COVID-19 w Polsce — sekwencja epidemii według modelu SIR

The paper aims to indicate the duration and intensity of the COVID-19 epidemic in Poland. To achieve this goal, the SIR interval model was used. The study was based on data from the Central Statistical Office, Hopkins University, and the Institute of Global Health at the University of Geneva. Based on the analysis results, it was established that the prognosis of the epidemic’s course largely depends on the size of the adopted R virus reproduction coefficient. The higher the coefficient, the more rapid the predicted course of the epidemic, the shorter the duration, and the greater the number of infected. And vice versa: the smaller the coefficient, the milder its course, the longer its duration, and the smaller the number of infected. For this reason, it is important to accurately assess the intensity of the epidemic’s development measured by the virus renewal rate, depending on the nature and intensity of interpersonal contacts. Perhaps in a given country different values of the coefficient for urbanized and rural areas should be used.

Investigation of Robustness of Nanoelectronic Structures Based on Resonant Tunneling Elements

Multi-input logic gates based on two-level logic cells MOBILE have short (picosecond) switching times and higher functionality due to the ability to implement logic functions with fewer gates. This creates good prospects for the development of ultra-high-speed FPGAs with a high degree of integration, which are required for organizing high-performance computing. However, the extremely high sensitivity of resonant tunneling elements to changes in the energies of quantum states requires an assessment of the stability of such structures to external influences in real operation. In this work, the problem of assessing the stability of nanoelectronic structures that include resonant tunneling elements is considered. The method for studying the robustness of logic cells MOBILE based on a resonant tunneling diode and an НВТ transistor was proposed, making it possible to find an external interval estimate of the output voltage of the device under study for given interval models of the initial components. The technique is based on the use of systems of topological and parametric equations written in finite increments. It was shown that the proposed decomposition principle for the initial interval model ensures the algorithmic solvability of the problem posed. A computational algorithm for calculating processes in a two-level logical cell MOBILE has been developed. The algorithm provides for step-by-step integration of interval differential equations and solution of interval nonlinear algebraic equations at each step of integration using Kaucher interval arithmetic. The obtained results of the study of processes in a two-level logic cell MOBILE create prerequisites for expanding the field of application of resonant tunneling devices in high-speed monolithic integrated circuits.

A Short-Term Solar Photovoltaic Power Optimized Prediction Interval Model Based on FOS-ELM Algorithm

Solar energy conversion efficiency has improved by the advancement technology of photovoltaic (PV) and the involvement of administrations worldwide. However, environmental conditions influence PV power output, resulting in randomness and intermittency. These characteristics may be harmful to the power scheme. As a conclusion, precise and timely power forecast information is essential for the power networks to engage solar energy. To lessen the negative impact of PV electricity usage, the offered short-term solar photovoltaic (PV) power estimate design is based on an online sequential extreme learning machine with a forgetting mechanism (FOS-ELM) under this study. This approach can replace existing knowledge with new information on a continuous basis. The variance of model uncertainty is computed in the first stage by using a learning algorithm to provide predictable PV power estimations. Stage two entails creating a one-of-a-kind PI based on cost function to enhance the ELM limitations and quantify noise uncertainty in respect of variance. As per findings, this approach does have the benefits of short training duration and better reliability. This technique can assist the energy dispatching unit list producing strategies while also providing temporal and spatial compensation and integrated power regulation, which are crucial for the stability and security of energy systems and also their continuous optimization.

A Particle Swarm Optimisation with Linearly Decreasing Weight for Real-Time Traffic Signal Control

Nowadays, traffic congestion has become a significant challenge in urban areas and densely populated cities. Real-time traffic signal control is an effective method to reduce traffic jams. This paper proposes a particle swarm optimisation with linearly decreasing weight (LDW-PSO) to tackle the signal intersection control problem, where a finite-interval model and an objective function are built to minimise spoilage time. The performance was evaluated in real-time simulation imitating a crowded intersection in Dalian city (in China) via the SUMO traffic simulator. The simulation results showed that the LDW-PSO outperformed the classical algorithms in this research, where queue length can be reduced by up to 20.4% and average waiting time can be reduced by up to 17.9%.

