Displaying publications 1 - 20 of 311 in total

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  1. Fulltext A Microwave Ring-Resonator Sensor for Non-Invasive Assessment of Meat Aging
    Jilnai MT, Wen WP, Cheong LY, ur Rehman MZ
    Sensors (Basel), 2016;16(1).
    PMID: 26805828 DOI: 10.3390/s16010052
    The assessment of moisture loss from meat during the aging period is a critical issue for the meat industry. In this article, a non-invasive microwave ring-resonator sensor is presented to evaluate the moisture content, or more precisely water holding capacity (WHC) of broiler meat over a four-week period. The developed sensor has shown significant changes in its resonance frequency and return loss due to reduction in WHC in the studied duration. The obtained results are also confirmed by physical measurements. Further, these results are evaluated using the Fricke model, which provides a good fit for electric circuit components in biological tissue. Significant changes were observed in membrane integrity, where the corresponding capacitance decreases 30% in the early aging (0D-7D) period. Similarly, the losses associated with intracellular and extracellular fluids exhibit changed up to 42% and 53%, respectively. Ultimately, empirical polynomial models are developed to predict the electrical component values for a better understanding of aging effects. The measured and calculated values are found to be in good agreement.
    Matched MeSH terms: Models, Statistical
  2. Water treatment and artificial intelligence techniques: a systematic literature review research
    Ismail W, Niknejad N, Bahari M, Hendradi R, Zaizi NJM, Zulkifli MZ
    Environ Sci Pollut Res Int, 2023 Jun;30(28):71794-71812.
    PMID: 34609681 DOI: 10.1007/s11356-021-16471-0
    As clean water can be considered among the essentials of human life, there is always a requirement to seek its foremost and high quality. Water primarily becomes polluted due to organic as well as inorganic pollutants, including nutrients, heavy metals, and constant contamination with organic materials. Predicting the quality of water accurately is essential for its better management along with controlling pollution. With stricter laws regarding water treatment to remove organic and biologic materials along with different pollutants, looking for novel technologic procedures will be necessary for improved control of the treatment processes by water utilities. Linear regression-based models with relative simplicity considering water prediction have been typically used as available statistical models. Nevertheless, in a majority of real problems, particularly those associated with modeling of water quality, non-linear patterns will be observed, requiring non-linear models to address them. Thus, artificial intelligence (AI) can be a good candidate in modeling and optimizing the elimination of pollutants from water in empirical settings with the ability to generate ideal operational variables, due to its recent considerable advancements. Management and operation of water treatment procedures are supported technically by these technologies, leading to higher efficiency compared to sole dependence on human operations. Thus, establishing predictive models for water quality and subsequently, more efficient management of water resources would be critically important, serving as a strong tool. A systematic review methodology has been employed in the present work to investigate the previous studies over the time interval of 2010-2020, while analyzing and synthesizing the literature, particularly regarding AI application in water treatment. A total number of 92 articles had addressed the topic under study using AI. Based on the conclusions, the application of AI can obviously facilitate operations, process automation, and management of water resources in significantly volatile contexts.
    Matched MeSH terms: Models, Statistical
  3. Ancestry-informative marker (AIM) SNP panel for the Malay population
    Yahya P, Sulong S, Harun A, Wangkumhang P, Wilantho A, Ngamphiw C, et al.
    Int J Legal Med, 2020 Jan;134(1):123-134.
