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The growing amount of electronic waste (e-waste) poses considerable risks to the environment and human health, especially when treated inadequately. However, it is difficult to assess the significance of these issues without quantitative understanding of spatiotemporal patterns of e-waste generation. This paper proposes a new model to estimate in-use stock of electric household appliances (HAs) and e-waste generation at the level of 1 km × 1 km grids by coupling geographic information system (GIS) and material flow analysis (MFA). We took Xiamen, a rapidly urbanized city in China, as a case and the results showed that demands for HAs increased from 1980, peaked in 2016, and then declined. In-use HAs exhibited a logistic growth and significantly increased in both spatial extent and intensity. E-waste generation kept rising until 2019, and its spatial center expanded outward from downtown to suburban areas. Our study highlights that a dynamic and spatial model is useful for designing effective policies for e-waste management by providing spatiotemporal details of e-waste types and generation magnitudes and explicitly recognizing generation hotspots in cities.

The literature contains different methods for estimating logistic functions parameters to fit the power‐speed characteristics of a wind turbine (WT). However, their disadvantages are: (1) they require a large amount of supervisory control and data acquisition data; (2) the parameter range needed for constrained optimization is not systematically determined; and/or (3) they do not guarantee monotonically increasing relationship between wind speed and predicted WT output power. This paper proposes a systematic approach to fitting the five‐ and the six‐parameter logistic functions to power‐speed data of WTs. The authors introduce new limits on the parameters of these functions, which guarantee their monotonicity. Most of these limits are determined analytically and are obtained from manufacturer datasheets. Afterwards, these limits are passed as optimization constraints for subspace trust region algorithm. The results show that the authors' method generally provides better accuracy with mean absolute percentage error values below 0.02 for the five‐parameter logistic function and below 0.005 for the six‐parameter logistic function. The authors present accurate fits for a group of WTs of different ratings (from 275 kW to 3000 kW) and 12 unique manufacturers, which proves the versatility of the authors' method.

Background A novel coronavirus disease (COVID-19) has caused huge damage to public health around the world. Revealing the transmission dynamics of COVID-19 and control efficiency is important for containing the spread of the virus. Methods By using a logistic growth model, we estimated the transmission parameters of COVID-19 in China and six other countries (Republic of Korea, Iran, Italy, Spain, France and Germany). The transmission parameters represent the maximum daily increase rate in the early stages of the epidemic and the control efficiency under human intervention. The control efficiency was determined by the significant decrease of the daily increase rate in time and cumulative cases. Results We found the daily increase rate of cumulative cases of COVID-19 decreased significantly in both time and cumulative cases in all countries, but the decreasing trend was not further reduced in other countries except for China and Republic of Korea. The response of the daily increase rate to control measures was much earlier than the number of new cases. Conclusions Our results suggested that lockdown at the epicenter and social distancing effectively reduced the spread of COVID-19 in the early stage, but identification and isolation of patients, suspected cases and people with close contact at a community level is essential in further reduction of the daily increase rate of COVID-19.

Background: Recent Coronavirus Disease 2019 (COVID-19) pandemic has inflicted the whole world critically. Although India has been listed amongst the top ten highly affected countries to date, one cannot rule out COVID-19 associated complications in the near future. Aim & Objective: We aim to build the COVID-19 severity model employing logistic function which determines the inflection point and help in the prediction of the future number of confirmed cases. Methods and Material: An empirical study was performed on the COVID-19 patient status in India. We performed the study commencing from 30 January 2020 to 12 July 2020 for the analysis. Exploratory data analysis (EDA) tools and techniques were applied to establish a correlation amongst the various features. The acute stage of the disease was mapped in order to build a robust model. We collected five different datasets to execute the study. Results: We found that men were more prone to get infected with the coronavirus disease as compared to women. On 165-days based analysis, we found a trending pattern of confirmed, recovered, deceased and active cases of COVID-19 in India. The as-developed growth model provided an inflection point of 72.0 days. It also predicted the number of confirmed cases as 17,80,000.0 in the future i.e. after 12th July. A growth rate of 32.0 percent was obtained. We achieved statistically significant correlations amongst growth rate and predicted COVID-19 confirmed cases. Conclusions: This study demonstrated the effective application of EDA and analytical modeling in building a mathematical severity model for COVID-19 in India. © 2021, Indian Association of Preventive and Social Medicine. All rights reserved.

Modelling stated preferences is an almost mystical science and as there is no data explaining how the sustainable feature in homes would effectively encourage homebuyers to invest in sustainable housing, it is important to investigate the buyers’ willingness to pay (WTP) for sustainable housing. The study of stated preferences often requires the use of specialised software or proprietary programs, which can be difficult and/or expensive to use. This study proposes to re-purpose the ‘support.CEs’ package, a program written in the R programming language, from its agronomic roots to measure home buyer preferences for sustainable housing. These are demonstrated through a stated preference discrete choice experiment of choosing model houses with differing levels of energy savings, renewable energy generation, landscaping, soundproofing, ventilation, and price differences. A pilot study was performed using an online survey, constructed using the LMA design tool provided in the ‘support.CEs’ package. The survey was also separated into six blocks of six questions each to reduce the cognitive burden on respondents. The survey was distributed through social media channels. Preliminary results with a limited sample of 20 respondents with mixed income, age, and occupational demographics, analysed using the package’s clogit function, that performs conditional logit estimations, have shown that the results have a statistically reliable adjusted rho-squared value and that all coefficients show the expected signs. From this study, it can be concluded that the ‘support.CEs’ package can be used to model home buyer preferences and that adequate blocking allows for the measurement of a higher number of variables despite having smaller sample sizes.