Basketball Sports Injury Prediction Model Based on the Grey Theory Neural Network

Sports injuries will have an impact on the consistency and systemicity of the training process, as well as athlete training and performance improvement. Many talented athletes have had their careers cut short due to sports injuries. Preventing sports injuries is the best way for basketball players to reduce sports injuries. Many coaches and athletes on sports teams, on the other hand, are unaware of the importance of sports injury prevention. They only realize that the body’s sports functions are abnormal when it suffers from sports injuries. As a result, this paper proposes a gray theory neural network-based athlete injury prediction model. First, from the standpoint of a single model, the improved unequal interval model is used to predict sports injury by optimizing the unequal interval model in gray theory. The findings show that it is a good predictor of sports injuries, but it is a poor predictor of the average number of injuries. Following that, in order to overcome the shortcomings of a single model, a gray neural network combination model was used. A combination model of the unequal time interval model and BP neural network was determined and established. The prediction effect is significantly improved by combining the gray neural network mapping model and the coupling model to predict the two characteristics of sports injuries. Finally, simulation experiments show that the proposed method is effective.

Reliability evaluation for colliery machine based on fuzzy interval model

The mechanical reliability design method is a common method, and it is the most direct and effective method to carry out the mechanical reliability design at present. The reliability optimization design is an effective optimization design method which is developed in combination with the reliability design theory on the basis of the conventional optimization design. Taking the antifriction bearing as an example, this paper systematically expounds all kinds of mechanical reliability design methods. Through comprehensive analysis of various factors that affect the reliability of colliery machine, the index system of colliery machine reliability evaluation is established. Because of the complexity and diversity of the use of colliery machine in the evaluation system, and the fuzziness of human thinking, it is difficult to give the deterministic evaluation information in numerical form, so this paper also analyzes the design method of colliery mechanical reliability, and puts forward an interval fuzzy evaluation method for colliery mechanical reliability evaluation to avoid overload operation of colliery machine and ensure safety production and safety of workers. The simulation results show that the inherent law of reliability is effectively characterized by this method, and it provides a evidence for security produce and scientific decision-making of colliery mechine.

Numerical Solution of an Interval-Based Uncertain SIR (Susceptible–Infected–Recovered) Epidemic Model by Homotopy Analysis Method

This work proposes an interval-based uncertain Susceptible–Infected–Recovered (SIR) epidemic model. The interval model has been numerically solved by the homotopy analysis method (HAM). The SIR epidemic model is proposed and solved under different uncertain intervals by the HAM to obtain the numerical solution of the model. Furthermore, the SIR ODE model was transformed into a stochastic differential equation (SDE) model and the results of the stochastic and deterministic models were compared using numerical simulations. The results obtained were compared with the numerical solution and found to be in good agreement. Finally, various simulations were done to discuss the solution.

DETERMINING OPTIMAL MUNICIPAL SOLID WASTE MANAGEMENT SCENARIO BASED ON BEST-WORST METHOD

Municipal solid waste (MSW) management is one of the most important issues in urban environments, especially in developing countries. In this work, a comprehensive assessment framework for MSW management is proposed to evaluate and screen the optimal scenario. The best-worst method (BWM) is utilized to determine the optimal weight of each criterion for each disposal scenario. However, the original BWM model is difficult to be solved globally. A linear model is presented to solve the model and an interval model is employed to verify the optimality of the linear model. The results indicate that the results of the linear model and interval model are consistent. A case study of MSW disposal in Qingdao City is used to demonstrate the application of the proposed method. The results indicate that a combination of landfilling, incineration technology with energy recovery facility is preferred for the current MSW management in Qingdao from the chosen criteria. The framework proposed in this work can be assisted to help the decision-makers to identify the priority sequence of MSW management scenarios.