MyMedR

Displaying publications 61 - 80 of 417 in total

Abstract:

Sort:

Fulltext Accurate Indoor-Positioning Model Based on People Effect and Ray-Tracing Propagation

Firdaus F, Ahmad NA, Sahibuddin S

Sensors (Basel), 2019 Dec 15;19(24).
PMID: 31847488 DOI: 10.3390/s19245546

Wireless local area networks (WLAN)-fingerprinting has been highlighted as the preferred technology for indoor positioning due to its accurate positioning and minimal infrastructure cost. However, its accuracy is highly influenced by obstacles that cause fluctuation in the signal strength. Many researchers have modeled static obstacles such as walls and ceilings, but few studies have modeled the people's presence effect (PPE), although the human body has a great impact on signal strength. Therefore, PPE must be addressed to obtain accurate positioning results. Previous research has proposed a model to address this issue, but these studies only considered the direct path signal between the transmitter and the receiver whereas multipath effects such as reflection also have a significant influence on indoor signal propagation. This research proposes an accurate indoor-positioning model by considering people's presence and multipath using ray-tracing, we call it (AIRY). This study proposed two solutions to construct AIRY: an automatic radio map using ray tracing and a constant of people's effect for the received signal strength indicator (RSSI) adaptation. The proposed model was simulated using MATLAB software and tested at Level 3, Menara Razak, Universiti Teknologi Malaysia. A K-nearest-neighbor (KNN) algorithm was used to define a position. The initial accuracy was 2.04 m, which then reduced to 0.57 m after people's presence and multipath effects were considered.

Matched MeSH terms: Cluster Analysis
Fulltext Solving text clustering problem using a memetic differential evolution algorithm

Mustafa HMJ, Ayob M, Albashish D, Abu-Taleb S

PLoS One, 2020;15(6):e0232816.
PMID: 32525869 DOI: 10.1371/journal.pone.0232816

The text clustering is considered as one of the most effective text document analysis methods, which is applied to cluster documents as a consequence of the expanded big data and online information. Based on the review of the related work of the text clustering algorithms, these algorithms achieved reasonable clustering results for some datasets, while they failed on a wide variety of benchmark datasets. Furthermore, the performance of these algorithms was not robust due to the inefficient balance between the exploitation and exploration capabilities of the clustering algorithm. Accordingly, this research proposes a Memetic Differential Evolution algorithm (MDETC) to solve the text clustering problem, which aims to address the effect of the hybridization between the differential evolution (DE) mutation strategy with the memetic algorithm (MA). This hybridization intends to enhance the quality of text clustering and improve the exploitation and exploration capabilities of the algorithm. Our experimental results based on six standard text clustering benchmark datasets (i.e. the Laboratory of Computational Intelligence (LABIC)) have shown that the MDETC algorithm outperformed other compared clustering algorithms based on AUC metric, F-measure, and the statistical analysis. Furthermore, the MDETC is compared with the state of art text clustering algorithms and obtained almost the best results for the standard benchmark datasets.

Matched MeSH terms: Cluster Analysis
Fulltext NMF-Based Approach for Missing Values Imputation of Mass Spectrometry Metabolomics Data

Xu J, Wang Y, Xu X, Cheng KK, Raftery D, Dong J

Molecules, 2021 Sep 24;26(19).
PMID: 34641330 DOI: 10.3390/molecules26195787

In mass spectrometry (MS)-based metabolomics, missing values (NAs) may be due to different causes, including sample heterogeneity, ion suppression, spectral overlap, inappropriate data processing, and instrumental errors. Although a number of methodologies have been applied to handle NAs, NA imputation remains a challenging problem. Here, we propose a non-negative matrix factorization (NMF)-based method for NA imputation in MS-based metabolomics data, which makes use of both global and local information of the data. The proposed method was compared with three commonly used methods: k-nearest neighbors (kNN), random forest (RF), and outlier-robust (ORI) missing values imputation. These methods were evaluated from the perspectives of accuracy of imputation, retrieval of data structures, and rank of imputation superiority. The experimental results showed that the NMF-based method is well-adapted to various cases of data missingness and the presence of outliers in MS-based metabolic profiles. It outperformed kNN and ORI and showed results comparable with the RF method. Furthermore, the NMF method is more robust and less susceptible to outliers as compared with the RF method. The proposed NMF-based scheme may serve as an alternative NA imputation method which may facilitate biological interpretations of metabolomics data.