    PMID: 31760471 DOI: 10.1007/s00414-019-02184-0
    Ancestry-informative markers (AIMs) can be used to infer the ancestry of an individual to minimize the inaccuracy of self-reported ethnicity in biomedical research. In this study, we describe three methods for selecting AIM SNPs for the Malay population (Malay AIM panel) using different approaches based on pairwise FST, informativeness for assignment (In), and PCA-correlated SNPs (PCAIMs). These Malay AIM panels were extracted from genotype data stored in SNP arrays hosted by the Malaysian node of the Human Variome Project (MyHVP) and the Singapore Genome Variation Project (SGVP). In particular, genotype data from a total of 165 Malay individuals were analyzed, comprising data on 117 individual genotypes from the Affymetrix SNP-6 SNP array platform and data on 48 individual genotypes from the OMNI 2.5 Illumina SNP array platform. The HapMap phase 3 database (1397 individuals from 11 populations) was used as a reference for comparison with the Malay genotype data. The accuracy of each resulting Malay AIM panel was evaluated using a machine learning "ancestry-predictive model" constructed by using WEKA, a comprehensive machine learning platform written in Java. A total of 1250 SNPs were finally selected, which successfully identified Malay individuals from other world populations with an accuracy of 90%, but the accuracy decreased to 80% using 157 SNPs according to the pairwise FST method, while a panel of 200 SNPs selected using In and PCAIMs could be used to identify Malay individuals with an accuracy of approximately 80%.
    Matched MeSH terms: Models, Statistical
  4. Prediction of breast cancer risk based on common genetic variants in women of East Asian ancestry
    Wen W, Shu XO, Guo X, Cai Q, Long J, Bolla MK, et al.
    Breast Cancer Res, 2016 12 08;18(1):124.
    PMID: 27931260
    BACKGROUND: Approximately 100 common breast cancer susceptibility alleles have been identified in genome-wide association studies (GWAS). The utility of these variants in breast cancer risk prediction models has not been evaluated adequately in women of Asian ancestry.

    METHODS: We evaluated 88 breast cancer risk variants that were identified previously by GWAS in 11,760 cases and 11,612 controls of Asian ancestry. SNPs confirmed to be associated with breast cancer risk in Asian women were used to construct a polygenic risk score (PRS). The relative and absolute risks of breast cancer by the PRS percentiles were estimated based on the PRS distribution, and were used to stratify women into different levels of breast cancer risk.

    RESULTS: We confirmed significant associations with breast cancer risk for SNPs in 44 of the 78 previously reported loci at P 

    Matched MeSH terms: Models, Statistical
  5. Fulltext Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation
    Illias HA, Zhao Liang W
    PLoS One, 2018;13(1):e0191366.
    PMID: 29370230 DOI: 10.1371/journal.pone.0191366
    Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site.
    Matched MeSH terms: Models, Statistical
  6. Temporal patterns and source apportionment of nitrate-nitrogen leaching in a paddy field at Kelantan, Malaysia
    Hussain H, Yusoff MK, Ramli MF, Abd Latif P, Juahir H, Zawawi MA
    Pak J Biol Sci, 2013 Nov 15;16(22):1524-30.
    PMID: 24511695
    Nitrate-nitrogen leaching from agricultural areas is a major cause for groundwater pollution. Polluted groundwater with high levels of nitrate is hazardous and cause adverse health effects. Human consumption of water with elevated levels of NO3-N has been linked to the infant disorder methemoglobinemia and also to non-Hodgkin's disease lymphoma in adults. This research aims to study the temporal patterns and source apportionment of nitrate-nitrogen leaching in a paddy soil at Ladang Merdeka Ismail Mulong in Kelantan, Malaysia. The complex data matrix (128 x 16) of nitrate-nitrogen parameters was subjected to multivariate analysis mainly Principal Component Analysis (PCA) and Discriminant Analysis (DA). PCA extracted four principal components from this data set which explained 86.4% of the total variance. The most important contributors were soil physical properties confirmed using Alyuda Forecaster software (R2 = 0.98). Discriminant analysis was used to evaluate the temporal variation in soil nitrate-nitrogen on leaching process. Discriminant analysis gave four parameters (hydraulic head, evapotranspiration, rainfall and temperature) contributing more than 98% correct assignments in temporal analysis. DA allowed reduction in dimensionality of the large data set which defines the four operating parameters most efficient and economical to be monitored for temporal variations. This knowledge is important so as to protect the precious groundwater from contamination with nitrate.
