Forecasting Practices in Textile and Apparel Export Industry a Systematic Review

Sustainability in textile and apparel is an ideal that requires organizational effort starting from eco-design, encompassing manufacturing, distribution, and consumption. However, in the circular economy, the idea further goes to reuse the raw material. Sustainability is still an evolving subject in apparel and textile, which needs to investigate from many angles. Excess inventory at the supplier's end also impacts sustainability and needs due attention from researchers and practitioners to ponder. Applying the correct forecast technique and minimum errors results in better financial performance and reduced environmental pollution, impacting the triple bottom line in the true sense. The current study uses a systematic review on textile and apparel forecasting, highlighting the earlier research, thus contributing to the literature on sustainability and supply chain management.

Ideology Restart: Liberal Project and Pandemic. Lessons of 2020

The present article is an abridged version of the paper of the “Russian Idea” site editors staff devoted to the ideological transformation in the Western countries and Russia in 2020, – the year of COVID pandemic and dramatic elections in the USA. The paper was drawn before the results of the elections were made public, but still it contains the correct forecast of the electoral victory of the liberal establishment representative. The authors also made the hypothesis confirmed by further course of events, that the winner of the ideological contest of 2020 would be the ideology described by the authors as the “new Atlantism”, – the doctrine about the Atlantic coalition interests priority over the national interests of the countries composing the coalition. The paper also forecasts the defeat of populism and Trumpism: in fact, having initiated the new cold war against China the supporters of those trends in the USA will surrender the initiative to their ideological opponents who are much more experienced in leading cold wars. On the basis of these conclusions the authors make the assumption of which new ideologies opposing the liberal establishment would be adopted by the conservative-minded intellectuals, and which conservative strategies could be of current importance for Russia as well.

Extended Range Forecast of Monsoon at Smaller Spatial Domains over India for Application in Agriculture

The performance of operational extended range forecast (ERF) issued by IMD is evaluated for the southwest monsoon 2020. The early onset of monsoon over the Bay of Bengal and the normal onset of monsoon over Kerala with slightly rapid progress northward are very well captured in the ERF with two to three weeks lead time. The ERF also captured very well the transitions from normal to weaker phase of monsoon in July, the active phase of monsoon in entire August was well anticipated in ERF with a lead time of 3 weeks. The active monsoon condition in the second half of September associated with delayed withdrawal from northwest India was also reasonably well captured in the ERF. Quantitatively, the ERF shows significant skill up to three weeks on all India levels. The spatial distribution of met-subdivision level mean forecast skill of predicting above normal, normal and below normal categories in terms of correct (forecast and observed category matching) and partially correct (forecast and observed category out by one category) for the 36 subdivisions during the entire monsoon season of 2020 is found to be 89%, 83%, 80% and 78% for week 1 to week 4 forecasts respectively. The wrong forecasts (forecast and observed category out by two categories) are found to be between 11% in week 1 to 22% in week 4 forecast. Thus, the met-subdivision level forecast shows useful skill and is being used operationally for agrometeorological advisory services of IMD.

Performance Evaluation of a Nowcasting Modelling Chain Operatively Employed in Very Small Catchments in the Mediterranean Environment for Civil Protection Purposes

The Hydro-Meteorological Centre (CMI) of the Environmental Protection Agency of Liguria Region, Italy, is in charge of the hydrometeorological forecast and the in-event monitoring for the region. This region counts numerous small and very small basins, known for their high sensitivity to intense storm events, characterised by low predictability. Therefore, at the CMI, a radar-based nowcasting modelling chain called the Small Basins Model Chain, tailored to such basins, is employed as a monitoring tool for civil protection purposes. The aim of this study is to evaluate the performance of this model chain, in terms of: (1) correct forecast, false alarm and missed alarm rates, based on both observed and simulated discharge threshold exceedances and observed impacts of rainfall events encountered in the region; (2) warning times respect to discharge threshold exceedances. The Small Basins Model Chain is proven to be an effective tool for flood nowcasting and helpful for civil protection operators during the monitoring phase of hydrometeorological events, detecting with good accuracy the location of intense storms, thanks to the radar technology, and the occurrence of flash floods.

Concerning the informativeness of the methods for predicting prognosis of peritonitis

The analysis of literature data on the most common general clinical and specific systems for predicting the outcome of peritonitis was carried out. The informativeness of the methods for predicting the outcome of peritonitis was performed according to the parameters of sensitivity and specificity (Se and Sp). The sum of the proportions of correct forecast results characterizes the accuracy of the method (Ac). We also calculated the predictive value of a positive result (PPV; hereinafter referred to as predictive value), which is the proportion (probability) of unfavorable outcomes among all patients with a high risk of death. The article also demonstrates the assessment of predictive ability using the example of four common scales for predicting the outcome of secondary peritonitis. The results of the study showed that none of the studied systems for predicting the outcome of peritonitis is universal and absolutely reliable. Despite the fact that all the studied systems (scales) are used in clinical practice, none of them can completely satisfy surgeons, primarily in the choice of access, the volume of surgery and the option for its completion.

