Sine cosine optimization algorithm combined with balloon effect for adaptive position control of a cart forced by an armature-controlled DC motor.

For a car that is propelled by an armature-controlled DC motor This study proposes an adjustable linear positioning control. In this paper, to optimize the parameters of the car's position controller the sine cosine optimization algorithm (SCA) is utilized, with support from the Balloon effect (BE), The BE is incorporated to enhance the responsiveness of the traditional sine cosine optimization algorithm when faced with external disturbances and variations in system parameters. In the proposed approach, the determined value of the open loop transfer function of the motor and the updated values of the controller gains serve as the basis for the modified sine cosine algorithm's objective function (OF). Under the influence of changes in motor parameters and step load disturbances, the system using the suggested controller is evaluated. Results from simulations and experiments show that the proposed adaptive controller, which implements the modified sine cosine algorithm, enhances the system's overall performance in the presence of load disturbances and parameter uncertainties.

Are mega-events super spreaders of infectious diseases similar to COVID-19? A look into Tokyo 2020 Olympics and Paralympics to improve preparedness of next international events.

Tokyo Summer Olympics and Paralympics have raised social issues regarding the potential rise in COVID-19 cases in Japan and risks associated with the safe organization of mega sporting events during the pandemic, such as the FIFA World Cup Qatar 2022. This study investigates the Tokyo Summer Olympics as a unique case study to clarify the drivers of infectivity and provide guidelines to host countries for the safe organization of subsequent international sporting events. The result here reveals that Tokyo and Japan did not experience a rise in confirmed cases of COVID-19 due to the hosting of the Summer Olympics. Still, transmission dynamics seems to be mainly driven by the high density of population (about 1.2%, p-value <0.001) like other larger cities in Japan (result confirmed with Mann-Whitney U test, significance at 0.05). Our study provided evidence that hosting mega sporting events during this COVID-19 pandemic is safe if strictly maintained the precautions with non-pharmaceutical (and pharmaceutical) measures of control of infections. The Tokyo Summer Olympics hosting will be exemplary for next international events due to the successful implementation of preventive measures during COVID-19 pandemic crisis.

Current stress minimization for isolated dual active bridge DC-DC converter.

This paper presents a new phase-shift modulation for isolated dual active bridge (DAB) direct current-direct current (DC-DC) converter. The proposed technique aims to minimize the maximum current stress of the converter, which could directly increase the efficiency and reduce the device losses. This modulation technique controls the converter power through only two phase-shift angles or two degrees of freedom; one phase shift is used between the legs of its first bridge and the other one between the legs of the second bridge. Although the traditional single-phase shift (SPS) technique has only one degree of freedom, it suffers from many drawbacks in terms of high current stress and reverse circulating power flow, which decrease the converter efficiency. On the other hand, increasing the number of phase-shift angles can enhance the system performance but also increase the control complexity. Thus, a comparative analysis between the proposed modulation technique and the traditional SPS was conducted; the new method showed better performance in terms of current stress reduction, along with implementation simplicity.

Financial logistics models based on systematic approach improving management solutions.

Background: Some firms with good growth opportunities and additional funds could have difficulties accessing external finance. One possible way to enhance their financial inclusion could be an exciting approach to planning the money reserve collected on a firm's account. Methods: This article aims to disclose the introduction of financial logistics as the new theoretical field of management science. The authors present, in this paper, the key findings on the development of logistical models of an optimum money reserve calculation taking into account digital transformation and industry 4.0 technologies and optimization methods. Results: The monetary reserve models are analogies of models of storekeeping in supply chains. The specific area of the theoretical research of logistics is shown in this paper, which could be disclosed as the subject of financial logistics as a science. The authors consider the term "Financial Logistics" based on logistics theory and money demand. Conclusions: Authors suggest the methodology of studying the nature of both financial and material flows of resources by comparing the relevant formulas. From the researchers' points of view, financial logistics could be defined as the theory of managing the cash flows based on the logistical models for calculating a corporation's cash reserve. The authors find it interesting to expand the conditions for calculating financial flows since the uncertainty of external market conditions always influences actual commercial activity.

A simulation study of techno-economics and resilience of the solar PV irrigation system against grid outages.

Water is the most fundamental need for better yield in agriculture. Worldwide, diesel and electricity are typically used to pump water which contributes to atmospheric pollution. Besides, a power outage affects the irrigation process badly. Without water, the crop may wither away, causing a substantial economic loss. This paper discusses the resilience of a solar PV system during a power outage. HOMER Pro software was used to perform the techno-economic analysis of solar-based irrigation for four major divisions of Bangladesh, while 1-hour power outage was assigned in REopt lite to model the survivability of the system against the grid outage. The simulation outcomes showed that the energy cost is $0.1496/kWh, $0.1502/kWh, $0.1557/kWh, and $0.1576/kWh for Rajshahi, Sylhet, Dhaka, and Chattogram, respectively. About 45% of excess electricity can be stored after fulfilling all requirements. The system is more economical than a microgrid-based water pumping system and a diesel-based system, and the photovoltaic system is technically and economically suitable to pump water if the nearest grid connection is impossible. When connected to the main utility grid, the system can survive without grid power for several hours, subject to daytime outages.

Multi-variant differential evolution algorithm for feature selection.

This work introduces a new population-based stochastic search technique, named multi-variant differential evolution (MVDE) algorithm for solving fifteen well-known real world problems from UCI repository and compared to four popular optimization methods. The MVDE proposes a new self-adaptive scaling factor based on cosine and logistic distributions as an almost factor-free optimization technique. For more updated chances, this factor is binary-mapped by incorporating an adaptive crossover operator. During the evolution, both greedy and less-greedy variants are managed by adjusting and incorporating the binary scaling factor and elite identification mechanism into a new multi-mutation crossover process through a number of sequentially evolutionary phases. Feature selection decreases the number of features by eliminating irrelevant or misleading, noisy and redundant data which can accelerate the process of classification. In this paper, a new feature selection algorithm based on the MVDE method and artificial neural network is presented which enabled MVDE to get a combination features' set, accelerate the accuracy of the classification, and optimize both the structure and weights of Artificial Neural Network (ANN) simultaneously. The experimental results show the encouraging behavior of the proposed algorithm in terms of the classification accuracies and optimal number of feature selection.

Performance Evaluation of Probabilistic Methods Based on Bootstrap and Quantile Regression to Quantify PV Power Point Forecast Uncertainty.

This paper presents two probabilistic approaches based on bootstrap method and quantile regression (QR) method to estimate the uncertainty associated with solar photovoltaic (PV) power point forecasts. Solar PV output power forecasts are obtained using a hybrid intelligent model, which is composed of a data filtering technique based on wavelet transform (WT) and a soft computing model (SCM) based on radial basis function neural network (RBFNN) that is optimized by particle swarm optimization (PSO) algorithm. The point forecast capability of the proposed hybrid WT+RBFNN+PSO intelligent model is examined and compared with other hybrid models as well as individual SCM. The performance of the proposed bootstrap method in the form of probabilistic forecasts is compared with the QR method by generating different prediction intervals (PIs). Numerical tests using real data demonstrate that the point forecasts obtained from the proposed hybrid intelligent model can be effectively used to quantify PV power uncertainty. The performance of these two uncertainty quantification methods is assessed through reliability.