bluuebunny/crossref_metadata_2025 huggingface dataset:
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

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

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

The 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

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

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

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

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

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

This 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

Classical 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

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

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

Synthetic 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

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

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.
Robust Hotelling’s T2 statistic based on M-estimator
Authors: Mokhtar Mohd Aizat Ahlam Mohamad , Yusoff Nur Syahidah , Liang Chuan Zun
Published: August 2021

Hotelling’s T
2 statistic is the multivariate generalization of the student’s t-statistic. Hotelling’s T
2 statistics is a method for testing hypotheses about multidimensional means. However, the classical Hotelling’s T
2 statistic is very sensitive to the presence of outliers. In order to overcome this limitation, a modification is needed so that Hotelling’s T
2 is robust. In this paper, classical Hotelling’s T
2 statistic has been modified by substituting mean vector and covariance matrix with a robust estimator. M-estimator has been used for this modification. The performance of modified Hotelling’s T
2 statistic has been compared with the classical Hotelling’s T
2 statistic and discussed in this paper to illustrate the advantage of modified Hotelling’s T
2 statistic towards outliers. The performance of modified Hotelling’s T
2 statistic is better than classical Hotelling’s T
2 when number of sample, n and dimension, p is small.
Time Truncated Life Test for New Two-Sided Group Chain Sampling Plan (NTSGChSP-1) using Minimum Angle Method
Authors: Aziz Nazrina , Teh Mohd Azri Pawan , Zain Zakiyah
Published: August 2021

This study constructs new two-sided group chain acceptance sampling plans (NTSGChSP-1) for generalized exponential distribution using minimum angle method. The minimum angle method is slightly advantageous over the previous methods of minimizing consumer’s and procedure’s risk, as it considers the optimal number of groups, g associated with the smallest angle, θ. In theory, an operating characteristics (OC) curve created with the smallest angle, θ closely resembles the ideal OC curve as the OC curve protects both parties, producer and consumer. The findings show that the optimal number of groups, g depends on the combinations of the design parameters. Plus, there is also a pattern for the optimal number of groups, g, where the optimal number of groups, g decreases as the specified constant, a increases. The two significant findings eventually assist the experimenter in designing the experiment on how many number of items should be selected for the inspection before making a final decision.
Provincial nitrogen footprints highlight variability in drivers of reactive nitrogen emissions in Canada
Authors: McCourt Sibeal , MacDonald Graham K
Published: August 2021

Nitrogen (N) footprints are one method to quantify consumer driven reactive nitrogen (Nr) emissions. Canada is a highly urbanized yet economically natural resource-dependent country, providing an illustrative case study to examine attribution of Nr emissions to per capita consumption, either domestically or abroad. Yet, considered only at the national scale, N footprints may obscure absolute and relative contributions of local drivers to Nr emissions. We apply a top-down N footprint approach drawing from national N budgets, emissions inventories, and agricultural statistics to estimate sub-national (provincial) drivers of Nr emissions across Canada. We calculate per capita provincial Nr footprints from four primary sectors in 2018: (a) crop production, (b) animal production, (c) wastewater treatment, and (d) fossil fuel burning. We estimate that Canada’s total N footprint is 995.7 Gg Nr yr−1, which equates to an average per capita footprint nationally of 27.1 kg Nr capita−1 yr−1. The largest national contributions come from a few key (sub)sectors, including transport, beef consumption, and wastewater treatment. Provincial per capita N footprints vary widely, with the largest (Saskatchewan 50.3 kg Nr cap−1 yr−1) more than double the smallest (Ontario 22.0 kg Nr cap−1 yr−1). Most variation across provinces is due to the fossil fuels sector, including emissions from energy generation and the oil and gas industry. We therefore compare our top-down approach for the fossil fuels sector with bottom-up N footprints and territorial emissions methodologies. Per capita N emissions vary considerably across these approaches. For example, Alberta’s per-capita fossil fuel Nr emissions are 45.9, 23.0, and 6.3 kg Nr cap−1 yr−1 using territorial, top-down and bottom-up footprint approaches, respectively. This analysis demonstrates the challenges of attributing Nr emission for export-oriented economies. Our study provides novel insights on sub-national drivers of Nr emissions, emphasizing the need to consider how heterogeneous geographic contexts contribute to national N footprints.
AN IMPROVEMENT OF PRODUCTIVITY BY REAL TIME MACHINE MONITORING SYSTEM: A case study of printing industry
Authors: Pararach Sawat , Muttamara Apiwat , Kaewprapha Phisan
Published: August 2021

