Comparison Of Time Series Research Articles

OPEB Accounting and Teacher Diversity

Despite extensive literatures documenting the importance of teacher diversity and teacher compensation, few studies explore relationships between the two. Fewer still examine effects of governmental accounting standards, even though these standards evolve regularly and could have substantial implications for how school districts allocate resources. I use staff- and district-level data from California to explore the effects on the teaching force of a change in accounting standards that required districts to recognize the costs of retiree health benefits (and other postemployment benefits, or OPEBs) as employees earned them. By making the true costs of this deferred compensation more apparent, this reform may have changed teachers’ incentives or encouraged districts offering such benefits to shift expenditures toward other aspects of compensation or working conditions more highly valued by relatively novice teachers (e.g., higher salaries). I illustrate how such reforms could impact the diversity of the teaching force by showing how teachers of different races and genders were likely to be differentially affected by deferred compensation policies due to differences in previous experience (and thus proximity to retirement). However, comparative interrupted time series analyses do not show that districts affected by the change in accounting standards saw their teaching staffs diversify at different rates than other districts. Thus, while the costs of deferred compensation are likely an important—and underdiscussed—factor in determining the diversity of the teaching force, accounting reforms alone are unlikely to moderate their impacts on the race and gender composition of teachers, at least in the medium term.

Impacts of climate and land cover variability and trends on springtime East Asian dust emission over 1982–2010: A modeling study

Mineral dust is an important player in global air quality and climate, but little is known about its variability and trends under the simultaneous influence of climate and land cover changes, particularly in East Asia where intense land cover change has occurred in recent decades. Here we determined the interannual variability and trends in dust emission from the main East Asian dust sources and examined to what extent these variations and trends were driven by climatic or land cover changes. We first optimized the dust emission module in the GEOS-Chem chemical transport model with better representation of dust physics and dependence on vegetation and wind variability. We then conducted simulations to quantify the impacts of climate and land cover variability and trends on springtime dust emission over 1982–2010 from the Teklimakan Desert, Gobi Desert and the whole East Asian desert domain. We found that dust emission from Gobi decreased substantially from the 1980s to 1990s, and then slightly increased toward the 2000s, leading to an overall reduction by 35% over the whole period. These changes were predominantly driven by meteorological changes and the effects of land cover change were negligible. Dust emission from Teklimakan increased from the 1980s to 1990s, but decreased afterward, leading to an overall reduction by 20%, albeit with large interannual variability. The overall trend was attributable to a consistent reduction in dust emission due to enhanced vegetation density in the periphery of the desert, accompanied by larger but more varying changes due to meteorological factors. Time series comparison and regression analysis indicated that in both deserts (especially Gobi), surface wind speed, followed by total precipitation, was the most important meteorological factor controlling dust variability and trends, with the weakening of wind playing the largest role in the overall decline in dust emission over the whole period. Overall, the interannual variability and trends of East Asian dust emission are largely shaped by climatic factors, with the land cover playing secondary but locally important roles, especially in the semiarid and non-desert regions undergoing rapid land cover change and desertification.

Application of Wind Tunnel Device for Evaluation of Biokinetic Parameters of Running

The aim of the study was the application of high-tech wind tunnel device to identify the changes in the biokinetic parameters of running performed on the specially designed treadmill. The research was carried out in the “Planica Nordic Centre—PNC” in the wind tunnel system, where the AirRunner Assault treadmill, which was equipped with four sensors measuring the vertical and horizontal ground reaction forces, was installed. To obtain biokinetic data, the runners performed the treadmill’s run under conditions of airflow directed at each participant’s back (backwind speeds +3 m/s and +5 m/s) and the chest (headwind speeds −5 m/s and −7 m/s). The runner’s speed was measured via image analysis using a DSLR camera and markers on the belt of the treadmill. Additionally, a high-speed camera synchronised to the force acquisition system was used to analyse the contact phase via comparison of foot placement and time series of the ground reaction forces. The contact phases of the running step were found to be longer than the flight phases, with their duration ranging from 0.15 to 0.20 s and the maximum forces at take-off were found to be greater than when running with the backwind. It should be noted that the application of high-tech devices wind tunnel and treadmill were found to be sufficiently accurate to perform kinetic measurements of running parameters in changing conditions, such as resistance and assistance (facilitating).