To understand and approach the spread of the SARS-CoV-2 epidemic, machine learning offers fundamental tools. This study presents the use of machine learning techniques for projecting COVID-19 infections and deaths in Mexico. The research has three main objectives: first, to identify which function adjusts the best to the infected population growth in Mexico; second, to determine the feature importance of climate and mobility; third, to compare the results of a traditional time series statistical model with a modern approach in machine learning. The motivation for this work is to support health care providers in their preparation and planning. The methods compared are linear, polynomial, and generalized logistic regression models to describe the growth of COVID-19 incidents in Mexico. Additionally, machine learning and time series techniques are used to identify feature importance and perform forecasting for daily cases and fatalities. The study uses the publicly available data sets from the John Hopkins University of Medicine in conjunction with the mobility rates obtained from Google’s Mobility Reports and climate variables acquired from the Weather Online API. The results suggest that the logistic growth model fits best the pandemic’s behavior, that there is enough correlation of climate and mobility variables with the disease numbers, and that the Long short-term memory network can be exploited for predicting daily cases. Given this, we propose a model to predict daily cases and fatalities for SARS-CoV-2 using time series data, mobility, and weather variables.

This article uses precise and novel data on country-level Cloud IaaS and PaaS revenue to measure the intensive margin of technology diffusion across countries and within countries over time. We horse race diffusion models and find that cloud diffusion exhibits both Log-Log and Logistic Growth patterns. We use cross validation on nearly 100 features to determine what correlates with cross-country differences. We find that increases in features impacting Gross Domestic Product, Internet Connectivity, and Human Capital are associated with increases in intensity of cloud adoption. We finally compare the relative impacts of these variables using a random coefficients model. Although correlative, our algorithmic research design motivates data-driven hypothesis generation and further causal work regarding how policymakers can encourage more cloud computing adoption and technology adoption more broadly.

Merging behavior requires multiple tasks such as cognition, decision-making, and driving operation. Previously, driving assistance systems, which instruct drivers on making accelerations, have been studied to support the decision-making task. The importance of improving driver comfort with adjusting system variables has been revealed through these studies. The present study aims to propose assistance methods for merging, which decreases driver’s workload and difficulty in decision-making. The proposed methods recognize drivers’ decision ambiguity using a decision-making model for respective drivers and instruct them on acceleration to decrease the ambiguity. First, we develop a decision-making model to predict where drivers merge based on a logistic function. Furthermore, we propose acoustic assistance methods, which instruct the acceleration and deceleration. The systems continuously calculate the optimal instruction based on driving history from the beginning of the assistance. Driving simulator experiments demonstrated that drivers’ workload and decision ambiguity decreased with our proposed methods.

The ongoing depletion of fossil resources, the energetic autonomy of countries, soaring prices for petroleum and climate change have stimulated research and development on renewable energy as biofuels. In this work, a bibliometric analysis of the Web of Science database was carried out to identify the research related to sugar beet to biofuels. The equation logistic was used to quantitatively describe the growth of the publication of sugar beet in the biofuels field. The results show that the publications of sugar beet for biofuel have a rate of growth of 0-1898 year-1. Germany and the USA were the countries with high influence for research in the field of sugar beet for biofuels. The journal Zuckerindustrie was the referent to publications in the field of study. From 2003 to 2019, the exponential growth of publications was found, this profile of growth can be attributed to the development of renewable energy and the relevance of global warming, security energetic and laws that promoted clean energy. This work shows that the logistic equation can be used to predict the evolution of publications in the field of study.Keywords: Beta vulgaris, beets, root beets, biofuel, fodder beetJEL Classifications: Q16, Q20, Q42DOI: https://doi.org/10.32479/ijeep.11013

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.

MicroRNAs (miRNAs) exert an enormous influence on cell differentiation, biological development and the onset of diseases. Because predicting potential miRNA-disease associations (MDAs) by biological experiments usually requires considerable time and money, a growing number of researchers are working on developing computational methods to predict MDAs. High accuracy is critical for prediction. To date, many algorithms have been proposed to infer novel MDAs. However, they may still have some drawbacks. In this paper, a logistic weighted profile-based bi-random walk method (LWBRW) is designed to infer potential MDAs based on known MDAs. In this method, three networks (i.e., a miRNA functional similarity network, a disease semantic similarity network and a known MDA network) are constructed first. In the process of building the miRNA network and the disease network, Gaussian interaction profile (GIP) kernel is computed to increase the kernel similarities, and the logistic function is used to extract valuable information and protect known MDAs. Next, the known MDA matrix is preprocessed by the weighted K-nearest known neighbours (WKNKN) method to reduce the number of false negatives. Then, the LWBRW method is applied to infer novel MDAs by bi-randomly walking on the miRNA network and the disease network. Finally, the predictive ability of the LWBRW method is confirmed by the average AUC of 0.939 3 (0.006 1) in 5-fold cross-validation (CV) and the AUC value of 0.976 3 in leave-one-out cross-validation (LOOCV). In addition, case studies also show the outstanding ability of the LWBRW method to explore potential MDAs.

Predicting time-dependent survival probability of a breast cancer patient using information such as primary tumor size, grade, node spread status, and patient age at the time of surgery can be of immense help in managing life expectations and strategizing postoperative treatment. However, for moderate-sized clinical datasets the application of standard Kaplan-Meier theory to determine survival probability as a function of multiple cofactors can become challenging when continuous variables like tumor diameter and survival time are segmented into a large number of narrow intervals, a problem commonly termed the curse of dimensionality. We circumvent this problem by modeling the patient-to-patient distribution of primary tumor diameter with a realistic, right-skewed function, and then matching the diameter-marginalized survival with the mean Kaplan-Meier survival for the data. We apply this procedure on a recent clinical data from 1875 breast cancer patients and develop parameters that can be readily used to estimate post-surgery survival for an arbitrary time length. Finally, we show that the observed fraction of node-positive patients can be quantitatively explained within a simple tumor growth and metastasis framework. Employing two different tumor growth models from the literature (i.e., Gompertz and logistic growth models), we utilize the observed fraction-node-positive data to determine metastasis rates from the surface of a primary tumor and its patient-to-patient distribution.

