Real Trends Research Articles

Do changes in STEC diagnostics mislead interpretation of disease surveillance data in Switzerland? Time trends in positivity, 2007 to 2016.

BackgroundLaboratory-confirmed cases of Shiga toxin-producing Escherichia coli (STEC) have been notifiable to the National Notification System for Infectious Diseases in Switzerland since 1999. Since 2015, a large increase in case numbers has been observed. Around the same time, syndromic multiplex PCR started to replace other diagnostic methods in standard laboratory practice for gastrointestinal pathogen testing, suggesting that the increase in notified cases is due to a change in test practices and numbers.AimThis study examined the impact of changes in diagnostic methods, in particular the introduction of multiplex PCR panels, on routine STEC surveillance data in Switzerland.MethodsWe analysed routine laboratory data from 11 laboratories, which reported 61.9% of all STEC cases from 2007 to 2016 to calculate the positivity, i.e. the rate of the number of positive STEC tests divided by the total number of tests performed.ResultsThe introduction of multiplex PCR had a strong impact on STEC test frequency and identified cases, with the number of tests performed increasing sevenfold from 2007 to 2016. Still, age- and sex-standardised positivity increased from 0.8% in 2007 to 1.7% in 2016.ConclusionIncreasing positivity suggests that the increase in case notifications cannot be attributed to an increase in test numbers alone. Therefore, we cannot exclude a real epidemiological trend for the observed increase. Modernising the notification system to address current gaps in information availability, e.g. diagnostic methods, and improved triangulation of clinical presentation, diagnostic and serotype information are needed to deal with emerging disease and technological advances.

Informing the design of a long-term population density monitoring protocol for a Nationally Endangered grasshopper: removal sampling as a basis for estimating individual detection probabilities

Imperfect detection of individuals in threatened wild populations is common and can obscure real population trends when it is unaccounted for in population monitoring, and therefore impede conservation decision making. For many threatened insects, there is a lack of biological information or available long-term data to inform how best to practice data collection and population monitoring. Here, we inform the design of a long-term population density monitoring protocol for Brachaspis robustus, a Nationally Endangered grasshopper endemic to the Mackenzie Basin of New Zealand. We use removal sampling (repeated visual searches of a predefined area where any individuals found are temporarily removed to achieve successive depletion) during a single austral summer season (November to March) to rapidly quantify seasonal and demographic visual detectability. Juvenile instars dominated population composition in all months except December and males represented > 50% of monthly captures. Adult females were 2–3 times larger than adult males, and 79% of those captured were found during the first search of an area compared to only 52% of adult males. The odds of detecting an individual increased by 6% per 1 mm of body length. Removal sampling was found to be an effective method for rapidly informing future long-term monitoring design for a visually cryptic, threatened insect. Recommendations include monitoring adult females as an index of population size, restricting monitoring to when adult abundance peaks (November and December), and conducting multiple monitoring events within peak months to counter the effects of daily and seasonal variation and imperfect detection.

Explicit Analytical Equations for Single Port Hybrid Rocket Combustion Chamber Sizing

Hybrid rockets have several advantages with respect to current propulsion systems like simplicity, safety, reliability, environmental friendliness, and lower cost. To size the combustion chamber, it is fundamental to understand how the length, the external diameter, the volume loading, and the length-to-diameter ratio vary with the design parameters like scale, burning time, average mixture ratio, initial oxidizer flux, and propellant combination. The equations available in the literature are not in explicit form with respect to the aforementioned design parameters, and sometimes they can be misinterpreted by hybrid rocket engineers. Moreover, it is not possible to determine the instantaneous and average characteristic velocities during the burn without a numerical time integration. To show explicitly the real trends, a set of analytical equations has been developed. The key step is the definition of the relation between initial and average mixture ratio and the asymptotic treatment with respect to the ratio between the external and internal diameters. Moreover, an approximate explicit semi-analytical expression of the instantaneous and average characteristic velocities is provided. The explicit analytical equations are validated with the exact implicit solutions showing good agreement and exact asymptotic behavior.

