bluuebunny/crossref_metadata_2025_split huggingface dataset:
Closure properties of bonded sequential insertion-deletion systems
Authors: Fong Wan Heng , Yosman Ahmad Firdaus , Hassim Hazzirah Izzati Mat 
Published: August 2021

Abstract
Through the years, formal language theory has evolved through continual interdisciplinary work in theoretical computer science, discrete mathematics and molecular biology. The combination of these areas resulted in the birth of DNA computing. Here, language generating devices that usually considered any set of letters have taken on extra restrictions or modified constructs to simulate the behavior of recombinant DNA. A type of these devices is an insertion-deletion system, where the operations of insertion and deletion of a word have been combined in a single construct. Upon appending integers to both sides of the letters in a word, bonded insertion-deletion systems were introduced to accurately depict chemical bonds in chemical compounds. Previously, it has been shown that bonded sequential insertion-deletion systems could generate up to recursively enumerable languages. However, the closure properties of these systems have yet to be determined. In this paper, it is shown that bonded sequential insertion-deletion systems are closed under union, concatenation, concatenation closure, λ-free concatenation closure, substitution and intersection with regular languages. Hence, the family of languages generated by bonded sequential insertion-deletion systems is shown to be a full abstract family of languages.
Direct product of finite intuitionistic fuzzy normed normal subrings
Authors: Alhaleem Nour Abed , Ahmad Abd Ghafur 
Published: August 2021

Abstract
In this paper, we define direct product of finite intuitionistic fuzzy normed normal subrings and investigate relevant properties. We also provide some essential operations specially subset, complement and intersection related to direct product of finite intuitionistic fuzzy normed normal subrings. Then we generalize the relation between the intuitionistic characteristic function and direct product of finite intuitionistic fuzzy normed normal subrings. Finally, we construct many fundamental properties which characteristic direct product of finite intuitionistic fuzzy normed normal subrings.
The entropy of rough neutrosophic multisets
Authors: Alias Suriana , Mohamad Daud , Shuib Adibah 
Published: August 2021

Abstract
The entropy of rough neutrosophic multisets is introduced to measure the fuzziness degree of rough multisets information. The entropy is defined in two ways, which is the entropy of rough neutrosophic multisets generalize from existing entropy of single value neutrosophic set and the rough neutrosophic multisets entropy based on roughness approximation. The definition is derived from being satisfied in the following conditions required for rough neutrosophic multisets entropy. Note that the entropy will be null when the set is crisp, while maximum if the set is a completely rough neutrosophic multiset. Moreover, the rough neutrosophic multisets entropy and its complement are equal. Also, if the degree of lower and upper approximation for truth membership, indeterminacy membership, and falsity membership of each element decrease, then the sum will decrease. Therefore, this set becomes fuzzier, causing the entropy to increase.
On the spectral properties of the differential operators with involution
Authors: Abdullayeva Cholpon , Turdueva Begimai , Ahmedov Anvarjon 
Published: August 2021

Abstract
In this paper we deal with the problems of the eigenfunction expansions related to the differential operators with involution. The mean value formula for the eigenfunction is obtained with application of the transformation methods of the operators in the symmetric regions. The obtained formula is applied to estimate the eigenfunctions of the given differential operator in the ball. For domains with smooth boundary, the solution to these differential operator problems involves eigenfunction expansions associated with biharmonic-type operator with Navier boundary conditions.
On the equiconvergence of the spectral decomposition of the distributions connected with elliptic differential operators on the torus with Fourier integral
Authors: Akhmedov Abdulkasim , Salleh Zuki , Rakhimov Abdumalik 
Published: August 2021

Abstract
In this paper, we deal with the problems of the expansions of the periodic distributions. We obtained sufficient conditions for the equiconvergence of the spectral decompositions of the distributions connected with the elliptic differential operator on the torus with Fourier integral in the classes of the Sobolev.
Visualization of Multivariate Time Series pollutant variables in Malaysia
Authors: Rahim Ulya Abdul , Masseran Nurulkamal 
Published: August 2021

Abstract
Visualization and exploratory analysis is a crucial preliminary part of any data analysis process. Several visualization approaches have been introduced to evaluate the behaviors of time-dependent data. However, the visualization technique tends to be challenging when the data are high-dimensional and voluminous. Environmental data such as pollutant variables are often collected in multi-variables form and over time, resulting in a form of multivariate time-series data. To deal with this issue, this study provides several graphical approaches and methods which include the plots of multiple individually on a time-series, correlation matrix visualization and smoothing multivariate time-series. A case study involving data on air-pollution variables in Klang, Malaysia have been analyzed. The results found the all the methods able to provide an informative visualization on the behavior of multivariable time series of pollutant data.
Modeling urban household income in Malaysia using parametric approach
Authors: Safari Muhammad Aslam Mohd , Masseran Nurulkamal , Hussain Saiful Izzuan 
Published: August 2021

