Market capitalization shock effects on open innovation models in e-commerce: Golden cut q-rung orthopair fuzzy multicriteria decision-making analysis

This research paper analyzes revenue trends in e-commerce, a sector with an annual sales volume of more than 340 billion dollars. The article evaluates, despite a scarcity of data, the effects on e-commerce development of the ubiquitous lockdowns and restriction measures introduced by most countries during the pandemic period. The analysis covers monthly data from January 1996 to February 2021. The research paper analyzes relative changes in the original time series through the autocorrelation function. The objects of this analysis are Amazon and Alibaba, as they are benchmarks in the e-commerce industry. This paper tests the shock effect on the e-commerce companies Alibaba in China and Amazon in the USA, concluding that it is weaker for companies with small market capitalizations. As a result, the effect on estimated e-trade volume in the USA was approximately 35% in 2020. Another evaluation considers fuzzy decision-making methodology. For this purpose, balanced scorecard-based open financial innovation models for the e-commerce industry are weighted with multistepwise weight assessment ratio analysis based on q-rung orthopair fuzzy sets and the golden cut. Within this framework, a detailed analysis of competitors should be made. The paper proves that this situation positively affects the development of successful financial innovation models for the e-commerce industry. Therefore, it may be possible to attract greater attention from e-commerce companies for these financial innovation products.Ministry of Education and Science of the Russian Federatio

Вычисление истинного уровня значимости предикторов при проведении процедуры спецификации уравнения регрессии

The paper is devoted to a new randomization method that yields unbiased adjustments of p-values for linear regression models predictors by incorporating the number of potential explanatory variables, their variance-covariance matrix and its uncertainty, based on the number of observations. This adjustment helps to control type I errors in scientific studies, significantly decreasing the number of publications that report false relations to be authentic ones. Comparative analysis with such existing methods as Bonferroni correction and Shehata and White adjustments explicitly shows their imperfections, especially in case when the number of observations and the number of potential explanatory variables are approximately equal. Also during the comparative analysis it was shown that when the variance-covariance matrix of a set of potential predictors is diagonal, i.e. the data are independent, the proposed simple correction is the best and easiest way to implement the method to obtain unbiased corrections of traditional p-values. However, in the case of the presence of strongly correlated data, a simple correction overestimates the true pvalues, which can lead to type II errors. It was also found that the corrected p-values depend on the number of observations, the number of potential explanatory variables and the sample variance-covariance matrix. For example, if there are only two potential explanatory variables competing for one position in the regression model, then if they are weakly correlated, the corrected p-value will be lower than when the number of observations is smaller and vice versa; if the data are highly correlated, the case with a larger number of observations will show a lower corrected p-value. With increasing correlation, all corrections, regardless of the number of observations, tend to the original p-value. This phenomenon is easy to explain: as correlation coefficient tends to one, two variables almost linearly depend on each other, and in case if one of them is significant, the other will almost certainly show the same significance. On the other hand, if the sample variance-covariance matrix tends to be diagonal and the number of observations tends to infinity, the proposed numerical method will return corrections close to the simple correction. In the case when the number of observations is much greater than the number of potential predictors, then the Shehata and White corrections give approximately the same corrections with the proposed numerical method. However, in much more common cases, when the number of observations is comparable to the number of potential predictors, the existing methods demonstrate significant inaccuracies. When the number of potential predictors is greater than the available number of observations, it seems impossible to calculate the true p-values. Therefore, it is recommended not to consider such datasets when constructing regression models, since only the fulfillment of the above condition ensures calculation of unbiased p-value corrections. The proposed method is easy to program and can be integrated into any statistical software package.Данная научная работа посвящена новому численному методу, вычисляющему несмещенные оценки p-значений для предикторов линейных регрессионных моделей с учетом числа потенциальных объясняющих переменных, их дисперсионно-ковариационной матрицы и степени ее неопределенности, основанной на числе рассматриваемых наблюдений. Такая поправка помогает ограничивать число ошибок 1-ого рода в научных исследованиях, значительно понижая число публикаций, декларирующих ложные зависимости в качестве истинных. Сравнительный анализ с такими существующими методами как поправка Бонферрони и поправка Шехата и Уайта явным образом демонстрирует их недостатки, особенно в случае, когда число потенциальных предикторов сравнимо с числом наблюдений. Также в процессе проведения сравнительного анализа было показано, что когда дисперсионно-ковариационная матрица набора потенциальных предикторов является диагональной, т.е. данные независимы, предложенная простая поправка является лучшим и самым легким в реализации методом для получения несмещенных корректировок традиционных p-значений. Однако, в случае присутствия сильно коррелированных данных простая поправка переоценивает истинные p-значения, что может приводить к ошибкам 2-ого рода. Также было выявлено, что исправленные p-значения зависят от числа наблюдений, числа потенциальных объясняющих переменных и выборочной дисперсионно-ковариационной матрицы. Например, если имеется только две потенциальных объясняющих переменных, конкурирующие за одну позицию в регрессионной модели, тогда, если они слабо коррелированы, исправленное p-значение будет ниже, чем в случае когда число наблюдений меньше и наоборот; если данные сильно коррелированы, случай с большим числом наблюдений будет показывать более низкое исправленное p-значение. С увеличением корреляции все поправки независимо от числа наблюдений стремятся к исходному p-значению. Данный феномен легко объяснить: с приближением коэффициента корреляции к единице две переменных практически линейно зависят друг от друга и в случае, если одна из них является значимой, то и другая почти наверняка будет демонстрировать такую же значимость. С другой стороны, если выборочная дисперсионно-ковариационная матрица стремится к диагональной и число наблюдений стремится к бесконечности, то предложенный численный метод будет возвращать поправки, близкие к простой поправке. В случае, когда число наблюдений много больше числа потенциальных предикторов, тогда поправка Шехата и Уайта дают примерно одинаковые поправки с предложенным численным методом. Однако, в намного более распространенных случаях, когда число наблюдений сравнимо с числом потенциальных предикторов, существующие методы демонстрируют достаточно значительные неточности. Когда число потенциальных предикторов больше доступного числа наблюдений, представляется невозможным рассчитать истинные p-значения. Вследствие этого рекомендуется не рассматривать такие наборы данных при построении регрессионных моделей, поскольку только выполнение вышеупомянутого условия обеспечивает расчет несмещенных корректировок p-значения. Предлагаемый метод полностью алгоритмизирован и может быть внедрен в любой пакет статистического анализа данных

