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Traditional tests fail to detect the presence of nonlinearities in series that are cointegrated, so in this paper a new procedure for cointegration tests is proposed by modifying the two-step Engle and Granger (EG) test (Engle and Granger in Econometrica 55:251–276, 1987), incorporating the RUR and the FB-RUR test of Aparicio et al. (J Time Ser Anal 27:545–576, 2006). The statistics of these non-parametric tests, which are constructed as functions of order statistics, endow the test with desirable properties such as invariance to non-linear transformations of the series and robustness to the presence of significant parameter shifts. As no prior estimation of the cointegrating parameter is required, the new tests lead to parameter-free asymptotic null distributions. Monte Carlo simulations are used to analyze the test properties and evaluate the power at different sample sizes. The robustness of the procedure is tested by performing a comparison of different tests of cointegration in real exchange rate relationships. These tests are able to find evidence of cointegration while standard cointegration tests fail to detect it. [ABSTRACT FROM AUTHOR]

Wildfires and deforestation are severe threats to global ecosystems. In Brazil, Cerrado (a tropical savanna) and Pantanal (a tropical wetland) biomes have undergone several changes over the years due to anthropic actions. Both deforestation in Cerrado biome and wildfires in Pantanal have increased lately. Some studies argue that both processes could be related, but there is a scarcity of quantitative analysis evaluating that. In this context, making use of machine learning techniques and temporal data obtained by Remote Sensing in the period 2000–2020, this study aimed to identify the interactions between Cerrado land use and land cover change in native vegetation and wildfires incidence in Pantanal. Our results corroborate that and show that wildfires in Pantanal were directly linked to large-scale and commodities agriculture conversion in Cerrado, as well as native vegetation loss and hydrological changes in Pantanal. [ABSTRACT FROM AUTHOR]

Climate change influences the composition of species and the phenology of insect. Species tend to appear earlier in spring and to be active later in autumn. However, species respond differently to climatic changes according to their ecological and behavioural performance. However, it has not yet been clarified which ecological characteristics determine which responses. In this study, we analysed potential phenological shifts of butterflies and burnet moths across Northern Austria over a period of four decades. To investigate this, we used extensive museum data and compared the time windows 1980 to 2000 and 2001 to 2022. We found species´ specific responses to climate change differing for spring and autumn. Species hibernating as imago or pupa as well as migratory habitat ubiquists showed particularly strong phenological responses. These species may become active immediately when weather conditions become suitable. The later occurrence of species during autumn is largely controlled by day length. Therefore, altering temperature regimes did not detectably influence autumn activity. Our study highlights that ecological specialists suffer most from climate change as these are least able to adapt to new thermal conditions and altered seasonality.

This paper presents a new scheme for dynamical systems and time series modeling and identification. It is based on artificial neural networks (ANN) and metaheuristic algorithms. This scheme combines the strength of ANN with the dexterity of metaheuristic algorithms. This fusion is renowned for its ability to detect complex patterns, which considerably improves accuracy, computational efficiency, and robustness. The proposed scheme deals with the curve fitting and addresses ANN's local minima problem. This approach introduces the identification concept using a fresh novel identification element, referred to as the error model. The proposed framework encompasses a parallel interconnection of two models. The principal sub-model is the elementary model, characterized by standard specifications and a lower resolution, designed for the data being examined. In order to address the resolution limitation and achieve heightened precision, a second sub-model, named the error model, is introduced. This error model captures the disparities between the primary model and considered data. The parameters of the proposed scheme are adjusted using metaheuristic algorithms. This technique is tested across many benchmark data sets to determine its efficacy. A comparative study along with benchmark approaches will be provided. Extensive computer studies show that the suggested strategy considerably increases convergence and resolution. [ABSTRACT FROM AUTHOR]