AbstractThe parametric approach is commonly used to estimate the actual distribution and thus measure income inequalities while analyzing the income distribution. The parametric estimate of the income distribution is useful as it helps in inferences about the estimated inequalities to be made more easily. Centered on household income survey data from 2007, 2009, 2012, and 2014, a comparative assessment of six parametric distribution candidates that represent urban household income in Malaysia was conducted in this research. The gamma, lognormal, Weibull, Dagum, Singh – Maddala, and generalized beta of the second kind distributions were some of the two, three, and four parameters considered in this study. Based on our analysis, it was found that the generalized beta of second kind distribution was the most adequate model for explaining the urban household incomes in Malaysia. Then, on the basis of the generalized beta of second kind distribution, income inequality analysis of the urban households in Malaysia was assessed using Lorenz curve and Gini coefficient.
Time series modeling using Box-Jenkins model for shariah compliant healthcare sector in Malaysia
Authors: Nadhirin Nur Fathin Shaida Muhammad , Ishak Norizarina , Hamzah Siti Raihana , Halim Nurfadhlina Abdul , Rasade Ahmad Fadly Nurullah 
Published: August 2021

Abstract
Most economic and financial time series are trended and not stationary and therefore the raw data need to be detrended by differencing. An appropriate and efficient model is a good practice for evaluating the stock price performance. This paper utilized the Box-Jenkins methodology for modeling the stock price for the healthcare sector in the Malaysian stock market. This study focused on one shariah compliant company, namely Hartalega Holdings Berhad, which is one of the major healthcare companies that manufactured clinical Nitrile gloves in Malaysia. The data of monthly stock price is collected from April 2008 until December 2020. The findings of this study showed that ARIMA(2,1,0) is the best model to represent the time series data according to the smallest values of AIC and SC as well as the satisfaction of Ljung-Box test statistics. Through this study, investors able to monitor the investment portfolio from estimating the stock price performance of healthcare sector, while considering the current healthcare economic situation in Malaysia.
Application of zero-truncated count data regression models to air-pollution disease
Authors: Zulki Alwani Z I , Ibrahim A I N , Yunus R M , Yusof F 
Published: August 2021

Abstract
Count data consist of non-negative integers that have many applications in various fields of studies. To handle count data, there are various statistical models that can be employed corresponding to the properties of the count data studied. Poisson regression model (PRM) is mostly used to model data with equidispersion, while negative binomial regression model (NBRM) is a model that is regularly employed to model over-dispersed count data. On the other hand, the usual count data regression models may not able to handle strictly positive counts. In this case, the appropriate model for the analysis of such data would be models truncated at zero. We are interested to study the relationship between pollution related disease with influential factors such as air pollution and climate variables in Johor Bahru, Malaysia, using these zero-truncated models, where the number of disease cases are strictly positive. In particular, the zero-truncated PRM and NBRM are used to determine the association between the number of dengue patients and their influential factors. From the study, zero-truncated NBRM is found to be the best model amongst the two models to model the relationship between the number dengue cases and air pollution and climate. Air pollution factors that significantly affect the number of cases for dengue are particulate matter (PM10) and sulfur dioxide. Also, humidity and temperature are the climate factors that significantly affect the number of dengue cases.
Forecasting Malaysian mortality rates using the Lee-Carter model with fitting period variants
Authors: Ibrahim Nur Shatikah Mohamad , Lazam Norazliani Md , Shair Syazreen Niza 
Published: August 2021

Abstract
This study aims to forecast Malaysian mortality rates by age and gender using the well-known Lee-Carter model. Data obtained from the Department of Statistics Malaysia which consists of central mortality rates by age and gender from year 1970 to 2018. Two different sets of fitting periods were determined based on the observations of changes in mortality index patterns over the years. The Set A consists of 24-year fitting period from 1970 to 1993 whereas Set B comprises 31-year fitting period from 1970 to 2000. The in-sample evaluation of the Lee-Carter model are performed using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) whereas the out-sample errors are calculated using the Mean Absolute Percent Errors (MAPE) and Root Mean Square Error (RMSE). Results show that the set A is better fitted into the Lee-Carter model than that of set B by having lower values of AIC and BIC, consequently produced more accurate out-sample forecast values for females. The Lee-Carter model is a reliable model for Malaysia data, however careful attention must be given when selecting the best fitting period.
Determination of Huffaz Academic Achievement Using Binary Logistic Regression Model
Authors: Zakaria Nurul Anasuhah , Ahmad Tahir , Awang Siti Rahmah , Safar Ajmain 
Published: August 2021

