bluuebunny/crossref_metadata_2025_split huggingface dataset:
High-output density partial superconducting motors for aviation systems
Authors: Terao Yutaka , Ishida Yusuke , Ohsaki Hiroyuki 
Published: April 2021

Abstract
This paper describes electromagnetic design of partial superconducting motors (PSCMs) using YBCO field coils and copper armature windings. It has simpler structure than that of fully superconducting motors (FSCMs), which have superconducting field/armature windings. The target output density is set over 16 kW/kg. For the first step, we designed the 3.0-, 5.0- and 9.0-MW superconducting motors by means of analytical equations. We used genetic algorithm to find the proper conditions, including magnetic flux density amplitude, machine diameter and rotation speed, for our optimal machine structure. The results show that the 5.0-and 9.0- MW PSCMs could reach over 16 kW/kg and in some 9.0-MW cases, we recognized that the output density of 26 kW/kg had been almost attained. After the optimal design, FEM analysis was conducted for some design results of the 5.0- and 9.0- MW PSCMs, in order to evaluate motor losses, copper and iron loss, as well as maximum magnetic flux density and motor output. The motor efficiency could reach over 98% in all the FEM design cases. Also, the output density values were almost the same as those in the design cases with analytical equations. These analytical and FEM design results refer that over 16 kW/kg is possible with PSCMs; if we should obtain the output density of over 20 kW/kg with the PSCM structure, however, the 9.0-MW PSCMs are indispensable.
Generalized Linear Model Approach for Time Series Count Data on Number of Foreign Tourists Modeling in West Java
Authors: Rohaeti E , Kurnia A , Sadik K , Sumertajaya I M 
Published: April 2021

Abstract
This study aimed to model the number of foreign tourist visits in West Java. The number of a tourist visit is part of the discrete data so that the normal distribution approach become less precise in common modeling. In this study, the number of tourist visits was carried out using the Generalized Linear Model approach, combining the Poisson distribution and negative binomial distribution with the identity and log link function. The effect of internal covariates due to a surge in the number of tourists in a given month was also added to the modeling. tscount: An R Package for Analysis of Count Time Series Following Generalized Linear Models. The results showed that the four models obtained were equally good based on the mean absolute percent error (MAPE) values, while the model obtained with the negative binomial distribution integral probability log link function is the best model based on the Akaike information criterion (AIC), Bayesian information criterion (BIC) and integral probability transform (PIT) histogram values. The negative binomial with the log link function approach was then used to model and to predict the number of foreign visits. Plot using negative binomial and log link function, has a value closer to the actual data plot, also strong with the smallest AIC and BIC values.
Generalized Structured Component Analysis to Analyze Measurement Models: Utilization of Health Insurance
Authors: Ferezagia D V , Safitri K A , Dewi N F , Anggara D 
Published: April 2021

Abstract
This study aims to analyze measurement models of the Utilization of Health Insurance. A survey is conducted annually by Statistics Indonesia in measuring the Utilization of Health Insurance through National Socio-Economic Survey (SUSENAS). Code 81301-81309 on SUSENAS Core is blocking code XIII with a description of Health Insurance Utilization. Y1: Do you have health insurance? Y2: have you ever used health insurance? Y3: What are the reasons you have never used health insurance? Y4: in the last year, have you ever been refused a health check? Y5: what are the reasons you experience refusal to check your health?, Y6: in the last year, have you ever used health insurance for hospitalization?, Y7: What are the reasons you never used health insurance for hospitalization?, Y8: in the last year, have you ever been refused hospitalization?, and Y9: what are the reasons you experience refusal to be hospitalized? Generalized Structured Component Analysis (GSCA) is part of SEM-based on components that have global least-square optimization criteria, which can be consistently minimizing sum squares residuals to obtain model parameter estimates. GSCA is a powerful analysis method. This is because it is not based on many assumptions such as variables that do not have to have multivariate normal distributions (indicators with category, ordinal, interval to ratio can be used on the same model), the amount of data does not have to be large. The results showed that some indicator variables had no significant effect on the construct variables. The loading factor > 0.6 are latent construct indicators that provide good convergent validity.
Spatial Interpolation for Missing Rainfall Data in Northern Region of Peninsular Malaysia
Authors: Mohamed Salleh Mohd Khaidir , Ahmad Radib Noor Fadhilah , Mohd Amin Nor Azrita 
Published: April 2021