Robust tests for time series comparison based on Laplace periodograms

Statistical comparison of time series is useful for the detection of mechanical damage and many other real-world applications. New methods have been proposed to check whether two semi-stationary time series have the same normalized dynamics. The proposed methods differ from traditional methods in that they are based on the Laplace periodogram, which is a robust tool to analyze the serial dependence of time series. Via the method of estimating equations, a generalized score statistic and an order selected statistic are developed for the comparison. Their asymptotic distributions under the null are obtained. The proposed methods are applicable to compare two semi-stationary time series which may be dependent on each other. They also can be used to compare two time series whose traditional spectral densities or autocovariance structures may not exist. A Monte Carlo simulation study illustrates the validity of the asymptotic results and the finite sample performance. The proposed methods have been applied to an analysis of non-stationary vibration signals for mechanical damage detection.

Taitoko, an advanced code for tsunami propagation, developed at the French Tsunami Warning Centers

This paper presents the new version of the operational code used at the Tsunami Warning Centers in French Polynesia and in mainland France. This nested multigrid code solves either the nonlinear shallow water or the weakly nonlinear Boussinesq equations in a spherical or Cartesian coordinates system. The objective is to simulate the propagation of tsunamis both in the context of tsunami early warning and in the context of research. The originality of this code is to simulate the tsunami propagation through a series of nested grids in which the numerical scheme is specified by the user. Three standard finite-difference methods and two finite volume schemes are available to take into account the dispersive nature of waves or to capture flow discontinuities. The performance of the five numerical schemes has been assessed through idealized 1D and 2D test cases. The convergence rate with grid refinement is evaluated for three of these tests. Diffusion and dispersion of the five schemes are evaluated through the 1D far-field propagation in a channel of constant depth. Finally, two-dimensional simulations of the 2011 Japanese tsunami are performed considering a homogeneous tectonic source. Coastal inundation is simulated in the bay of Kamaishi where run-up heights reached 10 m. The ability of the five schemes to propagate trans-Pacific tsunamis is evaluated through comparisons of time series between simulations and observations at DART buoys. In the context of tsunami early warning, numerical results show that standard finite-difference methods reproduce most of the observed time gauges and that numerical errors associated to these schemes are acceptable.

상장폐지 직전 이익조정: IFRS와 K-GAAP 채택 기간 간의 시계열 비교

재무적 곤경으로 인해 상장폐지에 직면한 기업은 이를 회피하기 위해 이익을 상향조정할 동기를 가질 수 있다. 본 연구는 상장폐지 직전 이익조정행태가 IFRS 도입 이후에 더 증가할 가능성이 있는지를 조사하고 있다. 관련된 선행연구는 원칙중심 회계기준인 IFRS는 개념적으로 경영자의 재량권을 억제하지만, 실무적 적용과정에서 경영자가 재량권을 남용할 위험도 있음을 지적하고 있다. 이 연구는 이익조정 동기가 증가할 수 있는 특수한 상황(상장폐지 사건)에서 IFRS 채택이 경영자의 이익조정 동기를 더 확대(또는 축소)하는지를 실증적으로 분석하는 것에 초점을 맞추고 있다. 2003년부터 2019년까지 표본선정기준을 만족하는 460개 상장폐지 표본을 대상으로, 상장폐지 직전 기간의 이익조정이 K-GAAP과 IFRS 기간 간에 차이가 있는지를 비교한 결과는 다음과 같다. 첫째, 상장폐지 직전 5년 동안 비정상발생액의 시계열적 추세를 비교한 결과, K-GAAP 기간의 비정상발생액은 상장폐지 시점에 근접할수록 음(-)의 값이 더 커지는 추세를 보였다. 하지만 IFRS 기간의 비정상발생액은 상장폐지 시점에 근접하더라도 유의한 감소추세를 보이지 않았다. 이러한 시계열적 추세는 IFRS 도입 이후 기간에서 상장폐지 직전 상향이익조정행태가 더 증가하고 있음을 의미한다. 둘째, 상장폐지 직전 연도의 재량적발생액이 K-GAAP과 IFRS 기간 간에 차이가 있는지를 비교한 결과, 상장폐지 직전 연도의 재량적발생액은 K-GAAP 기간보다 IFRS 기간에서 유의하게 컸고, 이러한 결과는 재무적 곤경으로 상장폐지 된 기업에서 더 강하게 나타났다. 전반적으로 상장폐지 위험에 큰 기업은 상장폐지를 모면할 목적으로 IFRS 채택으로 얻게 된 실무적 재량권을 기회주의적으로 사용하는 경향을 보였고, 이러한 결과는 상장폐지 직전 기간에서 투자자의 역선택 위험이 더 증가할 가능성을 시사하고 있다.