The biggest challenge in the world is population growth and determining how society and the state adapt to it as it directly affects the fundamental human rights such as food, clothing, housing, education, medical care, etc. The population estimates of any country play an important role in making the right decision about socio-economic and population development projects. Unpredictable population growth can be a curse. The purpose of this research article is to compare the accuracy process and proximity of three mathematical model such as Malthusian or exponential growth model, Logistic growth model and Least Square model to make predictions about the population growth of Bangladesh and India at the end of 21st century. Based on the results, it has been observed that the population is expected to be 429.32(in million) in Bangladesh and 3768.53 (in million) in India by exponential model, 211.70(in million) in Bangladesh and 1712.94(in million) in India by logistic model and 309.28 (in million) in Bangladesh and 2686.30 (in million) in India by least square method at the end of 2100. It was found that the projection data from 2000 to 2020 using the Logistic Growth Model was very close to the actual data. From that point of view, it can be predicted that the population will be 212 million in Bangladesh and 1713 million in India at the end of the 21st century. Although transgender people are recognized as the third sex but their accurate statistics data is not available. The work also provides a comparative scenario of how the state has adapted to the growing population in the past and how they will adapt in the future.

The wind turbine power curve (WTPC) is a mathematical model built to capture the input-output relationship between the generated electrical power and the wind speed. An adequately fitted WTPC aids in wind energy assessment and prediction since the actual power curve will differ from that provided by the manufacturer due to a variety of reasons, such as the topography of the wind farm, equipment aging, and multiple system faults. As such, this paper introduces a novel approach for WTPC modeling that combines Gaussian process (GP) regression, a class of probabilistic kernel-based machine learning models, and standard logistic functions. This semi-parametric approach follows a Bayesian reasoning, in the sense of maximizing the marginal likelihood to learn the parameters and hyperparameters through a variational sparse approximation to the GP model. Using real-world operational data, the proposed approach is compared with the state-of-the-art in WTPC modeling and with an alternative probabilistic approach based on generalized linear models and logistic functions. Finally, we evaluate the proposed model in its extrapolation ability for unmodelled data.

Introduction: With the advancement in internet technology, a large amount of information in the form of data and image is transferred from one end to the other. The information may be military, defense, medical, etc. which should be kept confidential by providing security. Objective: The aim of this article will be to provide security to the image. This is achieved by applying the image encryption method which converts the original information into an unreadable format. Methods: This work explores an efficient way of image encryption using a chaotic logistic function. A set of two chaotic logistic functions and 256 bit long external secret key are employed to enhance the security in the encrypted images. The initial condition of first logistic function has been obtained by providing the suitable weights to all bits of the secret key. The initial condition of second logistic function has been derived from the first chaotic logistic function. In this proposed algorithm, ten different operations are used to encrypt the pixel of an image. The outcome of the second logistic map decides the operation to be used in the encryption of the particular image pixel. Results: Various statistical parameters like NPCR, UACI and information entropy were calculated. Conclusion: Results show that the proposed algorithm provides an image encryption method with better security and efficiency for all real-time applications such as medical images.

With the spread of the new corona virus around the world, governments of various countries have begun to use the mathematical modeling method to construct some virus transmission models assessing the risks of spatial spread of the new corona virus COVID-19, while carrying out epidemic prevention work, and then calculate the inflection point for better prevention and control of epidemic transmission. This work analyzes the spread of the new corona virus in China, Italy, Germany, Spain, and France, and explores the quantitative relationship between the growth rate of the number of new corona virus infections and time. In investigating the dynamics of a disease such as COVID-19, its mathematical representation can be constructed at many levels of details, guided by the questions the model tries to help answer. Mathematical sophistication may have to yield to a more pragmatic approach closer to the ability to make predictions that inform public health policies. Background: In December 2019, the first Chinese patients with pneumonia of unknown cause is China admitted to hospital in Wuhan, Hubei Jinyintan, since then, COVID-19 in the rapid expansion of China Wuhan, Hubei, in a few months time, COVID-19 is Soon it spread to a total of 34 provincial-level administrative regions in China and neighboring countries, and Hubei Province immediately became the hardest hit by the new corona virus. In an emergency situation, we strive to establish an accurate infectious disease retardation growth model to predict the development and propagation of COVID-19, and on this basis, make some short-term effective predictions. The construction of this model has Relevant departments are helpful for the prevention and monitoring of the new corona virus, and also strive for more time for the clinical trials of Chinese researchers and the research on vaccines against the virus to eliminate the new corona virus as soon as possible. Methods: According to the original data change law, Establish a Logistic growth model, we collect and compare and integrate the spread of COVID-19 in China, Italy, France, Spain and Germany, record the virus transmission trend among people in each country and the protest measures of relevant government departments. Findings: Based on the analysis results of the Logistic model model, the Logistic model has a good fitting effect on the actual cumulative number of confirmed cases, which can bring a better effect to the prediction of the epidemic situation and the prevention and control of the epidemic situation. Interpretation: In the early stage of the epidemic, due to inadequate anti-epidemic measures in various countries, the epidemic situation in various countries spread rapidly. However, with the gradual understanding of COVI D -19, the epidemic situation began to be gradually controlled, thereby retarding growth.