Abstract
There have been increasing awareness among parents to send their children to dual-system tahfiz schools. However, mode of teaching in these schools may pose shortcomings to their students’ (huffaz) academic performances. Therefore, identification of huffaz’ intelligences, needs, curriculums, and learning styles offer in these schools are worthwhile to be investigated. The aims of this study are to identify dominant intelligence among huffaz that is based on Gardner’s multiple intelligence theory and to describe the relationship between academic achievement and Quran memorization performances of the huffaz. A number of 130 tahfiz students (79 male and 51 female) from four different tahfiz schools are the subjects in this study. Binomial logistic model is used to determine factors that influence huffazs’ academic performances. Results show huffazs’ Quranic performances (memorization) do influence their academic performances. Furthermore, naturalist intelligence is determined as the dominant intelligence among huffaz.
Forecasting on the crude palm oil production in Malaysia using SARIMA Model
Authors: Tayib Siti Amnah Mohd , Nor Siti Rohani Mohd , Norrulashikin Siti Mariam 
Published: August 2021

Abstract
Today, accurate prediction on the seasonal trend of the crude palm oil production is critical for the government and agriculturist management to aid in decision-making. The study aims to forecast the Malaysia crude palm oil production by using the Seasonal Autoregressive Integrated Moving Average model. The monthly data of Malaysia crude palm oil production were obtained from Malaysian Palm Oil Board, from January 2014 until September 2019. The Seasonal Autoregressive Integrated Moving Average model was applied to the data by using the Box-Jenkins approach. Based on the adequacy checking and accuracy testing, SARIMA(1,0,0)(0,1,1)12 is the best fitted model for the Malaysia crude palm oil production. As a result of the findings, the SARIMA(1,0,0)(0,1,1)12 model appears to be the best choice for decision makers to make reliable and accurate long-term forecasts on Malaysia crude palm oil production.
Modeling lifetime of parallel components system with covariate, right and interval censored data
Authors: Teong X H , Arasan J 
Published: August 2021

AbstractThis research aims to model the lifetime of parallel components system with covariates, right, and interval censored data. The lifetimes of the components are assumed to follow the exponential distribution, with constant failure rates. A simulation study is conducted to assess the performance of the maximum likelihood estimates, without and with midpoint imputation method at various sample sizes, censoring proportions, and number of components in the system. The combination which produces the best parameter estimates is then identified by comparing the bias, standard error and root mean square error of these estimates. The simulation results indicate that the midpoint imputation method produces more efficient and accurate parameter estimates with smaller bias, standard error and root mean square error. Also, in general, better estimates are obtained at low censoring levels, large sample sizes, and a high number of parallel components in the system. The proposed model is then fitted to a modified real data of diabetic retinopathy patients. Following that, the non-parametric log-rank test and Wald hypothesis test are carried out to check the significance of the covariate, age in the model. The results show that the model fits the data rather well and the age of patients has no significant effect on the survival time of the patients’ eyes.
Review on circular-linear regression models
Authors: Mohammad Hanin Hazwani , Satari Siti Zanariah , Yusoff Wan Nur Syahidah Wan 
Published: August 2021

AbstractClassical linear statistics method is no longer appropriate when handling circular data since the data is influenced by direction or angle. Considering the possibility of circular data appeared as dependent variable, it has resulted in the remodeling of classic linear regression model into circular-linear regression model over the past few decades. It is important to acknowledge these circular data characteristics as it can affect the descriptive and inference of statistical analysis. With the growing body of literature regarding this issue, this paper will review on circular-linear regression model by highlighting and exploring their benefits and limitations.
On probability distributions of wind speed data in Malaysia
Authors: Jalil Aimi Liyana , Hasan Husna 
Published: August 2021

Abstract
The rapid depletion of fossil fuel resources leads to environmental issues and impacts, making alternative resources such as wind energy to be one of the important renewable sources. The statistical characteristics of wind speed and the selection of suitable wind turbines are essential to evaluate wind energy potential and design wind farms effectively. Hence, an accurate assessment of wind energy and wind data analysis is crucial before a detailed analysis of energy potential is conducted. The probability distributions of wind speed data are considered, and its parameters are precisely estimated to achieve this aim. This study considers the most selected distribution, namely, Weibull, Gamma, and Logistic distributions. These distributions are fitted to the wind speed data for sixteen stations in Malaysia. The parameter estimation is performed by the maximum likelihood method. The efficiency of the model distribution is analysed. The goodness of fit test is performed using the Kolmogorov-Smirnov test. The results show that Gamma distribution is the most suitable distribution for the wind speed data in Malaysia as it fits the data well for thirteen stations. The Logistic distribution is found to be the best distribution for the other three stations. The graphical method also agrees with the analytical result.
Assessing rice production efficiency of the granary and non-granary areas in Malaysia using data envelopment analysis approach
Authors: Nodin M N , Mustafa Z , Hussain S I 
Published: August 2021

