Presence Of Trends Research Articles

Non-stationary models for hydrological extremes in the mountain rivers of the Argentinean Central Andes

ABSTRACT The assessment of extreme hydrological events relies on the assumption that time series are independent and identically distributed. However, empirical evidence contradicts this assumption, indicating the presence of nonstationarity, including trends, step changes, or both, in the river records of the Argentinean Central Andes. This study proposes the use of generalized additive models in location, scale and shape (GAMLSS) to model the annual maximum and minimum flow of seven rivers. Firstly, the presence of trends and step changes was assessed. Subsequently, the models were formulated considering time and various climatic indices as covariates. The findings reveal declining trends and negative jumps over the past 50 to 70 years. The location parameters of the models exhibit linear or smoothing dependence with time or climate indices covariates. Different modes of climate variability are associated with hydrological extremes. The nonstationary models provide a novel complementary framework for water resource planning in the region.

On the persistence of near-surface temperature dynamics in a warming world.

We consider issues related to the effect of climate change on the persistence of (trend-corrected) temperatures using global gridded data for both land and oceans. We first discuss how the presence of trends and additive noise affects inference about persistence. Ignoring a trend induces an upward bias, while not accounting for noise induces a downward bias. We show that the increase in persistence in the commonly used Warm Spell Duration Index is simply an artifact of increasing temperatures. To purge the impact of both trends and noise, we adopt a simple state-space model. Of separate interest, we document a much larger noise component for land than for oceans. The estimates of the persistence are much larger for oceans than for land. Inspection of the estimates across various subsamples and the application of tests for structural changes suggest the same pattern of persistence for both land and oceans across time, with few minor exceptions. Hence, our results show that surface temperature persistence has remained constant during the observed period.

Trends in major intensive care medicine journals: A machine learning approach

PurposeIntensive care medicine (ICM) has the particularity of being a multidisciplinary specialty and its literature reflects this multidisciplinarity. However, the proportion of each field in this literature and its trend dynamics are not known. The objective of this study was to analyze the ICM literature, extract latent topics and search for the presence of research trends. Material and methodsAbstracts of original articles from the top ICM journals, from their inception until December 31st, 2019, were included. This corpus was fed into a structural topic modeling algorithm to extract latent semantic topics. The temporal distribution was then analyzed and the presence of trends was searched by Mann-Kendall trends tests. ResultsFinally, 49,276 articles from 10 journals were included. After topic modeling analysis and experts' feedback, 124 research topics were selected and labeled. Topics were categorized into 19 categories, the most represented being respiratory, fundamental and neurological research. Increasing trends were observed for research on mechanical ventilation and decreasing trends for cardiopulmonary resuscitation. ConclusionsThis study reviewed all articles from major ICM journals in a comprehensive way. It provides a better understanding of ICM research landscape by analyzing the temporal evolution of latent research topics in the ICM literature.

Протестный медиа-активизм в современной России и перспективы его развития

The article attempts to study protest media activism in modern Russia, taking into account the manifestation of its new forms, such as online rallies, as well as an analysis of the prospects for its further application in the protest practice of modern Russia. The conclusion is made about the ambiguity of the use of new media technologies to consolidate the protest potential of the Russian society, which is associated with the presence of trends in the growth of the Internet users and the increase in state control over the sphere of public communications on the Internet.

Optimal Investment Timing for Flood Risk Adaptation in Presence of Trend, Seasonality and Stochastic Interest Rates

Optimal Investment Timing for Flood Risk Adaptation in Presence of Trend, Seasonality and Stochastic Interest Rates

THE INFLUENCE OF EROSION PROCESSES ON THE CHANGE IN THE PARAMETERS OF THE MAIN AGROCHEMICAL INDICATORS OF SOIL FERTILITY ON SLOPING AGRICULTURAL LANDS OF THE REPUBLIC OF BELARUS

The article presents the results of studies of changes in the main indicators of fertility of sod-podzolic soils of slope lands under the influence of erosion processes. Determined the ranges of their reduction depending on the degree of erosion, constituting up to 20–30 % on weakly eroded soils, up to 30–40 % on moderately eroded soils, and up to 40 % and more percent on heavily eroded soils.
 The presence of trends in multidirectional changes in the content of humus, mobile phosphorus, mobile potassium and mineral nitrogen in the conditions of industrial crops on soils of various genesis and the degree of manifestation of erosion processes has been established.