Forecasting of electricity consumption in assam by using mathematical and time series models and comparison of their predictive performances

Although energy exists in different forms in nature, the most important form of energy is Electricity. Considering the worldwide increasing demand for electricity with increasing population, rapid urbanization, rising incomes, changes in lifestyles and with infrastructural and industrial growth, forecasting electricity consumption becomes an important part of power sector of any country. In this paper, attempt is made to forecast the total electricity consumption in Assam with the help of two models, considering the time period from 1990-91 to 2016-17. The first one is Multiple Linear Regression model which considers the effects of Population and Per Capita Income on the electricity consumption in Assam while the second one is the Autoregressive Integrated Moving Average (ARIMA) model. By comparing the Average Relative Error of the two proposed models, the predictive performance of the ARIMA (0, 1, 2) model is found better than the Multiple Linear Regression model and ARIMA shows an annual average increasing rate of electricity consumption as 5.40% up to 2021.

Analysis of airborne pollen time series originating from Hirst-type volumetric samplers\u2014comparison between mobile sampling head oriented toward wind direction and fixed sampling head with two-layered inlet

In this study, we have compared time series of pollen concentration originating from two Hirst-type volumetric samplers that were equipped with different sampling heads. To collect airborne pollen, we have used classic sampler with mobile sampling head including wind vane and adapted sampler with fixed sampling head having two-layered inlet, like in Sigma-2 passive sampler. The devices were placed at the roof level, on the top of the building of the Faculty of Sciences located in Novi Sad, Serbia. The sampling of airborne pollen was performed from February until October 2019. Along with the pollen data, meteorological conditions were recorded with an automated weather station measuring solar radiation, air temperature, relative humidity, wind speed and precipitation. Time series of daily pollen concentrations expressed high correlations, although there were large differences on the hourly basis. Absolute difference between hourly values showed very weak correlation with relevant meteorological parameters: temperature, humidity, precipitation, wind speed and turbulent kinetic energy, leading to the conclusion that sampling with both heads was not affected by meteorological conditions. Counting the pollen grains from the whole sample and not just from 10% of the area, which is the minimum requirement, was done for the six days in the season and proved that error introduced by subsampling during analysis was the main reason for differences in time series. To conclude, replacing mobile sampling head with fixed sampling head having two-layered inlet does not notably affect the quantity of pollen recorded by the Hirst-type volumetric method.