Abstract
This study is aimed to estimate missing rainfall values for daily rainfall data from 30 selected rainfall stations. The daily rainfall data were obtained from the Department of Irrigation and Drainage Malaysia (DID) for the periods of 1999 to 2019. The missing values throughout the 20 years period were estimated using spatial interpolation methods. These methods include arithmetic average (AA), normal ratio (NR), inverse distance (ID) and coefficient of correlation (CC) weighting methods. The methods consider the distance between the target and the neighbourhood stations as well as the correlation between them. In determining the best spatial interpolation method, three tests for evaluating model performance have been used namely similarity index (S-index), mean absolute error (MAE) and root mean square error (RMSE). The homogeneity test using Standard normal homogeneity (SNHT), Buishand range (BR), Pettitt and Von Neumann (VNR) ratio are conducted to test the homogeneity of the rainfall data. The results show that the ID method is more efficient than the others method and 85% of the rainfall stations were homogenous based on this method. This study is important as it can be used to fill in the missing value rainfall data so that the conclusions that can be drawn from the data is valid.
Effects of Climatic Factors on Dengue Incidence: A Comparison of Bayesian Spatio-Temporal Models
Authors: Aswi Aswi , Sukarna  , Cramb Susanna , Mengersen Kerrie 
Published: April 2021

Abstract
Considering only the spatial component of diseases can identify areas with reduced or elevated risk, but not capture anything about temporal variation of risk which could be more or equally crucial. Hence, both spatial and temporal components of diseases need to be considered. Bayesian methods are useful due to the ease of specifying additional information, including temporal or spatial structure, through prior distributions. Here, we examine a range of different Bayesian spatio-temporal models available using CARBayes. Combinations of model formulations and climatic covariates were compared using goodness-of-fit measures, such as Watanabe Akaike Information Criterion (WAIC). Comparisons were made in the context of a substantive case study, namely monthly dengue fever incidence from January 2013 to December 2017 and climatic covariates in 14 geographic areas of Makassar, Indonesia. A spatio-temporal conditional autoregressive adaptive model combining rainfall and average humidity provided the most suitable model.
Investment and Margin Risk Analysis of Robusta Coffee Futures Contract at Jakarta Futures Exchange
Authors: Renggani Puspa , Sumertajaya I Made , Afendi Farit Mochamad , Budiarti Retno 
Published: April 2021

AbstractFutures trading is developing rapidly in various countries and has become one of the supports for economic growth. However, studies on investment in futures contracts in Indonesia, particularly in measuring risk are still limited. This study aims to calculate the maximum potential loss on investment and the estimated margin on robusta coffee futures at JFX using Value at Risk (VaR). The analysis used symmetrical and asymmetric GARCH (Exponential-GARCH, Threshold-GARCH, and GJR-GARCH). It used data of robusta coffee price, covering spot prices, March contract prices, and September contract prices for the 2016-2019 period. The result of the study indicates that the GARCH (1,1) is the most suitable model for the three robusta coffee prices. Based on the VaR value, the investment risk in the robusta spot is higher than the robusta contract. The margin value interval that may be applied to JFX robusta coffee based on spot price analysis, March contracts, and September contracts reach 2.39%-2.91%.
Multivariate generalized autoregressive score model (case study: vegetable oils and crude oil price data)
Authors: Jayanti D , Sadik K , Afendi F M 
Published: April 2021

Abstract
A multivariate econometric model can be used to forecast volatilities and dynamic correlations between various assets. Volatilities and dynamic correlations forecasting are important applied in risk management, hedging, asset allocation, and asset pricing options. This study uses a multivariate Generalized Autoregressive Score (GAS) model to analyze and forecast volatilities and dynamic correlations of the weekly prices of Crude Palm Oil, coconut oil, soybean oil, and crude oil. The GAS model is a new framework based on a score-driven time series model for updating time-varying parameters. Another multivariate econometric model is the Dynamic Conditional Correlation (DCC) GARCH model as a comparison. The empirical experiments regarding the distribution of return data show that the data can be approximated by the multivariate t-student distribution. The likelihood ratio test in the multivariate GAS model shows that the time-varying parameters in the GAS model are volatility, correlation, and location parameters. The evaluation performance model based on RMSE and MAE show that the multivariate GAS model has better performance in estimating and forecasting volatilities than the DCC GARCH model. Multivariate GAS models have better performance in estimating and forecasting the dynamic correlation on returns of CPO and soybean oil.
Forecasting the Number of Prisoners in Nganjuk With Integer-Valued Pth-Order Autoregressive (INAR(P))
Authors: Belinda Bella , Novita Mila 
Published: April 2021