Matched MeSH terms: Cluster Analysis
Fulltext A new hybrid fuzzy time series model with an application to predict PM10 concentration

Alyousifi Y, Othman M, Husin A, Rathnayake U

Ecotoxicol Environ Saf, 2021 Dec 20;227:112875.
PMID: 34717219 DOI: 10.1016/j.ecoenv.2021.112875

Fuzzy time series (FTS) forecasting models show a great performance in predicting time series, such as air pollution time series. However, they have caused major issues by utilizing random partitioning of the universe of discourse and ignoring repeated fuzzy sets. In this study, a novel hybrid forecasting model by integrating fuzzy time series to Markov chain and C-Means clustering techniques with an optimal number of clusters is presented. This hybridization contributes to generating effective lengths of intervals and thus, improving the model accuracy. The proposed model was verified and validated with real time series data sets, which are the benchmark data of actual trading of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and PM10 concentration data from Melaka, Malaysia. In addition, a comparison was made with some existing fuzzy time series models. Furthermore, the mean absolute percentage error, mean squared error and Theil's U statistic were calculated as evaluation criteria to illustrate the performance of the proposed model. The empirical analysis shows that the proposed model handles the time series data sets more efficiently and provides better overall forecasting results than existing FTS models. The results prove that the proposed model has greatly improved the prediction accuracy, for which it outperforms several fuzzy time series models. Therefore, it can be concluded that the proposed model is a better option for forecasting air pollution parameters and any kind of random parameters.

Matched MeSH terms: Cluster Analysis
Fulltext Effects of baseline correction algorithms on forensic classification of paper based on ATR-FTIR spectrum and Principal Component Analysis (PCA)

Lee, L.C., Liong, C-Y., Khairul, O., Jemain, A.A.

Pertanika Journal of Science & Technology, 2017;25(3):767-774.
MyJurnal

Spectral data is often required to be pre-processed prior to applying a multivariate modelling technique. Baseline correction of spectral data is one of the most important and frequently applied pre-processing procedures. This preliminary study aims to investigate the impacts of six types of baseline correction algorithms on classifying 150 infrared spectral data of three varieties of paper. The algorithms investigated were Iterative Restricted Least Squares, Asymmetric Least Squares (ALS), Low-pass FFT Filter, Median Window (MW), Fill Peaks and Modified Polynomial Fitting. Processed spectral data were then analysed using Principal Component Analysis (PCA) to visually examine the clustering among the three varieties of paper. Results show that separation among the three varieties of paper is greatly improved after baseline correction via ALS, FP and MW algorithms.

Matched MeSH terms: Cluster Analysis
Fulltext A Neighborhood and Machine Learning-Enabled Information Fusion Approach for the WSNs and Internet of Medical Things

Khan ZA, Naz S, Khan R, Teo J, Ghani A, Almaiah MA

Comput Intell Neurosci, 2022;2022:5112375.
PMID: 35449734 DOI: 10.1155/2022/5112375

Data redundancy or fusion is one of the common issues associated with the resource-constrained networks such as Wireless Sensor Networks (WSNs) and Internet of Things (IoTs). To resolve this issue, numerous data aggregation or fusion schemes have been presented in the literature. Generally, it is used to decrease the size of the collected data and, thus, improve the performance of the underlined IoTs in terms of congestion control, data accuracy, and lifetime. However, these approaches do not consider neighborhood information of the devices (cluster head in this case) in the data refinement phase. In this paper, a smart and intelligent neighborhood-enabled data aggregation scheme is presented where every device (cluster head) is bounded to refine the collected data before sending it to the concerned server module. For this purpose, the proposed data aggregation scheme is divided into two phases: (i) identification of neighboring nodes, which is based on the MAC address and location, and (ii) data aggregation using k-mean clustering algorithm and Support Vector Machine (SVM). Furthermore, every CH is smart enough to compare data sets of neighboring nodes only; that is, data of nonneighbor is not compared at all. These algorithms were implemented in Network Simulator 2 (NS-2) and were evaluated in terms of various performance metrics, such as the ratio of data redundancy, lifetime, and energy efficiency. Simulation results have verified that the proposed scheme performance is better than the existing approaches.