We are trying to test the capacity of a simplified macroscopic virus-centric model to simulate the evolution of the SARS CoV 2 epidemic (COVID 19) at the level of a country or a geographical entity, provided that the evolution of the conditions of its development (behaviors, containment policies) are sufficiently homogeneous on the considered territory For example, a uniformly deployed lockdown on the territory, or a sufficiently uniform overall crisis management The virus-centric approach means that we favor to model the population dynamic of the virus rather than the evolution of the human cases In other words, we model the interactions between the virus and its environment - for instance a specific human population with a specific behavior on a territory, instead of modeling the interactions between individuals Therefore, our approach assumes that an epidemic can be analyzed as the combination of several elementary epidemics which represent a different part of the population with different behaviors through time The modeling proposed here is based on the finite superposition of Verhulst equations commonly known as logistic functions and used in dynamics of population Modelling the lockdown effect at the macroscopic level is therefore possible Our model has parameters with a clear epidemiological interpretation, therefore the evolution of the epidemic can be discussed and compared among four countries: Belgium, France, Italy, and Spain Parameter optimization is carried out by a classical machine learning process We present the number of infected patients with SARS-CoV-2 and a comparison between data from the European Centre for Disease Prevention and Control and the modeling In a general formulation, the model is applicable to any country with similar epidemic management characteristics These results show that a simple two epidemics decomposition is sufficient to simulate with accuracy the effect of a lockdown on the evolution of the COVID-19 cases

Surgical invasion activates nociception, while anesthesia suppresses it. Under general anesthesia, stimulation, which is the balance between nociception and anti-nociception, induces responses, including activation of the autonomic nervous system. To evaluate the associations between stimulation (S) and the resultant responses (R), we examined R values, which were calculated using mathematical models of Stevens’ power law, Gompertz function and logistic function. The previously developed Nociceptive Response (NR) formula was applied as a modified logistic model. S values were calculated using a linear function in the NR formula. In a retrospective study, we developed an exponential model of Stevens’ power law and a sigmoidal model of Gompertz function using differential equations, by adjusting R values to correspond to NR values, in consecutive patients undergoing surgery under general anesthesia (n = 4,395). In a subsequent prospective study, we validated the superiority of R values of Gompertz function and the NR formula in an exponential model in adult patients undergoing tympanoplasty (n = 141) and laparoscopic cholecystectomy (n = 86). In conclusion, both modified logistic function and Gompertz function are likely appropriate mathematical models for representing responses to stimulation resulting from the balance between nociception/anti-nociception during surgical procedures under general anesthesia.

The Human System is within the Earth System. They should be modeled bidirectionally coupled, as they are in reality. The Human System is rapidly expanding, mostly due to consumption of fossil fuels (approximately one million times faster than Nature accumulated them) and fossil water. This threatens not only other planetary subsystems but also the Human System itself. Carrying Capacity is an important tool to measure sustainability, but there is a widespread view that Carrying Capacity is not applicable to humans. Carrying Capacity has generally been prescribed a priori, mostly using the logistic equation. However, the real dynamics of human population and consumption are not represented by this equation or its variants. We argue that Carrying Capacity should not be prescribed but should insteadbe dynamically derived a posteriori from the bidirectional coupling of Earth System submodels with the Human System model. We demonstrate this approach with a minimal model of Human–Nature interaction (HANDY). ▪ The Human System is a subsystem of the Earth System, with inputs (resources) from Earth System sources and outputs (waste, emissions) to Earth System sinks. ▪ The Human System is growing rapidly due to nonrenewable stocks of fossil fuels and water and threatens the sustainability of the Human System and to overwhelm the Earth System. ▪ Carrying Capacity has been prescribed a priori and using the logistic equation, which does not represent the dynamics of the Human System. ▪ Our new approach to human Carrying Capacity is derived from dynamically coupled Earth System–Human System models and can be used to estimate the sustainability of the Human System.

Taking internet banking as an example, we study diffusion of cost-saving technological innovations. We show that the diffusion of internet banking follows an S-shaped logistic curve as it penetrates a log-logistic bank-size distribution. We test the theoretical hypothesis with an empirical study of internet banking diffusion among banks across fifty U.S. states. Using an instrument-variable approach, we estimate the positive effect of average bank size on internet banking diffusion. The empirical findings allow us to examine the technological, economic, and institutional factors governing the diffusion process and explain the variation in diffusion rates across geographic regions.

In the present article, we deal with a generalization of the logistic function. Starting from the Riccati differential equation with constant coefficients, we find its analytical form and describe basic properties. Then we use the generalized logistic function for modeling some economic phenomena.

The allele frequency spectrum (AFS), or site frequency spectrum, is commonly used to summarize the genomic polymorphism pattern of a sample, which is informative for inferring population history and detecting natural selection. In 2013, Chen and Chen developed a method for analytically deriving the AFS for populations with temporally varying size through the coalescence time-scaling function. However, their approach is only applicable to population history scenarios in which the analytical form of the time-scaling function is tractable. In this paper, we propose a computational approach to extend the method to populations with arbitrary complex varying size by numerically approximating the time-scaling function. We demonstrate the performance of the approach by constructing the AFS for two population history scenarios: the logistic growth model and the Gompertz growth model, for which the AFS are unavailable with existing approaches. Software for implementing the algorithm can be downloaded at http://chenlab.big.ac.cn/software/.

The article studies logistic functions, processes and procedures accompanying all stages of full physical life cycle of new durable goods. The term ‘new’ means here an original product possessing new values for customers or a product – modification, which in comparison with the basic product has higher productivity, economical and ecological characteristics and possibilities of utilization. Durable goods include technical devices, such as production equipment and transportation means. The full physical life cycle is split into stages and step of their executions. Actually each stage is connected with logistic procedures: opportunity to get new materials and components at the project stage; estimating the number of parts taken from the basic product for the product-modification at the project stage; decision-making concerning out-sourcing and in-sourcing at the pre-production stage; evaluating the stock of material and technical resources at the enterprise at the production stage; expanding the existing distribution channels and searching for new ones at the marketing stage; calculating the need in material resources for warranty service at the operation stage. The article shows also certain lines in developing advance logistics, such reverse logistics, re-cycling logistics and green logistics. These lines van serve as land marks for developers of more economical and ecological technical devices, including transportation means, storage and shop equipment, as well as for developers of low-waste and logistic technologies.

