The transport sector is essential for socio-economic growth; however, the sector contributes a large portion of global carbon dioxide emissions. Consequently, electric vehicles have received increasing attention from various stakeholders in order to provide a solution to the current environmental impact of the transport industry. Current literature on electric vehicle powertrains show that electric vehicles are complex systems, with one of the most essential subsystems being the traction motor control mechanism. As a result, the objective of this paper is to add to the research conducted in the field of electric vehicle powertrains by carrying out comprehensive investigations into the suitability and performance of space vector modulation based direct torque control in the traction motor control system of an electric vehicle with a complete drive system. Initially, a conventional direct torque control mechanism was implemented for control of the traction motor system. The results of the investigation into implementation of conventional direct torque control highlighted the expected issues associated with the mechanism. Implementation of improvements to the conventional direct torque control model, such as the use of a space vector modulation based direct torque control model with closed loop torque control, field-weakening and sensorless control produced significantly favorable results, providing decreased torque and current ripples, operation over a wide speed range, and accurate speed control without the need for mechanical speed sensors. Such results demonstrate that the complete control mechanism investigated is well suited for use in electric vehicle applications. Direct torque control (DTC); DTC-SVM; Electric vehicles; Field-weakening control; Sensorless control; Traction motor control system Internal combustion engine vehicles (ICEVs) are the most commonly used type of vehicle; however, these vehicles cause tailpipe emissions, generating urban air pollution and contributing to greenhouse gases. Objectives and deadlines relating to the reduction of carbon emissions were provided to all countries by the United Nations Hybrid electric vehicles are a combination of conventional internal combustion engine technology, and electric power technology The performance of electric vehicles can be improved through the implementation of various mechanical design concepts. However, further efficiency and range improvements can also be achieved through EV powertrain optimization, with focus on EV powertrain electrification. One aspect in such optimization includes enhancement of EV driving range through the use of highly efficient electric motors As a result, a key component in the EV powertrain is the traction motor control system. Field oriented control (FOC) and direct torque control (DTC) are the two commonly utilized control techniques. This paper aims to present a study on the performance of DTC in electric vehicle applications, through the development of a complete traction motor control system. Various other research works have been conducted which investigate the traction motor control system of an EV. The authors in DTC presents promising results in various scenarios and studies analyzed in literature. Such studies make DTC based mechanisms worthy of attention and investigation. However, many of the aforementioned studies that have been presented consider small scale models, in which experimental testing forms the basis of the conclusions made. Even in cases in which simulation studies are presented, low power rating motors and systems are investigated. As a result, this paper aims to provide a study on the performance of a complete traction motor drive in which DTC-SVM is utilized. To the best of our knowledge, a study which investigates motor sizing through parameter matching, while also considering improvements to the control mechanism, including ripple reduction, field-weakening control, and sensorless control has not yet been reported. Investigations of all such aspects are conducted in this paper, while comprehensively considering the performance of all sections of the drive system, simulated over the entire speed range required, at realistic torque loads. As a result, the study provided is extremely comprehensive, considering the vast majority of sub-systems required in an EV traction motor drive. It is essential to understand the theoretical performance of control mechanisms in appropriately scaled systems, which are applicable for use in urban and highway driving conditions.Section 2Additionally, the dynamic performance specifications are also based on everyday use in urban areas and highways for example, the maximum highway speed limit is 120 km/h, and a 0-100 km/h acceleration time of less than 15 seconds is acceptable for smaller cars used for transportation 3Electric motors are used as traction motors in electric vehicles, enabling the transformation of electrical energy into mechanical energy The authors in m, g represents the gravitational acceleration (9.80 m/s2), the front area of the vehicle (m2) is denoted by A, f represents the tire rolling resistance coefficient., δ is the rotational inertial factor of the vehicle, 3).In order to determine the specifications of the selected motor, the maximum power of the motor must first be determined. The motor peak power must enable it to satisfy various vehicle specifications, which are the maximum velocity of the vehicle, the climbing performance of the vehicle, and the 0-100 km/h acceleration time of the vehicle k is the overload factor chosen. In addition to the maximum and rated power specifications of the motor, the maximum speed of the motor must also be determined. The maximum speed specification of the motor can be determined through the use of equation r is the rolling radius. The gear ratio can be chosen, and is not calculated specifically. However, the ratio has a minimum value which is required in order for the vehicle to meet its gradeability performance specification. This minimum limit is given in equation n is the rated speed of the motor. Utilizing equations As mentioned previously, the maximum power of the motor must be able to meet the power requirements in equations 4The electric machine and drive system in an EV powertrain are essential for enabling the dynamic performance specifications of the EV to be met. As these are core technologies in the EV powertrain, adequate operation of the vehicle requires a correctly designed and implemented traction motor control circuit The VVVF mechanism is a relatively simple method of control and implementation, which is widely used in various applications found in industry Improved dynamic control and a fast torque response, when compared to the VVVF method, can be achieved with the use of FOC. Furthermore, various other parameters can be controlled, which include the amplitude, position and frequency of the space vectors for the voltages, currents and magnetic flux Similar performance to that observed in FOC schemes, without the need for intensive on-line coordinate transformations can be achieved with the use of DTC However, DTC was preferred for implementation in the traction motor control mechanism being investigated in this paper. DTC allows for fast torque control of an induction motor, without the requirement for heavy on-line computation, as is required in FOC s-qss) are required for parameter estimation, and as a result, equation The block diagram in The look-up table implemented as part of the conventional DTC model studied in this paper is shown in The outputs of the hysteresis controllers are given by equations m. Circular flux trajectory DTC can also be achieved while operating the inverter in the overmodulation range. Based on the findings in Lastly, adequate