Digital technologies and automation integration is one of the promising directions to increase productivity in the manufacturing. A consulting project to promoting the adoption of digital technology under the policy of the Department of Industrial Promotion, and Thailand 4.0 to help small and medium entrepreneurs (SMEs) to assess and gain readiness to the use of cyber-physical including digital technology. The real-time data gathering system for monitoring and displaying information is presented. For example, IoT based sensors is applied in digital counting of machine-made goods. This data can be used to analyze the root cause of a manufacturing process problem, for example, time lost due to discontinuity of work or setting up. Best practices are observed at a printing factory. The implementation shows the effective data acquisition even the running speed of a machine is high. The owner and manager gain real-time access to the dashboard through their mobile phone or personal computer. Therefore, they can manage their jobs instantly and remotely. Moreover, the stored data can be downloaded in Excel format. The data can be used in process waste analysis. The numerical results of using this system, overall equipment productivity and efficiency (OEE) were increased by 10% to 15%, approximately.
Identification of Lime Leaf Diseases with Deep Learning Technique on Android Smartphone
Authors: Thammachot N , Bootkote P , Thanimkarn S , Preecha C
Published: August 2021

Leaf plant diseases are difficult to recognize because the symptoms of the disease appear very similar causing farmers to solve problems that don’t match the symptoms of the disease. This research presents the creation of a smartphone application for the identification of lime leaf diseases with deep learning technique. This uses 2 sets image data classified by plant disease specialist, 400 images for algorithm training and 100 images for algorithm testing for each disease. Algorithm development using Convolutional Neural Network that imitates the working of human brain neurons. This uses the supervised learning network type. The algorithm was developed based on the TensorFlow Framwork, Google’s open source library, and was used by Android Studio software to build smartphone applications in the Android operating system. For use, the algorithm that has been learned through the process of learning is embedded into the developed smartphone application. Then it was tested the application accuracy. The results of a smartphone application in the identification of lime leaf diseases found more than 90% accurate.
Physiochemical properties of vacuum fried durian (Durio Zibethinus Murr.) chips combined with microwave heating
Authors: Thongcharoenpipat C , Yamsaengsung R
Published: August 2021

This research evaluated the effects of oil temperature, ripeness of durian (Durio zibethinus Murr.), and microwave combined heating (MCH) on the physiochemical properties of vacuum fried durian chips. The oil temperature was varied from 90, 100, 110, and 120°C, while the ripeness was determined in term of total sugar (base on day of ripening). In addition, the effect of pretreatment of durian chips by freezing overnight -18°C before vacuum frying (VF) was also investigated.

From this study, it was found that VF at 120°C with the ripeness of day 3 gave the lowest shrinkage, and using MCH produced a higher rate of moisture loss compared to the condition without MCH. Even though it was found that the ripeness of durian lead to more oil absorption, the addition of MCH resulted in decreasing the permeability of oil into the product. Finally, an increase in the frying temperature lead to a slight significant increase in color change (p<0.05), while the microwave power intensity did not have a significant effect on the color change (p<0.05).
Determination of feasible sites for wind farm using WAsP in Uttaradit, Thailand
Authors: Khan T , Taweekun J , Theppaya T
Published: August 2021

Electricity production from the wind plays the potential role to reduce conventional way of electricity generation to reduce the negative impacts on the environment. In this study the data is collected from Uttaradit, the northern province of Thailand. The calculation in this study is based on ten-minutes interval data at 10 m observed during the year 2017-2019 by Thai meteorological department. Weibull distribution is applied to extract the monthly and yearly wind in the selected area by using wind atlas analysis and application program (WAsP). Moreover, in WAsP a stimulation was created to observe the comparison of potential of wind energy between three windiest sites located in the study area. The results generated show that the mean wind speed is 2.35m/s and the gradient wind direction from north and south are quite distinct. The annual energy production calculation of three sites in Uttaradit by WAsP estimates about 2125.815MWh, 3379.621MWh and 3369.235MWh respectively by using Bonus 300kW Mk III wind turbine. This paper suggests and supports the suitable sites and wind turbine for the electricity generation in Uttaradit. Hence, it will not only reduce the stress on conventional methods but also create awareness regarding the potential of wind energy.
Analysis of the Tools to Support Systematic Literature Review in Software Engineering
Authors: Stefanovic Darko , Havzi Sara , Nikolic Danilo , Dakic Dusanka , Lolic Teodora
Published: August 2021