Oil Price Predictors: Machine Learning Approach

The paper proposes a machine-learning approach to predict oil price. Market participants can forecast prices using such factors as: US key rate, US dollar index, S&P500 index, VIX index, US consumer price index. After analyzing the results and comparing the accuracy of the model first, we can conclude that oil prices in 2019-2022 will have a slight upward trend and will generally be stable. At the time of the fall in June 2012 the  price of Brent fell to a minimum of 17 months. The reason for this was the weak demand for oil futures, which was caused by poor data on the state of the US labor market. Keywords: oil price shocks, economic growth, oil impact, factors, dollar index, inflation; key rate; volatility index; S&P500 index. JEL Classification: C51, C58, F31, G12, G15 DOI: https://doi.org/10.32479/ijeep.759

Thermal calculations of underground oil pipelines

Operation of oil pipelines in the frozen soil causes heat exchange between the pipeline and the soil and formation of a melt zone which leads to deformation of pipelines. Terms of construction and operation of oil pipelines are greatly related to their temperature conditions. In this regard it is necessary to know the laws of formation of thawing halos around oil pipelines. Thus, elucidation of laws of formation of thawing halos around oil pipelines and determination of optimal conditions for their installation during construction in areas of permafrost in the north of Tyumen region is a very urgent task. The authors developed an algorithm and a computer program for construction of the temperature field of the frozen soil. Some problems have been solved basing on the obtained dependences and graphs of the dependence were constructed. Research and calculations made on the underground oil pipeline construction allowed the authors to give recommendations aimed at increasing the reliability of oil pipelines

Digital Echelons and Interfaces within Value Chains: End-to-End Marketing and Logistics Integration

[EN] The goals of real business in the context of the digital transformation of international logistics networks and marketing channels have necessitated the application of a scientifically based theoretical approach to the development of a formalized description acceptable for predictive planning based on leading indicators. In the context of globalization and interstate and regional economic unions, this will lead to achieving the maximum end-to-end integration of digital platforms. Based on the analysis, the article presents the integration of digital logistics and marketing approaches with the mathematical models of the ecosystem organization of economic relations. The features of the organization of economic relations between contractors involved in the execution of virtual transactions and the material movement of resources were analyzed. The researchers considered prerequisites for the analytical description of interconnections between the participants of digital platforms in cross border e-commerce. The authors' approach is based on the idea of both a sales funnel in marketing and a conversion funnel in digital transformation. Considering the integration of logistics and marketing, authors offer the definition of business echelons as stages of the consumer value creation. The theoretical contribution of this article consists in constructing a mathematical description of business echelons along the entire value chain. The developed analytical description of business echelons is acceptable both for embedding a digital management support system into various software products, and for conducting in-depth analysis and finding optimal solutions.The research of S.E.B., S.M.S. and I.V.K. is partially funded by the Ministry of Science and Higher Education of the Russian Federation under the strategic academic leadership program Priority 2030 (Agreement 075-15-2021-1333 dated 30 September 2021).Barykin, SE.; Smirnova, EA.; Chzhao, D.; Kapustina, IV.; Sergeev, SM.; Mikhalchevsky, YY.; Gubenko, AV.... (2021). Digital Echelons and Interfaces within Value Chains: End-to-End Marketing and Logistics Integration. Sustainability. 13(24):1-18. https://doi.org/10.3390/su132413929S118132