Matched MeSH terms: Cluster Analysis
Fulltext Dynamic spatiotemporal trends of dengue transmission in the Asia-Pacific region, 1955-2004

Banu S, Hu W, Guo Y, Naish S, Tong S

PLoS One, 2014;9(2):e89440.
PMID: 24586780 DOI: 10.1371/journal.pone.0089440

BACKGROUND: Dengue fever (DF) is one of the most important emerging arboviral human diseases. Globally, DF incidence has increased by 30-fold over the last fifty years, and the geographic range of the virus and its vectors has expanded. The disease is now endemic in more than 120 countries in tropical and subtropical parts of the world. This study examines the spatiotemporal trends of DF transmission in the Asia-Pacific region over a 50-year period, and identified the disease's cluster areas.
METHODOLOGY AND FINDINGS: The World Health Organization's DengueNet provided the annual number of DF cases in 16 countries in the Asia-Pacific region for the period 1955 to 2004. This fifty-year dataset was divided into five ten-year periods as the basis for the investigation of DF transmission trends. Space-time cluster analyses were conducted using scan statistics to detect the disease clusters. This study shows an increasing trend in the spatiotemporal distribution of DF in the Asia-Pacific region over the study period. Thailand, Vietnam, Laos, Singapore and Malaysia are identified as the most likely clusters (relative risk = 13.02) of DF transmission in this region in the period studied (1995 to 2004). The study also indicates that, for the most part, DF transmission has expanded southwards in the region.
CONCLUSIONS: This information will lead to the improvement of DF prevention and control strategies in the Asia-Pacific region by prioritizing control efforts and directing them where they are most needed.

Matched MeSH terms: Cluster Analysis
Statistical validation for the profiling of heroin by associating simulated postcut samples with the corresponding precut sample

Chan KW, Tan GH, Wong RC

J Forensic Sci, 2013 Jan;58 Suppl 1:S199-207.
PMID: 23013257 DOI: 10.1111/j.1556-4029.2012.02285.x

Statistical validation is crucial for the clustering of unknown samples. This study aims to demonstrate how statistical techniques can be optimized using simulated heroin samples containing a range of analyte concentrations that are similar to those of the case samples. Eight simulated heroin distribution links consisting of 64 postcut samples were prepared by mixing one of two mixtures of paracetamol-caffeine-dextromethorphan at different proportions with eight precut samples. Analyte contents and compositional variation of the prepared samples were investigated. A number of data pretreatments were evaluated by associating the postcut samples with the corresponding precut samples using principal component analysis and discriminant analysis. Subsequently, combinations of seven linkage methods and five distance measures were explored using hierarchical cluster analysis. In this study, Ward-Manhattan showed better distinctions between unrelated links and was able to cluster all related samples in very close distance under the known links on a dendogram. A similar discriminative outcome was also achieved by 90 unknown case samples when clustered via Ward-Manhattan.

Matched MeSH terms: Cluster Analysis
Fulltext Investigating cultural evolution using phylogenetic analysis: the origins and descent of the southeast Asian tradition of warp ikat weaving

Buckley CD

PLoS One, 2012;7(12):e52064.
PMID: 23272211 DOI: 10.1371/journal.pone.0052064

The warp ikat method of making decorated textiles is one of the most geographically widespread in southeast Asia, being used by Austronesian peoples in Indonesia, Malaysia and the Philippines, and Daic peoples on the Asian mainland. In this study a dataset consisting of the decorative characters of 36 of these warp ikat weaving traditions is investigated using Bayesian and Neighbornet techniques, and the results are used to construct a phylogenetic tree and taxonomy for warp ikat weaving in southeast Asia. The results and analysis show that these diverse traditions have a common ancestor amongst neolithic cultures the Asian mainland, and parallels exist between the patterns of textile weaving descent and linguistic phylogeny for the Austronesian group. Ancestral state analysis is used to reconstruct some of the features of the ancestral weaving tradition. The widely held theory that weaving motifs originated in the late Bronze Age Dong-Son culture is shown to be inconsistent with the data.

