bluuebunny/crossref_metadata_2025_split huggingface dataset:
Micro-mesoporous submicron silica particles with pore size tunable in a wide range: synthesis, properties and prospects for LED manufacturing
Authors: Eurov Daniil A , Kurdyukov Dmitry A , Medvedev Alexander V , Kirilenko Demid A , Tomkovich Maria V , Golubev Valery G 
Published: February 2021

Abstract
An approach has been developed that allows the synthesis of submicron spherical silica particles with a controlled micro-mesoporous structure possessing a large specific surface area (up to 1300 m2 g−1). Particle synthesis is carried out by the hydrolysis of a mixture of various organosilanes mostly associated either with CTAB or with each other. A change in the concentration of CTAB in the reaction mixture apparently leads to a change in the formation mechanism of nuclei for the silica particle growth, which allows for varying the diameter of the synthesized particles in the range from 40–450 nm. The effect of the composition of the silica precursor ([3-(methacryloyloxy)propyl]trimethoxysilane, (3-aminopropyl)triethoxysilane and tetraethoxysilane) on the formation process and porosity of the resulting particles is studied. It was shown that by simply varying the ratio of organosilanes in the composition of the precursor, one can control the pore diameter of the particles in a wide range from 0.6–15 nm. The large-pore (up to 15 nm) silica particles are used as a matrix for the spatial distribution of luminescent carbon dots. The incorporation of carbon dots into SiO2 particles prevents their aggregation leading to emission quenching after drying, thus allowing us to obtain highly luminescent composite particles. LEDs based on the obtained composite material show bright visible luminescence with spectral characteristics similar to that of a commercial cold white LED.
Nanonization of a chemically synthesized flavone HMDF (3-hydroxy-3′,4′-methylenedioxyflavone) by entrapping within calcium phosphate nanoparticles and exploring its antioxidant role on neural cells in vitro and zebrafish in vivo
Authors: Patra Mousumi , Banik Milon , Bandopadhyay Pathikrit , Dutta Debanjan , Mukherjee Riya , Das Sreeparna , Begum Naznin Ara , Basu Tarakdas 
Published: February 2021

Abstract
The chemical synthesis of 3-hydroxy-3′,4′-methylenedioxyflavone (HMDF) was reported to generate a modified flavone of potent antioxidant activity with significant neuropharmacological properties. In this study, HMDF was nanonized by entrapping within calcium phosphate nanoparticles (CPNPs). HMDF-CPNPs were of (i) size 25 nm, (ii) zeta potential (−) [22 ± 3] mV and (iii) entrapment efficiency 67%. HMDF-CPNPs, but not HMDF alone, inhibited the in vitro activity of acetylcholinesterase enzymes to break down the major neurotransmitter compound acetylcholine. Moreover, nanonized HMDF had more antioxidant activity than bulk HMDF, as observed from its ability to protect mouse neural (N2A) cells from oxidative damage caused by H2O2 exposure at the levels of cell viability, intracellular reactive oxygen species, mitochondrial membrane potential, cell cycle stages, nuclear integrity and neural connectivity. An in vivo study on zebrafish larvae (Denio rerio) also demonstrated that H2O2-mediated larval death was checked by HMDF-CPNP treatment. These results, therefore, suggest that HMDF-CPNPs may be developed as a potential antioxidant, particularly as a neuroprotectant.
Orientation-dependent mechanical response of graphene/BN hybrid nanostructures
Authors: Patra Lokanath , Mallick Govind , Sachdeva Geeta , Shock Cameron , Pandey Ravindra 
Published: February 2021