Abstract
The most used time series model is the time series model which assumes the variables being tested are continuous in a discrete time period and produces continuous values. Whereas in many applications, such as number of monthly accidents, number of doctor visits per year a person makes, etc., needed a discrete time series model to handle discrete variables and produce discrete values as well. Time series model that handles count or non-negative integer data is the Integer-valued Autoregressive model with the pth-order or INAR(p). INAR(p) is the counterpart of AR(p) model for integer data. To get integer results, this model uses binomial thinning operator which implements probabilistic operations with discrete distribution that are suitable to model count data such as Poisson and Binomial, also use median forecasting method. Model parameters will be estimated using the Yule-Walker method. In this research, INAR(p) time series model will be applied to number of prisoners in Nganjuk from April 2013 until July 2016 to help tackle overcapacity problems in prisons that led to many negative impacts. Through model specification, the best model for forecast the case is INAR(2). In this data, based on the measure of AIC, BIC, and AICc, the INAR(2) model achieved better performance than its AR(2) counterpart.
The application of medoid-based cluster validation in desirable dietary pattern data
Authors: Budiaji Weksi , Riyanto Rifqi Ahmad , Suherna  
Published: April 2021

Abstract
A desirable dietary pattern (DDP) index is an index to measure the balance and variance of the nutrition intake of an individual. This index is composed of the calory values of protein, fat, and carbohydrates. Grouping individuals based on the DDP index is required to measure and improve an individual food security state. We took 14 individual purposively as samples to fill a set of DDP questioner. They were asked about their daily food consumption. They were grouped based on the DDP variables. A 3-dimensional plot showed that there were three to four clusters. Then, medoid-based partitioning algorithms, namely partitioning around medoids (PAM) and simple k-medoids (SKM), were applied in the data set. The inputted distances were generalized distance function to vary the distance options. The cluster results were then validated by medoid-based shadow value validation. This index was comparable to the 3-dimensional plot such that four clusters were opted as the most suitable number of clusters. The barplot of the cluster results showed that cluster 1 was characterized by an abundance of fat, while cluster 2 had very sufficient carbohydrates. Cluster 3 and 4 were two clusters with opposite characteristics where the former had a shortage of protein, fat, and carbohydrates, while the latter had an abundance of them.
Supporting of Waste Management in Indonesia Using Self Organizing Map for Clustering Analysis
Authors: Primandari Arum Handini , Kesumawati Ayundyah , Purwaningsih Tuti 
Published: April 2021

Abstract
The amount of waste in Indonesia is continuously increasing, along with the increasing population and welfare. The government already takes various efforts to control the amount of garbage; for example, they prohibit using disposable plastic in any form. Mapping waste generation and waste composition hopefully will help the government (or local government) make a more precise policy. Therefore, this research employed a modern clustering analysis that was Self Organizing Maps (SOM). SOM is an Artificial Neural Network (ANN) that uses unsupervised learning to reduce data dimensions into two dimensions. SOM produces low-dimensional visualizations of high-dimensional data. The data of waste generation and composition were obtained from the National Waste Management Information System in 2017-2018. The variables processed were the daily average of waste per square kilometer (ton/km2), the daily average of waste per person (kg/person), and the daily average of waste per sub-district area (tons/sub-district). The researchers built the 3x3 hexagonal topology. The districts of Brebes, Buleleng, Cilacap, and Jepara were grouped into areas with high waste generation compared to other districts. Meanwhile, based on the composition of garbage, Morowali, Sinjai, and Palangkaraya districts were the districts where the composition of food waste, plastic waste, and textile waste was high. Tableau was used to visualize the result into map.
Electricity Consumption Modelling in Kendari using The Backpropagation Method on The Artificial Neural Network
Authors: Ruslan R , Laome L , Usman I , Harisa E W 
Published: April 2021