A thorough and accurate review of the literature is a necessary condition for relevant research. However, the literature review is a laborious, error-prone process. For this reason, various software tools are being developed to support researchers in conducting literature reviews. In this paper, three tools to support a systematic literature review in software engineering were analysed and evaluated using the DESMET methodology. Tools were found by conducting mapping study and were selected since they were most promising to support an overall systematic literature review process. Although the tools do not yet support the whole systematic review process on a high level, they provide a reasonable basis for further development. It is needed to set features that need to be supported by the tools.
An Analysis of the Impacts of Solar Electricity Generation on the Electricity Industry for Thailand
Authors: Aungyut P , Wattana B
Published: August 2021

This paper provides an analysis of the impacts of solar electricity generation on the electricity industry for Thailand. In this paper, three scenarios (REF, Solar2015 and Solar2018) are developed to represent an increased levels of electricity produced from solar energy. A Low Emissions Analysis Platform (LEAP) model is employed in this paper to assess the impacts for the period 2019-2037. This paper assesses the scenario impacts in terms of diversification of electricity generation, generation technology mix, primary energy requirement and CO2 emissions. The analysis reveals that greater role of solar energy in the electricity generation would have positive impacts on the Thai electricity generation from several aspects including improving the diversification of primary energy supply for electricity generation, reducing fossil fuel consumptions for power production, less reliance on fossil fuel sources and environmentally friendly electricity generation. For example, 20% share of solar in fuel mix for power generation would result in a 17% reduction in fossil fuel consumption. Furthermore, it would contribute to a 16% decrease in CO2 emissions and 28% reduction in SO2 emissions. Despite several benefits, there are a number of emerging barriers for promoting solar based electricity generation in Thailand. These include inefficient and unreliable nature of solar energy for baseload electricity demand, high-initial cost, unsupportable grid infrastructure and unfavourable regulatory framework. This paper, therefore, recommends that development of energy storage technologies, improvement of grid flexibility and the revision of the regulations to support solar energy business could be effective strategies in order to address the barriers facing the Thai electricity industry.
Inactivation of Escherichia coli by several types of gas bubbles
Authors: Naewkanya P. , Petiraksakul A.
Published: August 2021

The ability of gas bubbles to inactivate Escherichia coli suspended in distilled water at varied temperature and several types of gas, namely carbon dioxide, nitrogen and argon, was investigated. The results of the experiment showed that carbon dioxide gas bubbles gave the better inactivation of Escherichia coli than other gasses and the number of surviving Escherichia coli was reduced by approximately 0.76-log from the treatment of carbon dioxide gas bubbles at 4-10°C and gas flowrate of 2.5 ml/min for 40 min. The pH of solution after treatment with carbon dioxide gas bubbles for 40 min was 3.8. Furthermore, the number of surviving Escherichia coli was reduced by approximately 1.62-log when the solution temperature was increased to 50°C with the same condition of the treatment of carbon dioxide gas bubbles at 4-10°C. It was found that the addition of carbon dioxide gas bubbles together with increasing the solution temperature can significantly reduce the number of Escherichia coli. The most suitable of inactivation kinetics reaction was first order and the rate constant, k, at 4-10°C, 30°C and 50°C was 0.0220, 0.0013 and 0.0475 min -1, respectively. The decimal reduction time D at 4-10°C, 30°C and 50°C was 45.45, 769.23 and 21.05 min, respectively.
The Degradation of Austenitic Stainless Steel at High Temperature in Simulated Carbon Monoxide Containing Atmosphere of Biomass-to-Liquid Plants
Authors: Treewiriyakitja Paweena , Thongyoug Penpisuth , Pokwitidkul Suwijak , Tungtrongpairoj Jennarong
Published: August 2021