Visibility Graph Analysis of Particle Size Distribution During Flocculation for Water Treatment

Water and wastewater physicochemical treatments often rely on coagulation and flocculation to generate aggregates adequate for separation. Floc development may be assessed by particle size distribution (PSD) using dynamic light scattering techniques (DLS) available in commercial equipment. The DLS output data, however, often presents high variability, which may hinder comparisons of the aggregation time series for different conditions. The Visibility Graph (VG), a novel approach to be applied within the sanitation context, may be an alternative to disclose properties of these highly oscillatory monitoring results. In this study, after defining ideal shear rates and mixing times for the treatability of high-turbidity test water, we monitored PSD after coagulation using metallic salts (ferric chloride and ferric sulfate) and a natural coagulant (Opuntia cochenillifera). PSD data was converted to visibility networks and measurements were obtained to describe these time series by the VG technique. Although no series patterns were found, the VG approach shed some light onto the PSD through time, but no inferences were scaled to the treatability aspect. The limitations for the VG method in this study are mainly due to the small time series; thus, we endorse that visibility network analysis may be a promising technique within the environmental and sanitation context.

Tight lower bounds for dynamic time warping

Dynamic Time Warping (DTW) is a popular similarity measure for aligning and comparing time series. Due to DTW’s high computation time, lower bounds are often employed to screen poor matches. Many alternative lower bounds have been proposed, providing a range of different trade-offs between tightness and computational efficiency. LB_KEOGH provides a useful trade-off in many applications. Two recent lower bounds, LB_IMPROVED and LB_ENHANCED, are substantially tighter than LB_KEOGH. All three have the same worst case computational complexity—linear with respect to series length and constant with respect to window size. We present four new DTW lower bounds in the same complexity class. LB_PETITJEAN is substantially tighter than LB_IMPROVED, with only modest additional computational overhead. LB_WEBB is more efficient than LB_IMPROVED, while often providing a tighter bound. LB_WEBB is always tighter than LB_KEOGH. The parameter free LB_WEBB is usually tighter than LB_ENHANCED. A parameterized variant, LB_Webb_Enhanced, is always tighter than LB_ENHANCED. A further variant, LB_WEBB*, is useful for some constrained distance functions. In extensive experiments, LB_WEBB proves to be very effective for nearest neighbor search.

Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A comparison of time series forecasting methods

BackgroundThe COVID-19 pandemic conditions are still prevalent in Iran and other countries and the monitoring system is gradually discovering new cases every day. Therefore, it is a cause for concern around the world, and forecasting the number of future patients and death cases, although not entirely accurate, helps the governments and health-policy makers to make the necessary decisions and impose restrictions to reduce prevalence. MethodsIn this study, we aimed to find the best model for forecasting the number of confirmed and death cases in Iran. For this purpose, we applied nine models including NNETAR, ARIMA, Hybrid, Holt-Winter, BSTS, TBATS, Prophet, MLP, and ELM network models. The quality of forecasting models is evaluated by three performance metrics, RMSE, MAE, and MAPE. The best model is selected by the lowest value of performance metrics. Then, the number of confirmed and the death cases forecasted for the 30 next days. The used data in this study is the absolute number of confirmed, death cases from February 20 to August 15, 2020. ResultsOur findings suggested that based on existing data in Iran, the suitable model with the lowest performance metrics for confirmed cases data obtained MLP network and the Holt-Winter model is the suitable model for forecasting death cases in the future. These models forecasted on September 14, 2020, we will have 2484 new confirmed and 114 new death cases of COVID-19. ConclusionAccording to the results of this study and the existing data, we concluded that the MLP and Holt-Winter models had the lowest error in forecasting in comparison to other methods. Some models had fitted poorly in the test phase and this is because many other factors that are either not available or have been ignored in this study and can affect the accuracy of forecast results. Based on the trend of data and forecast results, the number of confirmed cases and death cases are almost constant and decreasing, respectively. However, due to disease progression and ignoring the recommendations and protocols of the Ministry of health, there is a possibility of re-emerging this disease more seriously in Iran and this requires more preventive care.