Abstract
Graphene-based hybrid van der Waals structures have emerged as a new class of materials for novel multifunctional applications. In such a vertically-stacked heterostructure, it is expected that its mechanical strength can be tailored by the orientation of the constituent monolayers relative to each other. In this paper, we explore this hypothesis by investigating the orientation dependence of the mechanical properties of graphene/h-BN heterostructures together with that of graphene and h-BN bilayers. The calculated results simulating the pull-out experiment show a noticeable dependence of the (out-of-plane) transverse mechanical response, which is primarily governed by the interlayer strength, on the stacking configurations. The degree of the dependence is directly related to the nature of the interlayer interactions, which change from covalent to covalent polar in going from graphene bilayer to graphene/BN to BN bilayer. In contrast, molecular dynamics simulations mimicking nanoindentation experiments predict that the in-plane mechanical response, which mainly depends on the intra-layer interactions, shows little or no dependence on the stacking-order. The BN monolayer is predicted to fracture before graphene regardless of the stacking pattern or configuration in the graphene/BN heterostructure, affirming the mechanical robustness of graphene. Thus, the graphene-based hybrid structures retain both stiffness and toughness required for a wide range of optoelectromechanical applications.
Plant efficiency: a sensitivity analysis of the capacity factor for fusion power plants with high recirculated power
Authors: Mulder R.A. , Melese Y.G. , Lopes Cardozo N.J. 
Published: February 2021

Abstract
The plant efficiency of a nuclear fusion power plant is considered. During nominal operation, the plant efficiency is determined by the thermodynamic efficiency and the recirculated power fraction. However, on average the reactor operates below the nominal power, even when the long shutdown periods for large maintenance are left outside the averaging. Hence, next to the recirculated power fraction the capacity factor must be factored in. An expression for the plant efficiency which incorporates both factors is given. It is shown that the combination of high recirculated power fraction and a low capacity factor, results in poor plant efficiency. This is due to the fact that in a fusion reactor the recirculated power remains high if it runs at reduced output power. It is argued that, at least for a first generation of power plants, this combination is likely to occur. Worked out example calculations are given for the models of the power plant conceptual study. Finally, the impact on the competitiveness of fusion on the energy market is discussed. This analysis stresses the importance of the development of plant designs with low recirculated power fraction.
A Novel Hybrid Mgstar-Rnn Model for Forecasting Spatio-Temporal Data
Authors: Laily V O N , Suhartono  , Pusporani E , Atok R M 
Published: February 2021

Abstract
Time series data have a time dimension and space dimension called Spatio-temporal data. GSTAR is one of the models that can be used to analyze spatio-temporal data. One of development for this model is the Multivariate Generalized Spaced-Time Autoregressive (MGSTAR). The MGSTAR model has limitations of not being able to model a nonlinear time series, this problem can be overcome by applying a hybrid model on MGSTAR. This research aims to propose a hybrid MGSTAR-RNN model, where the MGSTAR model as a linear component and RNN as a nonlinear component. This research focused on a simulation study to evaluate the performance of MGSTAR and MGSTAR-RNN model. Several scenarios be experimented, i.e simulation in data with different variable component and location. The result show that hybrid MGSTAR-RNN model is better than individual MGSTAR model. In general, it is in line with the latest results of the 2018 M4 forecasting competition show that combined models or hybrid models tend to provide more accurate forecast performance than forecast results with individual models.
Parameter Estimation of Multivariate Adaptive Regression Spline (MARS) with Stepwise Approach to Multi Drug-Resistant Tuberculosis (MDR-TB) Modeling in Lamongan Regency
Authors: Yasmirullah S D P , Otok B W , Purnomo J D T , Prastyo D D 
Published: February 2021

Abstract
Tuberculosis (TB) is an infectious disease, caused by mycobacterium tuberculosis that affects various organs, especially the lungs. If TB treatment is not done thoroughly by the patient, then it can lead to death. Tuberculosis disease could not be healed cause TB bacteria to double immunity against anti-TB drugs, called multi-drug resistant (MDR). One identification issue toward TB infection chain is a TB case distribution analysis using mathematical modeling. The method used in this study was Multivariate Adaptive Regression Spline (MARS). Multivariate Adaptive Regression Spline is one type of non-parametric regression techniques, where the model does not assume the functional relationship between response and predictor variables, and has a flexible functional structure as well. Modeling aims to determine the factors that have the most significant influence on MDR-TB cases in Lamongan regency, as well as predict the incidence of MDR-TB in each sub-regency. The results show, the best model has a combination of BF = 28, MI = 2 and MO = 3, based on the minimum GCV, which is 5.26E-06. Furthermore, the model is statistically proper according to the criteria of APER and Press’s Q.
An Empirical Study to Determine Crop Insurance Premium Using Survival Analysis
Authors: Hasanah P , Azka M , Herliansyah R , Payung N L 
Published: February 2021