Heatwaves in Southern Armenia in the context of climate change

AbstractThe quantitative and qualitative evaluations of long‐term trends and spatial distribution of heatwaves (HW) are carried out in Southern Armenia (Syunik province). For this purpose, the daily average maximum air temperature data (𝑇max) from four meteorological stations (Sisian, Goris, Kapan, and Meghri) are studied for the 1936–2019 period. The HW catchment algorithm and a new concept of HW (which is called potential HWs [PHWs]) are developed. The presence of trends and their significance for different HW measures are assessed utilizing linear and exponential trends, Sen's slope, and Mann–Kendall (MK) trend test. The results show that HWs become increasingly frequent and intense. Moreover, they have extended duration, as they emerge earlier and fade later in Syunik province. Significant variation trends of HW are observed (e.g., the frequency of HWs increased up to five times), especially after the 1990s in southern and central parts of the province. Contrary to this, relatively weaker or no trends occurred in the northern parts. Our methodological approach successfully determines the HW measures' trends in the province based on the relief features and altitude differences. The outcomes highlight their notable impact on the progression of HW. Overall, this study provides significant insights into understanding HW's variations and their transformation in the mountainous areas.

Accounts Receivable in the Russian Economy: Regional Trends

Accounts receivable, being both a sales and financial category, is a key aspect of the development of modern market relations. At the same time, accounts receivable has become a complex object of only microeconomic research. Insufficient knowledge of accounts receivable at the macro level determined the relevance of the research. The subject area of the research is the total accounts receivable of Russian organizations. The aim of the study is to group Russian regions and types of economic activities depending on the conditions of conducting sales and payment activities. The methodology consists of a systematic approach, which allowed a comprehensive review of the object of research and macroeconomic analysis, which ensured the reliability and validity of the conclusions of the work. As a result, the author defines the total accounts receivable of organizations in the economy as an independent object of macroeconomic research. Based on the results of the analysis for 2000–2019, the author concludes that there is a high degree of correlation between sales revenue and accounts receivable of Russian organizations, as well as the presence of trends in the increasing importance of accounts receivable in sales and the reduction of its overdue part in the finances of organizations. The share of accounts receivable in the total sales revenue of organizations is taken as a generalized characteristic of the conditions for doing business in the sales activities of organizations, and in payment activities — the share of overdue total accounts receivable of organizations. The author’s matrix for express assessment of the conditions for conducting sales and payment activities of organizations in the economy constitutes the scientific novelty of the study. It made it possible to group the types of activities, regions and districts of the Russian Federation, depending on the specified conditions. The prospect for future research of the total accounts receivable of organizations in the economy is the search for tools to determine its optimal volume and structure to timely identify the factors of the upcoming crises of non-payment and overproduction, both in the economy as a whole and in individual commodity markets.

A geospatial analysis approach for the operational assessment of solar ORC systems. Case study: Performance evaluation of a two-stage solar ORC engine in Greece

Renewable energy potential depends on many variables that vary both in space and time. Therefore, geospatial analysis approaches may effectively support proper planning and technoeconomic analysis. In this study, a simulation model of a two-stage Organic Rankine Cycle (ORC) engine combined with evacuated tube solar collectors was developed and applied throughout Greece. The simulation model uses daily meteorological data for a thirty-four years reference period. These data are downscaled in hourly time step and used to predict the system's performance in spatially distributed form based on experimental data. The obtained results are statistically analysed and a set of ranking maps presenting various aspects of the integrated system performance is created. The results indicate that there is a profound spatial variability. The interannual average power production ranges spatially between 17.4 and 27.7 kWh/d, the total efficiency between 0.022 and 0.028 and the corresponding energy cost between 0.41 and 0.7 €/kWh. Finally, the effect of climate variability on energy production performance and the presence of trends are investigated. The proposed approach was proven to be an effective tool for the assessment of the feasibility and potential of a solar heat-to-power engine for Greece and to be expanded in other regions as well.

