, Nawi NM

PLoS One, 2021;16(8):e0255269.
PMID: 34358237 DOI: 10.1371/journal.pone.0255269

The Sine-Cosine algorithm (SCA) is a population-based metaheuristic algorithm utilizing sine and cosine functions to perform search. To enable the search process, SCA incorporates several search parameters. But sometimes, these parameters make the search in SCA vulnerable to local minima/maxima. To overcome this problem, a new Multi Sine-Cosine algorithm (MSCA) is proposed in this paper. MSCA utilizes multiple swarm clusters to diversify & intensify the search in-order to avoid the local minima/maxima problem. Secondly, during update MSCA also checks for better search clusters that offer convergence to global minima effectively. To assess its performance, we tested the MSCA on unimodal, multimodal and composite benchmark functions taken from the literature. Experimental results reveal that the MSCA is statistically superior with regards to convergence as compared to recent state-of-the-art metaheuristic algorithms, including the original SCA.
Fulltext A new SIR-based sigmoid power control game in cognitive radio networks

Al-Gumaei YA, Noordin KA, Reza AW, Dimyati K

PLoS One, 2014;9(10):e109077.
PMID: 25286044 DOI: 10.1371/journal.pone.0109077

Interference resulting from Cognitive Radios (CRs) is the most important aspect of cognitive radio networks that leads to degradation in Quality of Service (QoS) in both primary and CR systems. Power control is one of the efficient techniques that can be used to reduce interference and satisfy the Signal-to-Interference Ratio (SIR) constraint among CRs. This paper proposes a new distributed power control algorithm based on game theory approach in cognitive radio networks. The proposal focuses on the channel status of cognitive radio users to improve system performance. A new cost function for SIR-based power control via a sigmoid weighting factor is introduced. The existence of Nash Equilibrium and convergence of the algorithm are also proved. The advantage of the proposed algorithm is the possibility to utilize and implement it in a distributed manner. Simulation results show considerable savings on Nash Equilibrium power compared to relevant algorithms while reduction in achieved SIR is insignificant.
Fulltext A new compact and low phase imbalance microstrip coupler for 5G wireless communication systems

I Yahya S, Zubir F, Nouri L, Yusoff Z, Chaudhary MA, Assaad M, et al.

PLoS One, 2023;18(12):e0296272.
PMID: 38134045 DOI: 10.1371/journal.pone.0296272

Microstrip couplers play a crucial role in signal processing and transmission in various applications, including RF and wireless communication, radar systems, and satellites. In this work, a novel microstrip 180° coupler is designed, fabricated and measured. The layout configuration of this coupler is completely new and different from the previously reported Rat-race, branch-line and directional couplers. To obtain the proposed coupler, the meandrous coupled lines are used and analyzed mathematically. To improve the performance of our coupler, an optimization method is used. The designed coupler is very compact with an overall size of 0.014λg2. The obtained values of S21 and S31 are -3.45 dB and -3.75 dB, respectively at the operating frequency, while the fractional bandwidth (FBW) is 56.2%. It operates at fo = 1.61 GHz (suitable for 5G applications) and can suppress harmonics up to 2.17fo. Another advantage of this coupler is its low phase imbalance, while the phase difference between S21 and S31 is 180°± 0.023°. Therefore, our device is a balanced coupler with ±0.3 dB magnitude unbalance at its operating frequency. It is important to note that it is very difficult to find a coupler that has all these advantages at the same time. The proposed 180° coupler is fabricated and measured. The comparison shows that the measurement and simulation results are in good agreement. Therefore, the proposed coupler can be easily used in designing high-performance 5G communication systems.
Fulltext A new method of mark detection for software-based optical mark recognition