Matched MeSH terms: Cluster Analysis
Fulltext Complex Network Theory Applied to the Growth of Kuala Lumpur's Public Urban Rail Transit Network

Ding R, Ujang N, Hamid HB, Wu J

PLoS One, 2015;10(10):e0139961.
PMID: 26448645 DOI: 10.1371/journal.pone.0139961

Recently, the number of studies involving complex network applications in transportation has increased steadily as scholars from various fields analyze traffic networks. Nonetheless, research on rail network growth is relatively rare. This research examines the evolution of the Public Urban Rail Transit Networks of Kuala Lumpur (PURTNoKL) based on complex network theory and covers both the topological structure of the rail system and future trends in network growth. In addition, network performance when facing different attack strategies is also assessed. Three topological network characteristics are considered: connections, clustering and centrality. In PURTNoKL, we found that the total number of nodes and edges exhibit a linear relationship and that the average degree stays within the interval [2.0488, 2.6774] with heavy-tailed distributions. The evolutionary process shows that the cumulative probability distribution (CPD) of degree and the average shortest path length show good fit with exponential distribution and normal distribution, respectively. Moreover, PURTNoKL exhibits clear cluster characteristics; most of the nodes have a 2-core value, and the CPDs of the centrality's closeness and betweenness follow a normal distribution function and an exponential distribution, respectively. Finally, we discuss four different types of network growth styles and the line extension process, which reveal that the rail network's growth is likely based on the nodes with the biggest lengths of the shortest path and that network protection should emphasize those nodes with the largest degrees and the highest betweenness values. This research may enhance the networkability of the rail system and better shape the future growth of public rail networks.

Matched MeSH terms: Cluster Analysis
Fulltext Dataset of the first de novo transcriptome assembly of the arillode of Baccaurea motleyana

Matra DD, Ritonga AW, Natawijaya A, Poerwanto R, Sobir, Widodo WD, et al.

Data Brief, 2019 Feb;22:332-335.
PMID: 30596128 DOI: 10.1016/j.dib.2018.12.031

Baccaurea motleyana Müll. Arg. (rambai) is one of the underutilized fruit natives to Indonesia, Thailand, and Malaya Peninsula and it is mostly cultivated in Java island (Lim, 2012) [1]. The edible part of fruits is white and reddish arillodes in which having sweet to acid-sweet tastes. However, nucleotide as well as transcriptome information of this species is still scarce, no information has been deposited in GenBank. In this data article, we performed for the first time of de novo assembly of transcriptome using paired-end Illumina technology. The assembled contigs were constructed using Trinity and after filtering and clustering, produced 37,077 contigs. The contig ranged 201-4972 bp and N50 has 696 bp. The contig was annotated with several database such as SwissProt, TrEMBL, nr and nt NCBI databases. The raw reads were deposited in DDBJ with DRA numbers, DRA007358. The assembled contigs of transcriptome are deposited in the DDBJ TSA with accession number, IADP01000001-IADP01037077 and also can be accessed at http://rujakbase.id.

Matched MeSH terms: Cluster Analysis
Complexity in financial asset returns: Evidence from the compass rose

Batten JA, Lucey BM, Peat M

Chaos, 2018 Dec;28(12):123109.
PMID: 30599539 DOI: 10.1063/1.5029226

The compass rose pattern in financial data may indicate the presence of a nonlinear, possibly chaotic, data generating mechanism. The analysis of three key financial asset and denoised returns, gold, the Great British Pound/US dollar spot exchange rate, and the Standard & Poor's 500 stock index, reveals that over four equivalent subperiods, from 1996 to 2015, the compass rose pattern changes. This finding provides an opportunity to establish how noise affects financial time series. We conclude that the compass rose pattern is unlikely the product of an underlying nonlinear structure, since there is evidence of nonlinearity in all time periods, even those where the compass rose pattern is not evident. Therefore, the compass rose patterns, seen in the denoised data, suggest that the presence of noise masks the underlying dynamics of the asset returns.

Matched MeSH terms: Cluster Analysis
Fulltext Nature of Complex Network of Dengue Epidemic as a Scale-Free Network

Malik HAM, Abid F, Mahmood N, Wahiddin MR, Malik A

Healthc Inform Res, 2019 Jul;25(3):182-192.
PMID: 31406610 DOI: 10.4258/hir.2019.25.3.182