operation of the DTC system requires that a certain DC voltage level can be supplied to the inverter, which must also be calculated. Investigation into the maximum modulation index of circular flux trajectory direct torque control is provided by the researchers in 5A 1 MHz sampling frequency was utilized in this study, as the sampling frequency has a major impact on the ripples observed. The stator flux trajectory shown is equivalent to the theoretical stator flux trajectory of conventional DTC, shown in The DTC control mechanism implemented produces expected results for conventional DTC, implemented with the use of hysteresis controllers. The speed response obtained is a very favorable result, as the reference speed is tracked with very little overshoot and no steady-state error. In addition, the developed torque of the motor responds quickly to changes in the load torque with minimal overshoot. The stator flux trajectory observed perfectly matches the theoretical result expected. The stator flux vector rotates in a circular manner, contained within the hysteresis band controller limits. However, despite some of the favorable results obtained, issues such as high torque ripple, high current ripple (harmonic distortion), and variable switching frequency were observed. In addition, it was observed that a high-speed processor is required for adequate implementation of conventional DTC. The issues observed are undesirable for implementation in EV applications. The authors in 6The results obtained from the simulation of conventional DTC highlighted the disadvantages associated with the use of hysteresis controllers in the system, as well as the issues created from a variable switching frequency. Such disadvantages, including high electromagnetic torque, stator flux and current ripples, as well as current distortions are a common trend in literature and are documented by the researchers in As the DTC-SVM technique enables improved performance  with simple algorithm complexity, it is chosen for investigation in this paper. The objective is to mitigate the issues observed in the conventional DTC system investigated. However, there are various other suitable control methods given in DTC-SVM techniques correctly select switching states for the voltage source inverter with the implementation of a voltage modulator The DTC-SVM system proposed utilizes the same flux and torque estimator model discussed in conventional DTC, and as a result, the equations are not redefined in this section. Conventional DTC enables the adjustment of the developed electromagnetic torque, through the control of the angle between the stator and rotor magnetic flux vectors  are noticed, with the method also enabling a constant switching frequency. As a result, the DTC-SVM technique presented significantly improves the CDTC structure initially simulated and is more applicable for implementation in EV systems. The DTC-SVM system can therefore be further investigated for use in EV systems through the integration of field-weakening and sensorless control. Despite the favourable results obtained, further investigation into the improvement of the DTC mechanism can be made. Such investigation may include the implementation of DTC-SVM with both closed-loop torque and flux control as presented in 8P is the number of pole pairs in the induction motor, σ is the total leakage factor  of an induction motor, when utilizing vector control mechanisms, is limited by the maximum current rating of the motor windings as well as the maximum inverter voltage 9The fundamental structure of the DTC-SVM mechanism is unchanged when field-weakening is added; however, a dynamic flux reference calculator, as well as the torque limits discussed in the previous section are also incorporated into the model. The inverter voltages observed in the simulation remained unchanged as compared to those observed in the DTC-SVM mechanism when field-weakening control had not been implemented. The field-weakening method implemented adjusts the stator flux in a manner which is inversely proportional to the rotor speed. However, adjustment of the flux only takes place in operating conditions in which the rotor speed is above the specified base speed. As a result, when the field-weakening control is in operation, the stator flux should be adjusted in a hyperbolic manner. Such adjustment of the stator flux can be seen in the stator flux magnitude graph, shown in The field-weakening technique implemented produced favorable results, allowing the traction motor to correctly operate over the entire speed range required. Additionally, the torque is correctly limited, in order to ensure that the motor maintains stable operation throughout the entire drive cycle tested. The results achieved are notable, as field-weakening is essential in traction motor drives utilized in EVs. The results obtained show that a DTC-SVM mechanism, with the implementation of field-weakening control, presents favorable results when applied in electric vehicle systems. The complete mechanism exhibits low torque, current and flux ripples, while achieving fast dynamic response and stable acceleration throughout the speed range required.10P is the number of pole pairs, and As an EV system could be considered as a hostile environment, sensorless DTC offers various advantages, which include a reduction in hardware complexity, cost, maintenance, and size of the machine drive, while also allowing for improved noise immunity, and increased reliability 11The structure of the DTC-SVM mechanism remains the same when sensorless control is added; however, a dynamic speed estimator is also included in the model, based on equation The inverter voltages observed in the simulation remained the same as compared to those observed in the DTC-SVM mechanism when sensorless control had not been implemented. The sensorless DTC-SVM results obtained are a significant advancement in the traction motor drive system implemented. Sensorless control is majorly advantageous in EV systems, enabling reduced hardware complexity, reduced cost, and less maintenance requirements 12The objective of this paper is to investigate the performance of direct torque control in the traction motor control system of a battery electric vehicle. The paper presented contains a parameter matching stage, and investigation into both conventional direct torque control, as well as direct torque control utilizing a space vector modulation strategy in electric vehicle drives. Investigation of conventional direct torque control indicated that the control mechanism, while producing a fast torque response with low steady-state speed error, exhibited high electromagnetic torque and current ripples, which is undesirable in electric vehicle applications. However, the implementation of improvements to the control mechanism, with the use of direct torque control with a space vector modulation technique showed significantly favorable results. In addition, field-weakening control, and well as sensorless control can also be implemented in conjunction with the system in order to present a complete traction motor control system. The complete control system investigated presented favorable results, and is suitable for use in electric vehicle systems. The results indicated that a fast torque response is achieved, with a perfectly controlled field-weakening mechanism, and sensorless control with accuracy of greater than 98% across the entire speed range. Some steady-state torque error can be noticed, and can be said to be a characteristic of the control scheme investigated. Further investigation can be made into the implementation of different direct torque control systems in the complete traction motor drive mechanism. Other systems include model predictive based direct torque control, sliding mode based direct torque control and deadbeat direct torque control. Simulation of such techniques will be included in later articles as comprehensive investigations are required.Matthew L. De Klerk & Akshay K. Saha: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.No data was used for the research described in the articleThe authors declare no conflict of interest.https://doi.org/10.1016/j.heliyon.2022.e09265.Supplementary content related to this article has been published online at


