Trend of the spread of COVID-19 in Indonesia using the machine learning prophet algorithm

Based on information on the <span>BNPB website on 2 September 2020, the positive rate for coronavirus disease (COVID-19) in Indonesia reached 25.25% on 30 August 2020. This is a big challenge for the Indonesian government to reduce the positivity rate to meet the standards safe accepted by World Health Organization (WHO) is 5%. To ensure the accuracy of government policies, accurate data predictions are needed. Therefore, the prophet's machine learning algorithm can be used to see trends in the spread of COVID-19 in the next one year. This algorithm has a fairly high level of accuracy because the data contains time variables which are adjusted to the dataset. In several previous research, the dataset was vast uncertain and small. Meanwhile in this research, data was taken from 2 March 2020 to 12 February 2021 on the KawalCOVID19 website. This data is used to predict from 13 February 2021 to 12 February 2022. There are 3 data used; namely data confirmed, recovered and died. Based on the analysis, the confirmed patient was 22.60-42.11%, died amounted to 21.67%-39.00%, and recovered by 22.53-41.82%. The prediction percentage that the average cases died was 2.43% every day. The accuracy of data confirmed was 43.97%, died was 72.50% and recovered was 84.24%.</span>

Matrix model partition function by a single constraint

In the recent study of Virasoro action on characters, we discovered that it gets especially simple for peculiar linear combinations of the Virasoro operators: particular harmonics of $${\hat{w}}$$ w ^ -operators. In this letter, we demonstrate that even more is true: a singlew-constraint is sufficient to uniquely specify the partition functions provided one assumes that it is a power series in time-variables. This substitutes the previous specifications in terms of two requirements: either a string equation imposed on the KP/Toda $$\tau $$ τ -function or a pair of Virasoro generators. This mysterious single-entry definition holds for a variety of theories, including Hermitian and complex matrix models, and also matrix models with external matrix: the unitary and cubic Kontsevich models. In these cases, it is equivalent to W-representation and is closely related to super integrability. However, a similar single equation that completely determines the partition function exists also in the case of the generalized Kontsevich model (GKM) with potential of higher degree, when the constraint algebra is a larger W-algebra, and neither W-representation, nor superintegrability are understood well enough.

Low-cost material as active substrates for the removal of phosphorus in synthetic effluents: a proposal for social treatment technology

Considering the importance of the development of simplified technologies and social control in sanitation actions, this study investigated the use of laterite for phosphorus removal in synthetic effluents, through adsorption, as a low-cost alternative with the possibility of reusing the generated effluent, for communities where access to sanitation is limited. In the experimental design, the variables pH, contact time, granulometry and laterite dosage were used. Factorial planning was used for processing, for optimization and desirability. It was observed that the removal efficiency did not have significant interference in relation to the pH and contact-time variables. The kinetics of the batch experiments showed that the ideal contact time was 6.4 hours and pH of around 4. The adsorption capacity was plotted against equilibrium concentration for the Freundlich and Langmuir isotherms. The Langmuir isotherm was more suitable for phosphorus adsorption. The results show that laterite was effective in phosphorus adsorption in the order of removal of 87%, showing itself to be a potential adsorbent material. Keywords: laterite, phosphate adsorption, simplified effluent treatment.

Technical analysis of shovel working time towards inter-burden production achievement in open-pit coal mine

Shovel production can be affected by several factors, such as shovel working time and working conditions. The shovel production for the inter-burden removal target did not achieve at mining operations in Melak, East Kalimantan. This study’s main objective is to find the causes of the inadequate production and the efforts to improve. In this case study, the high delay due to the waiting for truck maneuvering caused the lack of shovel production. Some corrections were needed on the width of the loading point, and the loading method required to be replaced with double-side loading. Changes in the width of the loading point and loading method have an impact on increasing shovel productivity. In addition, a digger working time simulation is performed based on secondary data. Calculation of total production achievement is based on the simulation of time categories with working, standby, and repair time variables.

The Effect of Physical Education and Sports Teachers' Web-Technological Pedagogy Content Knowledge on Online Learning Readiness

Aim: The aim of this study was to examine whether the Web-Technological Pedagogy Content Knowledge of Physical Education and Sports Teachers had an Effect on Online Learning Readiness in terms of various demographic variables such as gender, school level and tenure. Methods: In line with this purpose, 180 Physical Education and Sports Teachers voluntarily participated in the research. In the research, Web-Technological Pedagogy Content Knowledge and Online Learning Readiness Scales were used as a data collection tool besides the personal information form prepared by the researcher. In the analysis of the data, descriptive statistics and Pearson Moments Multiplication Correlation Coefficient (r), Linear Regression Analysis, T-Test and One-Way ANOVA were used. It was determined that the participants had a high level of Web-Technological Pedagogy Content Knowledge and Online Learning Readiness. Results: While the results of the analysis showed that the level of web-technological pedagogy content knowledge did not differ according to gender and service time variables, it was determined that the level of online learning readiness was higher in women. In addition, it was determined that there was a positive and moderate relationship between Online Learning Readiness and Web-Technological Pedagogy Content Knowledge. Conclusion: As a result, it was determined that 38% of online learning readiness was explained by web-technological pedagogy content knowledge. Keywords: Readiness, Web-technological pedagogy content knowledge, Physical education and sports, Online learning

