Improved SEIR Model Based on Recovery Rate Optimization to Predict COVID-19

When using the traditional SEIR infectious disease model to predict the trend of novel coronavirus pneumonia epidemic, numerous initial parameters need to be tuned, and the parameters cannot change over time during the prediction process, which reduces the accuracy of the model. Firstly, thesis used a logistic model to preprocess the SEIR model parameters and proposed a SEIR model based on time series recovery rate optimization with a new parameter of effective immunity rate. Secondly, the model was trained with epidemic data from domestic and foreign provinces and cities, and the usability of the model was demonstrated experimentally, and the mean absolute percentage error (MAPE) and goodness of fit (R2) were used to compare with other models, which proved the superiority of the model prediction and indicated further research directions. © 2022 IEEE.

Trajectory Tracking Analysis of Fractional-Order Nonlinear PID Controller for Single Link Robotic Manipulator System

Increasing demand for automation is being observed especially during the recent scenarios like the Covid-19 pandemic, wherein direct contact of the healthcare workers with the patients can be life-threatening. The use of robotic manipulators facilitates in minimizing such risky interactions and thereby providing a safe environment. In this research work, a single link robotic manipulator (SLRM) system is taken, which is a nonlinear multi–input–multi–output system. In order to address the limitations like heavy object movements, uncontrolled oscillations in positional movement, and improper link variations, an adaptive fractional-order nonlinear proportional, integral, and derivative (FONPID) controller has been suggested. This aids in the effective trajectory tracking of the performance of the SLRM system under step input response. Further, by tuning the controller gains using genetic algorithm optimization (GA) based on the minimum objective function (JIAE ) of the integral of absolute error (IAE) index, the suggested controller has been made more robust for trajectory tracking performance. Finally, the comparative analysis of the simulation results of proportional & integral (PI), proportional, integral, & derivative (PID), fractional-order proportional, integral, & derivative (FOPID), and the suggested FONPID controllers validated that the FONPID controller has performed better in terms of minimum JIAE and lower oscillation amplitude in trajectory tracking of positional movement of SLRM system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Assessment of effective reactive power reserve in power system networks under uncertainty applying coronavirus herd immunity optimizer (CHIO) for operation simulation

The Reactive Power Reserve (RPR) is a very important indicator for voltage stability and is sensitive to the operating conditions of power systems. Thorough understanding of RPR, specifically Effective Reactive Reserve (ERR) under intermittent Wind Power (WP) and uncertain demand is essential and key focus of this research. Hence, a stochastic multivariate ERR assessment and optimization problem is introduced here. The proposed problem is solved in three stages: modeling of multivariate uncertainty, studying the stochastic behavior of ERR and optimizing ERR. The volatilities associated with WP generation and consumer demand are modeled explicitly, and their probability distribution function is discretized to accommodate structural uncertainty. A combined load modeling approach is introduced and extended further to accommodate multi-variability. The impact of these uncertainties on ERR is assessed thoroughly on modified IEEE 30 and modified Indian 62 bus system. A non-linear dynamic stochastic optimization problem is formulated to maximize the expected value of ERR and is solved using ‘Coronavirus Herd Immunity Optimizer (CHIO)'. The impact of the proposed strategy on stability indices like the L-index, Proximity Indicator (PI) are analyzed through various case studies. Further, the effectiveness of the proposed approach is also compared with the existing mean value approach. Additionally, the performance of CHIO is confirmed through exhaustive case studies and comparisons. © 2023 Elsevier B.V.

Design and Development of Polymer-Based Optical Fiber Sensor for GAIT Analysis

In the present scenario like COVID-19 pandemic, to maintain physical distance, the gait-based biometric is a must. Human gait identification is a very difficult process, but it is a suitable distance biometric that also gives good results at low resolution conditions even with face features that are not clear. This study describes the construction of a smart carpet that measures ground response force (GRF) and spatio-temporal gait parameters (STGP) using a polymer optical fiber sensor (POFS). The suggested carpet contains two light detection units for acquiring signals. Each unit obtains response from 10 nearby sensors. There are 20 intensity deviation sensors on a fiber. Light-emitting diodes (LED) are triggered successively, using the multiplexing approach that is being employed. Multiplexing is dependent on coupling among the LED and POFS sections. Results of walking experiments performed on the smart carpet suggested that certain parameters, including step length, stride length, cadence, and stance time, might be used to estimate the GRF and STGP. The results enable the detection of gait, including the swing phase, stance, stance length, and double supporting periods. The suggested carpet is dependable, reasonably priced equipment for gait acquisition in a variety of applications. Using the sensor data, gait recognition is performed using genetic algorithm (GA) and particle swarm optimization (PSO) technique. GA- and PSO-based gait template analyses are performed to extract the features with respect to the gait signals obtained from polymer optical gait sensors (POGS). The techniques used for classification of the obtained signals are random forest (RF) and support vector machine (SVM). The accuracy, sensitivity, and specificity results are obtained using SVM classifier and RF classifier. The results obtained using both classifiers are compared. © 2023 Mamidipaka Hema et al.