Integrated assessment models have identified carbon capture and storage (CCS) as an important technology for limiting climate change. To achieve 2 °C climate targets, many scenarios require tens of gigatons of CO2 stored per year by mid-century. These scenarios are often unconstrained by growth rates, and uncertainty in global geologic storage assessments limits resource-based constraints. Here we show how logistic growth models, a common tool in resource assessment, provide a mathematical framework for stakeholders to monitor short-term CCS deployment progress and long-term resource requirements in the context of climate change mitigation targets. Growth rate analysis, constrained by historic commercial CO2 storage rates, indicates sufficient growth to achieve several of the 2100 storage targets identified in the assessment reports of the Intergovernmental Panel on Climate Change. A maximum global discovered storage capacity of approximately 2700 Gt is needed to meet the most aggressive targets, with this ceiling growing if CCS deployment is delayed.

Pig pen is fast becoming a very lucrative system of farming in Nigeria, most especially in the South-Eastern region of the country. This form of animal farming is very cost effective and does not need much training before someone ventures into it, as pigs ...

Lifetime analyses frequently apply a parametric functional description from measured data of the Kaplan-Meier non-parametric estimate (KM) of the survival probability. The cumulative Weibull distribution function (WF) is the primary choice to parametrize the KM. but some others (e.g. Gompertz, logistic functions) are also widely applied. We show that the cumulative two-parametric Weibull function meets all requirements. The Weibull function is the consequence of the general self-organizing behavior of the survival, and consequently shows self-similar death-rate as a function of the time. The ontogenic universality as well as the universality of tumor-growth fits to WF. WF parametrization needs two independent parameters, which could be obtained from the median and mean values of KM estimate, which makes an easy parametric approximation of the KM plot. The entropy of the distribution and the other entropy descriptions are supporting the parametrization validity well. The goal is to find the most appropriate mining of the inherent information in KM-plots. The two-parameter WF fits to the non-parametric KM survival curve in a real study of 1180 cancer patients offering satisfactory description of the clinical results. Two of the 3 characteristic parameters of the KM plot (namely the points of median, mean or inflection) are enough to reconstruct the parametric fit, which gives support of the comparison of survival curves of different patient’s groups.

The paper considers a multi-stage mathematical-informational task “Tent-like and logistic functions”, the implementation of which is aimed at developing students’ creativity. The multi-stage mathematical information task (MMIS) is a specially composed sequence of tasks, exercises, problems and didactic situations that connect various types of creative activity with each other. It is carried out by the MMIZ student under the guidance of a teacher and is aimed at developing his creativity. This multi-step task contains seven steps. Mathematical, informational and artistic activities in the course of performing MMIZ are aimed at developing the most important creative qualities - originality and flexibility of thinking. Solving the tasks proposed in MMIZ, the student breaks the stereotypes of thinking, puts forward hypotheses, carries out their verification by analytical methods and illustrates the results with the help of computer experiments, which is also aimed at developing his creativity. It is important to note that as part of a multi-stage assignment, the student is invited to design educational activities that develop his creative qualities. Creative qualities are necessary for a future university graduate who will face rapidly changing conditions of the social space and the modern labor market, which will require him to be able to creatively solve complex professional problems. When performing MMIZ, the student becomes a mathematician, programmer and computer artist, which increases his motivation for the studied disciplines and understanding of their deep integration connections. When performing MMIZ, the practical importance of student research is noted - a mathematical model of population development is built.

The article is devoted to the issues of theoretical generalization and clarification of the essence and content of logistic business processes of trade enterprises, as well as the formulation of some practical recommendations for improving the management of logistic business processes of trade enterprises. The need to build a comprehensive, appropriately organized and structured system of interconnected and connected with the external economic environment logistic business processes of each (including - trade) enterprise is emphasized.Insufficient formation of the theoretical basis on the issue of the essence of logistic business processes of enterprises, their features in trade enterprises and the development of mechanisms of so-called. logistic management. The analysis revealed the presence of contradictions in the interpretation of the concept of "logistic business processes", as well as differences in species diversity, content and methods of implementation of logistic business processes of wholesale and retail trade.Based on the essence of the term "process" in economic activity, a refined author's interpretation of the business process is proposed, as well as an original basic model of business process management. The key role of the category "function of the enterprise's activity" in the formation of the typology of business processes of enterprises, in particular - trade, is proven.The objective validity and expediency of allocating a group of logistic business processes in the array of business processes of trade enterprises, which have independent significance or provide logistical support for the implementation of commercial functions of a wholesaler or retailer, has been established. The approaches of some scientists to the classification of business processes of trade enterprises are analyzed, the essence and features of logistic business processes of trade enterprises are specified. The need for clear identification and regulation of logistic business processes of trade enterprises is emphasized, author's recommendations for the formation of a list of logistic business processes, taking into account the type and scale of trade activities of the business entity, are formulated.

Supplemental Digital Content is available in the text, Objective: The Joint United Nations Programme on HIV/AIDS-supported Spectrum software package is used by most countries worldwide to monitor the HIV epidemic. In Spectrum, HIV incidence trends among adults (aged 15–49 years) are derived by either fitting to seroprevalence surveillance and survey data or generating curves consistent with case surveillance and vital registration data, such as historical trends in the number of newly diagnosed infections or AIDS-related deaths. This article describes development and application of the case surveillance and vital registration (CSAVR) tool for the 2019 estimate round. Methods: Incidence in CSAVR is either estimated directly using single logistic, double logistic, or spline functions, or indirectly via the ‘r-logistic’ model, which represents the (log-transformed) per-capita transmission rate using a logistic function. The propensity to get diagnosed is assumed to be monotonic, following a Gamma cumulative distribution function and proportional to mortality as a function of time since infection. Model parameters are estimated from a combination of historical surveillance data on newly reported HIV cases, mean CD4+ at HIV diagnosis and estimates of AIDS-related deaths from vital registration systems. Bayesian calibration is used to identify the best fitting incidence trend and uncertainty bounds. Results: We used CSAVR to estimate HIV incidence, number of new diagnoses, mean CD4+ at diagnosis and the proportion undiagnosed in 31 European, Latin American, Middle Eastern, and Asian-Pacific countries. The spline model appeared to provide the best fit in most countries (45%), followed by the r-logistic (25%), double logistic (25%), and single logistic models. The proportion of HIV-positive people who knew their status increased from about 0.31 [interquartile range (IQR): 0.10–0.45] in 1990 to about 0.77 (IQR: 0.50–0.89) in 2017. The mean CD4+ at diagnosis appeared to be stable, at around 410 cells/μl (IQR: 224–567) in 1990 and 373 cells/μl (IQR: 174–475) by 2017. Conclusion: Robust case surveillance and vital registration data are routinely available in many middle-income and high-income countries while HIV seroprevalence surveillance and survey data may be scarce. In these countries, CSAVR offers a simpler, improved approach to estimating and projecting trends in both HIV incidence and knowledge of HIV status.