On the Distribution Modeling of Heavy-Tailed Disk Failure Lifetime in Big Data Centers

It has become commonplace to observe frequent multiple disk failures in big data centers in which thousands of drives operate simultaneously. Disks are typically protected by replication or erasure coding to guarantee a predetermined reliability. However, in order to optimize data protection, real life disk failure trends need to be modeled appropriately. The classical approach to modeling is to estimate the probability density function of failures using nonparametric estimation techniques such as kernel density estimation (KDE). However, these techniques are suboptimal in the absence of the true underlying density function. Moreover, insufficient data may lead to overfitting. In this article, we propose to use a set of transformations to the collected failure data for almost perfect regression in the transform domain. Then, by inverse transformation, we analytically estimated the failure density through the efficient computation of moment generating functions, and hence, the density functions. Moreover, we developed a visualization platform to extract useful statistical information such as model-based mean time to failure. Our results indicate that for other heavy-tailed data, the complex Gaussian hypergeometric distribution and classical KDE approach can perform best if the overfitting problem can be avoided and the complexity burden is overtaken. On the other hand, we show that the failure distribution exhibits less complex Argus-like distribution after performing the Box-Cox transformation up to appropriate scaling and shifting operations.

Understanding house price appreciation using multi-source big geo-data and machine learning

Understanding house price appreciation benefits place-based decision makings and real estate market analyses. Although large amounts of interests have been paid in the house price modeling, limited work has focused on evaluating the price appreciation rate. In this study, we propose a data-fusion framework to examine how well house price appreciation potentials can be predicted by combining multiple data sources. We used data sets including house structural attributes, house photos, locational amenities, street view images, transportation accessibility, visitor patterns, and socioeconomic attributes of neighborhoods to enrich our understanding of the real estate appreciation and its predictive modeling. As a case study, we investigate more than 20,000 houses in the Greater Boston Area, and discuss the spatial dependency of house price appreciations, influential variables and their relationships. In detail, we extract deep features from street view images and house photos using a deep learning model, merging features from multi-source data and modeling house price appreciation using machine learning models and the geographically weighted regression at two spatial scales: fine-scale point level and aggregated neighborhood level. Results show that the house price appreciation rate can be modeled with high accuracy using the proposed framework (R2=0.74 for gradient boosting machine at neighborhood-scale). We discovered that houses with low house prices and small house areas may have a higher house appreciation potential. Our results provide insights into how multi-source big geo-data can be employed in machine learning frameworks to characterize real estate price trends and help understand human settlements for policy-making.

Statistically-based methodology for revealing real contagion trends and correcting delay-induced errors in the assessment of COVID-19 pandemic.

COVID-19 pandemic has reshaped our world in a timescale much shorter than what we can understand. Particularities of SARS-CoV-2, such as its persistence in surfaces and the lack of a curative treatment or vaccine against COVID-19, have pushed authorities to apply restrictive policies to control its spreading. As data drove most of the decisions made in this global contingency, their quality is a critical variable for decision-making actors, and therefore should be carefully curated. In this work, we analyze the sources of error in typically reported epidemiological variables and usual tests used for diagnosis, and their impact on our understanding of COVID-19 spreading dynamics. We address the existence of different delays in the report of new cases, induced by the incubation time of the virus and testing-diagnosis time gaps, and other error sources related to the sensitivity/specificity of the tests used to diagnose COVID-19. Using a statistically-based algorithm, we perform a temporal reclassification of cases to avoid delay-induced errors, building up new epidemiologic curves centered in the day where the contagion effectively occurred. We also statistically enhance the robustness behind the discharge/recovery clinical criteria in the absence of a direct test, which is typically the case of non-first world countries, where the limited testing capabilities are fully dedicated to the evaluation of new cases. Finally, we applied our methodology to assess the evolution of the pandemic in Chile through the Effective Reproduction Number Rt, identifying different moments in which data was misleading governmental actions. In doing so, we aim to raise public awareness of the need for proper data reporting and processing protocols for epidemiological modelling and predictions.