Biomass fuel is effective renewable energy and being used for replacing fossil fuel energy. It can be produced from synthesis gas containing a high percentage of carbon monoxide (CO) and hydrogen (H2) in biomass-to-liquid plants. Austenitic stainless steel AISI 316L (Cr17% Ni 10% Mo 2%) is used for equipment parts in chemical and petrochemical industries due to good corrosion resistance at various operating conditions. The corrosion resistance of stainless steel may be degraded by the reduction reaction of the passive film and carbide formation from carbon diffusion, which leads to the intergranular corrosion on the steel surface. This research aims to study the degradation of stainless steel AISI 316L in a simulated carbon monoxide containing atmosphere at 15-45%CO and a sensitizing temperature of 800 °C. Before the test, the samples were preoxidized in the air at 800 °C for 6 hours. An electrochemical reactivation (EPR) technique was used to analyze for detecting sensitization. The mass change of AISI 316L slightly increased after the reduction test. Besides, the high carbon diffusion was shown on the steel surfaces as chromium carbides at the high percentage of carbon monoxide.
A study on activated carbon from Borassus flabellifer shell by chemical activation with potassium hydroxide for supercapacitor
Authors: Chaitammachok C. , Taweekun J. , Maliwan K. , Theppaya T.
Published: August 2021

The supercapacitor is significant in daily life. It is an alternative option that can storage energy. This research focused on studying the effect of Potassium hydroxide concentration content increasing surface area of the Borassus flabellifer shell charcoal (BFC), power density and energy density of activated carbon as electrode and cycles. Borassus flabellifer shell as agricultural waste was carbonized with tube furnace at nitrogen atmosphere by heated at 400 °C for 1 hour. Activated carbon with KOH (the mass ratio of KOH: BFC; 1:1, 1:2, 1:3, 4:1 and 5:1) (BAC) was heated at 900 °C for 2 hours. The results of the research show that suitable concentration of KOH: BFC to activated with higher pore volume and higher surface area. BAC- 5 (5:1) is the highest pore volume of 1679.61 m 2/g and the highest pore volume of 0.737 (cm 3/g) but BAC-4 (4:1) is the best because it has a similar surface area and is economical, the highest pore volume of 1607.092 m 2/g and the highest pore volume of 0.686 (cm 3/g). BAC-1, BAC-2, BAC-3, BAC-4 and BAC-5 have average pore diameters, respectively, around of 1.609 nm., 1.641 nm., 1.742 nm., 1.708 nm. and 1.754 nm. The average pore size of products is microporous.
X-ray and infrared spectroscopy study on contamination of asbestos in Thai commercial cosmetic talc powder product
Authors: Boonruang C , Won-in K , Thumanu K , Dararutana P
Published: August 2021

It is well-known that asbestos such as asbestosis and mesothelioma is a fiber causing lung diseases. Talc is used for commercial applications for example paints, plastics, papers, ceramics, construction materials, and cosmetics. It is well-known that the cosmetic talc powder is used for preventing diaper rash as a deodorant. The sampling in this work has been done from various commercial products in Thailand. Their microstructure and elemental composition are analyzed using a scanning electron microscope coupled with an energy dispersive X-ray spectrometer (SEM-EDS). A small-angle X-ray scattering (SAXS) and infrared spectroscopy (IR) based on synchrotron radiation are also carried out to determine their phase composition and functional groups. It has been found that fibers with asbestos-like shapes are present in some

samples. The main composition of oxygen, silicon, and magnesium in samples are in the form of the functional groups that associate with absorption band in the range of 3600-3700 cm -1 of IR spectra. It is proposed that there are 6 from 17 samples that possibly have asbestos contamination.
Screening analysis of pesticides in the protected area of Zobnatica Lake
Authors: Mihajlović I , Živančev N , Sremački M , Milovanović D , Špánik I , Novaković M , Petrović M
Published: August 2021