Abstract
The performance of rice production efficiency between two homogenous regions (granaries and non-granaries) at the regional level in Malaysia is evaluated by applying the data envelopment analysis (DEA) approach. In general, the granary area receives much attention to achieve sustainable rice development compared to non-granaries. The period observed from 2009 to 2018 related to the comprehensive government transformation in strengthening paddy and rice value-chain after the 2007-2008 international food crisis. Under the new National Agrofood Policy (NAFP) 2011-2020, more agricultural assistance and development expenditure was allotted. The total of eighteen regions was classified into eight granaries and ten regions for non-granaries. The output-input oriented analysis under CRS and VRS specifications were performed. From CRS and VRS technical efficiency scores ratio, the scale efficiencies were calculated. The result shows that eight (44.44%) out of eighteen regions maintain high efficiency with scores greater than 0.9000 for both specifications. Meanwhile, under ‘pure’ technical efficiency (VRS), eleven (61.11%) out of eighteen regions maintain high efficiency with scores greater than 0.9000. For productivity status, twelve (66.67%) out of eighteen areas are optimum and increasing productivity approach and the remaining six (33.33%) are decreasing productivity. This empirical study also demonstrates substantial performance variations between output (rice production) and production factors (land, planting frequency, irrigation areas, total subsidies and incentives received). Overall, the Malaysia paddy and rice industry’s efficiency performance can be considered still inefficient but has the potential to maximize the production through rationalizing the inputs used and obtain production efficiency, especially the inefficient regions.
A synthetic data generation procedure for univariate circular data with various outliers scenarios using Python programming language
Authors: Zulkipli N S , Satari S Z , Wan Yusoff W N S 
Published: August 2021

AbstractSynthetic data is artificial data that is created based on the statistical properties of the original data. The aim of this study is to generate a synthetic or simulated data for univariate circular data that follow von Mises(VM)distribution with various outliers scenario using Python programming language. The procedure of formulation a synthetic data generation is proposed in this study. The synthetic data is generated from various combinations of seven sample size,nand five concentration parameters,K.Moreover, a synthetic data will be generated by formulating a data generation procedure with different condition of outliers scenarios. Three outliers scenarios are proposed in this study to introduce the outliers in synthetic dataset by placing them away from inliers at a specific distance. The number of outliers planted in the dataset are fixed with three outliers. The synthetic data is randomly generated by using Python library and package which are ‘numpy’, ‘random’ and von Mises’. In conclusion, the synthetic data of univariate circular data from von Mises distribution is generated and the outliers are successfully introduced in the dataset with three outliers scenarios using Python. This study will be valuable for those who are interested to study univariate circular data with outliers and choose Python as an analysis tool.
An actuarial multi-state transition modelling in long-term care study
Authors: Chan L G , Tham Z W 
Published: August 2021

Abstract
With the growing numbers of elderly and numbers of disability among the elderly in the population, long-term care services are at great demand now. Long-term care provides a variety of services by helping the elderly or non-elderly with medical illnesses or disabilities who are unable to foster themselves for prolonged terms. Since Malaysia’s aged care and health care industry still has ways to go in contrast to other countries, such as Australia, therefore, understand the long-term care services could be helpful to provide quality lifestyle for the elderly in the future. We study the multi-state transition model which is the common model used on health-related data. Four levels of core activity restrictions are including in the model to allow the probability of disability to be included in the long-term care cost projection. Estimation of transition intensities are provided by fitting the Australian data with Gompertz and Weibull models. The graphs of actual and fitted transition probabilities are presented for better illustration. We concluded that Gompertz model has the better fit for both male and female in our study. Some limitations and recommendations have been outlined for future studies.
Survival analysis for diabetic retinopathy in diabetic patients using Extended Cox Model
Authors: Permai S D , Randa B J 
Published: August 2021

Abstract
One of the disorders of the eye that may occur to the diabetes patients is diabetic retinopathy. Diabetic retinopathy can cause vision loss and even blindness to the diabetes patients. At first, diabetic retinopathy may not have any symptoms at all. But diabetic retinopathy is the disorder which cause of blindness besides of cataracts, glaucoma and macular degeneration. The objective of this research was to determine the factors that can be used to delay diabetic retinopathy. There are several covariates that used in this research. One of the survival analysis method that can be used is Cox Proportional Hazard model. However, in this research, the result of Cox Proportional Hazard model showed that some covariates are not significant in the Cox Proportional Hazard model and the model did not fulfil the assumption, that is a constant hazard ratio over time. Therefore, an analysis was carried out using the Extended Cox Model. The results of Extended Cox model showed that all variables are concede a significant effect in delaying diabetic retinopathy in the Extended Cox model. Therefore, the Extended Cox model is more appropriate in this research than Cox Proportional Hazard model.