    Matched MeSH terms: Models, Statistical
  7. Environment and air pollution: health services bequeath to grotesque menace
    Qureshi MI, Rasli AM, Awan U, Ma J, Ali G, Faridullah, et al.
    Environ Sci Pollut Res Int, 2015 Mar;22(5):3467-76.
    PMID: 25242593 DOI: 10.1007/s11356-014-3584-2
    The objective of the study is to establish the link between air pollution, fossil fuel energy consumption, industrialization, alternative and nuclear energy, combustible renewable and wastes, urbanization, and resulting impact on health services in Malaysia. The study employed two-stage least square regression technique on the time series data from 1975 to 2012 to possibly minimize the problem of endogeniety in the health services model. The results in general show that air pollution and environmental indicators act as a strong contributor to influence Malaysian health services. Urbanization and nuclear energy consumption both significantly increases the life expectancy in Malaysia, while fertility rate decreases along with the increasing urbanization in a country. Fossil fuel energy consumption and industrialization both have an indirect relationship with the infant mortality rate, whereas, carbon dioxide emissions have a direct relationship with the sanitation facility in a country. The results conclude that balancing the air pollution, environment, and health services needs strong policy vistas on the end of the government officials.
    Matched MeSH terms: Models, Statistical*
  8. Utilizing shared interacting domain patterns and Gene Ontology information to improve protein-protein interaction prediction
    Roslan R, Othman RM, Shah ZA, Kasim S, Asmuni H, Taliba J, et al.
    Comput Biol Med, 2010 Jun;40(6):555-64.
    PMID: 20417930 DOI: 10.1016/j.compbiomed.2010.03.009
    Protein-protein interactions (PPIs) play a significant role in many crucial cellular operations such as metabolism, signaling and regulations. The computational methods for predicting PPIs have shown tremendous growth in recent years, but problem such as huge false positive rates has contributed to the lack of solid PPI information. We aimed at enhancing the overlap between computational predictions and experimental results in an effort to partially remove PPIs falsely predicted. The use of protein function predictor named PFP() that are based on shared interacting domain patterns is introduced in this study with the purpose of aiding the Gene Ontology Annotations (GOA). We used GOA and PFP() as agents in a filtering process to reduce false positive pairs in the computationally predicted PPI datasets. The functions predicted by PFP() were extracted from cross-species PPI data in order to assign novel functional annotations for the uncharacterized proteins and also as additional functions for those that are already characterized by the GO (Gene Ontology). The implementation of PFP() managed to increase the chances of finding matching function annotation for the first rule in the filtration process as much as 20%. To assess the capability of the proposed framework in filtering false PPIs, we applied it on the available S. cerevisiae PPIs and measured the performance in two aspects, the improvement made indicated as Signal-to-Noise Ratio (SNR) and the strength of improvement, respectively. The proposed filtering framework significantly achieved better performance than without it in both metrics.
    Matched MeSH terms: Models, Statistical*
  9. Prediction of scour below submerged pipeline crossing a river using ANN
    Azamathulla HM, Zakaria NA
    Water Sci Technol, 2011;63(10):2225-30.
    PMID: 21977642
    The process involved in the local scour below pipelines is so complex that it makes it difficult to establish a general empirical model to provide accurate estimation for scour. This paper describes the use of artificial neural networks (ANN) to estimate the pipeline scour depth. The data sets of laboratory measurements were collected from published works and used to train the network or evolve the program. The developed networks were validated by using the observations that were not involved in training. The performance of ANN was found to be more effective when compared with the results of regression equations in predicting the scour depth around pipelines.
    Matched MeSH terms: Models, Statistical*
  10. Optimisation of the supercritical extraction of toxic elements in fish oil
    Hajeb P, Jinap S, Shakibazadeh Sh, Afsah-Hejri L, Mohebbi GH, Zaidul IS
    Food Addit Contam Part A Chem Anal Control Expo Risk Assess, 2014;31(10):1712-22.