To ensure the safe use of structures, it is essential to develop efficient damage identification techniques. In this paper, a brand-new approach to identifying structural deterioration based on static displacement is proposed. First, the relationship between the displacement variation and the damaged element is derived from the static response equations before and after damage. Subsequently, the optimal achievable displacement variation is defined to determine the damage location in the structure. A progressive elimination strategy is suggested to identify the real damaged parts and weed out the pseudo-damaged elements by measuring the distance between the measured and the best possible displacement variation. After determining the damage location, the corresponding damage extent can be calculated by a system of linear equations. The proposed approach has been tested on a beam structure and truss structure using simulated and experimental data. Compared with the existing static sensitivity method, the suggested method does not result in misjudgment and has higher identification accuracy. It has been demonstrated that the suggested approach is effective at locating and assessing the extent of structural damage. The identification of bridge structure damage has attracted extensive attention recently ,2,3. AccFor the static displacement-based method, Lee and Eun  proposedFor the vibration frequency-based method, Wang et al.  presenteFor the vibration mode-based approach, Unger et al.  detectedFor the dynamic flexibility-based method, Ahmadi-Nedushan and Fathnejat  presenteAs the best achievable theory-based method, Lim and Thomas  suggesteIn this work, a novel damage diagnosis approach is proposed using the best achievable displacement variation. The improvements to the suggested technique concentrate on the following two features when compared to the best feasible eigenvector methods currently used ,27,28,29For a structure before and after damage, the static response equations can be expressed as:Substituting Equation (1) into (5), while excluding the high-order component, givesSubstituting Equation (1) into (6) givesEquation (7) can be rewritten asIn Equation (8), the stiffness change As mentioned before, The link between the displacement variation and the damaged elements is established by Equation (12). The physical meaning of Equation (12) is crucial and useful for damage localization. It is observed that Equation (12) is valid merely when Based on Equation (16), the best feasible displacement variation can be defined to determine whether or not In which If Note that a linear approximation that ignores the higher-order term is used in the derivation from Equation (5) to (6). Therefore, to obtain a more accurate damage coefficient, the acceleration formula derived from flexibility disassembly perturbation ,31 shoulTo further assess the final damaged pieces, the calculated values of the damage coefficients can corroborate the findings of the damage localization based on the aforementioned best achievable hypothesis.As a summary, a step-by-step description of the whole approach is illustrated as follows:Step 1: Construct the finite element model of the structure to obtain the global stiffness matrix Step 2: Perform the static test on the structure before and after damage to obtain the displacement Step 3: For the combination of the possible damaged elements, use the best achievable theory described by Equations (16)–(23) to identify the genuine injured components and exclude the pseudo-damaged elements.Step 4: Calculate the relevant damage coefficients  for the detected damaged elements using Equations (24) and (25).3; cross-sectional area—0.21 m2; moment of inertia—8.575 × 10−3 m4. The decrease in the elastic modulus before and after damage simulates structural deterioration. It is assumed that the static vertical load of 4000 kN is applied at node 13, as shown in The proposed damage identification method is illustrated numerically using the beam structure in Two damage scenarios are then simulated to verify the proposed method. The first damage scenario estimates a 20% reduction in the elastic modulus of element 13. Using the displacement data without noise, The proposed method is further validated using an experimental beam carried out by Le et al. . This exUsing the measured deflection data, A steel truss construction is constructed, as shown in Using the measured displacement data of the undamaged and damaged structure, In this paper, a novel method of structural damage identification based on the best achievable theory is proposed. According to the static response equations before and after damage, the displacement variation can be expressed as a linear combination of a series of vectors associated with the damaged elements. In this study, the distance between the measured and the best practicable displacement variation is calculated to determine the best achievable displacement variation for detecting the damage location using the best achievable theory. For the multi-damage case, a successive elimination approach is proposed to determine the true damaged elements and exclude the pseudo-damaged elements. When the best achievable theory is used to determine the damage location, the corresponding damage extents can be easily calculated by solving the linear equations. The suggested approach has been effectively validated on a beam structure and truss structure using simulated and experimental data. Analyzing the numerical and experimental results allows for the following conclusions: (1) Compared with the dynamic method, the proposed method only requires the measurement of the static displacement parameters of the structure under gravity loads. The measurement process is simpler and more accurate than the dynamic method. (2) The damaged element or its combination will correspond to the minimum value of the distances between the measured and the optimal achievable displacement variation. This principle can be successfully used to determine the location of damage in the structure. (3) The fault extent may be accurately calculated using the Moore–Penrose inverse and the acceleration formula once the fault location has been identified through the best feasible theory. (4) Compared with the existing static sensitivity method, the proposed method has higher reliability and calculation accuracy. The suggested approach may effectively reduce the potential for error in the current static sensitivity technique and can obtain damage coefficient values closer to the true values. It has been indicated that the proposed method may be an effective tool in structural damage detection in practice. Future research directions could focus on the following aspects. To further assess the reliability of this method, the first step is to apply it to real engineering issues. The second is to discuss the influence of the number and location of static displacement observation points on the identification accuracy of this method. Based on this, the economical layout of displacement observation points can be studied to facilitate the implementation of this method. The third is to apply this technique to several kinds of structures, such as composite structures, plate structures, and arch structures.


