STL Decomposition and SARIMA Model: The Case for Estimating Value-at-Risk of Covid-19 Increment Rate in DKI Jakarta

In this research, we propose an extreme values measure, the Value-at-Risk (VaR) based Seasonal Trend Loess (STL) Decomposition and Seasonal Autoregressive Integrated Moving Average (SARIMA) models, which is more sensitive to the seasonality of extreme value than the conventional VaR. We consider the problem of the seasonality and extreme value for increment rate of Covid-19 forecasting. For stakeholder, government and regulator, VaR estimation can be implemented to face the extreme wave of new positive Covid-19 in the future and minimize the losses that possibly affected in term of financial and human resources. Specifically, the estimation of VaR is developed with the difference lies on parameter estimators of STL and SARIMA model. The VaR has coverage probability as well as close 1-α. Thus, we propose to set α as parameter to estimate VaR. Consequently, the performance of VaR will depend not only on parameter model but also α. Our aim estimates VaR with minimum α based on correct VaR value. Numerical analysis is carried out to illustrate the estimative VaR.

Forecasting the COVID-19 Increment Rate in DKI Jakarta Using Non-Robust STL Decomposition and SARIMA Model

The Corona virus known as COVID-19 was first present in Wuhan, China at this time has troubled many countries and its spread is very fast and wide. Data on daily confirmed COVID-19 cases were collected from the DKI Jakarta province between early May 2020 and late January 2021. The daily increase in confirmed COVID-19 cases has a percentage of the value of increase in total cases. In this study, modeling and analysis of forecasting the increment rate in daily number of new cases COVID-19 DKI Jakarta was carried out using the Seasonal-Trend Loess (STL) Decomposition and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. STL Decomposition is a form of algorithm developed to help decompose a Time Series, and techniques considering seasonal and non-stationary observation. The results of the best forecasting accuracy are proven by STL-ARIMA, there are MAPE and MSE which only have an error value of 0.15. This proposed approach can be used for consideration for the DKI Jakarta government in making policies for handling COVID-19, as well as for the public to adhere to health protocols.

Ultra-high rate of temperature increment from superparamagnetic nanoparticles for highly efficient hyperthermia

The magneto-thermal effect, which represents the conversion of magnetostatic energy to heat from magnetic materials, has been spotlighted for potential therapeutic usage in hyperthermia treatments. However, the realization of its potential has been challenged owing to the limited heating from the magnetic nanoparticles. Here, we explored a new-concept of magneto-thermal modality marked by low-power-driven, fast resonant spin-excitation followed by consequent energy dissipation, which concept has yet to be realized for current hyperthermia applications. We investigated the effect of spin resonance-mediated heat dissipation using superparamagnetic Fe3O4 nanoparticles and achieved an extraordinary initial temperature increment rate of more than 150 K/s, which is a significant increase in comparison to that for the conventional magnetic heat induction of nanoparticles. This work would offer highly efficient heat generation and precision wireless controllability for realization of magnetic-hyperthermia-based medical treatment.

Protein Digestibility and Biochemical Characteristics of Soybean in Three Conditions of Fermentation Processing Step: An in Vitro Study

Protein digestibility of soybean (soaked "S", boiled "B", and fermented "F" soybeans) was changed as 20.58 ± 0.25%, 48.71 ± 0.04%, and 50.21 ± 0.45%, respectively in the preparation of soybean fermentation. After simulated digestion, the increment rate of protein digestibility of both B and F was comparable and higher than that of S accompanying by the accumulations of small protein sub-fractions and essential amino acids. Interestingly, bioactivity parameters of all digested fractions increased by around 2 to 4-fold when digestion stages were progressed with overall F showed the maximum values. Processing not only improves the palatability but also increases protein utilization, the bioavailability of nutrients, and healthy support. The study verified the effect of processing and the benefits of soybean and fermented soybean beyond their basic nutrients which could be claimed as functional foods with higher protein digestibility and indispensable amino acids as well as potential bioactivities.

Digital Transformation Modelling in the Context of Slowbalization

The article studies modern concepts and theories of digital transformation of society, analyzes structural changes in the economy during the fourth industrial revolution, presents different views of experts on the expected positive results of Industry 4.0 in the context of globalization. The authors suggest models and correlate increment rate dynamics with the information globalization index in the world and individual countries, and identify trends in the development of digital transformation in the context of slowbalization. The study considers possible consequences and risks of Industry 4.0 and ways to level them, outlines the priority areas of digital transformation to ensure high competitiveness of national economies in the period of slowbalization.