Особливості соціодемографічного «портрета» осіб, які шукають шлюбного партнера в інтернет-мережi

The aim of the article is to find out the peculiarities of the socio-demographic «portrait» of the people who are searching for a marriage partner online. Research methods and techniques are the methodological tools of the research which included the analysis and generalization of works devoted to the study of the virtual discourse of the people searching for a marriage partner online; content analysis of personal data in the context of identifying the peculiarities of language self-presentation of the users searching for a marriage partner online. Data collection for the empirical study was carried out on the site Love.ua.The sample of the research consisted of 120 Internet pages on the dating site (50 female and 70 male profiles). All obtained empirical data were analyzed separately for the sample of men and the sample of women. The significance of the difference was calculated using Fisher’s φ-test. The results of the research made it possible to determine the peculiarities of the socio-demographic «portrait» of the people who are searching for a marriage partner online. Conclusions. The identified gender characteristics in the answers to the questionnaires of a dating site reflect the real socio-demographic trends in Ukrainian society. It was found that for most men, the situation of online dating is a continuation of a real life situation, expanding its capabilities. Women mostly turn to a dating site as an alternative (compensation) to a real life situation. Adolescents and young people use the site as a «training» platform, learning to flirt and trying different tactics of behavior towards people of the opposite sex. It is investigated that when creating the self-presentation on a dating site, the author is limited by the framework of the template (questionnaire) that regulates communicative behavior, but he is given relative autonomy to create his own image. The users of a site usually avoid answering questions related to their social status, education and financial status. However, in the case of a serious attitude to acquaintance, this information is necessary, because it relates to the similarity of a social base of future partners.

Investigation into valuation of land using remote sensing and GIS in Madurai, Tamilnadu, India

ABSTRACT Assessing the value of property is important in the real estate activity. Important factor to assess the property value is the location of the property. In India, value of a property is mainly evaluated by the guideline value and the market value. This paper addresses the effect of different parameters that affect the market value in Madurai Corporation area. It also addresses the implementation of urban valuation on a Geographic Information System for better decision-making. The valuation of the land depends upon the popularity and also the facilities around in that area. The spatial data constitutes the location information of the assets and the attributes that contain the details of the assets. Quantum GIS software is used for the storage and analysis of the data. The effective use of GIS along with reliable data and predictive tools can provide a robust and near realistic solution in predicting real estate trends. It is found that, for land assets, the Geographic Information System approach is effective. In this paper, we have used the statistical methods in order to find the market value and modelled value using Voronoi Polygon Method.

Removal of Herbicides from Landfill Leachate in Biofilters Stimulated by Ammonium Acetate

At a former Danish polluted landfill, a field experiment using biofilters as an ex-situ remediation strategy for leachate water was carried out. The leachate water was polluted with phenoxy acids, mecoprop, dichlorprop, and their impurities originated from previous years of disposal of production wastes. Three individual biofilters were set up and each was filled with different a support material, e.g., sand, stonewool, and peat amended sand. The sand biofilter was spiked with ammonium acetate in pulses lasting a week to stimulate biomass growth and thereby enhance the removal of the phenoxy acids. The effects on removal and enantioselectivity were studied during a 69-day sampling campaign. Results showed that stimulation of the microbial community with ammonium acetate provided a boost, hence removal in the sand biofilter increased after the dosing whereas the stonewool and peat biofilters showed generally low removal. The highest removal was observed after stimulation in the sand biofilter for both herbicides. After a starting period, the removal was compound-specific but ranged from 60–100%. The final concentrations exceeded the drinking water limits slightly (0.25 µg L−1) (mecoprop and 2-(2/4-chlorophenoxy)propanoic acid), while it was considerably below the limit for all other compounds (2-(2-methylphenoxy)propanoic acid and dichlorprop). Enantioselective fractions were already 0.41, and 0.75 for mecoprop and dichlorprop, respectively, in the inlet, probably due to in-situ degradation in the landfill—Mecoprop showed some enrichment of the (R)-enantiomer in the sand biofilter whereas no real trends were seen in the stonewool and peat biofilter. Only minor alterations in enantiomeric fractions were observed for dichlorprop in all three biofilters. This experiment shows that it is feasible to remove micropollutants from landfill leachates and it is possible to stimulate biomass and thereby initiate and obtain increased removal faster.

Recent declines in global water vapor from MODIS products: Artifact or real trend?