Abstract
Kendari is the capital city of Southeast Sulawesi province with significant population growth. Population growth has resulted in higher demand for electricity, while Kendari city electricity source is only obtained from diesel power plants, of course, the amount of electricity available is limited compared to other power plants. For this reason, it is necessary to model the consumption of electricity in the city of Kendari, as well as forecasting peak loads at certain times of the day. In fact, the assumption of the data cannot be fully fulfilled, even though the assumption has been handled. For this reason, forecasting using the backpropagation method in artificial neural networks provides a solution when the assumptions of statistical data are not fulfilled. The purpose of this article is to model electricity consumption in Kendari city using the back propagation method on artificial neural networks. The best modelling results use a network structure with 10 input layer units and 4 hidden layer units with the smallest mean square error of 0.000145. The highest average peak load on a daily basis occurs at 8.00 PM is 75,593 MWh, while the lowest electricity load that occurs at 05.00 AM is 64,203 MWh. Forecasting of the mean of electricity consumption the next week will produce 69,079 MWh on Monday, 69,381 MWh on Tuesday, 68,550 MWh on Wednesday, 69,124 MWh on Thursday, 68,110 MWh on Friday, 67,927 MWh on Saturday, and 68,833 MWh on Sunday.
Carbon cycle dynamics during episodes of rapid climate change
Authors: Meissner K J , Brook E , Finkelstein S A , Rae J 
Published: April 2021

Abstract
Past climate records reveal many instances of rapid climate change that are often coincident with fast changes in atmospheric greenhouse gas concentrations, suggesting links and positive feedbacks between the carbon cycle and the physical climate system. The carbon reservoirs that might have played an important role during these past episodes of rapid change include near-surface soil and peatland carbon, permafrost, carbon stored in vegetation, methane hydrates in deep-sea sediments, volcanism, and carbon stored in parts of the ocean that are easily ventilated through changes in circulation. To determine whether similar changes might lie in store in our future, we must gain a better understanding of the physics, biogeochemistry, dynamics, and feedbacks involved in such events. Specifically, we need to ascertain the main natural sources of atmospheric carbon dioxide and methane linked to rapid climate events in the paleoclimate record, and understand the mechanisms, triggers, thresholds, and feedbacks that were involved. Our review contributes to this focus issue by synthesizing results from nine studies covering a broad range of past time episodes. Studies are categorized into (a) episodes of massive carbon release millions of years ago; (b) the transition from the last glacial to the current interglacial 19 000–11 000 years ago; and (c) the current era. We conclude with a discussion on major remaining research challenges and implications for future projections and risk assessment.
Study on the possibility of converting rice husks to organic fertilizer using bacteria Pseudomonas aeregenosa and Celullomonas flavigena
Authors: Taimooz Sulaf Hamid , Kareem Basim Mohammed , Muaen Al-Khaikany Salwan Abdul , Shadood Shahlaa Nassar 
Published: April 2021

Abstract
Peel rice known Balsbos remnants Majarh are considered, one of the causes of environmental pollution in Qadisiyah province due to the large spread of this Majarh region because it is considered the agricultural environment ideal for rice so research aimed at solving environmental pollution Balsbos problem by re-use this article (Sbos) and considered as a raw material in the preparation of Bacteria fermented with bacteria pseudomonas
aeregenosa And Celullomonas
flavigena. Where preliminary research results showed that the fermentation medium is loaded Celullomonas
flavigena Of the plant and the soil and its effect on the growth parameters of the barley plant where the results were good for the height of the plant and the dry weight of the vegetative total and the total root compared to the bacterial fermentation medium pseudomonas
aeregenosa. Which is followed by the effect and these two mediums are correlated with other fermentation mediums for the center of the cypress and the middle of the spousal paste and the bacterial medium. All these circles are loaded with the same bacteriostatic species.
Effect of Aqueous Extract of Truffle on Some Antioxidants in Rats Exposed to Oxidative Stress induced by Hydrogen peroxide
Authors: Ayed Hani Sabbar , Khalaf Abdulkhaleq Sulaimain , Taki Mohammed Taha Mohammed 
Published: April 2021