Optimized Layout: A Genetic Algorithm for Industrial and Business Application

Covid-19 epidemic has harmed the global economy. Particularly, the restaurant sector has been severely impacted by the rapid spread of the virus. The use of digital technology (DT) has been utilized to execute risk-reduction methods as service innovation tools. In this work, a genetic algorithm optimization is used to cope with the problems caused by the restrictions due to Covid-19 to optimize the management of the spaces in commercial and industrial structures. The approach through the GA involves the selection of the best members of a population that change genome based on the epochs. The digitization of a commercial or industrial environment becomes an optimal methodology for carrying out virtual design of work environments. Digitization thus becomes a strategy for the reduction of both monetary and time cost. Focusing on the case study, satisfactory results emerge supported by tests that reveal an appreciable robustness and open new scenarios for different applications of the methodology developed in this work, always in the context of optimal management of industrial and commercial spaces. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Covid19 Reproduction Number: Credibility Intervals by Blockwise Proximal Monte Carlo Samplers

Monitoring the Covid19 pandemic constitutes a critical societal stake that received considerable research efforts. The intensity of the pandemic on a given territory is efficiently measured by the reproduction number, quantifying the rate of growth of daily new infections. Recently, estimates for the time evolution of the reproduction number were produced using an inverse problem formulation with a nonsmooth functional minimization. While it was designed to be robust to the limited quality of the Covid19 data (outliers, missing counts), the procedure lacks the ability to output credibility interval based estimates. This remains a severe limitation for practical use in actual pandemic monitoring by epidemiologists that the present work aims to overcome by use of Monte Carlo sampling. After interpretation of the nonsmooth functional into a Bayesian framework, several sampling schemes are tailored to adjust the nonsmooth nature of the resulting posterior distribution. The originality of the devised algorithms stems from combining a Langevin Monte Carlo sampling scheme with Proximal operators. Performance of the new algorithms in producing relevant credibility intervals for the reproduction number estimates and denoised counts are compared. Assessment is conducted on real daily new infection counts made available by the Johns Hopkins University. The interest of the devised monitoring tools are illustrated on Covid19 data from several different countries. IEEE

Chaotic Binary Ant Lion Optimizer Approach for Feature Selection on Medical Datasets with COVID-19 Case Study

Binary version of the ant lion optimizer (ALO) are suggested and utilized in wrapper-mode to pick the best feature subset for classification. ALO is a recently developed bio-inspired optimization approach that mimics ant lion hunting behavior. Furthermore, ALO balances exploration and exploitation utilizing a unique operator to explore the space of solutions adaptively for the best solution. The difficulties of a plethora of noisy, irrelevant, and misleading features, as well as the capacity to deal with incorrect and inconsistent data in real-world subjects, provide rationale for feature selection to become one of the most important requirements. A difficult machine learning problem is to choose a subset of important characteristics from a vast number of features that characterize a dataset. Choosing the most informative markers and conducting a high-accuracy classification across the data may be a difficult process, especially if the data is complex. The feature selection task is usually expressed as a bio-objective optimization challenge, with the goal of enhancing the performance of the prediction model (data training fitting quality) while decreasing the number of features used. Various evaluation criteria are employed to determine the success of the suggested approach. The findings show that the suggested chaotic binary algorithm can explore the feature space for optimum feature set efficiently. © 2022 IEEE.