Tracking COVID-19 using online search

Previous research has demonstrated that various properties of infectious diseases can be inferred from online search behaviour. In this work we use time series of online search query frequencies to gain insights about the prevalence of COVID-19 in multiple countries. We first develop unsupervised modelling techniques based on associated symptom categories identified by the United Kingdom’s National Health Service and Public Health England. We then attempt to minimise an expected bias in these signals caused by public interest—as opposed to infections—using the proportion of news media coverage devoted to COVID-19 as a proxy indicator. Our analysis indicates that models based on online searches precede the reported confirmed cases and deaths by 16.7 (10.2–23.2) and 22.1 (17.4–26.9) days, respectively. We also investigate transfer learning techniques for mapping supervised models from countries where the spread of the disease has progressed extensively to countries that are in earlier phases of their respective epidemic curves. Furthermore, we compare time series of online search activity against confirmed COVID-19 cases or deaths jointly across multiple countries, uncovering interesting querying patterns, including the finding that rarer symptoms are better predictors than common ones. Finally, we show that web searches improve the short-term forecasting accuracy of autoregressive models for COVID-19 deaths. Our work provides evidence that online search data can be used to develop complementary public health surveillance methods to help inform the COVID-19 response in conjunction with more established approaches.

Comparative analysis of greenhouse gas emission inventory for Pakistan: Part II agriculture, forestry and other land use and waste

Understanding Pakistan's greenhouse gas (GHG) emission status is critical for identifying the national targets for GHG mitigation with respect to the nationally determined contributions (NDCs). This study focuses on the development of Pakistan's GHG (CO2, CH4, and N2O) inventories for agriculture, forestry and other land use (AFOLU) and waste sectors using 2006 IPCC Guidelines. This study should be seen as a direct continuation of the preceding one (Part I [ Available online at: https://doi.org/10.1016/j.accre.2020.05.002]) which discusses energy and industrial processes and product use in compliance with the 2006 and 1996 IPCC Guidelines. It also provides sector-specific comparative time series (1994–2017) analysis of GHG inventories, identification of key categories, and national GHG emissions trend for Pakistan. The results indicate an average relative difference (on average for time series 1994–2017) of 19% and 6% in total GHG emissions (CO2-eq) from AFOLU and waste sector respectively between 2006 and 1996 IPCC Guidelines. The absolute difference over the entire time series for AFOLU and waste sector was in the range of 3–67 Mt CO2-eq and 1–7 Mt CO2-eq respectively. Findings further reveal that the quantity of national GHG emissions by 2006 IPCC Guidelines is 10% lower on average for complete time series compared to 1996 IPCC Guidelines. The average relative difference for total national emissions of CO2, CH4 and N2O is −1%, 9%, and 48% respectively. Key category analysis based on 2006 IPCC Guidelines estimates identified three categories, each contributing ≥10% to the level assessment in the latest year 2017 and accounting for approximately half of the national GHG emissions. In order to further improve the reliability of GHG inventories, Pakistan needs to move from 1996 to 2006 IPCC Guidelines under a higher Tier approach particularly for identified key categories.

Evaluating the Impact of New York's Executive Order on Face Mask Use on COVID-19 Cases and Mortality: a Comparative Interrupted Times Series Study.

BackgroundOn April 17, 2020, the State of New York (NY) implemented an Executive Order that requires all people in NY to wear a face mask or covering in public settings where social distancing cannot be maintained. Although the Centers for Disease Control and Prevention recommended face mask use by the general public, there is a lack of evidence on the effect of face mask policies on the spread of COVID-19 at the state level.ObjectiveTo assess the impact of the Executive Order on face mask use on COVID-19 cases and mortality in NY.DesignA comparative interrupted time series analysis was used to assess the impact of the Executive Order in NY with Massachusetts (MA) as a comparison state.ParticipantsWe analyzed data on COVID-19 in NY and MA from March 25 to May 6, 2020.InterventionThe Executive Order on face mask use in NY.Main MeasuresDaily numbers of COVID-19 confirmed cases and deaths.Key ResultsThe average daily number of confirmed cases in NY decreased from 8549 to 5085 after the Executive Order took effect, with a trend change of 341 (95% CI, 187–496) cases per day. The average daily number of deaths decreased from 521 to 384 during the same two time periods, with a trend change of 52 (95% CI, 44–60) deaths per day. Compared to MA, the decreasing trend in NY was significantly greater for both daily numbers of confirmed cases (P = 0.003) and deaths (P < 0.001).ConclusionsThe Executive Order on face mask use in NY led to a significant decrease in both daily numbers of COVID-19 confirmed cases and deaths. Findings from this study provide important evidence to support state-level policies that require face mask use by the general public.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11606-020-06476-9.