Volatility estimation and quantile regression are relevant active research areas in statistics, machine learning and econometrics. In this work, we propose two procedures to estimate the local variances in generic regression problems by using kernel smoothers. The proposed schemes can be applied in multidimensional scenarios (not just for time series analysis) and easily in a multi-output framework as well. Moreover, they enable the possibility of providing uncertainty estimation using a generic kernel smoother technique. Several numerical experiments show the benefits of the proposed methods, even compared with the benchmark techniques. One of these experiments involves a real dataset analysis. [ABSTRACT FROM AUTHOR]

Context. Problem of traffic prediction in a city is closely connected to the tasks of transportations in a city as well as air pollution detection in a city. Modern prediction models have redundant complexity when used for separate stations, require large number of measuring stations, long measurement period when predictions are made hourly. Therefore, there is a lack of method to overcome these constraints. The object of the study is a city traffic. Objective. The objective of the study is to develop a method for traffic prediction, providing models for traffic quantification at measuring stations in the future under data and resource constraints. Method. The method for agent-oriented traffic prediction under data and resource constraints was proposed in the paper. This method uses biLSTM models with input features, including traffic data obtained from agent, representing target station, and other agents, representing informative city stations. These agents are selected by ensembles of decision trees using Random Forest method. Input time period length is proposed to set using autocorrelation data. Results. Experimental investigation was conducted on traffic data taken in Madrid from 59 measuring stations. Models created by the proposed method had higher prediction accuracy with lower values of MSE, MAE, RMSE and higher informativeness compared to base LSTM models. Conclusions. Obtained models as study results have optimal number of input features compared to the known models, do not require complete system of city stations for all roads. It enables to apply these models under city traffic data and resource constraints. The proposed solutions provide high informativeness of obtained models with practically applicable accuracy level. [ABSTRACT FROM AUTHOR]

Volatility estimation and quantile regression are relevant active research areas in statistics, machine learning and econometrics. In this work, we propose two procedures to estimate the local variances in generic regression problems by using kernel smoothers. The proposed schemes can be applied in multidimensional scenarios (not just for time series analysis) and easily in a multi-output framework as well. Moreover, they enable the possibility of providing uncertainty estimation using a generic kernel smoother technique. Several numerical experiments show the benefits of the proposed methods, even compared with the benchmark techniques. One of these experiments involves a real dataset analysis.

Vegetation, especially forest ecosystems, plays an important role in the global energy flow and material cycle. The vegetation index (VI) is an important index reflecting the dynamic change in vegetation and directly reflects the response of ecosystem to global climate change. The Greater Khingan Mountains Forest region is located in the northeast of China. It is the largest primeval forest region in China, which is well preserved and less affected by human activities. It is of great significance to study the driving mechanism of forest vegetation change for future ecological prediction and management. In this study, GIMMS NDVI data were used to explore the characteristics of nonlinear temporal and spatial variation of NDVI in the Greater Khingan Mountains and its relationship with climatic factors. Firstly, the EEMD method was used to analyze the characteristics of vegetation change in the study area from 1982 to 2015. Secondly, the relationship between vegetation change and climate was discussed by using precipitation and temperature data. The results showed that the following: (1) from 1982 to 2015, the interannual change in vegetation in the Greater Khingan Mountains presented a trend of slow fluctuation and gradual decrease (SLOPE = −0.1645/10,000, p < 0.01). (2) The spatial distribution of vegetation change had obvious geographical differences, and in the central region, the overall distribution characteristics had an obvious browning trend, and in the northwest and southeast, the distribution characteristics had a green trend. (3) The correlation analysis results of vegetation change and climate factors showed that NDVI change was significantly positively correlated with temperature and precipitation; additionally, NDVI change was more correlated with temperature with a range of 0.8–1 than precipitation. (4) The results of vegetation attribution analysis in four typical areas of the study area showed that the following: the coniferous forest area has good cold tolerance and drought tolerance, the correlation between vegetation change and climate factors (temperature, precipitation) was not the strongest, which was 0.537 and 0.828, respectively. The ecological transition area and the broad-leaved forest area, which was located at the edge of the study area, have relatively fragile ecosystems, showed a strong correlation with precipitation, and the correlation coefficients reached 0.670 and 0.632, respectively. The surface water resources provide favorable conditions for the growth of vegetation, it showed a weak correlation with precipitation, and the correlation coefficient was 0.5349. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the extent to which poor pupils in England are clustered in schools with others like them. It is based on a segregation index of pupils eligible for free school meals for every year for which official national data is available. The trend over time has been published before up to 2019, and this paper extends the analysis to 2021, covering both the Covid-19 era so far and the beginning of transitional arrangements for Universal Credit, which have led to a substantial increase in the number of pupils eligible for free school meals. Results show that the segregation of poor pupils between secondary schools has continued to decline annually – a decline that started with the onset of Pupil Premium funding. This decline in segregation has not occurred for other possible indicators of disadvantage, such as pupils having a special educational need or disability, which are not addressed by Pupil Premium funding. Clustering disadvantaged pupils together in parts of a national school system has been linked to worse pupil outcomes overall, lower aspirations, less ethnic cohesion, and reduced trust in society by students. So, this ongoing reduction is encouraging, and is likely to lead to a lower poverty attainment gap in academic outcomes. However, the reduction in 2020 and 2021 is "false" to some extent, based mostly on a sudden increase in the number of pupils officially classed as poor, rather than an improvement in their distribution or evenness. It is, therefore, important to retain Pupil Premium funding or something like it for the time being to see what happens to the attainment gap. And the apparent success of this funding scheme could have implications for school systems worldwide that value fairness in the provision of national opportunities for education. [ABSTRACT FROM AUTHOR]