Monitoring shrimp growth with control charts in aquaculture

This work proposes a method for the statistical monitoring of the weekly weight of shrimp in a fish farm. The design of control charts for shrimp growth presents several challenges, the main ones being the presence of trend and autocorrelation. Several control charts are proposed, the most efficient being a chart based on the residuals of a second order autoregression whose parameters vary with the week. A control chart with less computational complexity is also provided. It is simply based on growth rates and works well at detecting negative weight changes. Real data from a large shrimp farm has been used for both the design and the evaluation.

Modeling Eects of Climatic Variables on Tea Production in Kenya Using Linear Regression Model with Serially Correlated Errors

Aims/ Objectives: To formulated a linear regression model to capture the relationship between tea production and climatic variables in terms of ARIMA.Place and Duration of Study: Department of Mathematics and Actuarial Science, Catholic University of Eastern Africa, Nairobi, Kenya, between June 2019 and April 2021.Methodology: The study used time-series data for mean annual temperature, mean annual rainfall, humidity, solar radiation, and NDVI, collected from six counties, namely Embu, Kakamega, Kisii, Kericho, Meru, and Nyeri.Results: The study ndings noted that there is a presence of trend and seasonality for all the data. The scatter plot matrix for all the climatic variables for all the counties under the study indicated that tea production has a linear relationship with most climatic variables. Model t of the data indicated statistical signicance when tea production data is dierenced. A second linear model with tea production data deseasoned has mixed results in terms of a signicancetest. The variation of independent variables with tea production yielded very low values, suggesting that the data used has many variabilities.Conclusion: The study ndings show the climatic variables can be used to forecast tea production.Recommendation: Future studies may combine the analysis with other statistical modeling procedures such as the GARCH models.

Modelling and applications for non-stationary time series in the presence of trend and period

In this paper, we study the estimation of the non-stationary time series in the presence of trend and period. We write the classical decomposition time series model as a partial linear model with an unknown parameter. Then we approximate the trend term with B-spline and obtain the estimators of the period, periodic sequence and trend. The asymptotic behaviors of the estimators are given in our paper, including the consistency of period estimation and the asymptotic behavior of the estimated periodic sequence and trend. We illustrate the superiority of our method through simulations and the applications to two real data examples show the utility of our method.

Trend and nonstationary relation of extreme rainfall: Central Anatolia, Turkey

The frequency of extreme rainfall occurrence is expected to increase in the future and neglecting these changes will result in the underestimation of extreme events. Nonstationary extreme value modelling is one of the ways to incorporate changing conditions into analyses. Although the definition of nonstationary is still debated, the existence of nonstationarity is determined by the presence of significant monotonic upward or downward trends and/or shifts in the mean or variance. On the other hand, trend tests may not be a sign of nonstationarity and a lack of significant trend cannot be accepted as time series being stationary. Thus, this study investigated the relation between trend and nonstationarity for 5, 10, 15, and 30 min and 1, 3, 6, and 24 h annual maximum rainfall series at 13 stations in Central Anatolia, Turkey. Trend tests such as Mann–Kendall (MK), Cox–Stuart (CS), and Pettitt’s (P) tests were applied and nonstationary generalized extreme value models were generated. MK test and CS test results showed that 33% and 27% of 104 time series indicate a significant trend (with p < 0.01–p < 0.05–p < 0.1 significance level), respectively. Moreover, 43% of time series have outperformed nonstationary (NST) models that used time as covariate. Among five different time-variant nonstationary models, the model with a location parameter as a linear function of time and the model with a location and scale parameter as a linear function of time performed better. Considering the rainfall series with a significant trend, increasing trend power may increase how well fitted nonstationary models are. However, it is not necessary to have a significant trend to obtain outperforming nonstationary models. This study supported that it is not necessarily time series to have a trend to perform better nonstationary models and acceptance of nonstationarity solely depending on the presence of trend may be misleading.