The aim of study was to characterize pollution of water in protected area of Zobnatica Lake in the vicinity of Backa Topola, Serbia. The sampling campaigns were conducted in spring and summer 2019 in order to identify organic pollutants in water samples. Samples were prepared by liquid-liquid extraction and analyzed by GC-MS in SCAN and SIM mode. Screening analyses indicated the presence of pesticides: terbuthylazine, alpha-cypermethrin, aminomethyl phosphonic acid (AMPA) and urea. Terbuthylazine was detected in both sampling campaigns. Results specified the need for comprehensive water monitoring program in order to determine specific pollutants in protected area of Lake Zobnatica caused water pollution by agricultural activities.
Investigation of the Cr volatilisation using the ceramic phase collector technique
Authors: Thublaor T , Wiman P , Nanna C , Jaijitmun W , Krittiyanitsakun S , Siripongsakul T
Published: August 2021

An effect of chromium contamination in the lanthanum strontium manganite (LSM) cathode which is a component of solid oxide fuel cell (SOFC) leads to degradation of the device. This is caused by volatilisation of Cr-species from the iron-chromium alloy at high temperature. In this work, the ceramic phase collector technique was used to evaluate the Cr volatilisation. To prove the effectiveness of this method, AISI 430 ferritic stainless steel samples with and without cobalt (Co) spinel coatings were used to compare the Cr condensation in the LSM ceramic. The coatings were produced by electroplating followed by a heat-treatment process. The samples were characterised using SEM with EDX and XRD to confirm the microstructure and the formation of Co spinel. It was found that chromium migration was restrained for coated samples, in contrast the Cr-species deposited in the LSM for uncoated samples. The chromium volatilisation evaluated by the contents of Cr species in the LSM collector was compared to the traditional condensation technique which analysed chemical contents by inductively coupled plasma-mass spectrometry (ICP-MS).
Circular economy as possible solution for asbestos burden
Authors: Zoraja B , Živančev M , Ubavin D , Nakomčić-Smaragdakis B
Published: August 2021

Asbestos has been used worldwide in a large number of products throughout the 1970 s and 1980s. After the proven influence of asbestos materials on diseases that could have a fatal outcome was confirmed, bans on the use and production of these materials began. However, there are still significant quantities of asbestos waste and materials in the environment and there is a need for safe and permanent solution. As a support to sustainable development by minimizing waste production and harmful emissions as well as by reducing the consumption of natural resources, the circular economy (CE) is recognized in the EU as a leading solution. The main meaning of CE in waste management field is to turning waste into a resource. Considering above-mentioned, this paper presents how to resolve a problem of asbestos waste that is currently and will continue to be present. Instead of occupying the space, which is also a valuable resource, with landfills that also raise cost, asbestos can be used in construction material industry. Paper presents estimated amounts of asbestos waste, proposal for appropriate waste collection, treatment and market placing of a new product in Serbia. The conclusion of the paper is that the presented model of circular economy for asbestos in Serbia is feasible, in this way it would be possible to achieve some incomes from material selling and, also, releasing some space. In addition, for the implementation of this model of asbestos management, it is necessary to have Government involvement through investment in infrastructure and improvement of legislation.
Power brushing and chemical denture cleansers induced color changes of pre-polymerized CAD/CAM denture acrylic resins
Authors: Alfouzan Afnan F , AlNouwaisar Alhanouf N , AlAzzam Njood F , Al-Otaibi Hanan N , Labban Nawaf , Alswaidan Mashaal H , Al Taweel Sara M , Alshehri Huda A
Published: August 2021

Denture wearers are advised to follow the protocol of using both mechanical and chemical hygiene methods. In this study, the in-vitro color stability of heat-cured, light-cured and newly developed pre-polymerized CAD/CAM acrylic resin base materials were evaluated after exposure to mechanical brushing and chemical denture cleansers. Two polymethyl methacrylate (PMMA) (heat-cured, and pre-polymerised CAD/CAM) and one urethane dimethacrylate based resin denture base material were subjected to mechanical brushing, followed by immersion in chemical denture cleansers (Corega, 5.25% Sodium hypochlorite (NaOCl), and 0.2% chlorhexidine gluconate (CHG)) and thermal-cycling to simulate one-year of normal prosthesis use. Baseline and final color measurements were determined and the difference in color was calculated using bench-top UV light visible spectrophotometer. The highest (29.69 ± 1.84) and lowest (19.03 ± 8.78) mean ΔE was observed with light-cured and CAD/CAM materials immersed in 0.2% CHG, respectively. Tukey’s post-hoc test showed that heat cured and light-cured resins immersed in either of the denture cleansers showed no significant differences (p > 0.05) in the mean ΔE values. On the contrary, CAD/CAM materials immersed in either of the denture cleansers demonstrated significant differences in the mean ΔE values (p ≤ 0.05). A statistically significant interaction between the combination of materials and denture cleansers (F = 4.890; p = 0.001) was observed. The color stability of the pre-polymerized CAD/CAM acrylic discs is comparatively better than the conventional acrylic resin materials. The changes in the color values of all the tested materials were above the clinically acceptable range, regardless of the type of denture cleanser used.
An open-endcap blade trap for radial-2D ion crystals
Authors: Xie Yuanheng , Cui Jiafeng , D’Onofrio Marissa , Rasmusson A J , Howell Stephen W , Richerme Philip
Published: August 2021