    PMID: 25090228 DOI: 10.1080/19440049.2014.942707
    This study aims to optimise the operating conditions for the supercritical fluid extraction (SFE) of toxic elements from fish oil. The SFE operating parameters of pressure, temperature, CO2 flow rate and extraction time were optimised using a central composite design (CCD) of response surface methodology (RSM). High coefficients of determination (R²) (0.897-0.988) for the predicted response surface models confirmed a satisfactory adjustment of the polynomial regression models with the operation conditions. The results showed that the linear and quadratic terms of pressure and temperature were the most significant (p < 0.05) variables affecting the overall responses. The optimum conditions for the simultaneous elimination of toxic elements comprised a pressure of 61 MPa, a temperature of 39.8ºC, a CO₂ flow rate of 3.7 ml min⁻¹ and an extraction time of 4 h. These optimised SFE conditions were able to produce fish oil with the contents of lead, cadmium, arsenic and mercury reduced by up to 98.3%, 96.1%, 94.9% and 93.7%, respectively. The fish oil extracted under the optimised SFE operating conditions was of good quality in terms of its fatty acid constituents.
    Matched MeSH terms: Models, Statistical
  11. Real-time signal processing for fetal heart rate monitoring
    Ibrahimy MI, Ahmed F, Mohd Ali MA, Zahedi E
    IEEE Trans Biomed Eng, 2003 Feb;50(2):258-62.
    PMID: 12665042
    An algorithm based on digital filtering, adaptive thresholding, statistical properties in the time domain, and differencing of local maxima and minima has been developed for the simultaneous measurement of the fetal and maternal heart rates from the maternal abdominal electrocardiogram during pregnancy and labor for ambulatory monitoring. A microcontroller-based system has been used to implement the algorithm in real-time. A Doppler ultrasound fetal monitor was used for statistical comparison on five volunteers with low risk pregnancies, between 35 and 40 weeks of gestation. Results showed an average percent root mean square difference of 5.32% and linear correlation coefficient from 0.84 to 0.93. The fetal heart rate curves remained inside a +/- 5-beats-per-minute limit relative to the reference ultrasound method for 84.1% of the time.
    Matched MeSH terms: Models, Statistical
  12. A statistical experiment design approach for optimizing biodegradation of weathered crude oil in coastal sediments
    Mohajeri L, Aziz HA, Isa MH, Zahed MA
    Bioresour Technol, 2010 Feb;101(3):893-900.
    PMID: 19773160 DOI: 10.1016/j.biortech.2009.09.013
    This work studied the bioremediation of weathered crude oil (WCO) in coastal sediment samples using central composite face centered design (CCFD) under response surface methodology (RSM). Initial oil concentration, biomass, nitrogen and phosphorus concentrations were used as independent variables (factors) and oil removal as dependent variable (response) in a 60 days trial. A statistically significant model for WCO removal was obtained. The coefficient of determination (R(2)=0.9732) and probability value (P<0.0001) demonstrated significance for the regression model. Numerical optimization based on desirability function were carried out for initial oil concentration of 2, 16 and 30 g per kg sediment and 83.13, 78.06 and 69.92 per cent removal were observed respectively, compare to 77.13, 74.17 and 69.87 per cent removal for un-optimized results.
    Matched MeSH terms: Models, Statistical
  13. The absorbed dose in femur exposed to diagnostic radiography
    Salehi Z, Yusoff AL
    Radiat Prot Dosimetry, 2013;154(3):396-9.
    PMID: 23012482 DOI: 10.1093/rpd/ncs239
    A femur phantom made of wax and a real human bone was used to study the dose during radiographical procedures. The depth dose inside the phantom was determined using DOSXYZnrc, a Monte Carlo simulation software. The results were verified with measurements using TLD-100H. It was found that for 2.5 mm aluminium filtered 84-kVp X-rays, the radiation dose in the bone reached 57 % higher than the surface dose, i.e. 3.23 mGy as opposed to 2.06 mGy at the surface. The use of real bone introduces variations in the bone density in the DOSXYZnrc model, resulting in a lower attenuation effect than expected from solid bone tissues.