Feedstock planning and optimization of a sustainable distributed configuration biorefinery for biojet fuel production via ATJ process

In recent years, the production and consumption of fossil jet fuel have increased as a consequence of a rise in the number of passengers and goods transported by air. Despite the low demand caused by the coronavirus 2019 pandemic, an increase in the services offered by the sector is expected again. In an economic context still dependent on scarce oil, this represents a problem. There is also a problem arising from the fuel's environmental impact throughout its life cycle. Given this, a promising solution is the use of biojet fuel as renewable aviation fuel. In a circular economy framework, the use of lignocellulosic biomass in the form of sugar-rich crop residues allows the production of alcohols necessary to obtain biojet fuel. The tools provided by process intensification also make it possible to design a sustainable process with low environmental impact and capable of achieving energy savings. The goal of this work was to design an intensified process to produce biojet fuel from Mexican lignocellulosic biomass, with alcohols as intermediates. The process was modeled following a sequence of pretreatment/hydrolysis/fermentation/purification for the biomass-ethanol process, and dehydration/oligomerization/hydrogenation/distillation for ethanol-biojet process under the concept of distributed configuration. To obtain a cleaner, greener, and cheaper process, the purification zone of ethanol was intensified by employing a vapor side stream distillation column and a dividing wall column. Once designed, the entire process was optimized by employing the stochastic method of differential evolution with a tabu list to minimize the total annual cost and with the Eco-indicator-99 to evaluate the sustainability of the process. The results show that savings of 5.56% and a reduction of 1.72% in Eco-indicator-99 were achieved with a vapor side stream column in comparison with conventional distillation. On the other hand, with a dividing wall column, savings of 5.02% and reductions of 2.92% in Eco-indicator-99 were achieved. This process is capable of meeting a demand greater than 266 million liters of biojet fuel per year. However, the calculated sale price indicates that this biojet fuel still does not compete with conventional jet fuel produced in Mexico. © 2022 Society of Chemical Industry and John Wiley & Sons, Ltd. © 2022 Society of Chemical Industry and John Wiley & Sons, Ltd.

Simulation-based optimisation: analysis of the emergency department resources under COVID-19 conditions

The emergency department (ED) is the most important section in every hospital. The ED behaviour is adequately complex, because the ED has several uncertain parameters such as the waiting time of patients or arrival time of patients. To deal with ED complexities, this paper presents a simulation-based optimisation-based meta-model (S-BO-BM-M) to minimise total waiting time of the arriving patients in an emergency department under COVID-19 conditions. A full-factorial design used meta-modelling approach to identify scenarios of systems to estimate an integer nonlinear programming model for the patient waiting time minimisation under COVID-19 conditions. Findings showed that the S-BO-BM-M obtains the new key resources configuration. Simulation-based optimisation meta-modelling approach in this paper is an invaluable contribution to the ED and medical managers for the redesign and evaluates of current situation ED system to reduce waiting time of patients and improve resource distribution in the ED under COVID-19 conditions to improve efficiency. Copyright © 2023 Inderscience Enterprises Ltd.

Deep Learning Optimization for Lens-Free Holographic Imaging and SARS-CoV-2 Biosensing

Rapid biosensing assays to detect SARS-CoV-2 are critical in mitigating the impact of the pandemic. Here, we use a lens-free holographic microscope coupled with deep learning in a rapid and sensitive assay to detect SARS-CoV-2. © 2022 The Author(s)

Improving Effectiveness of Simulation-Based Inference in the Massively Parallel Regime

Simulation-based Inference (SBI) is a widely used set of algorithms to learn the parameters of complex scientific simulation models. While primarily run on CPUs in High-Performance Compute clusters, these algorithms have been shown to scale in performance when developed to be run on massively parallel architectures such as GPUs. While parallelizing existing SBI algorithms provides us with performance gains, this might not be the most efficient way to utilize the achieved parallelism. This work proposes a new parallelism-aware adaptation of an existing SBI method, namely approximate Bayesian computation with Sequential Monte Carlo(ABC-SMC). This new adaptation is designed to utilize the parallelism not only for performance gain, but also toward qualitative benefits in the learnt parameters. The key idea is to replace the notion of a single ‘step-size’hyperparameter, which governs how the state space of parameters is explored during learning, with step-sizes sampled from a tuned Beta distribution. This allows this new ABC-SMC algorithm to more efficiently explore the state-space of the parameters being learned. We test the effectiveness of the proposed algorithm to learn parameters for an epidemiology model running on a Tesla T4 GPU. Compared to the parallelized state-of-the-art SBI algorithm, we get similar quality results in <inline-formula><tex-math notation="LaTeX">$\sim 100 \times$</tex-math></inline-formula> fewer simulations and observe <inline-formula><tex-math notation="LaTeX">$\sim 80 \times$</tex-math></inline-formula> lower run-to-run variance across 10 independent trials. IEEE

An optimal control theory approach for freight structure path evolution post-COVID-19 pandemic.

