Decision Making on Fuzzy Soft Simply* Continuous of Fuzzy Soft Multi-Function

Real world applications are dealing now with a huge amount of data, especially in the area of high dimensional features. In this article, we depict the simply*upper, the simply*lower continuous, we get several characteristics and other properties with respect to upper and lower simply*-continuous soft multifunctions. We also investigate the relationship between soft-continuous, simply*- continuous multifunction. We also implement fuzzy soft multifunction between fuzzy soft topological spaces which is Akdag’s generation of the notion. We are introducing a new class of soft open sets, namely soft simply*open set deduce from soft topology, and we are using it to implement the new approximation space called soft multi-function approach space. Simply*space for approximation based on a simply*open set. The world must adopt modern studies in order to confront epidemics. Accordingly, we presented a new decision proposal in this article, compared our proposed approach to the soft relationship introduced by approximation of Xueyou, and concluded that our approach is better. We also used our proposal in the medical application that was studied in this paper

Bayesian Convolution for Stochastic Epidemic Model

Dengue Hemorrhagic Fever (DHF) is a tropical disease that always attacks densely populated urban communities. Some factors, such as environment, climate and mobility, have contributed to the spread of the disease. The Aedes aegypti mosquito is an agent of dengue virus in humans, and by inhibiting its life cycle it can reduce the spread of the dengue disease. Therefore, it is necessary to involve the dynamics of mosquito's life cycle in a model in order to obtain a reli- able risk map for intervention. The aim of this study is to develop a stochastic convolution susceptible, infective, recovered-susceptible, infective (SIR-SI) mod- el describing the dynamics of the relationship between humans and Aedes aegypti mosquitoes. This model involves temporal trend and uncertainty factors for both local and global heterogeneity. Bayesian approach was applied for the parameter estimation of the model. It has an intrinsic recurrent logic for Bayesian analysis by including prior distributions. We developed a numerical computation and carry out simulations in WinBUGS, an open-source software package to perform Mar- kov chain Monte Carlo (MCMC) method for Bayesian models, for the complex systems of convolution SIR-SI model. We considered the monthly DHF data of the 2016–2018 periods from 10 districts in Kendari-Indonesia for the application as well as the validation of the developed model. The estimated parameters were updated through to Bayesian MCMC. The parameter estimation process reached convergence (or fulfilled the Markov chain properties) after 50000 burn-in and 10000 iterations. The deviance was obtained at 453.7, which is smaller compared to those in previous models. The districts of Wua-Wua and Kadia were consistent as high-risk areas of DHF. These two districts were considered to have a signifi- cant contribution to the fluctuation of DHF cases

Association between Temperature and Relative Humidity in Relation to COVID-19

The aim of this study is to determine the association between temperature and humidity in relation to COVID-19 above 3°C. This was carried out in the cities of Bandung and Surabaya which have temperatures of about 22°C to 31°C. Data was analyzed using descriptive analysis and the Pearson and Spearman correlation for normally and abnormally distributed data. The results showed that there was no association between people under monitoring (ODP)/close contact, patients under surveillance (PDP)/suspect, and COVID-19 confirmed cases in relation to the temperature and humidity in Bandung and Surabaya. Furthermore, there was no relationship between temperature and humidity with ODP, PDP, and COVID-19 Confirmed cases in both cities, because they had a comfortably wet category (RH > 70%). This results are expected to provide information to the government that weather cases in Indonesia (temperatures around 26°C–30°C with humidity > 60%) do not affect the spread of COVID-19. In addition, it is expected that further studies would be carried out on other factors that influence the spread of COVID-19 in Indonesia, for example, how the level of alternating flow in and/or out of the population into an area

SutteARIMA: A Novel Method for Forecasting the Infant Mortality Rate in Indonesia

This study focuses on the novel forecasting method (SutteARIMA) and its application in predicting Infant Mortality Rate data in Indonesia. It undertakes a comparison of the most popular and widely used four forecasting methods: ARIMA, Neural Networks Time Series (NNAR), Holt-Winters, and SutteARIMA. The data used were obtained from the website of the World Bank. The data consisted of the annual infant mortality rate (per 1000 live births) from 1991 to 2019. To determine a suitable and best method for predicting Infant Mortality rate, the forecasting results of these four methods were compared based on the mean absolute percentage error (MAPE) and mean squared error (MSE). The results of the study showed that the accuracy level of SutteARIMA method (MAPE: 0.83% and MSE: 0.046) in predicting Infant Mortality rate in Indonesia was smaller than the other three forecasting methods, specifically the ARIMA (0.2.2) with a MAPE of 1.21% and a MSE of 0.146; the NNAR with a MAPE of 7.95% and a MSE of 3.90; and the Holt-Winters with a MAPE of 1.03% and a MSE: of 0.083

Planar Construction of Extraordinary Magnetoresistance Sensor

A new version of the construction of the extraordinary magnetoresistance effect (EMR) based magnetic sensor has been proposed [2]. The differences between the original three dimensional (3D) construction and proposed 2D (planar) construction are presented. In proposed construction the metallic thin film (shunt) is coplanar with the semiconductor sensitive element. There are advantages of that planar construction like easier way of technological obtaining of the device. Another advantage is its application for EMR sensors based on new electronic materials like graphene and topological insulator thin films. The validity of the planar construction has been experimentally confirmed for model EMR sensors based on InSb/Ag structures. Comparison of the obtained experimental data with computational simulations of the EMR effect on planar model EMR sensors is performed Finite element method (FEM) is used as a tool for obtaining EMR effect simulations

A Methodology for Computational Architectural Design Based on Biological Principles

Biomimicry, where nature is emulated as a basis for design, is a growing area of research in the fields of architecture and engineering. The widespread and practical application of biomimicry as a design approach remains however largely unrealized. A growing body of international research identifies various obstacles to the employment of biomimicry as an architectural design method. One barrier of particular note is the lack of a clear definition and methodology of the various approaches to biomimicry that designers can initially employ. This paper attempts to link biological principles with computational design in order to present a design methodology that aids interested architects within the preliminary design phase

Hydrogen enrichment of a methane-air mixture by atmospheric pressure plasma for vehicle applications

International audienceDuring the last few years, the control of motor vehicles pollution has generated considerable research oriented towards the efficiency and the yield of the combustion techniques. A relatively new technology based on the plasma treatment of inlet gases appears to improve the operating conditions of internal combustion engines. The primary focus of this technique is the transformation of methane–air mixture into hydrogen-rich gas before the admission in the cylinder of the vehicle engine. This work describes an experimental investigation in CH4–air mixture using a 20 kHz sliding discharge plasma reactor at atmospheric pressure. After plasma treatment, the major gaseous: H2, CH4, CO, CO2 and H2O were analyzed and quantified using a micro-gas chromatograph (GC) and Fourier transform infrared absorption spectrometer (FTIR). Plasma treatment results show H2 enrichment in the range of 4–10% of the inlet gas mixture

Investigation of the thermophysical properties of binary system CO 2 and Hydro-fluoro-olefins

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