Objectives: Dengue epidemic is a dynamic and complex phenomenon that has gained considerable attention due to its injurious effects. The focus of this study is to statically analyze the nature of the dengue epidemic network in terms of whether it follows the features of a scale-free network or a random network.
Methods: A multifarious network of Aedes aegypti is addressed keeping the viewpoint of a complex system and modelled as a network. The dengue network has been transformed into a one-mode network from a two-mode network by utilizing projection methods. Furthermore, three network features have been analyzed, the power-law, clustering coefficient, and network visualization. In addition, five methods have been applied to calculate the global clustering coefficient.
Results: It has been observed that dengue epidemic follows a power-law, with the value of its exponent γ = -2.1. The value of the clustering coefficient is high for dengue cases, as weight of links. The minimum method showed the highest value among the methods used to calculate the coefficient. Network visualization showed the main areas. Moreover, the dengue situation did not remain the same throughout the observed period.
Conclusions: The results showed that the network topology exhibits the features of a scale-free network instead of a random network. Focal hubs are highlighted and the critical period is found. Outcomes are important for the researchers, health officials, and policy makers who deal with arbovirus epidemic diseases. Zika virus and Chikungunya virus can also be modelled and analyzed in this manner.

Matched MeSH terms: Cluster Analysis
Fulltext Zooplankton in seagrass and adjacent non-seagrass habitats in Tun Mustapha Park, Sabah, Malaysia

Sing, Lui Lo, Chen, Cheng Ann, Tzuen, Kiat Yap, Teruaki Yoshida

Borneo Journal of Marine Science and Aquaculture, 2020;4(1):6-13.
MyJurnal

A comparison of zooplankton abundance and community in the seagrass and non-seagrass areas of Limau-limauan and Bak- Bak waters within the newly established Tun Mustapha Marine Park was made during 15-17 May 2017. Samples were collected via horizontal tow of a 140 μm plankton net. Environmental variables (temperature, salinity, DO, pH, turbidity) showed no significant differences among the study sites. However, zooplankton showed increasing abundance from non-seagrass, seagrass edge, to seagrass areas at Limau-limauan, while abundance values were comparable among the stations at Bak-bak. Overall zooplankton abundance was significantly higher at the seagrass areas relative to the non-seagrass station at Limau-limauan (p < 0.005), while no statistical difference was found at Bak-Bak (p < 0.21). Mean canopy height was 3-fold higher (p < 0.001) at Limau-limauan than Bak-Bak, suggesting the importance of seagrass bed structural complexity in habitat preference for zooplankton. Cluster analysis revealed the zooplankton community from the seagrass area at Limau-limauan was different from that at seagrass edge and non-seagrass areas, which may be attributed to the influence of seagrass meadows in forming characteristic zooplankton compositions. Marked differences in zooplankton composition and abundance even in close vicinity of sites suggest the importance of local small-scale variations in seagrass habitats in shaping the zooplankton community.

Matched MeSH terms: Cluster Analysis
Fulltext RF-Based Moisture Content Determination in Rice Using Machine Learning Techniques

Azmi N, Kamarudin LM, Zakaria A, Ndzi DL, Rahiman MHF, Zakaria SMMS, et al.

Sensors (Basel), 2021 Mar 08;21(5).
PMID: 33800174 DOI: 10.3390/s21051875

Seasonal crops require reliable storage conditions to protect the yield once harvested. For long term storage, controlling the moisture content level in grains is challenging because existing moisture measuring techniques are time-consuming and laborious as measurements are carried out manually. The measurements are carried out using a sample and moisture may be unevenly distributed inside the silo/bin. Numerous studies have been conducted to measure the moisture content in grains utilising dielectric properties. To the best of authors' knowledge, the utilisation of low-cost wireless technology operating in the 2.4 GHz and 915 MHz ISM bands such as Wireless Sensor Network (WSN) and Radio Frequency Identification (RFID) have not been widely investigated. This study focuses on the characterisation of 2.4 GHz Radio Frequency (RF) transceivers using ZigBee Standard and 868 to 915 MHz UHF RFID transceiver for moisture content classification and prediction using Artificial Neural Network (ANN) models. The Received Signal Strength Indicator (RSSI) from the wireless transceivers is used for moisture content prediction in rice. Four samples (2 kg of rice each) were conditioned to 10%, 15%, 20%, and 25% moisture contents. The RSSI from both systems were obtained and processed. The processed data is used as input to different ANNs models such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest, and Multi-layer Perceptron (MLP). The results show that the Random Forest method with one input feature (RSSI_WSN) provides the highest accuracy of 87% compared to the other four models. All models show more than 98% accuracy when two input features (RSSI_WSN and RSSI_TAG2) are used. Hence, Random Forest is a reliable model that can be used to predict the moisture content level in rice as it gives a high accuracy even when only one input feature is used.