The article analyzes the low female labor participation in Mexico and proposes that it is due to labor discouragement caused by the wage gap. It is concluded that women are more likely to enter the labor market when the perception of the wage gap is reduced, and they withdraw when it increases. The importance of reducing the wage gap to increase female labor participation is highlighted. Additionally, it is mentioned that wage discrimination is still present in the world and that there are differences in the wage gap according to the sector and region of residence. [Extracted from the article]

Solar irradiation (Rs) is the electromagnetic radiation energy emitted by the Sun. It plays a crucial role in sustaining life on Earth by providing light, heat, and energy. Furthermore, it serves as a key driver of Earth's climate and weather systems, influencing the distribution of heat across the planet, shaping global air and ocean currents, and determining weather patterns. Variations in Rs levels have significant implications for climate change and long-term climate trends. Moreover, Rs represents an abundant and renewable energy resource, offering a clean and sustainable alternative to fossil fuels. By harnessing solar energy, we can actively reduce greenhouse gas emissions. However, the utilization of Rs comes with its own challenges that must be addressed. One problem is its variability, which makes it difficult to predict and plan for consistent solar energy generation. Its intermittent nature also poses difficulties in meeting continuous energy demand unless appropriate energy storage or backup systems are in place. Integrating large-scale solar energy systems into existing power grids can present technical challenges. Rs levels are influenced by various factors; understanding these factors is crucial for various applications, such as renewable energy planning, climate modeling, and environmental studies. Overcoming the associated challenges requires advancements in technology and innovative solutions. Measuring and harnessing Rs for various applications can be achieved using various devices; however, the expense and scarcity of measuring equipment pose challenges in accurately assessing and monitoring Rs levels. In order to address this, alternative methods have been developed with which to estimate Rs, including artificial intelligence and machine learning (ML) models, like neural networks, kernel algorithms, tree-based models, and ensemble methods. To demonstrate the impact of feature selection methods on Rs predictions, we propose a Multivariate Time Series (MVTS) model using Recursive Feature Elimination (RFE) with a decision tree (DT), Pearson correlation (Pr), logistic regression (LR), Gradient Boosting Models (GBM), and a random forest (RF). Our article introduces a novel framework that integrates various models and incorporates overlooked factors. This framework offers a more comprehensive understanding of Recursive Feature Elimination and its integrations with different models in multivariate solar radiation forecasting. Our research delves into unexplored aspects and challenges existing theories related to solar radiation forecasting. Our results show reliable predictions based on essential criteria. The feature ranking may vary depending on the model used, with the RF Regressor algorithm selecting features such as maximum temperature, minimum temperature, precipitation, wind speed, and relative humidity for specific months. The DT algorithm may yield a slightly different set of selected features. Despite the variations, all of the models exhibit impressive performance, with the LR model demonstrating outstanding performance with low RMSE (0.003) and the highest R2 score (0.002). The other models also show promising results, with RMSE scores ranging from 0.006 to 0.007 and a consistent R2 score of 0.999. [ABSTRACT FROM AUTHOR]