Прогнозування запасів промислового підприємства в умовах волатильності попиту

The aim of the article is to provide tools for obtaining reliable forecasts of the level of inventories of the enterprise in conditions of volatility in demand for products. Most types of demand for industrial products are unstable, so it is important to form stocks based on demand forecasts to reduce logistics risks. The results of the analyses. Analytical tools for forecasting maximum level of inventories in conditions of volatility of demand for products of machine-building enterprises have been developed, which provides an opportunity to obtain the most reliable sales forecast and estimate the maximum required stocks for a certain type of demand. The method, which is obtained by analytical tools, is based on a three-stage algorithm: a) identification of trends in a time series of sales; b) obtaining optimal sales forecasting models; c) plotting of interval forecasts of product sales and risk assessment of the formation of its maximum stocks. The developed methodology identifies logistics risks, which depend on sales forecasts, for nine machine-building enterprises of Ukraine. A method for statistical assessment of logistics risks of machine-building enterprises by confidence intervals has been developed, in which maximum stocks are determined by two confidence intervals of sales forecasts, and the risk of error is associated with the appropriate levels of reliability of these intervals. It is proposed to build the upper limits of two confidence intervals, for example, 95% and 99%, according to the forecast inventory level estimates, and to consider them as maximum inventory level estimates with corresponding probabilities. The risk of stock shortages is defined as the probability of going beyond the upper limit of the corresponding interval. It is proved that the dynamics of monthly or quarterly sales of enterprises can be typed by four patterns: the presence of seasonal fluctuations and trends; the presence of purely seasonal fluctuations without a pronounced trend; no seasonal fluctuations, but the presence of a trend; no seasonal fluctuations and trends. Conclusions and perspectives for further research. It is proved that the volatility of monthly or quarterly sales volumes of enterprises can be typed by four patterns: 1) the presence of seasonal fluctuations and trends; 2) the presence of purely seasonal fluctuations without a pronounced trend; 3) no seasonal fluctuations, but the presence of a trend; 4) no seasonal fluctuations and trends. Based on this, the theoretical and methodological principles and analytical tools for forecasting the maximum stocks of an industrial enterprise in conditions of demand volatility were improved. Keywords: seasonality, volatility, inventory level forecasting, maximum stocks, demand forecasting.

Гендерные различия параметров эмоционального интеллекта подростков

The present study examines the results of an empir-ical research of gender differences in parameters of emotional intelligence of a person entering adoles-cence. The analysis of theoretical and empirical studies on this topic in the works of foreign and Russian scientists is given. There is described the essence of “emotional intelligence” as a set of char-acteristics that provide emotional regulation of ac-tivity. The data of foreign and Russian researchers determining the features of development of an emo-tional sphere of adolescents and the possibility of relief and prevention of negative personal experi-ences in adolescence are presented. It is established that specific manifestations of emotional intelli-gence of a person in adolescence are expressed in the following: low level of self-motivation, presence of trends in development of empathy, weak ability to understand your own emotions and emotions of others, desire to manage the emotions of people around. Gender differences in parameters of emo-tional intelligence of adolescents are determined. It is more typical for boys to understand and regulate their own emotions, while girls tend to understand and regulate emotions of others.

Возможности динамического обучения деловому общению на английском языке студентов направлений «Экономика» и «Менеджмент»

Readiness for foreign-language business communication is an important indicator of professionalism in any field of work. In this regard, students of non-linguistic specialties feel the need for training professionally-oriented communication, which allows them to prove themselves in the role of a full-fledged participant in intercultural communication. The relevance of the study is due to the expansion of international cooperation, the presence of trends towards globalization of the world economy and, as a result, the increasing role of professional intercultural communication and changing requirements for university graduates. Pilot work was carried out on the basis of a technical university during the first semester at the Faculty of Engineering and Economics. The author has developed and integrated into practice the long term business game “Starting a business” as one of the technologies of dynamic learning, which provides effective formation of business communication skills in English for students of “Management” and “Economics” training programs. Pilot work has shown results that testify the effectiveness of dynamic learning in the formation of foreign language business communication skills.