Abstract
Crop insurance provides a solution to economic losses experienced by farmers due to the risk of crop failure. The sample of crop survival data from Sukaratu Village in Cianjur-West Java had been analyzed. This research was analyzed the probability of failure based on the empirical study of the survival model. As a result, the probability of a crop failure studied in Sukaratu Village was calculated at 3.27%. Then, the exponential regression model was used to get the waiting time between crop failure events. The crop insurance product was simulated under the rate of premiums and benefits of insurance in several cases.
Small area estimation with application to households with energy-saving lamps estimates in Java Island, Indonesia
Authors: Siagian T H , Wulansari I Y , Pasaribu E 
Published: February 2021

Abstract
An electricity outage hit Jakarta and some provinces on Java Island on Sunday, August 4, 2019. This blackout was the worst in many years. However, it gives positive effect that many people realize the importance of energy-saving. Since 2016, the Ministry of Energy and Mineral Resources of Indonesia has initiated the 10-percent Energy Saving National Movement in an effort to change the wasteful behavior of the Indonesian people in consuming energy. One effort to support this national movement is by using energy-saving lamps such as compact fluorescents (CFLs) and light-emitting-diodes (LEDs). CFLs and LEDs last longer and are significantly more efficient than incandescent lamps (ILs). In 2017, 92.49 percent of households in Indonesia stated that they use energy-saving lamps in their homes. However, only 70.02 percent of households stated to use energy saving lamps in all lamps installed in their homes. This indicator is calculated from the 2017 national socio economic survey-social resilience module (SSN17.HANSOS). However the SSN17.HANSOS can provide data only at the national and province levels owing to insufficient sample size. Small Area Estimation (SAE) method overcomes this problem and can produce reliable estimates for small domains. Due to the growing need for reliable and accurate estimates at district level, therefore this study aims to estimate the percentage of household that use energy-saving lamps in all lamps installed in their homes by district in Java Island using a SAE method. The result of the study can be used by local government for identifying districts in which the energy-saving programs need to be strengthened and monitoring improved.
Estimating Households that Handle Their Domestic Waste in Not Eco-Friendly Ways in Indonesia: An Application of Small Area Estimation Technique
Authors: Sukim  , Ubaidillah A , Kurniawan R , Siagian T H 
Published: February 2021

Abstract
Waste still becomes a big problem for Indonesia up to now. The Ministry of Environment and Forestry stated that the total volume of waste nationally is 175,000 tons per day or equivalent with 64 million tons per year if using the assumption of waste produced by each person per day is 0.7 kg. According to the 2010 population census, Indonesia’s population is 237 million and this figure is projected to reach around 318 million people in 2045. With a population that continues to grow it will certainly increase the amount of waste generation. Some policy interventions have been set up by the Indonesian government such as the Indonesian President Regulation Number 97/2017 which aims to reduce 30 percent of the country’s waste and to process and manage for at least 70 percent of the country’s waste in order to avoid it from being accumulated in the landfill. Households waste poses a threat to public health and the environment if they are not stored, collected and disposed properly. Data from the 2017 National Socio Economic Survey-Social Resilience Module (SSN17.HANSOS) shows that 68.3 percent of households in Indonesia handle their domestic waste in not eco-friendly ways. Due to lack of availability of reliable data on household waste management at district/city level in Indonesia, this study aims to derive model-based district level estimates of households that handle their domestic waste in not eco-friendly ways by utilizing Small Areas Estimation (SAE) technique.
Pancreatic Cancer Classification Using the Kernel-based Support Vector Machine (KSVM)
Authors: Khairi R , Fitri S G , Rustam Z 
Published: February 2021