As the COVID-19 pandemic fades, the aviation industry is entering a fast recovery period. To analyze airport networks’ post-pandemic resilience during the recovery process, this paper proposes a Comprehensive Resilience Assessment (CRA) model approach using the airport networks of China, Europe, and the U.S.A as case studies. The impact of COVID-19 on the networks is analyzed after populating the models of these networks with real air traffic data. The results suggest that the pandemic has caused damage to all three networks, although the damages to the network structures of Europe and the U.S.A are more severe than the damage in China. The analysis suggests that China, as the airport network with less network performance change, has a more stable level of resilience. The analysis also shows that the different levels of stringency policy in prevention and control measures during the epidemic directly affected the recovery rate of the network. This paper provides new insights into the impact of the pandemic on airport network resilience. In different countries and regions, COVID-19 resulted in a significant decline of travel frequencies and distances, and a plummeting number of transportation users . The infTo the best of our knowledge, a model combining performance and resilience assessment with the recovery of different regional airport networks (with their different levels of stringency policy of prevention and control measures in the post-epidemic period) has not been studied in the literature. Accordingly, we address the following three problems:How to develop and suggest a model to quantitatively evaluate the resilience and recovery of aviation network due to an epidemic.Which airport operational factors should be considered to evaluate resilience, such as actually airport network performance changes, in light of different levels of stringency policy of prevention and control measures?What is the impact of epidemic prevention and control on network recovery rates?This paper considers the Chinese airport network (CAN), European airport network (EAA) and the USA airport network (AAN) as examples in a comprehensive method to analyze each airport network’s performance, and explores the impact of factors such as different levels of stringency policy of prevention and control measures on the recovery time and resilience level of the aviation network. We selected a stringency index which considered the thirteen metrics  as the criteria to assess the stringency policy. Because ICAO predicted a V-shaped or U-shaped expected recovery curve for the aviation industry when the COVID-19 broke out , this paThe remainder of this paper is outlined as follows. In Section 2, the related literature is reviewed. In Section 3, the paper proposes a CRA method and analyzes airport networks recovery with performance loss and different prevention and control measures policies. In Section 4, we compared the changes of different airport networks performance and resilience. Section 5 summarizes our findings, addresses the limitations of the model, and proposes new paths for future research.To highlight this study’s positioning and contribution, we review the relevant literature on complex network resilience and recovery.Understanding the structure and dynamics of complex social technology systems with social connectivity attributes has been made easier using network methodology. ZhangJ2010Zhang et al. (2010) used complex network theory to analyze aviation networks . Their rPatriarca et al. (2018) suggested that studies on resilience should change their focus from defined resilience to modeling from a safety perspective   . When α<0.2,0.6] .The composite recovery index Although this study focuses on the impact of epidemic prevention and control strategies on airport networks, The performance recovery of the airport network at a specific period is calculated using Eqs Notes: GR, which considered the three capacities of absorption, adaptation and recovery, and different recovery stages [GR to assess the resilience level of the airspace sectors [Nan and Sansavini 2017) used a quantitative approach to assess the resilience of infrastructure and proposed a resilience evaluation model, y stages . Wang an sectors . Guo et 17 used aR) represents the ability of a network to maintain its basic stability after a disturbance, and is defined by RPerformance(t) is network performance at t time.Robustness (RAPIDP) measures the speed at which the network loses performance after disruption, which is defined by tr is the network recovery start time and td is the time of the disturbance.The performance loss in the disruptive phase (RAPIRP) measures how promptly the network recovers after a disruption, and is defined in tns is the time needed for recovery to initial performance.Rapidity (TAPL) is the performance loss of the system under disturbance, and is defined by RPerformance(t0) is the network’s initial performance.The time-averaged performance loss (RA) evaluates the performance of the network in reaching the stabilization phase, and is defined in The recovery ability (GRE) comprehensively measures the entire process before and after the network is subjected to external disturbances, and is defined in The resilience metric . The ANN closed multiple routes that affected Alaska and Hawaii. However, during the epidemic, the EAN had a more intensive degree of connectivity than the CAN and AAN, while the AAN partial airport network mapped the United States of America airport network with high connectivity and excellent network transmission performance.Eff, passenger throughput, and RPerformance to assess the change in the performance of the airport network.In Eff were almost unchanged during the epidemic, and RPerformance of CAN decreased the least (36.35%) amongst the networks. However, in terms of its comparative robustness, CAN under-performed. The EAN structure experienced considerable disruption during the epidemic, as indicated by the significant changes in Eff , suggesting that the AAN has lower performance than the EAN.The changes in the CAN are shown in refer to . The cloThe ICAO predicted the post-pandemic recovery time for civil aviation as probably taking until 2024 [Eqs We predicted the resilience of airport networks using the CRA model shown in GRE for each airport network given their differing epidemic prevention and control strategies  perspective, higher stringency policy has less network performance loss and faster recovery rate in the short term. However, this limits the recovery rates for a long time.In the early period of future global public health emergencies, countries/regions should jointly implement higher-stringency policies for prevention and control measures. CRA was used to model the recovery function of each network and predict its recovery time. The three networks use different stringency policies in their recovery strategies, reflecting their national conditions. Different recovery measures in the late recovery period do not significantly affect the recovery rate; however, prolonged and strict travel bans and other policies will significantly limit the recovery rate of the aviation industry. In addition, a widespread epidemic causes people to panic, which is detrimental to the recovery of the aviation industry. Different governments have adopted different policy responses based on different national conditions, such as population resources and health resource occupancy, which showed different degrees of recovery in the aviation network. Therefore, countries/regions should jointly discuss when to lift or reduce prevention control measures, build a vaccine passport system of mutual trust, not impose quarantines on countries/regions that have been free of the epidemic over a long period, and accelerate the recovery of international routes. This is conducive to the rapid recovery of the aviation industry. Ideally, national and regional health and epidemic prevention departments should cooperate to reduce the impact of COVID-19. In future, we will continue to study the influence of multiple disturbances on network resilience. Analyzing such multiple disturbance effects can help better respond to future global public health events.S1 File(ZIP)Click here for additional data file.