After the outbreak of COVID-19, the freight demand fell briefly, and as production resumed, the trucking share rate increased again, further increasing energy consumption and environmental pollution. To optimize the sudden changing freight structure, the study aims on developing an evolution model based on Markov's theory to estimate the freight structure post-COVID-19. The current study applies economic cybernetics to establish a freight structural adjustment path optimization model and solve the problem of how much freight transportation should increase each year under the premise that the total turnover of the freight industry continues to grow, and how many years it will take at least to reach a reasonable freight structure. The freight transport structure of China is used to examine the feasibility of the proposed model. The finding indicates that the development of China's freight transport structure is at an adjustment period and should enter a stable period by 2035 and the COVID-19 makes it harder to adjust the freight structure. Increasing the growth rate of the freight volume of railway and waterway transportation is the key to realizing the optimization of the freight structure, and the freight structure path optimization method can realize the rationalization of the freight structure in advance.

Deep Learning Optimization for Lens-Free Holographic Imaging and SARS-CoV-2 Biosensing

Rapid biosensing assays to detect SARS-CoV-2 are critical in mitigating the impact of the pandemic. Here, we use a lens-free holographic microscope coupled with deep learning in a rapid and sensitive assay to detect SARS-CoV-2. © 2022 The Author(s)

Understanding the Power of Evolutionary Computation for GPU Code Optimization

Achieving high performance for GPU codes requires developers to have significant knowledge in parallel programming and GPU architectures, and in-depth understanding of the application. This combination makes it challenging to find performance optimizations for GPU-based applications, especially in scientific computing. This paper shows that significant speedups can be achieved on two quite different scientific workloads using the tool, GEVO, to improve performance over human-optimized GPU code. GEVO uses evolutionary computation to find code edits that improve the runtime of a multiple sequence alignment kernel and a SARS-CoV-2 simulation by 28.9% and 29% respectively. Further, when GEVO begins with an early, unoptimized version of the sequence alignment program, it finds an impressive 30 times speedup-a performance improvement similar to that of the hand-tuned version. This work presents an in-depth analysis of the discovered optimizations, revealing that the primary sources of improvement vary across applications;that most of the optimizations generalize across GPU architectures;and that several of the most important optimizations involve significant code interdependencies. The results showcase the potential of automated program optimization tools to help reduce the optimization burden for scientific computing developers and enhance performance portability for domain-specific accelerators. © 2022 IEEE.

Social Intelligence as a Tool for Human Performance Optimization During Post COVID 19: A Review

Optimization of human performance today is the key ingredient to success when there is a huge downfall in economy due to novel corona virus. Life has become a struggle for continued existence, especially during the times of pandemic. In such conditions, people who are going out for work are more vulnerable. Scarceness of fund, illness, poverty, and pitiable employment opportunities is mainly for the people in the mounting pandemic times. As many employees are often trying to cope up with their health, economic, social, and psychological issues, intelligence comes in their way when they are dealing with the people and working hard in an organization. Social intelligence plays a significant role in their life, in getting along well with others. Hence, the review was done to see if social intelligence works as a tool for human performance optimization, especially for people who are going out and serving others. The secondary data indicate that employee's performance and social intelligence have a positive relationship. Proper optimization of human performance was seen in people with high social intelligence. Further, studies also indicated that social intelligence enhanced motivation and created values in employees which in turn helped in human performance optimization. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A New Immune Plasma Algorithm Based on Levy Flight

Immune Plasma algorithm (IP algorithm or IPA) is one of the most recently introduced intelligent optimization techniques. IP algorithm modeled by guiding the fundamental properties of the immune or convalescent plasma treatment that gains the researchers' attentions with the ongoing COVID-19 pandemic has been used for solving different types of problems. In this study, the plasma extraction mechanism of the IP algorithm was updated with the Levy flight and a new IPA variant also called Levy-IPA was introduced. The solving capabilities of the newly introduced IPA variant were tested over numerical optimization problems and then obtained results were compared with the results of standard IPA and other intelligent optimization techniques. Comparative studies showed that Levy flight significantly contributes to the performance of the IP algorithm. © 2022 IEEE.