Considering the retarding effect of natural resources, environmental conditions, and other factors on network growth, the capacity of network nodes to connect to new edges is generally limited. Inspired by this hindered growth of many real-world networks, two types of evolving network models are suggested with different logistic growth schemes. In the global and local logistic network, the total number of network edges and the number of edges added into the network at each step are in line with the Logistic growth, respectively. The most exciting feature of the Logistic growth network is that the growth rule of network edges is first fast, then slow and finally reaches the saturation value [Formula: see text]. Theoretical analysis and numerical simulation reveal that the node degrees of two new networks converge to the same results of the BA scale-free network, [Formula: see text], as the growth rate [Formula: see text] approaches to 0. The local logistic network follows a bilateral power-law degree distribution with a given value of [Formula: see text]. Meanwhile, for these two networks, it is found that the greater [Formula: see text] and [Formula: see text], the smaller the average shortest paths, the greater the clustering coefficients, and the weaker the disassortativity. Additionally, compared to the local logistic growth network, the clustering feature of the global logistic network is more obvious.

A simple model for predicting Coronavirus Disease 2019 (COVID-19) epidemic is presented in this study. The prediction model is presented based on the classic Susceptible-Infectious-Recovered (SIR) model, which has been widely used to describe the epidemic time evolution of infectious diseases. The original version of the Kermack and McKendrick model is used in this study. This included the daily rates of infection spread by infected individuals when these individuals interact with a susceptible population, which is denoted by the parameter β, while the recovery rates to determine the number of recovered individuals is expressed by the parameter γ. The parameters estimation of the three-compartment SIR model is determined through using a mathematical sequential reduction process from the logistic growth model equation. As the parameters are the basic characteristics of epidemic time evolution, the model is always tested and applied to the latest actual data of confirmed COVID-19 cases. It seems that this simple model is still reliable enough to describe the dynamics of the COVID-19 epidemic, not only qualitatively but also quantitatively with a high degree of correlation between actual data and prediction results. Therefore, it is possible to apply this model to predict cases of COVID-19 in several countries. In addition, the parameter characteristics of the classic SIR model can provide information on how these parameters reflect the efforts by each country to prevent the spread of the COVID-19 outbreak. This is clearly seen from the changes of the parameters shown by the classic SIR model.

Resource recovery from human excreta can advance circular economies while improving access to sanitation and renewable agricultural inputs. While national projections of nutrient recovery potential provide motivation for resource recovery sanitation, elucidating generalizable strategies for sustainable implementation requires a deeper understanding of country-specific overlap between supply and demand. For 107 countries, we analyze the colocation of human-derived nutrients (in urine) and crop demands for nitrogen, phosphorus, and potassium. To characterize colocation patterns, we fit data for each country to a generalized logistic function. Using fitted logistic curve parameters, three typologies were identified: (i) dislocated nutrient supply and demand resulting from high density agriculture (with low population density) and nutrient islands (e.g., dense cities) motivating nutrient concentration and transport; (ii) colocated nutrient supply and demand enabling local reuse; and (iii) diverse nutrient supply-demand proximities, with countries spanning the continuum between (i) and (ii). Finally, we explored connections between these typologies and country-specific contextual characteristics via principal component analysis and found that the Human Development Index was clustered by typology. By providing a generalizable, quantitative framework for characterizing the colocation of human-derived nutrient supply and agricultural nutrient demand, these typologies can advance resource recovery by informing resource management strategies, policy, and investment.

Entering the we-media era, there are more and more negative network public opinions in colleges and universities, and many colleges and universities have problems in dealing with network public opinions, especially lack of systematic thinking. Systematic response to the network public opinion of colleges and universities needs to proceed from four aspects, two of which are particularly important: one is based on the symbiosis theory, using the Logistic equation to demonstrate the effectiveness of multiagent monitoring of campus network public opinion. The second is to establish the index system and weights of campus network public opinion, and calculate the campus network public opinion into four levels through the calculation of the model, so as to improve the ability of colleges and universities in monitoring and early warning and quickly handling negative public opinions.

We-media network public opinion is a new force in the current social public opinion field that has an important impact on the guidance of social public opinion and social stability. Studying the periodic law of we-media network public opinion dissemination and constructing a quantitative model of we-media network public opinion dissemination stages provide the basis for guiding social public opinion and governing we-media network public opinion dissemination. Based on this, this paper explores the life cycle of we-media network public opinion evolution, analyzes the characteristics and connotations of each evolution stage, and determines the dominant indicators of we-media network public opinion evolution stages; in addition, this paper constructs a logistic quantitative model and its stage refinement model for the evolution and development of we-media network public opinion and uses MATLAB software to simulate the event of the academic fraud of the Chinese actor Zhai. This paper studies the four key points on the logistic curve of we-media network public opinion evolution and the five key intervals, analyzes the connotation of the quantified stage of each interval, and puts forward the governance strategy of we-media network public opinion events, through the simulation of initial values, growth rates and upper limits.