Loke SC, Kasmiran KA, Haron SA

PLoS One, 2018;13(11):e0206420.
PMID: 30412588 DOI: 10.1371/journal.pone.0206420

Software optical mark recognition (SOMR) is the process whereby information entered on a survey form or questionnaire is converted using specialized software into a machine-readable format. SOMR normally requires input fields to be completely darkened, have no internal labels, or be filled with a soft pencil, otherwise mark detection will be inaccurate. Forms can also have print and scan artefacts that further increase the error rate. This article presents a new method of mark detection that improves over existing techniques based on pixel counting and simple thresholding. Its main advantage is that it can be used under a variety of conditions and yet maintain a high level of accuracy that is sufficient for scientific applications. Field testing shows no software misclassification in 5695 samples filled by trained personnel, and only two misclassifications in 6000 samples filled by untrained respondents. Sensitivity, specificity, and accuracy were 99.73%, 99.98%, and 99.94% respectively, even in the presence of print and scan artefacts, which was superior to other methods tested. A separate direct comparison for mark detection showed a sensitivity, specificity, and accuracy respectively of 99.7%, 100.0%, 100.0% (new method), 96.3%, 96.0%, 96.1% (pixel counting), and 99.9%, 99.8%, 99.8% (simple thresholding) on clean forms, and 100.0%, 99.1%, 99.3% (new method), 98.4%, 95.6%, 96.2% (pixel counting), 100.0%, 38.3%, 51.4% (simple thresholding) on forms with print artefacts. This method is designed for bubble and box fields, while other types such as handwriting fields require separate error control measures.
Fulltext A new split based searching for exact pattern matching for natural texts

Hakak S, Kamsin A, Shivakumara P, Idna Idris MY, Gilkar GA

PLoS One, 2018;13(7):e0200912.
PMID: 30048486 DOI: 10.1371/journal.pone.0200912

Exact pattern matching algorithms are popular and used widely in several applications, such as molecular biology, text processing, image processing, web search engines, network intrusion detection systems and operating systems. The focus of these algorithms is to achieve time efficiency according to applications but not memory consumption. In this work, we propose a novel idea to achieve both time efficiency and memory consumption by splitting query string for searching in Corpus. For a given text, the proposed algorithm split the query pattern into two equal halves and considers the second (right) half as a query string for searching in Corpus. Once the match is found with second halves, the proposed algorithm applies brute force procedure to find remaining match by referring the location of right half. Experimental results on different S1 Dataset, namely Arabic, English, Chinese, Italian and French text databases show that the proposed algorithm outperforms the existing S1 Algorithm in terms of time efficiency and memory consumption as the length of the query pattern increases.
Fulltext A new trigonometric spline approach to numerical solution of generalized nonlinear Klien-Gordon equation

Mat Zin S, Abbas M, Majid AA, Ismail AI

PLoS One, 2014;9(5):e95774.
PMID: 24796483 DOI: 10.1371/journal.pone.0095774

The generalized nonlinear Klien-Gordon equation plays an important role in quantum mechanics. In this paper, a new three-time level implicit approach based on cubic trigonometric B-spline is presented for the approximate solution of this equation with Dirichlet boundary conditions. The usual finite difference approach is used to discretize the time derivative while cubic trigonometric B-spline is applied as an interpolating function in the space dimension. Several examples are discussed to exhibit the feasibility and capability of the approach. The absolute errors and L∞ error norms are also computed at different times to assess the performance of the proposed approach and the results were found to be in good agreement with known solutions and with existing schemes in literature.
Fulltext A newly emerging HIV-1 recombinant lineage (CRF58_01B) disseminating among people who inject drugs in Malaysia

Chow WZ, Takebe Y, Syafina NE, Prakasa MS, Chan KG, Al-Darraji HA, et al.