The present paper examines the economic growth and financial development relationship in two of the European Union's founding countries and Eurozone members, namely Germany and Luxembourg, by taking into consideration a large period, from 1970 to 2019. We motivate the choice of these specific two countries based on the similar rate of GDP per capita growth over the years, and also on the relevance of the financial sector in the total economy. Five different measures of financial development are employed to address the depth and efficiency of the financial sector. We apply Granger causality tests, using the cointegration and Vector Error-Correction (VEC) methodology. The empirical analysis indicates consistent results, as both in the case of Germany and Luxembourg it can be established Granger causality for the economic growth -- financial development nexus. There is also evidence of bi-directional relationship. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between the volunteer labour supply as a component of social capital accumulation and birth cohorts. Using cross-sectional data from Germany, we apply pseudo time series and panel methods to study the connection between volunteering, active membership status, and public and private good motivations to capture an apparently changing perception of volunteer work. Our results suggest that volunteering establishes itself as a stable behaviour. Active membership and motives to volunteer to do something for a common good have predictive power. The results suggest that the volunteer labour supply is associated with some kind of institutionalised structures and a public good orientation rather than 'just having a good time'. [ABSTRACT FROM AUTHOR]

The first case of coronavirus disease 2019 (COVID-19) in Saudi Arabia was reported on 2 March 2020. Since then, it has progressed rapidly and the number of cases has grown exponentially, reaching 788,294 cases on 22 June 2022. Accurately analyzing and predicting the spread of new COVID-19 cases is critical to develop a framework for universal pandemic preparedness as well as mitigating the disease's spread. To this end, the main aim of this paper is first to analyze the historical data of the disease gathered from 2 March 2020 to 20 June 2022 and second to use the collected data for forecasting the trajectory of COVID-19 in order to construct robust and accurate models. To the best of our knowledge, this study is the first that analyzes the outbreak of COVID-19 in Saudi Arabia for a long period (more than two years). To achieve this study aim, two techniques from the data analytics field, namely the auto-regressive integrated moving average (ARIMA) statistical technique and Prophet Facebook machine learning technique were investigated for predicting daily new infections, recoveries and deaths. Based on forecasting performance metrics, both models were found to be accurate and robust in forecasting the time series of COVID-19 in Saudi Arabia for the considered period (the coefficient of determination for example was in all cases more than 0.96) with a small superiority of the ARIMA model in terms of the forecasting ability and of Prophet in terms of simplicity and a few hyper-parameters. The findings of this study have yielded a realistic picture of the disease direction and provide useful insights for decision makers so as to be prepared for the future evolution of the pandemic. In addition, the results of this study have shown positive healthcare implications of the Saudi experience in fighting the disease and the relative efficiency of the taken measures. [ABSTRACT FROM AUTHOR]