Abstract
Pancreatic cancer is a disease in which malignant (cancerous) tumor cells develop in pancreatic tissue; organ behind the lower abdomen and in front of the spine, which helps the body use and store energy from food by producing hormones to control blood sugar levels and digestive enzymes to break down food. Usually, pancreatic cancer is rarely detected at an early stage. One sign of a person with pancreatic cancer is diabetes, especially if it coincides with rapid weight loss, jaundice, or pain in the upper abdomen that spreads to the back. Among various types of cancer, pancreatic cancer has the lowest survival rate of only about 3%-6% of those diagnosed who can survive for five years. If patients are diagnosed on time for treatment, their chances of survival will increase. There is a tumor marker commonly used to follow the course of pancreatic cancer, namely CA 19-9 which can be measured in the blood. Healthy people can have small amounts of CA 19-9 in their blood. High levels of CA 19-9 are often a sign of pancreatic cancer. But sometimes, high levels can indicate other types of cancer or certain noncancerous disorders, including cirrhosis and gallstones. Because a high level of CA 19-9 is not specific for pancreatic cancer, CA 19-9 cannot be used by itself for screening or diagnosis. It can help monitor the progress of your cancer and the effectiveness of cancer treatment. In this study, the Kernel-based Support Vector Machine method is used to classify CA19-9 blood test results into two sections including data on patients diagnosed with pancreatic cancer or normal patients. This method will get an accuracy of around 95%.
Classification of Cerebral Infarction Data Using K-Means and Kernel K-Means
Authors: Putri A M , Sari A G M , Rustam Z , Pandelaki J 
Published: February 2021

Abstract
A cerebral infarct is a circumscribed focus or area of brain tissue that dies as a result of localized hypoxia or ischemia due to cessation of blood flow. To diagnose the presence of cerebral infarction, it needs a CT scan result from the patient. But, in this study not only CT scan result will be used, machine learning also will be proposed to diagnosing cerebral infarction. Machine learning can be used to detect and classify of infarcts in the brain using features and label that obtained from the results of the CT scan. In this study, the machine learning method that will be used is K-Means and K-Means based on kernel or kernel K-Means. Kernel K-Means is the application of K-Means that modified by changing the inner product with kernel function. The CT scan result data used in this study was obtained from the Department of Radiology at Dr. Cipto Mangunkusumo Hospital (RSCM). The best result reached with kernel K-Means, it performed with different percentage of training data, started with 50%, 55%, until 95% data training. The average accuracy score of the kernel K-Means method attained an accuracy rate of 95.28%.
Comparison between Fisher’s Ratio and Information Gain with SVM classifier for 3 levels of enthusiasm classification through face recognition
Authors: Rustam Z , Kristina Andrea Laksmirani , Satria Y 
Published: February 2021

Abstract
The enthusiasm level of a person is an important measurement in real-world problems. This paper, therefore, presents face recognition classification for enthusiasm level, based on supervised machine learning, using Support Vector Machine (SVM) as a classifier, with a one-vs-one method because the data consists of more than two classes. In addition, Fisher’s Ratio and Information Gain are applied in the selection of contributive features, and the goals were to present an accuracy comparison between SVM and Fisher’s Ratio, as wells as with Information Gain, and the results showed the accuracy at 88,89%, and 80,95238%, respectively. This indicates the combination of SVM with Fisher Ratio to be better.
Prostate Cancer Classification Using Random Forest and Support Vector Machines
Authors: Rustam Z , Angie N 
Published: February 2021

Abstract
Nowadays, it gets more types of diseases in the medical sector. For this reason, the role of technology is very important in assisting medical staff to overcome the problem. This research discusses about Prostate Cancer. Prostate Cancer is suffered commonly by males. There are no exact causes how Prostate Cancer occurs in males, but there are several risk factors of a Prostate Cancer, such as age, ethnic group, family history, diet, smoking, and world area. In this research, the classification to diagnose Prostate Cancer is using two methods, those are Random Forest (RF) and Support Vector Machines (SVM). By comparing accuracy of those two methods, we will know which method is better with a dataset that we have from Al-Islam Bandung Hospital, Indonesia. The result is given that Random Forest has a better accuracy than Support Vector Machines. The accuracy shows 97.30% with 80% of data training.
Leroux Spatial Model for Mapping the Relative Risk of Dengue Fever in Makassar
Authors: Thamrin S A , Rosdiana  , Jaya A K , Ansariadi  
Published: February 2021