We present the design and experimental demonstration of an open-endcap radio frequency trap to confine ion crystals in the radial-two dimensional (2D) structural phase. The central axis of the trap is kept free of obstructions to allow for site-resolved imaging of ions in the 2D crystal plane, and the confining potentials are provided by four segmented blade electrodes. We discuss the design challenges, fabrication techniques, and voltage requirements for implementing this open-endcap trap. Finally, we validate its operation by confining up to 29 ions in a 2D triangular lattice, oriented such that both in-plane principal axes of the 2D crystal lie in the radial direction.
DeepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding
Authors: Zeng Min , Wu Yifan , Lu Chengqian , Zhang Fuhao , Wu Fang-Xiang , Li Min
Published: August 2021

Long non-coding RNAs (lncRNAs) are a class of RNA molecules with more than 200 nucleotides. A growing amount of evidence reveals that subcellular localization of lncRNAs can provide valuable insights into their biological functions. Existing computational methods for predicting lncRNA subcellular localization use k-mer features to encode lncRNA sequences. However, the sequence order information is lost by using only k-mer features. We proposed a deep learning framework, DeepLncLoc, to predict lncRNA subcellular localization. In DeepLncLoc, we introduced a new subsequence embedding method that keeps the order information of lncRNA sequences. The subsequence embedding method first divides a sequence into some consecutive subsequences and then extracts the patterns of each subsequence, last combines these patterns to obtain a complete representation of the lncRNA sequence. After that, a text convolutional neural network is employed to learn high-level features and perform the prediction task. Compared with traditional machine learning models, popular representation methods and existing predictors, DeepLncLoc achieved better performance, which shows that DeepLncLoc could effectively predict lncRNA subcellular localization. Our study not only presented a novel computational model for predicting lncRNA subcellular localization but also introduced a new subsequence embedding method which is expected to be applied in other sequence-based prediction tasks. The DeepLncLoc web server is freely accessible at http://bioinformatics.csu.edu.cn/DeepLncLoc/, and source code and datasets can be downloaded from https://github.com/CSUBioGroup/DeepLncLoc.
ARIC: accurate and robust inference of cell type proportions from bulk gene expression or DNA methylation data
Authors: Zhang Wei , Xu Hanwen , Qiao Rong , Zhong Bixi , Zhang Xianglin , Gu Jin , Zhang Xuegong , Wei Lei , Wang Xiaowo
Published: August 2021

Quantifying cell proportions, especially for rare cell types in some scenarios, is of great value in tracking signals associated with certain phenotypes or diseases. Although some methods have been proposed to infer cell proportions from multicomponent bulk data, they are substantially less effective for estimating the proportions of rare cell types which are highly sensitive to feature outliers and collinearity. Here we proposed a new deconvolution algorithm named ARIC to estimate cell type proportions from gene expression or DNA methylation data. ARIC employs a novel two-step marker selection strategy, including collinear feature elimination based on the component-wise condition number and adaptive removal of outlier markers. This strategy can systematically obtain effective markers for weighted $\upsilon$-support vector regression to ensure a robust and precise rare proportion prediction. We showed that ARIC can accurately estimate fractions in both DNA methylation and gene expression data from different experiments. We further applied ARIC to the survival prediction of ovarian cancer and the condition monitoring of chronic kidney disease, and the results demonstrate the high accuracy and robustness as well as clinical potentials of ARIC. Taken together, ARIC is a promising tool to solve the deconvolution problem of bulk data where rare components are of vital importance.