Regional Economy Condition in Indonesia during COVID-19 Pandemic: An Analysis using Teaching Learning-Based Fuzzy Geodemographic Clustering

COVID-19 has impacted Indonesia and caused an economic recession during 2020. The economic condition in Indonesia should be evaluated through the regional economic condition. One well-known approach to do a regional analysis is a geodemographic analysis using Fuzzy Geographically Weighted Clustering (FGWC). However, FGWC is still weak against the local optima, so it is necessary to use an optimisation algorithm to enhance it. This study proposes a new approach of FGWC enhancement using Elicit Teaching-Learning Based Optimisation (ETLBO) to analyse the regional economic condition in Indonesia. We compare ETLBO with previously implemented optimisation algorithms in FGWC, such as Particle Swarm Optimisation (PSO) and Intelligent Firefly Algorithm (IFA). This study found that ETLBO performs well in identifying Indonesia's regional economic condition. Moreover, the clustering results showed the difference of problematic sectors. We also found that the provinces in Java Island joined into a cluster and have problems in many sectors. This study can be used as the basis for the evaluation of regional economic conditions in Indonesia. © 2022 IEEE.

An evolutionary approach to the discretization of gene expression profiles to predict the severity of COVID-19

In this work, we propose to use a state-of-the-art evolutionary algorithm to set the discretization thresholds for gene expression profiles, using feedback from a classifier in order to maximize the accuracy of the predictions based on the discretized gene expression levels, while at the same time minimizing the number of different profiles obtained, to ease the understanding of the expert. The methodology is applied to a dataset containing COVID-19 patients that developed either mild or severe symptoms. The results show that the evolutionary approach performs better than a traditional discretization based on statistical analysis, and that it does preserve the sense-making necessary for practitioners to trust the results. © 2022 Owner/Author.

A novel transfer deep learning model with reinforcement-learning-based hyperparameter optimization for short-term load forecasting during the COVID-19 pandemic

The coronavirus disease 2019 pandemic has significantly impacted the world. The sudden decline in electricity load demand caused by strict social distancing restrictions has made it difficult for traditional models to forecast the load demand during the pandemic. Therefore, in this study, a novel transfer deep learning model with reinforcement-learning-based hyperparameter optimization is proposed for short-term load forecasting during the pandemic. First, a knowledge base containing mobility data is constructed, which can reflect the changes in visitor volume in different regions and buildings based on mobile services. Therefore, the sudden decline in load can be analyzed according to the socioeconomic behavior changes during the pandemic. Furthermore, a new transfer deep learning model is proposed to address the problem of limited mobility data associated with the pandemic. Moreover, reinforcement learning is employed to optimize the hyperparameters of the proposed model automatically, which avoids the manual adjustment of the hyperparameters, thereby maximizing the forecasting accuracy. To enhance the hyperparameter optimization efficiency of the reinforcement-learning agents, a new advance forecasting method is proposed to forecast the state-action values of the state space that have not been traversed. The experimental results on 12 real-world datasets covering different countries and cities demonstrate that the proposed model achieves high forecasting accuracy during the coronavirus disease 2019 pandemic. © 2022 - IOS Press. All rights reserved.

Optimal Chair Location Through a Maximum Diversity Problem Genetic Algorithm Optimization

The coronavirus disease (COVID-19) pandemic has challenged multiple aspects of our lives. Social distancing among other preventive measures for reducing the contagion probability have supposed a significant challenge for many establishments. Restaurants, schools, conferences are establishments founded by the congregation of participants, distributed in tables or chairs over a certain scenario. These enterprises now face an optimization problem in their daily routine, where they seek to maximize the interpersonal distance while also allocating the maximum number of assistants. The optimization of these distribution paradigms, such as the CLP (Chair Location Problem), has been defined as NP-Hard, therefore, the use of metaheuristic techniques, such as Genetic Algorithms is recommended for obtaining an optimal solution within a polynomial time. In this paper, a GA is proposed for solving the CLP, attaining an optimal solution that maximizes the interpersonal distance among assistants while also guaranteeing a minimum distance separation for reducing the contagion probability. Results of the proposed methodology and multiple fitness evaluation strategies prove its viability for attaining a valid distribution for these establishments, thus satisfying the main objectives of this research. © 2022, Springer Nature Switzerland AG.