    Matched MeSH terms: Models, Statistical*
  14. Fulltext Simultaneous time-temperature-thickness superposition theoretical and statistical modelling of convective drying of guava
    Kek SP, Chin NL, Yusof YA
    J Food Sci Technol, 2014 Dec;51(12):3609-22.
    PMID: 25477628 DOI: 10.1007/s13197-013-0923-0
    Modelling studies of guava drying and quality are presented using theoretical and statistical models by varying temperature from 55 to 75 °C and slice thickness from 3 to 9 mm. The quality of dried fruit was measured for its water activity, colour, vitamin C, and texture. The superposition technique with Midilli-Kucuk model showed efficiency in modelling the drying process with R (2)  = 0.9991. The second-order polynomial equations adequately described the quality of dried guava with regression coefficient, R (2)  > 0.7. Drying time was a good function of temperature and thickness (P 
    Matched MeSH terms: Models, Statistical
  15. Fulltext An Efficient Optimization Method for Solving Unsupervised Data Classification Problems
    Shabanzadeh P, Yusof R
    Comput Math Methods Med, 2015;2015:802754.
    PMID: 26336509 DOI: 10.1155/2015/802754
    Unsupervised data classification (or clustering) analysis is one of the most useful tools and a descriptive task in data mining that seeks to classify homogeneous groups of objects based on similarity and is used in many medical disciplines and various applications. In general, there is no single algorithm that is suitable for all types of data, conditions, and applications. Each algorithm has its own advantages, limitations, and deficiencies. Hence, research for novel and effective approaches for unsupervised data classification is still active. In this paper a heuristic algorithm, Biogeography-Based Optimization (BBO) algorithm, was adapted for data clustering problems by modifying the main operators of BBO algorithm, which is inspired from the natural biogeography distribution of different species. Similar to other population-based algorithms, BBO algorithm starts with an initial population of candidate solutions to an optimization problem and an objective function that is calculated for them. To evaluate the performance of the proposed algorithm assessment was carried on six medical and real life datasets and was compared with eight well known and recent unsupervised data classification algorithms. Numerical results demonstrate that the proposed evolutionary optimization algorithm is efficient for unsupervised data classification.
    Matched MeSH terms: Models, Statistical
  16. A modified binary particle swarm optimization for selecting the small subset of informative genes from gene expression data
    Mohamad MS, Omatu S, Deris S, Yoshioka M
    IEEE Trans Inf Technol Biomed, 2011 Nov;15(6):813-22.
    PMID: 21914573 DOI: 10.1109/TITB.2011.2167756
    Gene expression data are expected to be of significant help in the development of efficient cancer diagnoses and classification platforms. In order to select a small subset of informative genes from the data for cancer classification, recently, many researchers are analyzing gene expression data using various computational intelligence methods. However, due to the small number of samples compared to the huge number of genes (high dimension), irrelevant genes, and noisy genes, many of the computational methods face difficulties to select the small subset. Thus, we propose an improved (modified) binary particle swarm optimization to select the small subset of informative genes that is relevant for the cancer classification. In this proposed method, we introduce particles' speed for giving the rate at which a particle changes its position, and we propose a rule for updating particle's positions. By performing experiments on ten different gene expression datasets, we have found that the performance of the proposed method is superior to other previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also produces lower running times compared to BPSO.
    Matched MeSH terms: Models, Statistical*
  17. Fulltext Classification of community-acquired outbreaks for the global transmission of COVID-19: Machine learning and statistical model analysis
    Wang WC, Lin TY, Chiu SY, Chen CN, Sarakarn P, Ibrahim M, et al.
    J Formos Med Assoc, 2021 Jun;120 Suppl 1:S26-S37.
    PMID: 34083090 DOI: 10.1016/j.jfma.2021.05.010
    BACKGROUND: As Coronavirus disease 2019 (COVID-19) pandemic led to the unprecedent large-scale repeated surges of epidemics worldwide since the end of 2019, data-driven analysis to look into the duration and case load of each episode of outbreak worldwide has been motivated.