Abstract
One of the health problems in Makassar city is dengue fever (DF) disease. To find out areas in Makassar that are at high risk of spreading DF, a relative risk analysis can be done. Bayesian Conditional Autoregressive (CAR) is a model used in disease mapping by taking into account the smoothing of the estimated relative risk and using spatial information to obtain a better estimate of relative risk. The Leroux model is a prior used in the CAR model for estimating the relative risk of DF in Makassar city. The relative risk estimation is used for geographic mapping of DF. In this model random and spatial variables are used. The results of this study indicate that DF medium relative risk occurred in some sub-districts of Makassar city. It indicates that the population in these areas susceptible and more likely to be infected with DF disease. This gives a clue so the Health Department of Makassar city can provide more intensive treatment to medium relative risk sub-districts so that the spread of DF disease in Makassar can be overcome.
Estimation of Relative Risk of Dengue Fever in Makassar Using Localized Bayesian Autoregressive Conditional Model
Authors: Thamrin S A , Khaerati R , Jaya A K , Ansariadi  
Published: February 2021

Abstract
Analysis of the relative risk of the spread of dengue fever (DF) in Makassar city, Indonesia, needs to be done to see the which areas are at high risk of DF. Bayesian Autoregressive Conditional (CAR) is used in the mapping model of this disease. This model is Able to model of relative risk by taking into account the smoothing of that relative risk and entering spatial information to reduce the error of the estimated parameters in order the reliable relative risk models is Obtained. In this study, the relative risk value of the spread of DF was Analyzed using the localized Bayesian models CAR. Under this model the geographical mapping of DF in Makassar can be identified for each sub-district and shows that Makassar is still very vulnerable to DF.
Bias Not Linear Prediction Method The Best for Small Area on Empirical Suspect Spending Per Capita (Case: South Sulawesi Province District Gowa)
Authors: Nusrang M , Sudarmin  , Annas S , Asfar  , Rais Z 
Published: February 2021

Abstract
Indirect estimation is the estimation of parameters that use additional information about the same parameters in a small area. The combination of the basic assumption of random effects and fixed effects forms a mixed influence model. In this study, the data used are secondary data from the SUSENAS and Regression methods. The value of Average Root Mean Square Error (ARSME) estimator in the EBLUP estimator is smaller when compared to the direct estimator and simple linear regression. So it can be said that the EBLUP estimator is better than the direct estimator of per household at the District level in Gowa Regency based on SUSENAS data for 2017.
Morphometric Variations of Nerita Lineata Gmelin 1791 in Different Mangrove Age Groups in the Ecosystem
Authors: Yunus M , Samsi A N 
Published: February 2021

Abstract
Snail Nerita lineata is a snail that lives in the mangrove ecosystem. This snail is also found in the mangrove ecosystem in the Village of Tongke-Tongke, Sinjai Regency, South Sulawesi. This study aims to provide information about the morphometrics of snails N. lineata. This research was conducted for six months, from April to September 2019. There were three research stations, namely A, B, and C, which were grouped according to mangrove age. group A is mangrove ⩾10 years old, group B is mangrove ⩾20 years old, and group C is mangrove which is rove30 years old. Each station uses a large plot measuring 10m x 10m and inside there are four small plots measuring 0.5m x 0.5m. sampling is done every month. Morphometric measurements using a digital caliper with an accuracy of 0.01 mm and weighing using a digital scale with an accuracy of 0.1 gr. Morphometric parameters observed were shell length (SL), shell width (SW), spire height (SpH), columella length (CL), shell opening width (AW), shell opening length (AL), and snail weight (W). Data were analyzed for correlation and Paired Sample Test using SPSS software. Morphometric snails in mangrove groups A, B, and C are not much different. In the mangrove group A, there are several morphometric parameters that are not correlated with each other. In the mangrove groups B and C, the parameters of shell length (SL) and shell width (SW) do not correlate. The results of the Paired Sample Test showed the significant length of snail (SL) between mangrove A and mangrove B and length of snails in mangrove B and mangrove C. None of the results of paired sample test showed significant snail weight.