A Distance-based Method for Spatial Prediction in the Presence of Trend

A new method based on distances for modeling continuous random data in Gaussian random fields is presented. In non-stationary cases in which a trend or drift is present, dealing with information in regionalized mixed variables (including categorical, discrete and continuous variables) is common in geosciences and environmental sciences. The proposed distance-based method is used in a geostatistical model to estimate the trend and the covariance structure, which are key features in interpolation and monitoring problems. This strategy takes full advantage of the information at hand due to the relationship between observations, by using a spectral decomposition of a selected distance and the corresponding principal coordinates. Unconditional simulations are performed to validate the efficiency of the proposed method under a variety of scenarios, and the results show a statistical gain when compared with a more traditional detrending method. Finally, our method is illustrated with two applications: earth’s average daily temperatures in Croatia, and calcium concentration measured at a depth of 0–20 cm in Brazil. Supplementary materials accompanying this paper appear online.

Assessment of seasonal climate transference and regional influential linkages to land cover – Investigation in a river basin

In this study, the trend of annual and monthly rainfall; and trend of maximum and minimum temperature of the month have been detected. The presence of trends in annual rainfall time series are detected and evaluated using linear regression (LR), Mann-Kendall(M-K) test with Sen's slope estimator, and Innovative Trend analysis (ITA). Results of trend analysis by LR, M-K and ITA methods show close agreement. An inverse distance weighting (IDW) method in Arc GIS is used for developing spatial distribution of the trends over the entire study area of Sabarmati River basin, India. Result of the monthly rainfall shows reducing trend in the June month and shows increasing trend for the month of September in the study area. This is indicative of tendency of late starting and late vanishing of the monsoon season. Maximum temperature in the basin, in winter months show reducing trend, while in summer months, temperature show increasing trends. Results shown shifting of summer season (March to May) extending to the monsoon months. The Land Use Land cover is examined for regional influential linkages to climate change. The change in the seasonal climate bears significance for its inferences in agriculture field and water resources field for fulfilling the demand from available water resources in the river basin.

Towards Beijing 2022: Discussion on the growth in size of Paralympic Winter Games in terms of participating data from Örnsköldvik 1976 to PyeongChang 2018

Paralympic Winter Games (PWG) born in 1976. Across Games, media personnel has increased suggesting an increase in PWG size. However, the growth of PWG from Ornskoldsvik 1976 to PyeongChang 2018 in terms of participating data has not been discussed. This work aims to find if the growth in media coverage comes together with an increase in PWG size in terms of National Paralympic Committee, sports, medal events, and athletes. The presence of trend over PWG editions is evaluated with Mann-Kendall Trend Test and differences among sports and continents with Kruskal Wallis test. Showing a significant monotonic trend, the number of countries (p < .001, β = 0.81) and the number of participating athletes (p < .001, β = 7.40) seem to be good indicators of PWG. These two indicators are good descriptors of PWG size because related to sociocultural and political aspects. In contrast, number of medal events being related to many factors did not show statistically significant trend (p = .95); therefore, this indicator seems to be less appropriate to describe PWG size. If the whole history is considered, slow but significant changes in number of sports (p < .001, β = 0.08) suggest this indicator could be considered in describing Games size.

Trendlets: A novel probabilistic representational structures for clustering the time series data

Time series data is a sequence of values recorded systematically over a period which are mostly used for prediction, clustering, and analysis. The two essential features of a time series data are trend and seasonality. Preprocessing of the time series data is necessary for performing prediction tasks. In most of the cases, the trend and the seasonality are removed before applying the regression algorithms. The accuracy of such algorithms depends upon the functions used for the removal of trend and seasonality. Clustering of an unlabeled time series data with the presence of trend and seasonality is challenging. In this paper, we propose a probabilistic representational learning method for grouping the time series data. We introduce five terminologies in our method of clustering namely the trendlets, uplets, downlets, equalets and trendlet string. These elements are the representational building blocks of our proposed method. Experiments on the proposed algorithm are performed with the renewable energy data on the electricity supply system of continental Europe which includes the demand and inflow of renewable energy for the term 2012 to 2014 and UCR-2018 time series archive containing 128 datasets. We compared our proposed representational method with various clustering algorithms using the silhouette score. Mini-batch k-means and agglomerative hierarchical clustering algorithms show better performance in terms of quality, logical accordance with data and time taken for clustering.