Effect of altitude on the emission characteristics of a DI diesel engine

To evaluate the effects of altitude on common rail electronic-controlled diesel engine, an experiment was carried out on a test bench with the simulated altitude system. The results indicated that the emissions of CO and Soot increased, while the impact of altitude on NOx varied based on the specific loads and speed when altitude increased from 0m to 4000m. The increment rate of CO and Soot increased with altitude, and the impact of altitude was greater on CO and Soot than those of NOX. The average emissions of CO and Soot increased with altitude, while the average emission NOx first increased at 1000m then decreased with altitude. The average increasing rate of CO ,Soot and NOx was 14.5%~59.5%, 13.4%~25.6% , -5.6%~0.5% respectively.

The prediction for development of COVID-19 in global major epidemic areas through empirical trends in China by utilizing state transition matrix model

Background Since pneumonia caused by coronavirus disease 2019 (COVID-19) broke out in Wuhan, Hubei province, China, tremendous infected cases has risen all over the world attributed to its high transmissibility. We aimed to mathematically forecast the inflection point (IFP) of new cases in South Korea, Italy, and Iran, utilizing the transcendental model from China. Methods Data from reports released by the National Health Commission of the People’s Republic of China (Dec 31, 2019 to Mar 5, 2020) and the World Health Organization (Jan 20, 2020 to Mar 5, 2020) were extracted as the training set and the data from Mar 6 to 9 as the validation set. New close contacts, newly confirmed cases, cumulative confirmed cases, non-severe cases, severe cases, critical cases, cured cases, and death were collected and analyzed. We analyzed the data above through the State Transition Matrix model. Results The optimistic scenario (non-Hubei model, daily increment rate of − 3.87%), the cautiously optimistic scenario (Hubei model, daily increment rate of − 2.20%), and the relatively pessimistic scenario (adjustment, daily increment rate of − 1.50%) were inferred and modeling from data in China. The IFP of time in South Korea would be Mar 6 to 12, Italy Mar 10 to 24, and Iran Mar 10 to 24. The numbers of cumulative confirmed patients will reach approximately 20 k in South Korea, 209 k in Italy, and 226 k in Iran under fitting scenarios, respectively. However, with the adoption of different diagnosis criteria, the variation of new cases could impose various influences in the predictive model. If that happens, the IFP of increment will be earlier than predicted above. Conclusion The end of the pandemic is still inapproachable, and the number of confirmed cases is still escalating. With the augment of data, the world epidemic trend could be further predicted, and it is imperative to consummate the assignment of global medical resources to curb the development of COVID-19.

The Prediction for Development of COVID-19 in Global Major Epidemic Areas Through Empirical Trends in China by Utilizing State Transition Matrix Model

Background: Since pneumonia caused by coronavirus disease 2019 (COVID-19) broke out in Wuhan, Hubei province, China, tremendous infected cases has risen all over the world attributed to its high transmissibility. We aimed to mathematically forecast the inflection point (IFP) of new cases in South Korea, Italy, and Iran, utilizing the transcendental model from China. Methods: Data from reports released by the National Health Commission of the People’s Republic of China (Dec 31, 2019 to Mar 5, 2020) and the World Health Organization (Jan 20, 2020 to Mar 5, 2020) were extracted as the training set and the data from Mar 6 to 9 as the validation set. New close contacts, newly confirmed cases, cumulative confirmed cases, non-severe cases, severe cases, critical cases, cured cases, and death were collected and analyzed. We analyzed the data above through the State Transition Matrix model. Results: The optimistic scenario (non-Hubei model, daily increment rate of -3.87%), the cautiously optimistic scenario (Hubei model, daily increment rate of -2.20%), and the relatively pessimistic scenario (adjustment, daily increment rate of -1.50%) were inferred and modeling from data in China. The IFP of time in South Korea would be Mar 6 to 12, Italy Mar 10 to 24, and Iran Mar 10 to 24. The numbers of cumulative confirmed patients will reach approximately 20k in South Korea, 209k in Italy, and 226k in Iran under fitting scenarios, respectively. However, with the adoption of different diagnosis criteria, the variation of new cases could impose various influences in the predictive model. If that happens, the IFP of increment will be earlier than predicted above. Conclusion: The end of the pandemic is still inapproachable, and the number of confirmed cases is still escalating. With the augment of data, the world epidemic trend could be further predicted, and it is imperative to consummate the assignment of global medical resources to curb the development of COVID-19.