PLoS One, 2014;9(1):e85250.
PMID: 24465513 DOI: 10.1371/journal.pone.0085250

The HIV epidemic is primarily characterised by the circulation of HIV-1 group M (main) comprising of 11 subtypes and sub-subtypes (A1, A2, B-D, F1, F2, G, H, J, and K) and to date 55 circulating recombinant forms (CRFs). In Southeast Asia, active inter-subtype recombination involving three main circulating genotypes--subtype B (including subtype B', the Thai variant of subtype B), CRF01_AE, and CRF33_01B--have contributed to the emergence of novel unique recombinant forms. In the present study, we conducted the molecular epidemiological surveillance of HIV-1 gag-RT genes among 258 people who inject drugs (PWIDs) in Kuala Lumpur, Malaysia, between 2009 and 2011 whereby a novel CRF candidate was recently identified. The near full-length genome sequences obtained from six epidemiologically unlinked individuals showed identical mosaic structures consisting of subtype B' and CRF01_AE, with six unique recombination breakpoints in the gag-RT, pol, and env regions. Among the high-risk population of PWIDs in Malaysia, which was predominantly infected by CRF33_01B (>70%), CRF58_01B circulated at a low but significant prevalence (2.3%, 6/258). Interestingly, the CRF58_01B shared two unique recombination breakpoints with other established CRFs in the region: CRF33_01B, CRF48_01B, and CRF53_01B in the gag gene, and CRF15_01B (from Thailand) in the env gene. Extended Bayesian Markov chain Monte Carlo sampling analysis showed that CRF58_01B and other recently discovered CRFs were most likely to have originated in Malaysia, and that the recent spread of recombinant lineages in the country had little influence from neighbouring countries. The isolation, genetic characterization, and evolutionary features of CRF58_01B among PWIDs in Malaysia signify the increasingly complex HIV-1 diversity in Southeast Asia that may hold an implication on disease treatment, control, and prevention.
Fulltext A newton cooperative genetic algorithm method for in silico optimization of metabolic pathway production

Ismail MA, Deris S, Mohamad MS, Abdullah A

PLoS One, 2015;10(5):e0126199.
PMID: 25961295 DOI: 10.1371/journal.pone.0126199

This paper presents an in silico optimization method of metabolic pathway production. The metabolic pathway can be represented by a mathematical model known as the generalized mass action model, which leads to a complex nonlinear equations system. The optimization process becomes difficult when steady state and the constraints of the components in the metabolic pathway are involved. To deal with this situation, this paper presents an in silico optimization method, namely the Newton Cooperative Genetic Algorithm (NCGA). The NCGA used Newton method in dealing with the metabolic pathway, and then integrated genetic algorithm and cooperative co-evolutionary algorithm. The proposed method was experimentally applied on the benchmark metabolic pathways, and the results showed that the NCGA achieved better results compared to the existing methods.
Fulltext A normalization model for repeated letters in social media hate speech text based on rules and spelling correction

Mansur Z, Omar N, Tiun S, Alshari EM

PLoS One, 2024;19(3):e0299652.
PMID: 38512966 DOI: 10.1371/journal.pone.0299652

As social media booms, abusive online practices such as hate speech have unfortunately increased as well. As letters are often repeated in words used to construct social media messages, these types of words should be eliminated or reduced in number to enhance the efficacy of hate speech detection. Although multiple models have attempted to normalize out-of-vocabulary (OOV) words with repeated letters, they often fail to determine whether the in-vocabulary (IV) replacement words are correct or incorrect. Therefore, this study developed an improved model for normalizing OOV words with repeated letters by replacing them with correct in-vocabulary (IV) replacement words. The improved normalization model is an unsupervised method that does not require the use of a special dictionary or annotated data. It combines rule-based patterns of words with repeated letters and the SymSpell spelling correction algorithm to remove repeated letters within the words by multiple rules regarding the position of repeated letters in a word, be it at the beginning, middle, or end of the word and the repetition pattern. Two hate speech datasets were then used to assess performance. The proposed normalization model was able to decrease the percentage of OOV words to 8%. Its F1 score was also 9% and 13% higher than the models proposed by two extant studies. Therefore, the proposed normalization model performed better than the benchmark studies in replacing OOV words with the correct IV replacement and improved the performance of the detection model. As such, suitable rule-based patterns can be combined with spelling correction to develop a text normalization model to correctly replace words with repeated letters, which would, in turn, improve hate speech detection in texts.
Fulltext A novel 3D ray launching technique for radio propagation prediction in indoor environments