Matched MeSH terms: Cluster Analysis
Fulltext Unsupervised bivariate data clustering for damage assessment of carbon fiber composite laminates

May Z, Alam MK, Mahmud MS, Rahman NAA

PLoS One, 2020;15(11):e0242022.
PMID: 33186372 DOI: 10.1371/journal.pone.0242022

Damage assessment is a key element in structural health monitoring of various industrial applications to understand well and predict the response of the material. The big uncertainty in carbon fiber composite materials response is because of variability in the initiation and propagation of damage. Developing advanced tools to design with composite materials, methods for characterizing several damage modes during operation are required. While there is a significant amount of work on the analysis of acoustic emission (AE) from different composite materials and many loading cases, this research focuses on applying an unsupervised clustering method for separating AE data into several groups with distinct evolution. In this paper, we develop an adaptive sampling and unsupervised bivariate data clustering techniques to characterize the several damage initiations of a composite structure in different lay-ups. An adaptive sampling technique pre-processes the AE features and eliminates redundant AE data samples. The reduction of unnecessary AE data depends on the requirements of the proposed bivariate data clustering technique. The bivariate data clustering technique groups the AE data (dependent variable) with respect to the mechanical data (independent variable) to assess the damage of the composite structure. Tensile experiments on carbon fiber reinforced composite laminates (CFRP) in different orientations are carried out to collect mechanical and AE data and demonstrate the damage modes. Based on the mechanical stress-strain data, the results show the dominant damage regions in different lay-ups of specimens and the definition of the different states of damage. In addition, the states of the damage are observed using Scanning Electron Microscope (SEM) analysis. Based on the AE data, the results show that the strong linear correlation between AE and mechanical energy, and the classification of various modes of damage in all lay-ups of specimens forming clusters of AE energy with respect to the mechanical energy. Furthermore, the validation of the cluster-based characterization and improvement of the sensitivity of the damage modes classification are observed by the combined knowledge of AE and mechanical energy and time-frequency spectrum analysis.

Matched MeSH terms: Cluster Analysis
Profiles of Physical Fitness and Fitness Enjoyment Among Children: Associations With Sports Participation

De Meester A, Wazir MRWN, Lenoir M, Bardid F

Res Q Exerc Sport, 2020 Sep 09.
PMID: 32903170 DOI: 10.1080/02701367.2020.1788700

Purpose: The present study examined whether groups of children with different physical fitness and fitness enjoyment profiles could be identified and, if so, whether the different groups varied from one another in terms of organized sports participation. Method: Five hundred and fifty-eight 8-11-year-olds (56.99% boys) participated in this cross-sectional study. Physical fitness and fitness enjoyment were assessed with seven items from a standardized test battery and a pictorial scale containing pictures referring to the enjoyment in those seven physical fitness tests, respectively. To examine whether groups with different fitness and enjoyment profiles could be identified, we conducted cluster analyses based on children's standardized physical fitness and fitness enjoyment scores. A two-way ANCOVA (sex*cluster) was conducted to investigate differences in weekly organized sports participation among each of the identified groups while controlling for age. Results: Cluster analyses revealed two groups with aligned levels of physical fitness and fitness enjoyment (i.e., relatively low-low and relatively high-high) and two groups with unaligned levels (i.e., relatively low-moderate and relatively high-moderate), respectively. Both groups with relatively high fitness scores were found to spend significantly more time in organized sports (M = 2.01 h and 2.29 h, respectively) than the two groups with relatively low fitness scores (M = 1.08 h and 0.98 h, respectively), irrespective of their enjoyment levels. Conclusion: Increasing physical fitness levels (especially among those children with suboptimal enjoyment levels) may lead to increased organized sports participation, while increased organized sports participation in its turn may lead to higher fitness levels. As such, participation in sports programs should be promoted in children of all age groups.

Matched MeSH terms: Cluster Analysis
Fulltext Self-Reported Modifiable Risk Factors of Cardiovascular Disease among Older Adults in Malaysia: A Cross-Sectional Study of Prevalence and Clustering

Chan YY, Sahril N, Rezali MS, Kuang Kuay L, Baharudin A, Abd Razak MA, et al.