Atmospheric water vapor plays a key role in the global water and energy cycles. Accurate estimation of water vapor and consistent representation of its spatial-temporal variation are critical to climate analysis and model validation. This study used ground observational data from global radiosonde and sunphotometer networks to evaluate MODIS (MODerate-resolution Imaging Spectroradiometer) precipitable water vapor (PWV) products for 2000–2017. The products included the thermal-infrared (TIR) (Collection 6, C006) and its updated version (Collection 061, C061), and near-infrared (NIR) products (C061). Our results demonstrated that compared to its earlier version subject to sensor crosstalk problem, the C061_TIR data showed improved accuracy in terms of bias, standard deviation, mean absolute error, root mean square error, and coefficient of determination, regression slope and intercept. Among the PWV products, C061_NIR data achieved the best overall performance in accuracy evaluation. The C061_NIR revealed the PWV had a multi-year average of 2.50 ± 0.08 cm for the globe, 2.03 ± 0.06 cm for continents, and 2.70 ± 0.09 cm for oceans in 2000–2017. The PWV values yielded an increasing rate of 0.015 cm/year for the globe, 0.010 cm/year for continents, and 0.017 cm/year for oceans. Nearly 98.95% of the globe showed an increasing trend, 80.74% of statistical significance, mainly distributed within and around the tropical zones. The findings should be valuable for understanding of global water and energy cycles.

A remaining useful life prediction method with long-short term feature processing for aircraft engines

As one of the key components of aircraft, any failure of the engine can lead to serious accidents. The reliability and safety can be guaranteed by predicting the remaining useful life of the aircraft engine. The data-driven approaches are suitable for predicting the remaining useful life of the aircraft engine, but they generally suffer from the following challenges: (i) how to capture the real degradation trend of the engine; (ii) how to efficiently and fully utilize the temporal correlation between the sensor data; (iii) how to handle highly nonlinear data. In order to address these challenges, an effective data-driven remaining useful life prediction method is proposed in this paper. Firstly, a long-term differential technique is proposed to extract forward differential features, which fully reflects the actual degradation trend in the entire lifetime. Then, the Fibonacci window is proposed for short-term feature extension, which makes full use of the temporal correlation of the historical data and effectively reduces the extra computational load. Finally, the CatBoost algorithm is used to predict the remaining useful life on the highly nonlinear data, and superior prediction performance is obtained. In order to verify the effectiveness of the proposed method, the experiments are carried out on the aircraft engine dataset provided by NASA.

Pitfalls of judgment during the COVID-19 pandemic

Pitfalls of judgment during the COVID-19 pandemic

Comparing trends of perinatal mortality in two rural areas of Matlab, Bangladesh

PurposeSixteen million adolescents give birth each year, constituting 11% of all births worldwide. Adverse adolescent pregnancy outcomes are well-documented. Available data on adolescent pregnancies have mainly relied on self-reported age and retrospective survey data, which might not capture adolescent births accurately. This paper reports on trends in adolescent pregnancy and associated adverse birth outcomes in Matlab, Bangladesh, using data from the Matlab Heath and Demographic System (HDSS) which precisely documents maternal age.Design/methodology/approachThe study was conducted in the rural subdistrict of Matlab in Bangladesh. HDSS data were used to examine trends in adolescent motherhood (10–19 years) in the International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b) service areas (ISA) and government service areas (GSA) between 2007 and 2015. A total of 4,996 adolescent mothers were included in the analysis. Chi-square testing and binary logistic regression were used to document adolescent pregnancy trends and the differences in and causes of perinatal death.FindingsThe fertility rate was 27 per 1000 adolescent mothers in ISA and 20 per 1000 adolescent mothers in GSA, during the 9 years of the study period. The adjusted odd of an adolescent mother having a perinatal death in ISA, relative to GSA was 0.69. Significant determinants of perinatal death among adolescent mothers included maternal education, paternal education, mother’s age at first birth, asset score and distance from the nearest health facility.Originality/valueThis paper documents the real trend of adolescent pregnancy by capturing the accurate age at pregnancy for the first time in Bangladesh.

Three decades of data on phytoplankton and phycotoxins on the French coast: Lessons from REPHY and REPHYTOX