Expert systems are frequently used to make predictions in various areas. However, the practical robustness of expert systems is not as good as expected, mainly due to the fact that finding an ideal system configuration from a specific dataset is a challenging task. Therefore, how to optimize an expert system has become an important issue of research. In this paper, a new method called the robust design-based expert system is proposed to bridge this gap. The technical process of this system consists of data initialization, configuration generation, a genetic algorithm (GA) framework for feature selection, and a robust mechanism that helps the system find a configuration with the highest robustness. The system will finally obtain a set of features, which can be used to predict a pandemic based on given data. The robust mechanism can increase the efficiency of the system. The configuration for training is optimized by means of a genetic algorithm (GA) and the Taguchi method. The effectiveness of the proposed system in predicting epidemic trends is examined using a real COVID-19 dataset from Japan. For this dataset, the average prediction accuracy was 60%. Additionally, 10 representative features were also selected, resulting in a selection rate of 67% with a reduction rate of 33%. The critical features for predicting the epidemic trend of COVID-19 were also obtained, including new confirmed cases, ICU patients, people vaccinated, population, population density, hospital beds per thousand, middle age, aged 70 or older, and GDP per capital. The main contribution of this paper is two-fold: Firstly, this paper has bridged the gap between the pandemic research and expert systems with robust predictive performance. Secondly, this paper proposes a feature selection method for extracting representative variables and predicting the epidemic trend of a pandemic disease. The prediction results indicate that the system is valuable to healthcare authorities and can help governments get hold of the epidemic trend and strategize their use of healthcare resources. Infectious diseases, if not effectively monitored and controlled, often result in mass human infections and pose risks of mass mortality, economic recession, and depletion of medical resources. For instance, coronavirus disease 19 (COVID-19) first broke out in late 2019 in Wuhan, Hubei Province, China . MoreoveSince the Omicron variant began to sweep the world, the COVID-19 pandemic has intensified in many countries due to the high transmissibility of this variant. Despite the emergence of vaccines approved by emergency-use authorization (EUA), because human knowledge of this virus is still insufficient, many people are dying from it every day. In 2022, WHO once again warned the world that the spread of COVID-19 should be closely monitored to prevent a simultaneous increase in the number of moderate and severe cases and the number of deaths as the virus continues to mutate. WHO also suggested that all governments should adjust management policies and quarantine measures for severe cases in a timely manner. Therefore, health authorities’ ability to get hold of the development trends of the pandemic is particularly important.Nowadays, the main tasks of healthcare management authorities in epidemic prevention include infection prevention, spread prediction, infection control, treatment of confirmed cases, and mortality reduction . Until aThe recent years have seen a substantial development of expert systems and publication of outstanding research findings about such systems in all fields. A human expert can quickly find feasible solutions to a new problem based on his or her experiences that accumulate over time. The introduction of the LISP programming language by John McCarthy in 1960 ushered in the development of research on expert systems . Early eAmong the numerous techniques of expert systems, artificial intelligence systems (AIs) have received particular attention from researchers ,16,17. AIn the last decade, big data analysis was a new line of research. Advancements in this area of research have contributed to the growth of expert systems. With the development of data mining, many big data-driven expert systems have been proposed ,26,27. TDue to the outstanding contribution of AIs to various research fields, it has also received attention in the field of healthcare. For instance, Malki et al.  developeRecent research has shown that feature selection is the most representative technique of expert systems. Expert systems usually rely on supervised learning. They need to be given a set of training data to learn the relationship between “input features” and “outcomes” ,32.Feature selection is a technique for extracting relevant features in data. The basic concept of feature selection is to find distinctive case features in order to enhance the learning efficiency of the expert system ,34.If the system can omit certain unnecessary features, it can reduce the data comparison time and achieve a higher accuracy. The main advantage of feature selection lies in its ability to adopt supervised or hierarchical feature extraction algorithms to replace manual ways . The groIn an early application of feature selection, Siedlecki and Sklansky  used genAs to the applications of feature selection in recent years, Lin et al.  proposedZhang et al.  proposedA feature-selection-based expert system is a smart technique that can be used for describing structured data. It can converge in its own database by inputs of specific structures, so it can accept a wide range of real cases. With this characteristic, feature selection is most appropriate for experiments that involve observations with an expert system ,29. As tPrevious studies have shown that it is not easy to get impressive learning results from expert systems ,47,48,49In this paper, a modified expert system, called the robust design-based expert system, is proposed to address the above issues. In addition, a genetic algorithm (GA) framework and the Taguchi method are integrated into the system to optimize the performance of the system. A good system configuration can not only increase the system’s prediction accuracy but also ensure the stable quality of the system. Features selected by the system can support inferences of epidemic trends. Finally, the feasibility and efficiency of the proposed system is verified using COVID-19 as an example.In this study, a robust design-based expert system is proposed for pandemic prediction. The system architecture is shown in To enhance the predictive performance of the proposed system, this study applies a genetic algorithm (GA) to feature selection. GAs have been widely used as a means to optimize expert systems ,35,36. IFirst of all, the GA stochastically generates an initial set of feasible solutions , in which each feasible solution is called chromosome and coded by a value of 0 or 1 see . Later, Next, the GA uses the genes in the chromosomes to compute the next generation. The proposed system adopts the GA as a predictor because of its evolution mechanism including selection, crossover, and mutation. These mechanisms can help the system achieve a high prediction accuracy. Selection decides which chromosomes can survive or should be eliminated; crossover is used to exchange partial sections of the chromosomes among parents to create the chromosomes of the next generation. Finally, mutation selects one gene from chromosomes for mutation. The probability of mutation is usually very low. Through repetitive executions of the above genetic operation, offspring with better fitness can be generated, and this operation stops when the stopping rule is met.The GA framework designed for this system is as illustrated in Step 1. Design the structure of chromosomesIn order to obtain an optimal combination of features, we encode feature selection in chromosomes using 0 or 1, as shown in Step 2. Generate the initial populationBefore execution of the genetic algorithm, the system has to generate an initial population comprising n chromosomes, each containing randomly generated parameter values. Each chromosome represents a possible solution . Given a total of x features, each chromosome is represented by x genes, and each generation has n chromosomes. Through evolution from one generation to the next of each generation, a better solution can be progressively obtained.Step 3. Compute