Assessment of clay soils strengthening under triaxial cyclic loading

One of the main issues in the design of foundations is the correct forecast of the development of long-term deformations (settlement) of the foundations. Among the main factors affecting settlement, one can single out the neglect of changes in the rheological parameters of the soil in time and the neglect of the loading regime, as well as the processes of soil hardening in time. The work presents the results of a study of the behaviour of samples of clayey soil with a disturbed structure under conditions of triaxial cyclic loading, taking into account the strengthening effect. The relevance of the problem of studying the hardening process is associated with the assessment of the deformability and strength of clays with rheological properties. The tests were carried out in a pneumatic stabilimeter with an artificial soil created of paste. After preparing the samples, they were placed in a sealed container until the start of the test for a period of 1 to 5 days. To establish the ultimate strength of the soil, single static loads were carried out at a given value of the uniform pressure. The main control characteristics during the tests were the maximum and minimum values of the cycle voltages. If the sample did not fail during cyclic loading, it was destroyed by a stepped increase in the static load. The dependences of the change in the strength of a clay soil sample during the holding time before the start of the test were obtained at various values of all-round compression and the amplitude of the cycle. The strength of the soil with a change in the holding time increased up to 1.27 times.

Using Analysis Corrections to Address Model Error in Atmospheric Forecasts

This paper illustrates that analysis corrections, when applied as a model tendency term, can be used to improve nonlinear model forecasts and are consistent with the hypothesis that they represent an additive 6-h accumulation of model error. Comparison of mean analysis corrections with observational estimates of bias further illustrates the fidelity with which mean analysis corrections capture the model bias. While most previous implementations have explored the use of analysis corrections to correct forecast biases in short-range forecasts, this is the first implementation of the correction method using both a seasonal mean and random analysis correction for medium-range forecasts (out to 10 days). Overall, the analysis correction–based perturbations are able to improve forecast skill in ensemble and deterministic systems, especially in the first 5 days of the forecast where bias and RMSE in both lower-tropospheric temperature and 500 hPa geopotential height are significantly improved across all experiments. However, while the method does provide some significant improvement to forecast skill, some degradation in bias can occur at later lead times when the average bias at analysis time trends toward zero over the length of the forecast, leading to an overcorrection by the analysis correction–based additive inflation (ACAI) method. Additionally, it is shown that both the mean and random component of the ACAI perturbations play a role in adjusting the model bias, and that the two components can have a complicated and sometimes nonlinear interaction.

Analyzing the accuracy of bank reliability assessment based on official reporting

Subject. The article considers ratings of banks' reliability. Objectives. The aim is to evaluate the accuracy of existing methodology for bank reliability assessment based on official reporting, to identify patterns between indicators and factors that can affect the financial sustainability of a bank. Methods. The study draws on the comparative analysis of key indicators of bank's financial statements one year prior to the introduction of provisional administration, and evaluates the results of existing methods for analyzing the financial standing of banks. Results. The findings show that those methods that use only official reporting to assess the reliability of banks are not sufficient for short-term forecasting of financial stability. Ratings of the majority of agencies that rest on official reporting have a high percentage of erroneous results, therefore, rating agencies are not able to predict the regulator's decisions regarding a credit institution. Conclusions. Currently, there are no universal methods to determine reliability, which would provide a correct forecast of deteriorated financial position of the bank. It is important to use a systems approach, where financial reporting is not a key component.

Economic-mathematical model for predicting financial market dynamics

Study purpose.Existing approaches to forecasting dynamics of financial markets, as a rule, reduce to econometric calculations or technical analysis techniques, which in turn is a consequence of preferences among specialists, engaged in theoretical research and professional market participants, respectively. The main study purpose is developing a predictive economic-mathematical model that allows combining both approaches. In other words, this model should be estimated using traditional methods of econometrics and, at the same time, take into account the impact on the pricing process of the effect of clustering participants on behavioral patterns, as the basis of technical analysis. In addition, it is necessary that the created economic-mathematical model should take into account the phenomenon of existing historical trading levels and control the influence they exert on price dynamics, when it falls into local areas of these levels. Such analysis of price behavior patterns in certain areas of historical repeating levels is a popular approach among professional market participants. Besides, an important criterion of developing model’s potential applicability by a wide range of the interested specialists is its general functional form’s simplicity and, in particular, its components.Materials and methods. In the study, the market of the pound sterling exchange rate against the US dollar (GBP/USD) for the whole period of 2017 was chosen as the considered financial series, in order to forecast it. The presented economic-mathematical model was estimated by classical Kalman filter with an embedded neural network. The choice of these assessment tools can be explained by their wide capabilities in dealing with non-stationary, noisy financial market time series. In addition, applying Kalman filter is a popular technique for estimation local-level models, which principle was implemented in the newly model, proposed in article.Results. Using chosen approach of simultaneous applying Kalman filter and artificial neural network, there were obtained statistically significant estimations of all model’s coefficients. The subsequent model application on GBP/USD series from the test dataset allowed demonstrating its high predictive ability comparing with added random walk model, in particular judging by percentage of correct forecast directions. All received results have confirmed that constructed model allows effectively taking into account structural features of considered market and building good forecasts of future price dynamics.Conclusion. The study was focused on developing and improving apparatus of forecasting financial market prices dynamics. In turn, economic-mathematical model presented in that paper can be used both by specialists, carrying out theoretical studies of pricing process in financial markets, and by professional market participants, forecasting the direction of future price movements. High percentage of correct forecast directions makes it possible to use proposed model independently or as a confirmatory tool.