Vegetation, especially forest ecosystems, plays an important role in the global energy flow and material cycle. The vegetation index (VI) is an important index reflecting the dynamic change in vegetation and directly reflects the response of ecosystem to global climate change. The Greater Khingan Mountains Forest region is located in the northeast of China. It is the largest primeval forest region in China, which is well preserved and less affected by human activities. It is of great significance to study the driving mechanism of forest vegetation change for future ecological prediction and management. In this study, GIMMS NDVI data were used to explore the characteristics of nonlinear temporal and spatial variation of NDVI in the Greater Khingan Mountains and its relationship with climatic factors. Firstly, the EEMD method was used to analyze the characteristics of vegetation change in the study area from 1982 to 2015. Secondly, the relationship between vegetation change and climate was discussed by using precipitation and temperature data. The results showed that the following: (1) from 1982 to 2015, the interannual change in vegetation in the Greater Khingan Mountains presented a trend of slow fluctuation and gradual decrease (SLOPE = −0.1645/10,000, p < 0.01). (2) The spatial distribution of vegetation change had obvious geographical differences, and in the central region, the overall distribution characteristics had an obvious browning trend, and in the northwest and southeast, the distribution characteristics had a green trend. (3) The correlation analysis results of vegetation change and climate factors showed that NDVI change was significantly positively correlated with temperature and precipitation; additionally, NDVI change was more correlated with temperature with a range of 0.8–1 than precipitation. (4) The results of vegetation attribution analysis in four typical areas of the study area showed that the following: the coniferous forest area has good cold tolerance and drought tolerance, the correlation between vegetation change and climate factors (temperature, precipitation) was not the strongest, which was 0.537 and 0.828, respectively. The ecological transition area and the broad-leaved forest area, which was located at the edge of the study area, have relatively fragile ecosystems, showed a strong correlation with precipitation, and the correlation coefficients reached 0.670 and 0.632, respectively. The surface water resources provide favorable conditions for the growth of vegetation, it showed a weak correlation with precipitation, and the correlation coefficient was 0.5349.

Purpose – This empirical paper aims to examine the impact of interest rate (IR) and political instability (POLINS) on Palestine's domestic private investment. Design/methodology/approach – A set of econometric techniques of time series data are adopted to meet the study objectives. They include regression analysis, unit root tests, cointegration test, ARDL & Bound tests, VAR test and Granger causality test. Findings – The study's primary results complement the neoclassical approach, which states that the IR is negatively associated with domestic private investment. The empirical results reveal that there is no long-run relationship. Also, there is no causality between domestic investment and lending rates. Accordingly, these findings alert policymakers to draw a series of steps to minimize the IR at a minimum to stimulate investment for improved economic growth and development. Practical implications – There is still no national currency in Palestine. The Palestinian Monetary Authority (PMA) is advised to set an appropriate ratio of the IR for the currencies-in-circulation in Palestine for boosting investment and economic development. Originality/value – This paper provides new background information to both policymakers and researchers on the main determinants of investment in Palestine using econometric analysis. Accordingly, this critical issue is required to be examined in Palestine for stimulating investment.

• Proposes an interpretable machine learning (IML) approach. • Improves recurrent neural network framework for cryptocurrency forecasting. • Incorporated an attention mechanism-enhanced distribution function algorithm. • Proposes attention-based approach improved interpretability and accuracy. • The new approach has higher accuracy than the GRU and ARIMA approaches. To predict Ethereum price fluctuations, this study proposes a new two-stage Machine Learning approach using an improved convolutional neural network and a recurrent neural network framework, integrating an attention mechanism-based distribution function algorithm. We construct a dataset and perform model training, fitting, and forecasting. The results indicate that compared with traditional neural networks and time-series models such as GRU and ARIMA, respectively, this approach can effectively use the data information of digital cryptocurrency and improve the prediction accuracy and interpretability of attention-based allocation functions. This study contributes to the literature by offering a new approach for stakeholders. [ABSTRACT FROM AUTHOR]