Understanding of stand growth information is necessary for establishing forest management plans, but accurate models for estimating ingrowth are currently lacking in Korea. This research aims to develop an ingrowth estimation equation according to various forest types using nationwide forest monitoring data by the National Forest Inventory (NFI). A two-stage approach was developed based on the ingrowth database using permanent sample plots from the 5th (2006–2010) and 6th (2011–2015) NFI. In the first stage, the ingrowth probability was estimated using a logistic function. In the second stage, the ingrowth amount was estimated using a conditional function by regression analysis. In results, a logistic regression model based on the number of sampling plot which did not result in ingrowth (Model VI), was selected for an ingrowth probability estimation equation. After performing three types of statistical test to evaluate the ingrowth estimation equation suitability, three optimal models were selected based on their respective estimation ability: Coniferous Forest (Model IV), Broad-leaved Forest (Model VII), and Mixed Forest (Model VI). The estimation ability of the proposed estimation equation was statistically verified and showed no problems of suitability or applicability. If high-quality data are continuously accumulated for comparison and contrast with the present sampling plot data through the ongoing NFI system, this research can present a new direction in ingrowth modeling for Korean forests.

This article explores an efficient way of image encryption using chaotic logistic function. A set of two chaotic logistic functions and a 256 bit long external secret key are employed to enhance the security in the encrypted images. The initial condition of first logistic function has been obtained by providing the suitable weights to all bits of the secret key. The initial condition of second logistic function has been derived from first chaotic logistic function. In this proposed algorithm, ten different operations are used to encrypt the pixel of an image. The outcome of the second logistic map decides the operation to be used in the encryption of the particular image pixel. Various statistical parameter comparisons show that the proposed algorithm provides an image encryption method with better security and efficiency for all real-time applications.

Aim/purpose – The key aim of the paper is to examine the diffusion of the sustainable investing Exchange-Traded Funds (ETFs) on the European and US ETFs markets, with the special focus on the market shares of sustainable investing and conventional funds. Design/methodology/approach – The model of diffusion of innovation (logistic growth model) is applied. Monthly data on the assets of ETFs in the time period of 2006-2017 are used. Findings – Increasing assets of sustainable investing ETFs were identified in both exam-ined regions. The average value of assets was higher in the United States, but the Euro-pean market became larger in the late 2017. Exclusively for Europe, the diffusion of sustainable investing ETFs was confirmed for the entire analysed time period as the market share of this category was increasing in relation to the conventional funds. In the United States, the diffusion was short-lived and took place in the time period 2006-2008. Research implications/limitations – Applied diffusion models assume an S-shaped trajec-tory of the innovation’s diffusion and the estimations are sensitive to historical data. Originality/value/contribution – It is the first study to apply the methodological framework of innovation diffusion for the examination of the sustainable financial prod-ucts. It addresses an issue of switching between sustainable investing and conventional financial products that has not been examined previously.

Modeling of wind turbine power curve which shows the relationship between wind speed and its power output can be used as an important tool in monitoring and forecasting wind energy. A data-driven approach to find most probable probability distribution function (PDF) for wind speed and turbine power is presented in this study. Equations for the scale and shape parameters in the Weibull wind speed distribution and equations for the four parameters in the logistic function were obtained explicitly by maximum likelihood estimation (MLE) method. With help of a selected data set from the wind speed and the corresponding power output data which was collected over a period of a year, the values of the parameters were obtained by solving the equations by iteration procedures. The predicted powers by the obtained logistic function closely follow the measured turbine powers averaged at 5-min or 10-min. Monitoring turbine power output by the logistic function was also tested for the measured powers in other time duration.

The use of computers today is the most important and widespread means of storing, retrieving and circulating information through local and international networks and smart phones. Thus it is possible to intercept information through different networks or access to computers, whether independent or connected with the network for the purpose of viewing its contents or stealing information or to tamper with, and in this light must ensure the protection, reliability and credibility of information and preservation of the emergence of various means of protection such as the use of encryption and techniques of hiding or coverage of information. This research is based on a hybrid method between the Discrete Wavelets Translate technique (DWT) and the Chaos system based on the Least Significant Bit algorithm (LSB) to encrypt and hide a digital video file within another digital video that represents the cover by inserting the digital video file and segmenting it into a set of frames and then analyze each frame to its colors (red, green and blue) and then study the color values ​​of each frame and the process of encryption based on the equation of the logistics function (chaotic functions), and the application of the Discrete Wavelets Translate technique on the color slides of the cover frame. The hiding process is done using the Least Significant Bit algorithm based on the logistic function by calculating the series of random locations of cover area. The results showed that the hybridization of the Discrete Wavelets Translate technique and the chaos system based on the Least Significant Bit algorithm was an effective method for encrypting and hiding digital video files. After applying the work algorithms to a set of samples, the results showed consistency and compatibility in the encrypting and hiding process with the samples that was dealt with.

A new formulation of a Logistic deterministic S-N curve is applied to fatigue data of metallic materials from ancient Portuguese riveted steel bridges. This formulation is based on a modified logistic relation that uses three parameters to fit the low-cycle- (LCF), finite-life- and high-cycle-fatigue (HCF) regions. This model is compared to the Kohout-Vechet fatigue model, which has a refined adjustment from very low-cycle fatigue (VLCF) to very high-cycle fatigue (VHCF). These models are also compared with other models, such as, power law and fatigue-life curve from the ASTM E739 standard. The modelling performance of the S-N curves was made using the fatigue data considering the stress fatigue damage parameter for the materials from the Eiffel, Luiz I, F�o and Trez�i riveted steel bridges. Using a qualitative methodology of graphical adjustment analysis and another quantitative using the mean square error, it was possible to evaluate the performance of the mean S-N curve formulation. The results showed that the formulation of the S-N curve using the Logistic equation applied to the metallic materials from the old bridges resulted in a superior performance when compared with others models under consideration, both in the estimation of fatigue behaviour in the low-cycle fatigue (LCF) region and in the lowest mean square error.