the fitness of each chromosomeTo compute the fitness of each chromosome, we divide the case dataset into two subsets, including a training dataset and a test dataset. The training dataset is the main dataset for training the expert system, whereas the test dataset provides the subject to be tested by the expert system. The training dataset is larger than the test dataset.i can be computed through the following steps:(1)PL) for each case in the test dataset. For each case Compute the predicted level ((2)Compute the fitness of chromosome For any chromosome Step 4. Apply genetic operators to derive new offspringAfter a new generation is generated, the max fitness value searched for in the previous generation may be changed. As mentioned above, these genetic operators, including chromosome selection, crossover, and mutation, are intended to help generate new chromosomes. The selection operator determines whether a chromosome should be kept or eliminated depending on its fitness value. Chromosomes with a higher fitness value are more likely to survive. For crossover and mutation, the probabilities should be defined in advance.Step 5. Repeat Step 3 and Step 4 until the stopping rule is metStep 3 and Step 4 are iteratively executed until the stopping rule is satisfied. By the time that the expert system terminates the evolution based on the stopping rule, an optimal solution will be generated. This solution contains the finally selected features, which are most useful for the prediction of new cases and optimization of the weighting of features in the system.Step 6. Evolution is completedAfter genetic evolutions, the system outputs selected features. However, system configuration affects the solution performance of the GA framework and further reduce the robustness of the system.To enhance the robustness of the proposed system, a GA and the Taguchi method are integrated into the expert system.The Taguchi method  is utiliMSE) of the expected results based on the data from each run. The MSE value has a smaller-the-better characteristic. It is expressed as follows:After the configuration training is completed, the system will measure the mean-square error ) was installed as the runtime environment to implement the program. The proposed system was built using Evolver version 8.2 ) to process the genetic operations in the training. VBA  programming was also integrated to build the proposed expert system.Microsoft Excel 2016 (https://covid19.who.int (accessed on 30 June 2022). According to the statistics, the number of daily confirmed cases remains very high and COVID-19 is still severely spreading across the world , governments are most concerned about the rapid rise in confirmed cases. In the post-epidemic era, governments would pay particular attention to issues relating to the reduction of the mortality rate, including the treatment of severe cases and how to provide effective treatments. Therefore, this study uses the death rate as an important evaluation index and classifies the epidemic situation into different alert states as a reference for healthcare policy makers.The dataset comprises 819 days of COVID-19 data in Japan. We defined death rates for a daily number equivalent to or below 0.005 as low death rates (I), death rates between 0.005 and 0.01 as mid death rates (II), and death rates equivalent to or above 0.01 as high death rates (III). In the dataset, 83 cases were classified into the low death rate level (I), 34 cases into the mid death rate level (II), and 702 cases into the high death rate level (III). Death rate is defined as follows:In addition, this study uses demographic variables including population, population density, middle-aged population (middle age), population aged 65 or older (aged 65 older), population aged 70 or order (aged 70 older), cardiovascular disease death rate (CVD death rate), diabetes prevalence, hospital beds per thousand, and GDP per capital. The descriptive variables of COVID-19 collected in this study include new confirmed cases, hospital patients, ICU patients, people vaccinated, people fully vaccinated, and stringency index. These variables are used as the features of the COVID-19 dataset  To optimize training and system performance, using an appropriate system configuration is very important. Different configurations of parameters affect the performance of the system in finding a solution. Based on the Taguchi method , we adopAs shown in MSE value in each configuration of parameter levels was measured three times , Japan’s epidemic data from 22 January 2020 to 31 December 2021 was imported into the system to predict the epidemic trend over the next 120 days. The results are shown in It can be easily discovered from It can be discovered that the prediction of the proposed system was close to the real situations. As shown in Furthermore, the robust design-based expert system can give a warning when the number of death cases is about to rise ; and set up easily accessible PCR testing sites to instantly identify confirmed cases and provide medication. After the epidemic reaches its peak and begins to slow down through levels III and II with a decreasing death rate, the system suggests a monitoring level of I. Healthcare authorities are advised to progressively relax social management measures, lift the limitations on the economic activity of all industries, and also encourage people to conduct self-health management  was applied to test if there was any significant difference in prediction accuracy between datasets.To explore whether different compositions of the training dataset would affect the system’s predictive performance, we created three training datasets respectively consisting of 100%, 95%, and 90% of the training data. For each training dataset, the training was carried out five times, and the This study applied a robust design-based expert system with a feature selection learning mechanism to predict the possible trends of a pandemic disease. This system was designed to combine the strengths of the GA framework and the Taguchi method. In the case of Japan, the experimental results indicate that the proposed system can successfully capture the possible trends of COVID-19. The predicted results are close to the actual situations, meaning that the system’s capability is assured. With a unique design, the system can deliver good performance and stable quality. The prediction results of the system may be helpful for the government when estimating the death rate of the virus. Healthcare authorities can also employ the system to support formulation of epidemic management policies and allocation of medical resources.SN ratio can correctly reflect the differences between system configurations to further ensure the prediction quality of the system.Building an effective expert system is not easy ,43. ThisWe also found that although the GA architecture is suitable for learning feature selection when performing an expert system experiment, the amount of the training dataset must be sufficient to obtain good results. In addition, due to the selection, crossover, and mutation mechanisms designed in the GA framework, the evolution of each feature in the chromosome can be evaluated accurately. This is key to the success of the feature selection expert system. Hence, the GA framework can achieve a good performance.The proposed system can signal an alert when there is a rise in the level of death cases, which means that the number of severe cases has risen too. This alert has an important management implication for healthcare management authorities. If we can capture an imminent increase in the domestic death of the epidemic, the government can make necessary deployments in advance ,8. This Policy implications: In fact, the government’s healthcare resources are limited. If changes in the number of severe cases or death cases in the country cannot be correctly assessed, the government’s deployment of medical resources will be affected first. For example, when the epidemic is slowing down, health authorities can allow more patients with mild symptoms to stay in the hospital for treatment and observation and also delay their discharge time. Conversely, if the epidemic becomes more serious , health authorities need to deploy ahead of time and change the management policies for mild and severe patients. For instance, when medical resources are insufficient, some