The objectives of this work were to verify the adjustment of time series of monthly minimum (Tmín) and monthly maximum (Tmáx) air temperatures, in the State of Rio Grande do Sul, to the normal, log-normal, gamma and weibull probability distributions, to determine probabilities of occurrence and prepare maps with the interpolation of isolines. The Kolmogorov-Smirnov test was applied to 216 time series of Tmín and 216 time series of Tmáx (18 cities × 12 months). Based on the normal distribution, values of Tmín were determined, whose probability of occurrence of Tmín less than or equal to this value is 90%, and values of Tmáx, whose probability of occurrence of Tmáx greater or equal to this value is 90%. From these values, maps were prepared with interpolation of isolines with a difference of 0.5°C. The Tmín and Tmáx data adjust the normal, log-normal, gamma and weibull distributions, showing better adherence to the normal distribution. The maps with the isolines are useful to verify the probability of occurrence of harmful or favorable Tmín and Tmáx for agricultural crops in the State of Rio Grande do Sul, during the months of the year. [ABSTRACT FROM AUTHOR]

Copyright of Organizações Rurais & Agroindustriais is the property of Organizacoes Rurais & Agroindustriais and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objetive: To analyze the trends of COVID-19 in Brazil in 2020 by Federal Units (FU). Method: Ecological time-series based on cumulative confirmed cases of COVID-19 from March 11 to May 12. Joinpoint regression models were applied to identify points of inflection in COVID-19 trends, considering the days since the 50th confirmed case as time unit. Results: Brazil reached its 50th confirmed case of COVID-19 in 11 March 2020 and, 63 days after that, on May 12, 177,589 cases had been confirmed. The trends for all regions and FU are upward. In the last segment, from the 31st to the 63rd day, Brazil presented a daily percentage change (DPC) of 7.3% (95%CI= 7.2;7.5). For the country the average daily percentage change (ADPC) was 14.2% (95%CI: 13.8;14.5). The highest ADPC values were found in the North, Northeast and Southeast regions. Conclusions: In summary, our results show that all FUs in Brazil present upward trends of COVID-19. In some FUs, the slowdown in DPC in the last segment must be considered with caution. Each FU is at a different stage of the pandemic and, therefore, non-pharmacological measures should be adopted accordingly.

It is widely recognized that the design and application of suitable and robust science, technology and innovation (STI) policies and appropriate STI institutions promote development, economic growth and competitiveness in the long run. This paper analyses the dynamics of STI in Colombia over the 1995–2019 period to determine its relationship with its most important determinants and its collateral relationship with economic growth as an input affecting different issues; this work takes into account the creation of the new ministry of science, technology and innovation (MSTI) and uses different time series techniques. According to the analysis, a positive relationship exists between investments in research and development, STI activities, and the independence and transparency of STI management by the new MSTI, which could generate higher productivity, technological change, economic growth and development. The results of the models also demonstrate the long-run relationship and short-run dynamics related to STI investment and research results and the importance of transparency and independence. It is important to establish adequate STI governance and allow new ministries to play an important role to achieve a society based on knowledge that produces relevant research, technology and innovation based on the needs and resources of the country. [ABSTRACT FROM AUTHOR]

The more than seven thousand kilometers of the Brazilian coast constitute an area of unparalleled wealth in our country, where a large part of the Brazilian population is concentrated. It is in the coastal region where a large part of fishing, navigation, oil extraction, leisure and tourism takes place in Brazil. In this scenario, a knowledge of the oceanic conditions existing along our coastline becomes increasingly necessary. Several programs encourage and carry out activities related to the study of the oceans. In this work, the GOOS-BRASIL database, which is a national ocean observation system, will be analyzed. The objective of the present study is to model the forecast of the time series between 2005 and 2014 of average temperatures and salinities at one meter depth, collected from the ATLAS buoy (geographical position 19 ° 00S34 ° 00W) of the PIRATA network. Initially, stationarity and seasonality analyzes were made, to later elaborate forecasts. The exponential smoothing and Box-Jenkins models were used, which were evaluated by the metrics of the Mean Square Error and the Absolute Mean Percentage of Error. The temperature had Brown Linear Exponential Smoothing and salinity AR (1) and Simple Exponential Smoothing as the best results. [ABSTRACT FROM AUTHOR]