    METHODS: Using open data repository with daily infected, recovered and death cases in the period between March 2020 and April 2021, a descriptive analysis was performed. The susceptible-exposed-infected-recovery model was used to estimate the effective productive number (Rt). The duration taken from Rt > 1 to Rt 

    Matched MeSH terms: Models, Statistical
  18. Appropriateness of deep vein thrombosis (DVT) prophylaxis use among medical inpatients: a DVT risk alert tool (DRAT) study
    Diana Yap FS, Ng ZY, Wong CY, Muhamad Saifuzzaman MK, Yang LB
    Med J Malaysia, 2019 02;74(1):45-50.
    PMID: 30846662
    INTRODUCTION: Increasing incidence of Venous Thromboembolism (VTE) has complicated treatment courses for hospitalised patients. Despite recommendation to support deep vein thrombosis (DVT) risk assessment and appropriate use of prophylaxis in medical inpatients, it is either neglected or prescribed unnecessarily by the clinicians. This study aimed to assess and compare the appropriateness of DVT prophylaxis prescribing between usual care versus a pharmacist-driven DVT Risk Alert Tool (DRAT) intervention among hospitalised medical patients.

    METHODS: A prospective pre- and post-intervention study was conducted among medical inpatients in a Malaysian secondary care hospital. DVT and bleeding risks were stratified using validated Padua Risk Assessment Model (RAM) and International Medical Prevention Registry on Venous Thromboembolism (IMPROVE) Bleeding Risk Assessment Model. Pharmacist-driven DRAT was developed and implemented post-interventional phase. DVT prophylaxis use was determined and its appropriateness was compared between pre and post study using multivariate logistic regression with IBM SPSS software version 21.0.

    RESULTS: Overall, 286 patients (n=142 pre-intervention versus n=144 post-intervention) were conveniently recruited. The prevalence of DVT prophylaxis use was 10.8%. Appropriate thromboprophylaxis prescribing increased from 64.8% to 68.1% post-DRAT implementation. Of note, among high DVT risk patients, DRAT intervention was observed to be a significant predictor of appropriate thromboprophylaxis use (14.3% versus 31.3%; adjusted odds ratio=2.80; 95% CI 1.01 to 7.80; p<0.05).

    CONCLUSION: The appropriateness of DVT prophylaxis use was suboptimal but doubled after implementation of DRAT intervention. Thus, an integrated risk stratification checklist is an effective approach for the improvement of rational DVT prophylaxis use.

    Matched MeSH terms: Models, Statistical
  19. Classification of speech dysfluencies using LPC based parameterization techniques
    Hariharan M, Chee LS, Ai OC, Yaacob S
    J Med Syst, 2012 Jun;36(3):1821-30.
    PMID: 21249515 DOI: 10.1007/s10916-010-9641-6
    The goal of this paper is to discuss and compare three feature extraction methods: Linear Predictive Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC) and Weighted Linear Prediction Cepstral Coefficients (WLPCC) for recognizing the stuttered events. Speech samples from the University College London Archive of Stuttered Speech (UCLASS) were used for our analysis. The stuttered events were identified through manual segmentation and were used for feature extraction. Two simple classifiers namely, k-nearest neighbour (kNN) and Linear Discriminant Analysis (LDA) were employed for speech dysfluencies classification. Conventional validation method was used for testing the reliability of the classifier results. The study on the effect of different frame length, percentage of overlapping, value of ã in a first order pre-emphasizer and different order p were discussed. The speech dysfluencies classification accuracy was found to be improved by applying statistical normalization before feature extraction. The experimental investigation elucidated LPC, LPCC and WLPCC features can be used for identifying the stuttered events and WLPCC features slightly outperforms LPCC features and LPC features.
    Matched MeSH terms: Models, Statistical
  20. 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: Models, Statistical
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