A trend extraction method based on logistic functions and envelopes

A method of step characteristic trend extraction based on logistic functions and envelopes (LFEs) is proposed in this paper. Compared with the existing trend extraction methods, the LFE method can determine the starting position of the step trend using a logistic function and extract the local trend using upper and lower envelopes. This method enhances the extraction accuracy and reduces the computation time. To verify the effectiveness of the LFE method, a simulated signal with a step trend feature was compared with the five-spot triple smoothing method, wavelet transform method and empirical mode decomposition-based method. All of these methods were applied to a real shock signal. The results demonstrate that the LFE method can reliably and accurately extract the trends with step characteristics based on less prior knowledge. Moreover, the proposed technique is suitable for industrial online applications.

Fractal aspects of natural growth

The work presented here puts forward a fractal aspect of natural growth. The S-shaped pattern of a logistic function is analyzed in terms of several constituent logistic functions. The approach sheds light on two familiar phenomena: the undulatory evolution of growth, which gives rise to an alternation between high-and low-growth periods, and the increasingly noticeable shrinking life cycle of products. There are some economic and political implications for the European nations. A quantitative example is given for computer sales from Digital Equipment Corporation. The approach is further generalized to suggest that any growth process can be analyzed in terms of natural-growth subprocesses. Applied to human growth this analysis yields precise definitions for the timing of transitions such as babyhood to childhood, and childhood to adolescence.

The approximation of an isolated epidemic process wave using a combination of exponents

The most commonly used methods for the medium- and long-term forecasting of epidemic processes are based on the classical SIR (susceptible – infected – recovered) model and its numerous modifications. In this approach, the dynamics of the epidemic is approximated using the solutions of differential or discrete equations. The forecasting methods based on the approximation of data by functions of a given class are usually focused on obtaining a short-term forecast. They are not used for the long-term forecasts of epidemic processes due to their insufficient efficiency for forecasting nonstationary processes. In this paper, we formulated a hypothesis that the primary waves of the COVID-19 pandemic, which took place in a number of European countries, including the Republic of Belarus, in the spring-summer of 2020 are isolated and therefore can be regarded as processes close to stationary. On the basis of this hypothesis, a method of approximating isolated epidemic process waves by means of generalized logistic functions with an increased number of exponents was proposed. The developed approach was applied to predict the number of infected people in the Republic of Belarus for the period until August 2020 based on data from the beginning of the epidemic until June 12, 2020.

Omnichannelling and the Predominance of Big Retailers in the post-Covid Era

The Covid 19 pandemic has profoundly affected the competitive context of retail companies. Multiple changes have occurred both in consumer behaviour, in retail strategy and marketing channels. In particular, the pandemic has accelerated the use of digital technology in the processes of physical purchase and distribution of retail products, favouring new forms of integration between physical and online channels (so-called omnichannelling). Many brick&mortar stores have closed down, others have modified their original logistic functions in favor of the new digital integrated ones, with a smaller number of point of sales assuming the connotation of flagship stores with greater force. Some cases confirm the acceleration that took place towards the integration and redefinition of the roles of physical and online channels. The biggest retailers empower their predominance in the global markets. Smaller operators could renew their role by shaping new forms of collaboration to survive.

Extending the critical period for weed control model to better include weed succession using common sunflower as a mimic weed in high-yielding cotton

The critical period for weed control (CPWC) adds value to integrated weed management by identifying the period during which weeds need to be controlled to avoid yield losses exceeding a defined threshold. However, the traditional application of the CPWC does not identify the timing of control needed for weeds that emerge late in the critical period. In this study, CPWC models were developed from field data in high yielding cotton crops during three summer seasons from 2005 to 2008, using the mimic weed; common sunflower, at densities of 2 to 20 plants m−2. Common sunflower plants were introduced at up to 450 growing degree days (GDD) after crop planting and removed at successive 200 GDD intervals after introduction. The CPWC models were described using extended Gompertz and logistic functions that included weed density, time of weed introduction and time of weed removal (logistic function only) in the relationships. The resulting models defined the CPWC for late emerging weeds, identifying a period after weed emergence before weed control was required to prevent yield loss exceeding the yield-loss threshold. Where weeds emerged in sufficient numbers toward the end of the critical period, the model predicted that crop yield loss resulting from competition by these weeds would not exceed the yield-loss threshold until well after the end of the CPWC. These findings support the traditional practice of ensuring weeds are controlled before crop canopy closure, with later weed control inputs used as required.

Logistic Wavelets and Their Application to Model the Spread of COVID-19 Pandemic

In the present paper, we model the cumulative number of persons, reported to be infected with COVID-19 virus, by a sum of several logistic functions (the so-called multilogistic function). We introduce logistic wavelets and describe their properties in terms of Eulerian numbers. Moreover, we implement the logistic wavelets into Matlab’s Wavelet Toolbox and then we use the continuous wavelet transform (CWT) to estimate the parameters of the approximating multilogistic function. Using the examples of several countries, we show that this method is effective as a method of fitting a curve to existing data. However, it also has a predictive value, and, in particular, allows for an early assessment of the size of the emerging new wave of the epidemic, thus it can be used as an early warning method.

Challenges in Raw Material Treatment at the Mechanical Processing Stage

This paper concerns problems related to the mechanical processing of mineral raw materials. The aspects explored were limited to the analysis of comminution technologies in terms of their effectiveness and energy consumption, modeling and simulation approaches, the assessment of crushing results, and environmental aspects. This article includes investigation of new technologies of comminution, comparing HPGR, high-voltage pulses, and electromagnetic mills. In the area of modeling and optimization, special attention was paid to the approximation of the particle size distribution of crushing products by means of Weibull, log-normal, and logistic functions. Crushing products with an increased content of fines were well characterized by Weibull’s distribution, while log-normal function adequately described HPGR products with a relatively low content of fines.

Models For Forecasting The Number Of Russian Grandparents

Russian demographic statistics does not provide information about the number of grandparents. The aim of our study is to present models for forecasting their number. We used data from the Human Fertility Database to estimate the average age of a mother at the birth of her first child. Based on the simulated age of Russian women’s entry into grandparenthood, the time series of the number of Russian grandmothers was created. To obtain prospective estimates of the number of Russian grandmothers, we tested various models used in demography to forecast population size – mathematical (based on exponential and logistic functions) and statistical (based on statistical characteristics of time series). To estimate the number of grandmothers who are significantly involved in caring for grandchildren, we used data from the Federal statistical survey. Our results are as follows: 1) there is an increase in the age of entry into grandparenthood; 2) we estimated the size of potential grandmothers in different years and we found two models which are more appropriate for forecasting: linear trend model and average absolute growth model; 3) using these models, we predicted an increase in the number of both potential and active grandmothers in the next 5 years.

EXAMINATION OF THE EFFECT OF LOGISTICS FUNCTIONS ON FINANCIAL PERFORMANCE OF ORGANIZATION

The contributions of logistics functions to the performance of an organization have been the subject of research over the years. Thus, this present study further examined the effect of outbound logistics functions on financial performance of quoted manufacturing companies in Nigeria. Panel data regression analysis was employed to test the effect of logistics functions on financial performance of the selected companies over a period of five years (2015-2019). Logistic functions costs and financial performance indicators were extracted from secondary data. The findings of the study showed that logistics function has a positive and significant effect on financial performance of manufacturing companies in Nigeria. Therefore, the companies are implored to pay more attention to logistics functions when aiming at a better financial performance.