Natural ecosystems are currently experiencing unprecedented rates of anthropogenic disturbance. Given the potential ramifications of more frequent disturbances, it is imperative that we accurately quantify ecosystem responses to severe disturbance. Specifically, ecologists and managers need estimates of resistance and recovery from disturbance that are free of observation error, not biased by temporal stochasticity and that standardize disturbance magnitude among many disparate ecosystems relative to normal interannual variability. Here, I propose a statistical framework that estimates all four components of ecosystem responses to disturbance (resistance, recovery, elasticity and return time), while resolving all of the issues described above. Coupling autoregressive time series with exogenous predictors (ARX) models with impulse response functions (IRFs) allows researchers to statistically subject all ecosystems to similar levels of disturbance, estimate lag effects and obtain standardized estimates of resistance to and recovery from disturbance that are free from observation error and stochastic processes inherent in raw data. [ABSTRACT FROM AUTHOR]

Drawing on the notion of channel capability, we develop a theoretical ramework for understanding the interactions between mobile and traditional online channels for products with different characteristics. Specifically, we identify two channel capabilities-access and search capabilities-that differentiate mobile and online channels, and two product characteristics that are directly related to the channel capabilities-time criticality and information intensity. Based on this framework, we generate a set of predictions on the differential effects of mobile channel introduction across different product categories. We test the predictions by applying a counterfactual analysis based on vector autoregression to a large panel data set from a leading e-market in Korea that covers a 28-month period and contains all of the transactions made through the online and mobile channels before and after the mobile channel introduction. Consistent with our theoretical predictions, our results suggest that the performance impact of the mobile channel depends on the two product characteristics and the resulting product-channel fit. We discuss implications for theory and multichannel strategy. [ABSTRACT FROM AUTHOR]

The objective of this research study is to compare pollutant dispersion in a Gaussian model, using the wind rose from the city of Andacollo (Chile), with a model based on the chaos theory that uses raw public Andacollo station data from the Chilean National Air Quality Information System (SINCA, for its acronym in Spanish). The data consist of time series for particulate matter, temperature, relative humidity, pressure, solar radiation, and wind speed magnitude. The chaotic treatment of each series provides their corresponding Lyapunov exponent, Hurst coefficient, and correlation entropy. The chaotic approximation shows that entropies of meteorological variables act on that of the pollutant, causing an asymptotic decay according to the loss of persistence. This explains its localized thermal interactions. It is concluded that both models show similar predictions when comparing the decay of the pollutant PM10. [ABSTRACT FROM AUTHOR]

• Brazil registered 177,589 cases of COVID-19 between March 11 and May 12, 2020. • All Federative Units showed upward trends in accumulated cases of COVID-19. • The highest increments were found in the North, Northeast and Southeast regions. • Each Federative Unit in Brazil is at a different stage of the COVID-19 pandemic. To analyze the trends of COVID-19 in Brazil in 2020 by Federal Units (FU). Ecological time-series based on cumulative confirmed cases of COVID-19 from March 11 to May 12. Joinpoint regression models were applied to identify points of inflection in COVID-19 trends, considering the days since the 50th confirmed case as time unit. Brazil reached its 50th confirmed case of COVID-19 in 11 March 2020 and, 63 days after that, on May 12, 177,589 cases had been confirmed. The trends for all regions and FU are upward. In the last segment, from the 31st to the 63rd day, Brazil presented a daily percentage change (DPC) of 7.3% (95%CI= 7.2;7.5). For the country the average daily percentage change (ADPC) was 14.2% (95%CI: 13.8;14.5). The highest ADPC values were found in the North, Northeast and Southeast regions. In summary, our results show that all FUs in Brazil present upward trends of COVID-19. In some FUs, the slowdown in DPC in the last segment must be considered with caution. Each FU is at a different stage of the pandemic and, therefore, non-pharmacological measures should be adopted accordingly. [ABSTRACT FROM AUTHOR]