countries may advise patients with a mild illness to stay at home to avoid medical collapse. Take Japan as an example. The repeated changes in the epidemic situation have put tremendous pressure on the government’s patient care policy. This highlights the importance of situational judgment. Therefore, the government’s primary task in COVID-19 management is to get hold of changes in the death rate in its territory and then make immediate adjustments of health care policies according to the epidemic trend. This kind of timely and situation-based response is conducive to the deployment of medical resources and patient management.In the present, many countries have adopted policies that favor living with the virus. However, the spread and mutation of COVID-19 still pose a high threat. For example, despite the release of EUA-approved vaccines, many countries still experience large numbers of breakthrough infections after widespread vaccination. This phenomenon shows that epidemic control cannot rely solely on EUA-approved vaccines. All countries should be cautious in monitoring, isolation, and social management of confirmed cases. In the meantime, how to effectively get hold of the daily number of death cases becomes crucial and is an important task for governments when building an epidemic management information system. It determines whether governments can monitor the epidemic development in real time and adjust the degree of relaxation or contraction of medical resources at any time, so as to control the rate of virus transmission within the limits of national resources. Therefore, effective epidemic predictions with an appropriate support system can contribute greatly to epidemic control.Management implications: In this study, the features considered in the prediction of COVID-19 epidemic trends consisted of descriptive variables and demographic variables. From the dataset of Japan, the average prediction accuracy was 60%. The system shows better accuracy given three levels of alert state were considered . In addition, 10 representative features were also selected, resulting in a selection rate of 67% with a reduced rate of 33%. According to the experimental results, among the descriptive variables, new confirmed cases, ICU patients, and people vaccinated are important factors affecting the mortality of COVID-19 in Japan. In comparison, hospital patients and stringency index are not critical to the prediction result.Among the demographic variables, population, population density, hospital beds per thousand, middle age, aged 70 older, and GDP per capita are all critical to the mortality of COVID-19 in Japan. However, CVD death rate, diabetes prevalence, and aged 65 older are not significantly related to mortality.From the above findings, we can infer that, in addition to the descriptive variables that healthcare units are more concerned about , the demographic structure of a country also significantly affects the pandemic mortality. This suggests that governments should pay attention to the correlation between the country’s demographic structure and the important descriptive variables of COVID-19, because the mortality of the pandemic may increase significantly with increases in certain descriptive variables such as new confirmed cases and ICU patients.Therefore, the interactions between the abovementioned features and the differences in their importance should be a research topic worthy of further research.This study was subject to two main limitations. First, the descriptive variables of COVID-19 are aggregated statistical data, and the database is not classified by virus variants determined through gene sequencing. Second, the subject of this study was Japan but the spread of the epidemic varies from country to country. With a good predictive performance, the proposed system can be applied to predict the spread of new COVID-19 variants and explore the differences in death rate between different variants. Moreover, the differences in importance between features is also an interesting research issue. The system architecture of this study can be modified for the research of the above issues in the future.Expert systems are increasingly used in important applications. Healthcare management organizations across the world are seeking more accurate methods to predict the spread of epidemics and support policy adjustment. Considering the tremendous impacts that COVID-19 has brought to the world and the need for healthcare authorities to build a highly adaptive prediction system, this study proposes an enhanced artificial intelligence prediction technique called the robust design-based expert system. The GA framework and Taguchi method are integrated into this system to optimize the performance of the system. The epidemic data in Japan are employed to develop a prediction system for COVID-19. The prediction accuracy of proposed system was 60%. In addition, the feature selection rate of the system was 67% with a reduction of 33%. The experimental results indicate that the proposed system is effective in predicting the epidemic trend over the next four months (about 120 days). The proposed system can be utilized to support the prediction of epidemic trends as well as the deployment of resources. Future researchers can apply this system to analyze the epidemic trends in countries with more serious outbreaks and further explore the differences between countries and what can be improved with regard to the application of this expert system.


Despite the proposed ‘certainty’ in a city or region hosting a mega-event, there has long been issues of uncertainty surrounding the planning and implementation of what have until now essentially been mega-projects. Large events have found a variety of ways to adapt and respond to unforeseen circumstances due to political conflicts, planning of oversize venues, limited time of implementation, and legacies that are difficult to manage. Considering the further increased uncertainty surrounding the planning of mega-events as a result of the COVID-19 pandemic, this paper examines how an accompanying cultural component—e.g., the Cultural Olympiad—may help cities that plan for events like the Olympics transition toward diversified drivers and long-term legacy. In particular, the case of cultural offering in Milan between the two mega-events of Expo 2015 and the upcoming 2026 Milano-Cortina Olympic Games shows how host cities can address growing uncertainty through the expanded role of a cultural programme combined with the rescaling of traditional mega-event formats. With this purpose, 2012 London Cultural Olympiad has been selected as an antecedent example—with positive and negative outcomes—to critically review the relationships between the Olympics and the Cultural Olympiad, as well as their widespread spatial strategy and public engagement. In addition, the paper offers more general conclusions regarding learning potentials of jointly studying mega-events and cultural policy. Mega-events are extraordinary but recurrent occasions to study processes of urban change Roche . Even beUmbrellas, Incubators, Mothers and Killers as proposed by Jones and Ponzini , we adopt a qualitative perspective rather than a strict quantitative classification based on number of visitors, overall cost, media exposure or other parameters. The “ Ponzini . The “LiUmbrellas, Incubators, Mothers and Killers). This framework has been selected as it provides an articulated position from which to observe the mega-events and their effects over time with the purpose of deconstructing the spatial arrangements and cultural policy. In particular, we consider the combination of spatial strategies and cultural programmes within the analysed case studies of London and Milan by checking their multiple locations and contributions to the host cities and regions.Cultural events such as the Cultural Olympiad and the side Expo programmes face significant and multifaceted uncertainties, both in relation to mega-event and post-event planning processes, as well as in terms of public participation. Usually, this is visible in relation to the long-term development and mega-event legacy. Accordingly, we first completed a state-of-the-art literature review to explore the question of multiple uncertainties regarding the hosting of mega-events, that until recently has been in part overlooked. This background provides