Abstract
The current study dealt with using of hydrogen peroxide with 0.5% with drinking water to induce state of oxidative stress in male rats and estimate the ability of different concentrations of the aqueous extract of Truffle Water Extract to reduce the effects of oxidative stress during periods 0, 15, 30 days by measuring the following parameters in serum: Glutathione(GSH), Malondialdehyde (MDA), the activity of super oxide dismutase enzyme (SOD) and the peroxynitrate radical (ONOO). Fifty male rats with the age of 3-4 months and weight of 300-400 gm were distributed into 5 groups: group (1): control group received drinking tap water, group (2): treated with H2O2 0.5% in drinking water, group (3): treated with H2O2 and Truffle Water Extract 20%, group (4): treated with H2O2 and Truffle Water Extract 40%, group (5): treated with H2O2 and Truffle Water Extract 60%. Hydrogen peroxide treatment led to a significant decrease (P ≤ 0.05) of the GSH and SOD activities and increase of the MDA and ONOO levels in the serum during treatment periods compared with the control group. The results of using different concentrations of Truffle Water Extract on rats revealed slight protection from the oxidative stress induced by Hydrogen Peroxide, because its effect was affected on the level of GSH, SOD, MDA and ONOO levels in the blood serum in different experiment period.
Effect of added different levels of Graviola Leavs meal ( Annona
muricata L) on biochemical blood traits and bacteriological account in broilers chicken
Authors: Alkassar Ali Mehmood , Shaker Alaboudy Batool Abed Alnaby 
Published: April 2021

Abstract
The study was carried out at the Animal Production Department Faculty of agriculture university of kufa to investigate the effect of replacing(GLM) Graviola leavs meal ( Annona muricata) fruit with antibiotcs and antioxidant in the broiler diets. The experiment was conducted at the poultry farm of the Animal Production Department, College of Agriculture, university of Kufa. Starter diets ( 23.82% C.P and 3029.7 kcal ME/kg) feed were gave to the birds from week 1 to week 3 of age, and finisher diets which contained 20.94% C.P. and 3204.5 kcal ME/kg were gave from week 4 to week 5 of age. Three hundred sixty one day old Ross 308 chicks were randomly divided into six groups with three replicate (20 bird / replicate)per group, Each group was subjected to the one of the following treatments (T1) positive control contained antibiotics and antioxidant, (T2) negative control without antibiotics and antioxidant, (T3) T2 + 0.1% Graviola leavs powder, (T4) T2 + 0.2% GLP, (T5) T2 + 0.3% GLP, (T6) T2 + 0.4% GLP. Using Randomized completely Block Design(RCBD). Biochemical blood traits, benefits and harmful bacteria in small intestine. Result showed that there were significant decreasing (P < 0.05) in all treatments contained (GLM) in cholesterol, triglycerids, uric acid, albumen, globulin, and LDL, VLDL, ALPase, glucose, phosphorus and calicium in blood serum compromised with control treatment.
The Effect of Spraying the Bio stimulant humic acid on growth and yield of Four sunflower hybrids
Authors: Khalaf Issa Talib , Assal Bassima Athar 
Published: April 2021

Abstract
A field experiment was carried out in a salty loam at Mysiab which is located 3 km from Mysiab Technical college during the spring season of 2016/2017. The aim of the experiment was to study the influence of spraying the bio stimulant humic acid with the following levels (0, 2ml, 4ml and 6ml.litre−1), on growth and yield of four sunflower hybrids (Zuhraa, Qudas, Euflor and Cobon). Results showed that, excluding plant dry weight, Capitulum diameter and kernel weight where there were no significant differences between the spring levels 4ml and 6ml.litre−1. Take spring level 6ml.litre−1 significantly overcome the other levels on the remainder trails being studied. Excluding plant height, where there was no significant effect between the hybrids Zuhraa and Qudas; Zuhraa was superior with remainder characters among other hybrids. The interaction effect was significant with the over whole studied characters. The seed yield was 6.32 tone.hectare−1 for Zuhraa and Euflor. Thus we can introduce those two hybrids in breeding projects to achieve rales which could tolerate the region environmental stresses.