Decreased volume of hip fractures observed during COVID-19 lockdown.

The aim was to study the incidence and characteristics of fragility hip fractures admitted during COVID-19 lockdown. We analysed socio-demographic and baseline characteristics of patients suffering fragility hip fractures between March 1st and May 1st 2020, period most affected by COVID-19 confinement measures. Cases (n = 64) were compared with controls (n = 172) from corresponding periods in 2018 and 2019. Poisson Generalised Linear Model regression analysis was performed to compare daily case counts, and LOESS curves compared time series. No differences were found regarding age or gender distributions, pre-fracture living, ambulatory, functional or cognitive status. More patients in 2020 had a high-risk ASA score. Fewer cases [Reduction = 26% (95% CI 1-44%)] were admitted in 2020. The reduction seemed to correspond with confinement. Approximately, one quarter less fragility hip fractures were admitted to our hospital during lockdown. Patients' baseline status and socio-demographic characteristics were similar.

Effect of Collaborative Writing Combined with Blog Online Learning on Indonesian EFL Learners’ Writing Skill across Motivation

Abstract:&#x0D; &#x0D; This study aims to investigate collaborative writing combined with blog online learning on the writing skill of Indonesian English foreign language (EFL) learners’ across their motivation. This research included a quasi experimental with the comparative time series. A quasi experimental study was employed by involving 61 learners of Senior High School. This research applied in into two groups: experimental and control. The treatment was conducted at 7 sessions, including the pre-test and post-test. The scores of pre-test and post-test in writing were used as a base of quantitative data analysis whereas the learners’ responses to motivation questionnaire were used to categorize their motivation. The result revealed that the collaborative writing combined with blog online learning on the writing was effective to enhance the learners’ writing skill. In addition, to apply the combination of collaborative writing and online blog learning, teacher should not worry about different levels of motivation among students where the students were motivated to use language.&#x0D;

Optimizing the artificial neural network parameters using a biased random key genetic algorithm for time series forecasting

Artificial Neural Networks (ANN) is one of the most used methods in time series forecasting. Mostly, it is hard to determine the design and weight parameters of ANNs by experience. For this reason, ANN optimization is considered to find the best network design and parameter sets. In this study, a novel hybrid algorithm based on biased random key genetic algorithms is proposed for ANN optimization. The algorithm, which is named as BRKGA–NN, determines the number of hidden neurons, bias values of hidden neurons and the connection weights between nodes. To test the performance of the BRKGA–NN, the algorithm is compared with genetic algorithm based ANN, ANN with back-propagation, Support Vector Regression and Autoregressive Integrated Moving Average on some of the most known time series datasets. According to the results of forecasts, BRKGA–NN algorithm can produce better forecasts than the compared methods. In addition to the time series datasets, the results and comparisons of the forecasts with the proposed algorithm using real-life data are also presented.

Forecasting of electricity consumption in assam by using mathematical and time series models and comparison of their predictive performances

Although energy exists in different forms in nature, the most important form of energy is Electricity. Considering the worldwide increasing demand for electricity with increasing population, rapid urbanization, rising incomes, changes in lifestyles and with infrastructural and industrial growth, forecasting electricity consumption becomes an important part of power sector of any country. In this paper, attempt is made to forecast the total electricity consumption in Assam with the help of two models, considering the time period from 1990-91 to 2016-17. The first one is Multiple Linear Regression model which considers the effects of Population and Per Capita Income on the electricity consumption in Assam while the second one is the Autoregressive Integrated Moving Average (ARIMA) model. By comparing the Average Relative Error of the two proposed models, the predictive performance of the ARIMA (0, 1, 2) model is found better than the Multiple Linear Regression model and ARIMA shows an annual average increasing rate of electricity consumption as 5.40% up to 2021.