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km² resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e., offset) between in-situ soil temperature measurements, based on time series from over 1200 1-km² pixels (summarized from 8500 unique temperature sensors) across all the world’s major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in-situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world\u27s major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Albiglutide and cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease (Harmony Outcomes): a double-blind, randomised placebo-controlled trial

Background: Glucagon-like peptide 1 receptor agonists differ in chemical structure, duration of action, and in their effects on clinical outcomes. The cardiovascular effects of once-weekly albiglutide in type 2 diabetes are unknown. We aimed to determine the safety and efficacy of albiglutide in preventing cardiovascular death, myocardial infarction, or stroke. Methods: We did a double-blind, randomised, placebo-controlled trial in 610 sites across 28 countries. We randomly assigned patients aged 40 years and older with type 2 diabetes and cardiovascular disease (at a 1:1 ratio) to groups that either received a subcutaneous injection of albiglutide (30–50 mg, based on glycaemic response and tolerability) or of a matched volume of placebo once a week, in addition to their standard care. Investigators used an interactive voice or web response system to obtain treatment assignment, and patients and all study investigators were masked to their treatment allocation. We hypothesised that albiglutide would be non-inferior to placebo for the primary outcome of the first occurrence of cardiovascular death, myocardial infarction, or stroke, which was assessed in the intention-to-treat population. If non-inferiority was confirmed by an upper limit of the 95% CI for a hazard ratio of less than 1·30, closed testing for superiority was prespecified. This study is registered with ClinicalTrials.gov, number NCT02465515. Findings: Patients were screened between July 1, 2015, and Nov 24, 2016. 10 793 patients were screened and 9463 participants were enrolled and randomly assigned to groups: 4731 patients were assigned to receive albiglutide and 4732 patients to receive placebo. On Nov 8, 2017, it was determined that 611 primary endpoints and a median follow-up of at least 1·5 years had accrued, and participants returned for a final visit and discontinuation from study treatment; the last patient visit was on March 12, 2018. These 9463 patients, the intention-to-treat population, were evaluated for a median duration of 1·6 years and were assessed for the primary outcome. The primary composite outcome occurred in 338 (7%) of 4731 patients at an incidence rate of 4·6 events per 100 person-years in the albiglutide group and in 428 (9%) of 4732 patients at an incidence rate of 5·9 events per 100 person-years in the placebo group (hazard ratio 0·78, 95% CI 0·68–0·90), which indicated that albiglutide was superior to placebo (p<0·0001 for non-inferiority; p=0·0006 for superiority). The incidence of acute pancreatitis (ten patients in the albiglutide group and seven patients in the placebo group), pancreatic cancer (six patients in the albiglutide group and five patients in the placebo group), medullary thyroid carcinoma (zero patients in both groups), and other serious adverse events did not differ between the two groups. There were three (<1%) deaths in the placebo group that were assessed by investigators, who were masked to study drug assignment, to be treatment-related and two (<1%) deaths in the albiglutide group. Interpretation: In patients with type 2 diabetes and cardiovascular disease, albiglutide was superior to placebo with respect to major adverse cardiovascular events. Evidence-based glucagon-like peptide 1 receptor agonists should therefore be considered as part of a comprehensive strategy to reduce the risk of cardiovascular events in patients with type 2 diabetes. Funding: GlaxoSmithKline

Global maps of soil temperature.

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Resource queueing system M/M/∞ in random environment

In this paper the resource queueing system operating in a random environment is considered. When the environment changes its state, the arrival rate, the service rate, and resource requirements are changed. An approximation for the characteristic function of the probability distribution of the total amount of occupied resource is derived under the asymptotic condition of growing arrival rate and extremely frequent changes in the states of the random environment