In France, REPHY (Observation and Surveillance Network for Phytoplankton and Hydrology in coastal waters) and REPHYTOX (Monitoring Network for Phycotoxins in marine organisms) have been contributing to long-term time series on ocean health for more than 30 years. The aim of this paper is to describe these networks and to highlight their key results. Over the last 20 years, phytoplankton flora analysis on French coasts from the Channel to Mediterranean has shown that the five “emblematic” taxa are Chaetoceros, Skeletonema, Cryptophyceae, Leptocylindrus and Pseudo-nitzschia. The latter, together with the taxa of interest Dinophysis + Phalacroma, Alexandrium, and Karenia, have been consistently recorded along the entire French coastline. However, when taking into account frequency of occurrence some taxa exhibit more distinct geographical distributions. In particular, the occurrence of Phaeocystis appeared to be strongly specific to the northern coasts of the Channel. French coasts have been regularly affected since the 1980s by the presence of toxins in bivalve molluscs, leading to bans on fishing and sale of shellfish during periods of varying duration. Three categories of toxins were involved. PST and AST were absent from the French coasts, respectively before 1988 and 2000. DST (Diarrheic Shellfish Toxins) have affected many areas along the whole coast every year since 1987. For PST (Paralytic Shellfish Toxins), only a few areas have been affected, sometimes sporadically, since 1988 in the Channel, 1993 in the Atlantic, and 1998 in the Mediterranean. Many areas have been impacted since 2000 by AST (Amnesic Shellfish Toxins) episodes, mainly affecting scallops in the Channel and on Atlantic coasts. The patterns of change of shellfish toxicity episodes showed no real trend in any province over the entire period 1987–2018.

550 Real World Treatment Trends for Patients TBSA ≤ 20%: Evaluating Important Shifts in Care and Budget Impact of ASCS Adoption

Abstract Introduction Treatment pathways in burn care are typically determined based on burn center (BC) and patient characteristics, although decisions may be influenced by anecdotal experience, personal preference, and hospital policies/purchasing decisions. Health economic (HE) evaluations can support improved decision-making, identifying the most cost-effective interventions for tailored care. A novel burn care model (BEACON) was developed with burn surgeons over several years and validated through numerous publications, including an assessment of the HE impacts of autologous skin cell suspension (ASCS) use for definitive burn closure. To ensure that BEACON accurately represents the current state of care, it is vital to update data that underpins model projections. This study collected real world data on practice patterns and patient outcomes for the most commonly seen burns (TBSA ≤ 20%) to update the current understanding of standard of care (SOC) costs and outcomes and to refine estimates on the impact of ASCS use in TBSA ≤ 20% patients. Methods Data was collected from a 10% sample of BCs, including: BC and patient characteristics, resource use, inpatient costs, and length of stay (LOS). NBR based inputs in BEACON were updated to reflect survey data for patients with TBSA ≤ 20%, with the ability to view data as a national aggregate sample and across BC characteristics. BEACON estimates patient and BC costs and outcomes across a spectrum of patient profiles (age, gender, inhalation injury, comorbidity status, burn depth, TBSA) and combines information on each patient profile to understand annual budget impact. Key outcomes were compared across the survey sample and published NBR trends. Using the updated BEACON, the BC budget impact of ASCS in burns TBSA ≤ 20% was assessed. Results The survey was collected from 16+ BCs, focusing on inpatient encounters in 2018. LOS was lower than NBR estimates, with some centers reporting LOS per %TBSA far below 1 d/%TBSA. Using the detailed bottom-up estimation of cost from BEACON with survey data, trends suggest total hospital costs for SOC are lower than published NBR charges given shorter LOS and updated cost and resource use assumption. Conclusions Compared to NBR 8.0, contemporary data suggests that fewer small TBSA burns are being treated in the inpatient setting; those treated have a LOS below NBR estimates. When using real world data, the impact of ASCS use in burns TBSA ≤ 20% was still calculated to be cost saving to a BC overall, given reductions in LOS and number of definitive closure procedures. Incorporating ASCS into appropriate TBSA ≤ 20% procedures can still result in a positive financial impact for BCs. Applicability of Research to Practice

Monetary Policy Transmission on Real Trends in Serbia – VAR Analysis

Abstract The efficiency of channels of monetary transmission varies from country to country and is conditioned by a number of factors that determine the economic and financial system of a country. In order to achieve the set monetary policy objectives, а central bank takes certain measures and employs instruments of monetary policy. Those instruments, however, act indirectly and with a certain lag. Due to these limitations in monetary policy effects, the analysis of the monetary transmission is of essence in every country as it enables its designers to determine an optimum monetary regime. In this paper, an analysis of monetary transmission in the Republic of Serbia is made using the Vector autoregressive model (VAR model). The research conducted is significant due to a current issue of the impact of monetary policy on actual economic trends, both in the developed and developing countries. On the basis of the research it is concluded that, in the time period under observation, the biggest impact on the fluctuations in industrial production in Serbia is that of monetary aggregate, whereas the biggest impact on the fluctuation in prices is that of key policy rate movements. The results of the analysis provide guidelines to monetary authorities to take necessary steps to shorten a lag period and eliminate restrictions in transmitting monetary impulses into real economic values.