Int J Environ Res Public Health, 2021 07 27;18(15).
PMID: 34360235 DOI: 10.3390/ijerph18157941

The co-occurrence of multiple modifiable risk factors increases the risk of cardiovascular disease (CVD) morbidity or mortality. This study examines the prevalence and clustering of self-reported modifiable CVD risk factors among older adults in Malaysia. A total of 7117 adults aged ≥50 years participated in the National Health and Morbidity Survey (NHMS) 2018: Elderly Health, a community-based cross-sectional survey. Data were collected using a standardized structured questionnaire. Multivariable logistic regression was used to determine the factors associated with the clustering of self-reported modifiable CVD risk factors. The prevalence of self-reported diabetes, hypertension, hypercholesterolemia, overweight/obesity, and current smoking was 23.3%, 42.2%, 35.6%, 58.4%, and 17.5%, respectively. Overall, the prevalence of clustering of ≥1, ≥2, and ≥3 modifiable CVD risk factors was 83.3%, 75.4%, and 62.6%, respectively. Multivariable logistic regression analysis showed that men, 60-69 age group, urban dwellers, having no formal education, unemployed/retirees/homemakers, and being physically inactive were independently associated with self-reported modifiable CVD risk factors clustering. There are also ethnic differences in self-reported modifiable CVD risk factors clustering. Our findings underscore the necessity of targeted interventions and integrated strategies for early detection and treatment of modifiable CVD risk factors among older adults, considering age, sex, ethnicity, and socioeconomic status.

Matched MeSH terms: Cluster Analysis
Fulltext Distributed controller clustering in software defined networks

Abdelaziz A, Fong AT, Gani A, Garba U, Khan S, Akhunzada A, et al.

PLoS One, 2017;12(4):e0174715.
PMID: 28384312 DOI: 10.1371/journal.pone.0174715

Software Defined Networking (SDN) is an emerging promising paradigm for network management because of its centralized network intelligence. However, the centralized control architecture of the software-defined networks (SDNs) brings novel challenges of reliability, scalability, fault tolerance and interoperability. In this paper, we proposed a novel clustered distributed controller architecture in the real setting of SDNs. The distributed cluster implementation comprises of multiple popular SDN controllers. The proposed mechanism is evaluated using a real world network topology running on top of an emulated SDN environment. The result shows that the proposed distributed controller clustering mechanism is able to significantly reduce the average latency from 8.1% to 1.6%, the packet loss from 5.22% to 4.15%, compared to distributed controller without clustering running on HP Virtual Application Network (VAN) SDN and Open Network Operating System (ONOS) controllers respectively. Moreover, proposed method also shows reasonable CPU utilization results. Furthermore, the proposed mechanism makes possible to handle unexpected load fluctuations while maintaining a continuous network operation, even when there is a controller failure. The paper is a potential contribution stepping towards addressing the issues of reliability, scalability, fault tolerance, and inter-operability.

Matched MeSH terms: Cluster Analysis
Fulltext Chromatographic fingerprint and Chemometric approach for quality control of Tongkat Ali (Eurycoma Longifolia)

Nor Nasriah Zaini, Mardiana Saaid, Hafizan Juahir, Rozita Osman

Pertanika Journal of Science & Technology, 2017;25(103):345-354.
MyJurnal

Tongkat Ali (Eurycoma longifolia) is one of the most popular tropical herbal plants as it is believed to enhance virility and sexual prowess. This study looked examined chromatographic fingerprint of Tongkat Ali roots and its products generated using online solid phase-extraction liquid chromatography (SPE-LC) combined with chemometric approaches. The aim was to determine its quality. Pressurised liquid extraction (PLE) technique was used prior to online SPE-LC using polystyrene divinyl benzene (PSDVB) and C18 columns. Seventeen Tongkat Ali roots and 10 products (capsules) were analysed. Chromatographic dataset was subjected to chemometric techniques, namely cluster analysis (CA), discriminant analysis (DA) and principal component analysis (PCA) using 37 selected peaks. The samples were grouped into three clusters based on their quality. The PCA resulted in 11 latent factors describing 90.8% of the whole variance. Pattern matching analysis showed no significant difference (p>0.05) between the roots and products within the same CA grouping. The findings showed the combination of chromatographic fingerprint and chemometric techniques provided comprehensive evaluation for efficient quality control of Tongkat Ali formulation.

Matched MeSH terms: Cluster Analysis

Filters

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links