In this study; human capital, innovation and economic growth for Turkey's economy were investigated by time series analysis methods using the 1995-2018 period annual data. Real GDP per capita representing economic growth was taken as a dependent variable, domestic patent registration numbers representing innovation from independent variables, and students graduating from higher education representing the human capital variable. Besides, variables of labor and capital investments, which are considered as indicators of growth, are also included in the model. Extended Dickey-Fuller and Phillips- Perron unit root tests in determining the stability of variables, Johansen cointegration analysis in determining long term relationship, the existence of a cointegration relationship between variables, were used. Granger causality analysis was used to determine the causality relationship. As a result of the analyzes applied to the model, it was determined that there is a cointegration relationship between the variables. It has been observed that human capital and innovation have no effect on economic growth in the short term, but they have a positive effect in the long term, and capital investments and workforce affect economic growth both in the short and long term. As a result of the Granger causality analysis, bidirectional causality relationships between capital investments and economic growth, from the workforce to economic growth, from the workforce to capital investments, from human capital to innovation, were determined. [ABSTRACT FROM AUTHOR]

The Brazilian economy has been going through a series of shocks of a political nature in the last 10 years. This article assesses whether such shocks and whether exchange rate volatility had an effect on the value of listed companies in the post-2008 crisis. The results, estimated by OLS and GMM, point out that these political shocks and the volatility of the exchange rate exchange rates exercise statistically significant effects, causing significant reductions in the value of companies in Brazil. [ABSTRACT FROM AUTHOR]

Copyright of Revista Brasileira de Engenharia Agricola e Ambiental - Agriambi is the property of Revista Brasileira de Engenharia Agricola e Ambiental and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Background According to the WHO, low birth weight (<2500 gr) is a primary maternal health indicator as the cause of multiple morbi-mortality in the short and long-term. It is known that air pollution from road traffic (PM 10 , NO 2) and O 3 have an important impact on low birth weight (LBW), but there are few studies of this topic in Spain. The objective of this study is to determine the possible exposure windows in the gestational period in which there is greater susceptibility to urban air pollution and to quantify the relative risks (RR) and population attributable risks (PAR) of low birth weight associated with pollutant concentrations in Spain. Methods We calculated the weekly average births with low birth weight (ICD-10: P07.0-P07.1) for each Spanish province for the period 2001–2009, using the average weekly concentrations of PM 10 , NO 2 and O 3 , measured in the capital cities of the provinces. The estimation of RR and PAR were carried out using generalized linear models with link Poisson, controlling for the trend, seasonality and auto-regressive character of the series and for the influence of temperature during periods of heat waves and/or cold. Finally, a meta-analysis was used to estimate the global RR and PAR based on the RR obtained for each of the provinces. Results The RR for the whole of Spain is 1.104 (CI95%: 1.072, 1.138) for the association between LBW and PM 10 , and 1.091 (CI95%: 1.059, 1.124) for the association between NO 2 and LBW. Our results suggest that 5% of low birth weight births in the case of PM 10 and 8% in the case of NO 2 could have been avoided with a reduction of 10 μg/m3 in the concentrations of these pollutants. Conclusions The impact of the results obtained- with 6105 cases attributable to PM 10 and up to 9385 cases attributable to NO 2 in a period of 9 study years- suggest the need to design structural and awareness public health measures to reduce air pollution in Spain. Highlights • Air pollutants are increasing in our cities playing an active role on LBW. • Reducing 10 μg/m3 in PM10 could have avoided a 5% of LBW. • Reducing 10 μg/m3 in NO2 could have avoided a 8% of LBW. • In Spain 6105 LBW cases attributable to PM10 and 9385 to NO 2 in 9 years occurred. [ABSTRACT FROM AUTHOR]