EDUCATION GAME MATH MENGGUNAKAN ALGORITMA FUZZY SUGENO

The quality of education is very important for success in learning, by using various methods or tools to make it easier for students to understand discrete mathematics material, based on the 23% graduation rate of students who do not complete discrete mathematics at XYZ University, it is necessary to innovate learning applications, use The game education application provides convenience in developing learning media, namely the Digital Math Game, an Android-based mobile application that makes it easy for students to learn discrete mathematics. This application is equipped with several features, namely games to hone the extent to which students' ability to do discrete math exercises randomly, with the reward in the game, the questions are designed to be more difficult and graded, Fuzzy Sugeno algorithm calculates logic to determine rewards based on time variables and problem solving. This application has an AR (Augmented Reality) feature to provide 3D views and videos on discrete mathematics textbooks, 30 algorithm testing with 100% accuracy.

An Efficient Discrete Model to Approximate the Solutions of a Nonlinear Double-Fractional Two-Component Gross–Pitaevskii-Type System

In this work, we introduce and theoretically analyze a relatively simple numerical algorithm to solve a double-fractional condensate model. The mathematical system is a generalization of the famous Gross–Pitaevskii equation, which is a model consisting of two nonlinear complex-valued diffusive differential equations. The continuous model studied in this manuscript is a multidimensional system that includes Riesz-type spatial fractional derivatives. We prove here the relevant features of the numerical algorithm, and illustrative simulations will be shown to verify the quadratic order of convergence in both the space and time variables.

Cardiac Autonomic Modulation Response Before, During, and After Submaximal Exercise in Older Adults With Intellectual Disability

The analysis of the heart rate variability (HRV) consists of changes in the time intervals between consecutive R waves. It provides information on the autonomic nervous system regulation and it is a predictor of adverse cardiovascular events. Several studies analyzed this parameter in youth and adults with Intellectual Disability (ID). Nevertheless, there is a lack of information regarding the HRV before, during, and after exercise in older adults with ID. Therefore, we aimed to describe and compare the cardiac autonomic modulation before, during, and after the six-minute walk test (6MWT) in older adults with and without ID. Twenty-four volunteers with ID and 24 without ID (non-ID) participated in this study. HRV was assessed by R-R intervals at rest, during and after the 6MWT. At rest and recovery periods, the participants remained sited. The symbolic analysis was used to evaluate non-linear HRV components. The recovery HR kinetics was assessed by the mean response time, which is equivalent to time constant (τ)+time delay (TD). Between groups differences in HRV variables were not significant. During the recovery period, HR kinetics time variables showed significant better results in non-ID participants (TD: 6±5s vs. 15±11s; τ: 19±10s vs. 35±17s; and MRT: 25±9s vs. 50±11s, all p&lt;0.050). In conclusion, our results suggest that the HRV in older adults with and without ID is similar during rest, exercise, and recovery. Recovery HR kinetics after the 6MWT was slower in older adults with ID. The reason for these results may be a reduced post-exercise vagal rebound in older adults with ID.

Modeling tropospheric ozone and particulate matter in Tunis, Tunisia using generalized additive model

The main purpose of this paper is to analyze the sensitivity of tropospheric ozone and particulate matter concentrations to changes in local scale meteorology with the aid of meteorological variables (wind speed, wind direction, relative humidity, solar radiation and temperature) and intensity of traffic using hourly concentration of NOX, which are measured in three different locations in Tunis, (i.e. Gazela, Mannouba and Bab Aliwa). In order to quantify the impact of meteorological conditions and precursor concentrations on air pollution, a general model was developed where the logarithm of the hourly concentrations of O3 and PM10 were modeled as a sum of non-linear functions using the framework of Generalized Additive Models (GAMs). Partial effects of each predictor are presented. We obtain a good fit with R² = 85% for the response variable O3 at Bab Aliwa station. Results show the aggregate impact of meteorological variables in the models explained 29% of the variance in PM10 and 41% in O3. This indicates that local meteorological condition is an active driver of air quality in Tunis. The time variables (hour of the day, day of the week and month) also have an effect. This is especially true for the time variable “month” that contributes significantly to the description of the study area.

Modelling Electricity Consumption Forecasting Using the Markov Process and Hybrid Features Selection

Given the problem of electrical energy storage, it is critical to predict the amount of load required in order to have a reliable and stable power distribution network. Predicting electricity consumption of subscribers and analyzing their consumption behavior under the influence of various factors and time variables is important. Given the large volume of subscriber consumption data and the effective factors, it is only possible to analyze the data using new information technology tools such as data mining. In this paper, feature selection, clustering and Markov process techniques are used to model and predict the power consumption data of subscribers. First, the selection of a subset of effective features is based on the combined PCA approach and the Firefly algorithm. Subscribers are then clustered based on the features selected by the K-means. Finally, subscriber behavior patterns are modeled to predict consumption using the Markov process on high-risk clusters. This study is simulated based on the data of electricity subscribers in Bushehr-Iran Power Distribution Company. The simulation results show the superiority of the proposed model over other similar algorithms such as LASSO-QRNN and HyFIS. The accuracy of power consumption prediction in the proposed method is about 1% compared to LASSO-QRNN and about 0.5% compared to HyFIS.