Time irreversibility and complexity of heart rate variability

Intrioution. The heart rate variability is based on measuring (time) intervals between R-peaks (of RR-intervals) of an electrocardiogram and plotting a rhythmogram on their basis with its subsequent analysis by various mathematical methods. Using nonlinear methods in HRV and ECG analysis has proven to be very advantageous. Time irreversibility is a fundamental parameter of a system, it defines justification and necessity of applying nonlinear methods for analysis of a system’s dynamics. Objective. We propose an algorithm for testing the probability of a time series' irreversibility, showing its effectiveness in the process of HRV analysis. In this article, complexity of HRV will be described by two parameters: entropy EnRE [18] and correlation dimension D2 [19]. Naturally, the chosen parameters EnRE and D2 in no way can be used for comprehensive description of complexity of HRV, but we will be able to tress the necessary sufficiency of such an approach. Materials and methods. We used long-term HRV records by Massachusetts Institute of Technology – Boston’s Beth Israel Hospital (MIT-BIH) from [15], a free-access, on-line archive of physiological signals for Normal Sinus Rhythm (NSR) RR Interval, Congestive Heart Failure (CHF) RR Interval and Atrial Fibrillation (AF) Databases [16]. In [17], we have developed a special modification to the classic Mann-Whitney (MW) U-test in order to use the test for comparison of Time Series with an equal number of elements N – Time Series MW M-test. Here the new statistical -test was proposed for finding the probability of time series' irreversibility. Conclusion. In this article, we propose a statistical -test for assessment of probability of irreversibility of time series. It has been shown that the new statistical -test accurately identifies times series reversibility and irreversibility in known cases of synthetic data. For long-term HRV records of MIT-BIH database for NSR, CHF and AF groups, we have compared values of z-score, which statistically defines the limit of irreversibility of time series, and values of HRV complexity indicators: entropy EnRE [18] and correlation dimension D2 [19]. We have noted the following: HRV is time irreversible nonlinear dynamic process, with the exception of AF episodes; nonlinear indicators of HRV complexity – entropy EnRE and correlation dimension D2 – have been analyzed, and there is a conclusive difference between NSR and analyzed pathological states; analyzed time series have been presented in D2-z-EnRE phase space, and their reliable separability has been shown. It can be stated that the analyzed D2-z-EnRE phase space is sufficient for research of nonlinear HRV events in this case.

COVID-19 in medium-sized municipalities in the 14 health macro-regions of Minas Gerais, Brazil

The present study focused on the scenario of confirmed cases of SARS-CoV-2 infection in the state of Minas Gerais (MG), Brazil, from March 2020 to March 2021. We evaluated the evolution of COVID-19 prevalence and death in one municipality from each of the 14 health macro-regions of MG state. Socio-demographic characteristics and variables related to the municipalities were analyzed. The raw dataset used in this study was freely sourced from the website Brasil.io. From the raw dataset, two time series were extracted: the cumulative confirmed cases of COVID-19 and cumulative death counts, and they were compared to the state data using a nowcasting approach. In order to make time series comparisons possible, all data was normalized per 100,000 inhabitants. When analyzing in light of colored wave code interventions initiated in August 2020 in MG, for the majority of the municipalities, there was an absence of clear influence on prevalence and deaths. The national holidays in the first semester of 2020 had a small impact on the COVID-19 prevalence of the municipalities, but the holidays in the second semester of 2020 and beginning of 2021 caused important impacts on COVID-19 prevalence. The low number of ICU beds in some municipalities contributed to the higher number of deaths. The analysis showed here is expected to contribute to the improvement of decision making of the MG government, as it opened a huge possibility to have the total macro-regions and state data analyzed.