Geok TK, Hossain F, Chiat ATW

PLoS One, 2018;13(8):e0201905.
PMID: 30086170 DOI: 10.1371/journal.pone.0201905

Radio propagation prediction simulation methods based on deterministic technique such as ray launching is extensively used to accomplish radio channel characterization. However, the superiority of the simulation depends on the number of rays launched and received. This paper presented the indoor three-dimensional (3D) Minimum Ray Launching Maximum Accuracy (MRLMA) technique, which is applicable for an efficient indoor radio wave propagation prediction. Utilizing the novel MRLMA technique in the simulation environment for ray lunching and tracing can drastically reduce the number of rays that need to be traced, and improve the efficiency of ray tracing. Implementation and justification of MRLMA presented in the paper. An indoor office 3D layouts are selected and simulations have been performed using the MRLMA and other reference techniques. Results showed that the indoor 3D MRLMA model is appropriate for wireless communications network systems design and optimization process with respect to efficiency, coverage, number of rays launching, number of rays received by the mobile station, and simulation time.
Fulltext A novel LTE scheduling algorithm for green technology in smart grid

Hindia MN, Reza AW, Noordin KA, Chayon MH

PLoS One, 2015;10(4):e0121901.
PMID: 25830703 DOI: 10.1371/journal.pone.0121901

Smart grid (SG) application is being used nowadays to meet the demand of increasing power consumption. SG application is considered as a perfect solution for combining renewable energy resources and electrical grid by means of creating a bidirectional communication channel between the two systems. In this paper, three SG applications applicable to renewable energy system, namely, distribution automation (DA), distributed energy system-storage (DER) and electrical vehicle (EV), are investigated in order to study their suitability in Long Term Evolution (LTE) network. To compensate the weakness in the existing scheduling algorithms, a novel bandwidth estimation and allocation technique and a new scheduling algorithm are proposed. The technique allocates available network resources based on application's priority, whereas the algorithm makes scheduling decision based on dynamic weighting factors of multi-criteria to satisfy the demands (delay, past average throughput and instantaneous transmission rate) of quality of service. Finally, the simulation results demonstrate that the proposed mechanism achieves higher throughput, lower delay and lower packet loss rate for DA and DER as well as provide a degree of service for EV. In terms of fairness, the proposed algorithm shows 3%, 7 % and 9% better performance compared to exponential rule (EXP-Rule), modified-largest weighted delay first (M-LWDF) and exponential/PF (EXP/PF), respectively.
Fulltext A novel association rule mining approach using TID intermediate itemset

Aqra I, Herawan T, Abdul Ghani N, Akhunzada A, Ali A, Bin Razali R, et al.

PLoS One, 2018;13(1):e0179703.
PMID: 29351287 DOI: 10.1371/journal.pone.0179703

Designing an efficient association rule mining (ARM) algorithm for multilevel knowledge-based transactional databases that is appropriate for real-world deployments is of paramount concern. However, dynamic decision making that needs to modify the threshold either to minimize or maximize the output knowledge certainly necessitates the extant state-of-the-art algorithms to rescan the entire database. Subsequently, the process incurs heavy computation cost and is not feasible for real-time applications. The paper addresses efficiently the problem of threshold dynamic updation for a given purpose. The paper contributes by presenting a novel ARM approach that creates an intermediate itemset and applies a threshold to extract categorical frequent itemsets with diverse threshold values. Thus, improving the overall efficiency as we no longer needs to scan the whole database. After the entire itemset is built, we are able to obtain real support without the need of rebuilding the itemset (e.g. Itemset list is intersected to obtain the actual support). Moreover, the algorithm supports to extract many frequent itemsets according to a pre-determined minimum support with an independent purpose. Additionally, the experimental results of our proposed approach demonstrate the capability to be deployed in any mining system in a fully parallel mode; consequently, increasing the efficiency of the real-time association rules discovery process. The proposed approach outperforms the extant state-of-the-art and shows promising results that reduce computation cost, increase accuracy, and produce all possible itemsets.
Fulltext A novel hybrid classification model of genetic algorithms, modified k-Nearest Neighbor and developed backpropagation neural network