Purpose In machine learning applications, and in credit risk modeling in particular, model performance is usually measured by using cumulative accuracy profile (CAP) and receiving operating characteristic curves. The purpose of this paper is to use the statistics of the CAP curve to provide a new method for credit PD curves calibration that are not based on arbitrary choices as the ones that are used in the industry. Design/methodology/approach The author maps CAP curves to a ball–box problem and uses statistical physics techniques to compute the statistics of the CAP curve from which the author derives the shape of PD curves. Findings This approach leads to a new type of shape for PD curves that have not been considered in the literature yet, namely, the Fermi–Dirac function which is a two-parameter function depending on the target default rate of the portfolio and the target accuracy ratio of the scoring model. The author shows that this type of PD curve shape is likely to outperform the logistic PD curve that practitioners often use. Practical implications This paper has some practical implications for practitioners in banks. The author shows that the logistic function which is widely used, in particular in the field of retail banking, should be replaced by the Fermi–Dirac function. This has an impact on pricing, the granting policy and risk management. Social implications Measuring credit risk accurately benefits the bank of course and the customers as well. Indeed, granting is based on a fair evaluation of risk, and pricing is done accordingly. Additionally, it provides better tools to supervisors to assess the risk of the bank and the financial system as a whole through the stress testing exercises. Originality/value The author suggests that practitioners should stop using logistic PD curves and should adopt the Fermi–Dirac function to improve the accuracy of their credit risk measurement.

In the article the analysis of the systems of terms is conducted business process and logistic business process. It is set that business processin logistic activity is an aggregate of the successive, associate systematic carried out actions within the framework of realization of strategy of development, directed on forming and use of logistic potential during realization of transformations of financial stream with the purpose of creation of competitive services, able to satisfy external and internal users and provide the enterprise of achievement of strategic aims in the conditions of dynamic logistic environment. Certainly, that dominant now is conception of perfection of business process(Business Process Improvement), which is based on four approaches, directed on the increase of their productivity, efficiency and adapted: method of rapid analysis of decisions (FAST); бенчмаркінг; перепроектування (concentrated improvement); реінжиніринг business process (Business-process Reengineering). Summarizing opinions of different authors, a conclusion is formulated that reengineering, is the radical update of business processes in the context of acceleration of reaction of enterprise on changes in the requirements of users at the multiple cutting of costs of all of kinds, that takes a place at the terms of the concerted work of command of highly skilled, effectively explained, specialists, which develop and incarnate in activity of enterprise innovative and unusual ideas on the increase of level of competitiveness, optimization of logistic streams, growth of the productivity and quality of products and services, increase of satisfaction of clients. On the basis of analysis of front-rank practice, it is set that successful realization of reengineering of logistic бізнес-процесів of company provides for: optimization of sequence of logistic functions and operations, which is instrumental in reduction of duration of logistic cycles; optimization of volume of financial and financial charges; construction of flexible and adaptive processes in the logistic system; the clear distributing of functions and fixing of them is after responsible performers; optimization of co-operating with suppliers and users in the logistic system; co-ordination and synchronization of processes which are executed simultaneously. In the article it is conducted SWOT is an analysis of enterprise of LTD. «KHIMLABORREAKTIV» and audit of him logistic system. On the basis of the conducted logistic audit possibility of development, and introduction of project of reengineering, was made to order on this enterprise, creating a having a special purpose group with bringing in of command of specialists with experience of development and introduction of reengineering on analogical enterprises. It enabled to form and realize the algorithm of realization of strategy of reengineering business processin LTD. «KHIMLABORREAKTIV».

The paper deals with the parameterization of the calculation of the modal split in the four-step model determining the traffic prognosis. Multiple Logit model theory is the process of calculating the modal split, the model is in common use for transport modelling. Its advantage is that we can choose from more independent variables. The estimation of Logit function parameters is based on transport and sociological survey in Žilina region. The Biogeme program will be used for the calculation. The primary task is to create a set of Logit function performance parameters for Žilina region conditions. The choice of a specific transport mode of transport is expressed by the utility function. The function is used in a disaggregated model for individual groups of the population. Groups are characterized by their behaviour in the transport process. The disaggregated model involves simulating the behaviour of individuals in time, space, and their subsequent aggregation into the resulting transport relations of the territory. The modal split will be taken into account in the trip distribution by transport modes: car - driver, car - passenger, public transport, cycling and pedestrian transport.

We present an open-source computational tool for the 2D simulation of the Diffusive-Logistic Growth (DLG) model. The r.recovery module offers a complete environment for the simulation of forestry regeneration in conservation areas and includes a built-in tool for calibration and validation of the model parameters through the use of standard and freely available satellite imagery. It was implemented as an add-on to the GRASS software, a largely applied open-source Geographic Information System (GIS). To illustrate its application, we present a complete case study of forest regeneration carried out in the Espigao Alto State Park (EASP), Brazil, from which we assess typical values of forest diffusion and growth rate parameters, along with the prognostics of forest density status for the coming decades. We observe that the r.recovery tool can be advantageously applied by forestry managers and policy-makers as a form of acquiring technical and scientifically-based information for strategy development and decision-making.

Among the other logistic functions, safeguarding the logistic management is an essential task for manufacturing firms. The purpose of this study is to propose a hybrid simulation framework, which can help to forecast events that can interrupt the operational process and have negative impacts on the logistic delivery system. Therefore, a number of research studies have addressed the logistic performance evaluations of fleet management issues in the supply chain. However, most existing research lack inbuilt of hybrid simulation models in logistic management that take into consideration of dynamic feedback and interactions effects, that would allow assessing and addressing the reliability of the logistic delivery system. For this reason, the hybrid simulation paradigm is implemented for modelling the complex logistics management system. This study suggests a novel hybrid-modelling framework with the combination of agent-based modelling and system dynamics to address the dynamic risk effects in the logistic delivery. The main contribution of this study is to suggest a new framework for decision making in delivery management to compare the outcomes of different logistics risk process. This research enables managers and scholars to combine two methods of simulation to reconfigure a framework in new combinations to produce a solution that is useful for logistic operations. The proposed framework offers decision-makers an alternative way of logistics management.