The Prediction for Development of COVID-19 in Global Major Epidemic Areas Through Empirical Trends in China by Utilizing State Transition Matrix Model

Background: Since pneumonia caused by coronavirus disease 2019 (COVID-19) broke out in Wuhan, Hubei province, China, tremendous infected cases has risen all over the world attributed to high transmissibility. We managed to mathematically forecast the inflection point (IFP) of new cases in South Korea, Italy, and Iran, utilizing the transcendental model from China. Methods: Data from reports released by the National Health Commission of the People’s Republic of China (Dec 31, 2019 to Mar 5, 2020) and the World Health Organization (Jan 20, 2020 to Mar 5, 2020) were extracted as the training set and the data from Mar 6 to 9 as validation set. New close contacts, newly confirmed cases, cumulative confirmed cases, non-severe cases, severe cases, critical cases, cured cases, and death were collected and analyzed. All data were analyzed through the State Transition Matrix model. Results: the optimistic scenario (non-Hubei model, daily increment rate of -3.87%), the cautiously optimistic scenario (Hubei model, daily increment rate of -2.20%), and the relatively pessimistic scenario (adjustment, daily increment rate of -1.50%) were inferred and modeling from data in China. The IFP of time in South Korea would be Mar 6 to 12, Italy Mar 10 to 24, and Iran Mar 10 to 24. The numbers of cumulative confirmed patients will reach approximately 20k in South Korea, 209k in Italy, and 226k in Iran under fitting scenarios, respectively. However, with the adoption of different diagnosis criteria, the variation of new cases could impose various influences in the predictive model. If that happens, the IFP of increment will be earlier than predicted above. Conclusion: The end of the pandemic is still inapproachable, and the number of confirmed cases is still escalating. With the augment of data, the world epidemic trend could be further predicted, and it is imperative to consummate the assignment of global medical resources to curb the development of COVID-19.

Diseases of the circulatory system in the Kyrgyz Republic in the period of 2002–2017

Aim To analyze a tendency in circulatory system diseases (CSD) in the Kyrgyz Republic (KR).Materials and methods Medical statistics on morbidity, prevalence, mortality, and primary disability related with CSD in the adult population was obtained from the National Health Information Center (NHIC) of the Ministry of Health of the KR and the National Statistical Committee of the KR. For the purpose of comparative analysis, relative values were calculated per 100,000 and 10,000 population. A retrospective epidemiological study of prevalence, morbidity, mortality, and primary disability related with CSD for 2002-2017 was performed with calculation of the increase/decrease rate using the least square method for aligning the dynamic row. Also, the epidemiological situation of CSD was analyzed by sex and in 9 administrative divisions of the KR with calculation of the mean long-term incidence (MLTI) of CSD from 2007 through 2017. Statistical analysis was performed with specialized Statistica 10.0 and SPSS 11.5 software.Results CSD remain the major cause of death (51.6 % in 2017) and primary disability (19.68 % of overall primary disability) in the Kyrgyz population. Prevalence of CSD shows a pronounced upward trend (6.8% increment rate relative to 2002) whereas the incidence rate has increased with a moderate trend (3.5 % increment rate). The major structure of CSD is determined by essential hypertension (51.2 %), ischemic heart disease (28.0 %), and cerebrovascular diseases (10.0 %). CSD is more frequently observed in women (mean value for 2007-2017, 59.0%) than in men (41.0 %) (р<0.001). Mean long-term indexes of CSD significantly differ in different regions and cities of the KR. The highest primary morbidity and prevalence of CSD is observed in Bishkek (1546.90 / 0000 and 12415.40 / 0000). Relatively low levels of these values are found in the Talas Region (720.70 / 0000 and 3675.10 / 0000, respectively).Conclusion CSD remains the major cause of death and primary disability in the population of the KR. Prevalence of CSD shows a pronounced increasing trend whereas the increase in incidence rate is moderate. The major structure of CSD is determined by essential hypertension, ischemic heart disease, and cerebrovascular diseases. Incidence of CSD is statistically significantly higher for women than for men. Mean long-term indexes of CSD significantly differ in different regions and cities of the KR. Therefore, it is essential to enhance preventive measures in the KR; to identify individuals with hypertension at the level of primary care; and to motivate healthcare professionals to improve the quality of healthcare (CSD prevention, detection, and treatment).