Salari N, Shohaimi S, Najafi F, Nallappan M, Karishnarajah I

PLoS One, 2014;9(11):e112987.
PMID: 25419659 DOI: 10.1371/journal.pone.0112987

Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms, and artificial neural networks are considered as the most common and effective methods in classification problems in numerous studies. In the present study, the results of the implementation of a novel hybrid feature selection-classification model using the above mentioned methods are presented. The purpose is benefitting from the synergies obtained from combining these technologies for the development of classification models. Such a combination creates an opportunity to invest in the strength of each algorithm, and is an approach to make up for their deficiencies. To develop proposed model, with the aim of obtaining the best array of features, first, feature ranking techniques such as the Fisher's discriminant ratio and class separability criteria were used to prioritize features. Second, the obtained results that included arrays of the top-ranked features were used as the initial population of a genetic algorithm to produce optimum arrays of features. Third, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. The performance of the proposed model was compared with thirteen well-known classification models based on seven datasets. Furthermore, the statistical analysis was performed using the Friedman test followed by post-hoc tests. The experimental findings indicated that the novel proposed hybrid model resulted in significantly better classification performance compared with all 13 classification methods. Finally, the performance results of the proposed model was benchmarked against the best ones reported as the state-of-the-art classifiers in terms of classification accuracy for the same data sets. The substantial findings of the comprehensive comparative study revealed that performance of the proposed model in terms of classification accuracy is desirable, promising, and competitive to the existing state-of-the-art classification models.
Fulltext A novel investigation of the influence of corporate governance on firms' credit ratings

Alkhawaldeh AAK, Jaber JJ, Boughaci D, Ismail N

PLoS One, 2021;16(5):e0250242.
PMID: 33945537 DOI: 10.1371/journal.pone.0250242

Corporate governance is the way of governing a firm in order to increase its accountability and to avoid any massive damage before it occurs. The aim of this paper is to investigate the impact of capital structure, firms' size, and competitive advantages of firms as control variables on credit ratings. We investigate the role of corporate governance in improving the firms' credit rating using a sample of Jordanian listed firms. We split firms into four categories according to WVB credit rating. We use both the binary logistic regression (LR) and the ordinal logistic regression (OLR) to model credit ratings in Jordanian environment. The empirical results show that the control variables are strong determinants of credit ratings. When we evaluate the relationship between the governance variables and credit ratings, we found interesting results. The board stockholders and board expertise are moderately significant. The board independence and role duality are weakly significant, while board size is insignificant.
Fulltext A novel method for multifactorial bio-chemical experiments design based on combinational design theory

Wang X, Sun B, Liu B, Fu Y, Zheng P

PLoS One, 2017;12(11):e0186853.
PMID: 29095845 DOI: 10.1371/journal.pone.0186853

Experimental design focuses on describing or explaining the multifactorial interactions that are hypothesized to reflect the variation. The design introduces conditions that may directly affect the variation, where particular conditions are purposely selected for observation. Combinatorial design theory deals with the existence, construction and properties of systems of finite sets whose arrangements satisfy generalized concepts of balance and/or symmetry. In this work, borrowing the concept of "balance" in combinatorial design theory, a novel method for multifactorial bio-chemical experiments design is proposed, where balanced templates in combinational design are used to select the conditions for observation. Balanced experimental data that covers all the influencing factors of experiments can be obtianed for further processing, such as training set for machine learning models. Finally, a software based on the proposed method is developed for designing experiments with covering influencing factors a certain number of times.
Fulltext A novel microfluidic compact disc to investigate electrochemical property changes between artificial and real salivary samples mixed with mouthwashes using electrical impedance analysis

Thiha A, Ibrahim F, Joseph K, Petrović B, Kojić S, Dahlan NA, et al.