the basis from which to approach the two cases of London  and Milan . This paper does not aim to compare different case studies. We selected the only existing framework that connects mega-events with accompanying cultural events by discussing four types . The same framework has been used to critically analyse the planning of 2026 Milano-Cortina Winter Olympics and the potential learnings for the future. Milan has been selected due to this progression of hosting two separate mega-events, but with clear legacy during the interim period of expanding the city’s events and cultural policy. After the experimentation with widespread cultural side events to the official site of the Milan Expo 2015, the Milan-Cortina 2026 are the first Winter Olympic Games to follow many of the precepts of the 2020 Agenda . This analytical framework allows one to clearly highlight the criticalities as well as the potential learnings for Milan and the wider Olympic region in the future. As well, this approach identifies more generally the emerging role for cultural policy to play in not only supporting the Olympic Games but as one of the enduring legacies.The Great Planning Disasters, Hall  Jennings . TypicalAfter the social protests against mega-events in Brazil Vainer  and due Once seen as an aspect making these kinds of events seem less spectacular than other mega-events, this may now serve as a strategic advantage in their planning and implementation. Di Vita and Wilson  collect Events through the COVID-19 Pandemic: Evidence from Europe . Notably, Essen for the Ruhr 2010 ECoC and the Matera-Basilicata 2019 ECoC both took a regional approach to hosting their year of celebration by spreading events across a wider region—bringing greater attention to often overlooked areas and benefitting the overall programme through using existing resources. However, it is worth highlighting the experience of London and other host cities where the Olympic Games actually kept away visitors looking to avoid higher costs and complications from the Games, leading to a reduced number of tourists during the mega-event itself Vlachos . As a coOne Extraordinary Day event saw unique dance performances taking place across the city of London in iconic locations like the Millennium Bridge and City Hall building, both designed by Foster + Partners. These performances were suspended from the structures themselves, turning them into an active component rather than just a static backdrop. Olympic cities have long used the Games to show dramatic images of the city for promotion, and these cultural events can use sites not adapted for sporting venues while yet activating them in new and innovative ways. Beyond London, several historic sites such as Stonehenge and Hadrian’s Wall amongst others served as cultural event locations , the main 2015 Expo site was established as a single satellite platform between the Municipalities of Milan and Rho, within the Metropolitan City of Milan, 14 km from the centre. For the event celebration, the problem of physical and intangible disconnection between the Expo site and the rest of the city was addressed in multiple ways, in terms of infrastructure and mobility, as well as communication . However, these visions only implicitly shift from large redevelopments to widespread regeneration and branding of peripheral areas, with goals of socio-spatial equity Pasqui . At the ExpoinCittà present several key learnings for future host cities. Within the framework of four types of connections between mega-events and long-term cultural policies, we use the upcoming Milano-Cortina 2026 Winter Olympic Games to make these lessons more concrete, evident, and challenging. In particular, we summarize the arguments according to the spatial structure, long-term vision and public participation dimensions, as well as to further problematization of the four proposed types of relations between mega-events and accompanying cultural events. More general considerations regarding collecting data and learning opportunities conclude the paper. These conclusions intend to contribute to developing a Cultural Olympiad of the 2026 Winter Games in light of both the increased uncertainty and the lack of any existing plans to critically analyse.The examples of the London 2012 Cultural Olympiad and the Milan 2015 ExpoinCittà programme has expanded the Expo effects in space  and in time (in the following years and city agendas). Accordingly, while Milan, Cortina and a handful of other small towns will host the sport events of the 2026 Winter Games, the Cultural Olympiad could be used to accompany this diffused event approach and attract greater attention to these areas across the Alpine region than they would otherwise manage on their own. This widescale diffusion of events, particularly in locations often left out of the spotlight of such mega-events, has been highlighted as one of the positive elements of both the 2012 London Cultural Olympiad programme and the 2015 Milan ExpoinCittà programme.As noted above, Milan-Cortina 2026 represents a new approach as the first Winter Games to fully embrace the 2020 Olympic Agenda IOC , with twThe London case and its shortcomings also shows that such a strategy requires a long-term, legacy-oriented vision with a robust governance. London had already begun preparing the groundwork for their Cultural Olympiad within the original bid, seven years prior to hosting the Olympic Games, with cultural events being staged four years prior to the opening ceremony. Nonetheless, there was a substantial number of cultural organizations and activities that could not survive the post-event phase. In the case of Milano-Cortina, as of 2022, a cultural programme has not been planned as the city and regions, at the time of writing, are still grappling with the COVID-19 pandemic. However, the current situation not only presents hurdles to overcome, but also opportunities to develop. Digital events can take on a greater importance within a Cultural Olympiad, as well as activating the ‘unusual’, iconic or heritage spaces across the region and country (as seen in London and the UK).ExpoinCittà (first developed in Milan on the occasion of the 2015 Expo in order to utilize and activate thousands of public and private spaces within the metropolitan region) could be spread out to all the areas involved in hosting the 2026 Games. Nevertheless, such a spatially spread approach would require a stronger governance system involving representatives from multiple areas, as well as it could benefit from a spatial pre-selection of areas and actors to involve, so as to avoid the polarization of disparities between urban cores and peripheries that emerged during the Expo.The incremental and unplanned strategy of As seen in both the London and Milan case studies, this diffused approach can risk becoming quite disconnected from both itself as well as to the related mega-event. Close coordination and planning would be necessary to undertake such an initiative. A wide-spread cultural programme should also strive to build in sustained funding sources (not solely dependent upon the Olympics to achieve a long-term legacy). However, this potential input to the planning of the 2026 Cultural Olympiad in space and time has not been considered yet by the official event governance.ExpoinCittà programme of accompanying events through a specific and innovative e-participation digital platform  would help to provide greater insights into evolution of cultural policy and real effects of hosting a mega-event. The tracking of such data (starting from the level of public administrations) should be an element to include in future evaluations of mega-events. Access to more refined data would also allow for finer grained analysis to better understand the long-term cultural impact of mega-events in contributing to spatial trends in cities and wider regions over time. The case of Milan and the availability of data from the 2015 Expo has been critical in revealing the broader impact of that event on cultural policy, despite its more obvious shortcomings in reusing the Expo site itself. The continued study of these elements in light of the upcoming Milano-Cortina 2026 Winter Olympics can greatly expand upon the learnings of these processes and potentially present Milan as interesting case in terms of legacy of continuing cultural activities following a cultural mega-event.