Future Directions.

This final chapter focuses on the progress made so far in the area of suicide prevention and calls attention to considerations for future work. Recommendations for the implementation of national suicide prevention strategies are proposed, including close collaboration between countries within the same geographic region, as well as guidance from countries wherein national strategies have successfully implemented, such as the sharing of effective strategy templates. In addition, the value of accurate surveillance data in overcoming barriers, informing actions and responding to real time trends in suicide and self-harm is emphasized. The need for more systematic research into the efficacy of intervention and prevention approaches is also highlighted. Furthermore, the provision of governmental support to ensure long-term sustainability of national suicide prevention strategies is endorsed. Approaches to enhance the evaluation of the efficacy of national suicide prevention strategies and interdisciplinary partnerships and collaborations are discussed. Finally, recommended resources to assist in implementing and evaluating key components of national suicide prevention strategies are listed.

Forecasting multiple-well flow rates using a novel space-time modeling approach

A different approach based on existing spatiotemporal models is developed in this research for forecasting the production flow rates of a group of adjacent wells in a reservoir. Unlike the previous single-well analysis techniques, this new methodology tries to forecast the flow rates of a number of producing wells located in a certain neighborhood. It also takes into account the variety of flow rate patterns that may take place during the production period of the wells. Regarding the time-series nature of flow rate data and the spatial relationships between multiple wells in a proximity, the Space-Time Auto-Regressive Moving-Average technique, abbreviated as STARMA, is borrowed to employ in this research to capture the spatial inter-relationships of the wells and model the flow rate data of multiple wells simultaneously. Partitioning the field into different groups of adjacent wells based on the similarity of flow rate patterns is also recommended using the spatial clustering algorithms as an enhancing part of the proposed methodology. The capability of the proposed approach in forecasting the flow rates of multiple wells is verified using the real data of a case study including 38 active oil producing wells located in a reservoir in South West Iran. The forecasts generated by the proposed method are finally compared with the predictions obtained by the current ARIMA techniques. Based on findings of this research, the proposed space time modeling of multiple well flow rates helps in estimating the more accurate forecasts, when the single well analysis techniques fail in predicting the real trend of future flow rates.

The solitude of the Orphan: Ǧābir b. Ḥayyān and the Shiite heterodox milieu of the third/ninth–fourth/tenth centuries

AbstractThe community of Shiite alchemists gathered under the pen name of Ǧābir b. Ḥayyān produced an important corpus first studied by Paul Kraus, who dated it between the third/ninth and fourth/tenth centuries. The religious, doctrinal and political issues of the corpus – especially in the last two collections – show that the Ǧābireans were a real sectarian trend unknown to heresiographers. Kraus, along with some scholars after him, understood the Ǧābirean community to be an expression of Ismaili thought. This paper aims to reconsider: a) the religious and political affiliation of Ǧābir's alchemical community in the light of textual comparisons that show a close connection between the Ǧābireans and the esoteric tenets characterizing the Shiiteġuluwwas mirrored in the heresiographic sources of the late third/ninth and early fourth/tenth centuries, and in theĠulātliterary sources; and b) the last collection of the Ǧābirean corpus as a polemical outcome specific to the Shiite milieu between the lesser and the greater Occultation of the twelfth Imam.

Стохастические тренды на основе нечеткой математики

В настоящее время существует ряд способов определения трендов и экстремумов на стохастических временных рядах, что неудивительно, поскольку тренды временного ряда являются фундаментальной характеристикой динамики процесса, стоящего за ним. Реальные стохастические тренды совсем не похожи на идеальные математические, посколько в них случаются сбои. Это не смущает исследователя, изначально обладающего адаптивным восприятием фундаментальных свойств предельности, непрерывности, связности, тренда и т. д. Он поймет, когда нарушение несущественно и тренд продолжается, а когда нарушение прерывает тренд.В настоящей работе предлагается новый подход к распознаванию стохастических трендов, основанный на математической конструкции регрессионных производных для конечного временного ряда. Тренды ищутся с помощью производной по сценарию классического математического анализа.