PLoS One, 2023;18(2):e0280381.
PMID: 36795661 DOI: 10.1371/journal.pone.0280381

Diagnosing oral diseases at an early stage may lead to better preventive treatments, thus reducing treatment burden and costs. This paper introduces a systematic design of a microfluidic compact disc (CD) consisting of six unique chambers that run simultaneously from sample loading, holding, mixing and analysis. In this study, the electrochemical property changes between real saliva and artificial saliva mixed with three different types of mouthwashes (i.e. chlorhexidine-, fluoride- and essential oil (Listerine)-based mouthwashes) were investigated using electrical impedance analysis. Given the diversity and complexity of patient's salivary samples, we investigated the electrochemical impedance property of healthy real saliva mixed with different types of mouthwashes to understand the different electrochemical property which could be a foundation for diagnosis and monitoring of oral diseases. On the other hand, electrochemical impedance property of artificial saliva, a commonly used moisturizing agent and lubricant for the treatment of xerostomia or dry mouth syndrome was also studied. The findings indicate that artificial saliva and fluoride-based mouthwash showed higher conductance values compared to real saliva and two other different types of mouthwashes. The ability of our new microfluidic CD platform to perform multiplex processes and detection of electrochemical property of different types of saliva and mouthwashes is a fundamental concept for future research on salivary theranostics using point-of-care microfluidic CD platform.
Fulltext A novel oyster shell biocomposite for the efficient adsorptive removal of cadmium and lead from aqueous solution: Synthesis, process optimization, modelling and mechanism studies

Adeleke AO, Royahu CO, Ahmad A, Dele-Afolabi TT, Alshammari MB, Imteaz M

PLoS One, 2024;19(2):e0294286.
PMID: 38386950 DOI: 10.1371/journal.pone.0294286

This study highlights the effectiveness of oyster shell biocomposite for the biosorption of Cd(II) and Pb(II) ions from an aqueous solution. The aim of this work was to modify a novel biocomposite derived from oyster shell for the adsorption of Cd(II) and Pb(II) ions from aqueous solution. The studied revealed the specific surface BET surface area was 9.1476 m2/g. The elemental dispersive x-ray analysis (EDS) indicated that C, O, Ag, Ca were the predominant elements on the surface of the biocomposite after which metals ions of Cd and Pb were noticed after adsorption. The Fourier transform Irradiation (FT-IR) revealed the presence of carboxyl and hydroxyl groups on the surface. The effect of process variables on the adsorption capacity of the modified biocomposite was examined using the central composite design (CCD) of the response surface methodology (RSM). The process variables which include pH, adsorbent dose, the initial concentration and temperature were the most effective parameters influencing the uptake capacity. The optimal process conditions of these parameters were found to be pH, 5.57, adsorbent dose, 2.53 g/L, initial concentration, 46.76 mg/L and temperature 28.48°C for the biosorption of Cd(II) and Pb(II) ions from aqueous solution at a desirability coefficient of 1. The analysis of variance (ANOVA) revealed a high coefficient of determination (R2 > 0.91) and low probability coefficients for the responses (P < 0.05) which indicated the validity and aptness of the model for the biosorption of the metal ions. Experimental isotherm data fitted better to the Langmuir model and the kinetic data fitted better to the pseudo-second-order model. Maximun Cd(II) and Pb(II) adsorption capacities of the oyster shell biocomposite were 97.54 and 78.99 mg/g respectively and was obtained at pH 5.56 and 28.48°C. This investigation has provided the possibility of the utilization of alternative biocomposite as a sustainable approach for the biosorption of heavy metal ions from the wastewater stream.

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