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This study aims to propose an efficient method for solving reliability-based design optimization (RBDO) problems.

In the proposed algorithm, genetic algorithm (GA) is employed to search the global optimal solution of design parameters satisfying the reliability and deterministic constraints. The Kriging model based on U learning function is used as a classification tool to accurately and efficiently judge whether an individual solution in GA belongs to feasible region.

Compared with existing methods, the proposed method has two major advantages. The first one is that the GA is employed to construct the optimization framework, which is helpful to search the global optimum solutions of the RBDO problems. The other one is that the use of Kriging model is helpful to improve the computational efficiency in solving the RBDO problems.

Since the boundaries are concerned in two Kriging models, the size of the training set for constructing the convergent Kriging model is small, and the corresponding efficiency is high.

Project-driven supply chain risks pose a significant threat to the success of complex development projects, in terms of achieving key performances such as quality, time and efficiency. The purpose of this paper is to adopt a supply chain quality perspective in order to explore and better understand the unique attributes of risks associated with project-driven supply chains for continuously improving the quality of both processes and products.

Theoretically grounded in the framework of Bayesian Belief Networks and Game theory, this paper develops a structured process for assessing and managing risks in project-driven supply chains. The application of the proposed approach is demonstrated through a simulation case study conducted on the development project of Boeing 787 aircraft.

The conflicting incentives amongst stakeholders in a supply chain can jeopardise the success of a project and therefore, assessment of this category of risks classified as “Game theoretic risks” needs special consideration. Project-driven supply chain risks pose a significant threat to the success of complex projects. The results of the study clearly revealed that without mitigating the game theoretic risks, the main objective of timely completion of the Boeing 787 project was not materialised. Further, the lack of management expertise was the major factor contributing to the overall project costs including cost of quality.

The proposed process and analyses present a significant and original insight in terms of capturing the key determinants of both product and service quality such as product performance, convenience and reliability of service, timeliness, ease of maintenance, flexibility, and customer satisfaction and comfort. Propositions are developed for ascertaining the significance of information sharing in a project-driven supply chain, and a fair sharing partnership is introduced to help supply chain managers in managing game theoretic risks in order to achieve the goals of quality, time and efficiency.

This study aims to examine the impact of controversial governance practices on media coverage under a specific context. Based on the attribution theory, this study develops a theoretical framework to explore how antecedent factors can influence attribution process under a particular cultural context.

This paper presents a behavioral view of the media and corporate governance to demonstrate how media attributes different reasons for the same controversial governance practice in Chinese-specific context. Using 1,198 non-state-owned listed company observations in China as the study sample, cross-section data are used to build a multiple linear regression mode to test hypotheses.

The analysis indicates that the media imposes fewer penalties on founder-CEO firms than on non-founder-CEO firms for engaging in controversial governance practices, such as CEO compensation. CEO tenure negatively moderates the effect of CEO compensation on negative media coverage in non-founder-CEO firms. The positive media bias evidence for founder-CEO firms exists only when the firm is better performed.

This study’s contribution to the governance literature starts with its logical reasoning of basic assumptions in the agency theory, and that media penalty will arise when managers impose actions that against interests of shareholders or other stakeholders. This study shows that the rule is not always true. The findings also bridge the connection of governance literature and reputation literature to better explain how media can act as a social arbitration role.

This study provides insights into how belief and information of reputational evaluators affect attribution consequences on controversial governance practices. Moreover, this study looks beyond the internal elements and focuses on China’s traditional cultural context as well. Specifically, the authors concentrate on the attribution process by showing the importance of evaluators’ framing tendency with regard to controversial practices. The results extend the knowledge about how conformity makes media coverage shows a bias effect on interactions during the evaluation process.

The purpose of this paper is to implement a genetic algorithmic geared toward building an optimized investment portfolio exploring data set from stocks of firms listed on the Nigerian exchange market. To provide a research-driven guide toward portfolio business assessment and implementation for optimal risk-return.

The approach was to formulate the portfolio selection problem as a mathematical programming problem to optimize returns of portfolio; calculated by a Sharpe ratio. A genetic algorithm (GA) is then applied to solve the formulated model. The GA lead to an optimized portfolio, suggesting an effective asset allocation to achieve the optimized returns.

The approach enables an investor to take a calculated risk in selecting and investing in an investment portfolio best minimizes the risks and maximizes returns. The investor can make a sound investment decision based on expected returns suggested from the optimal portfolio.

The data used for the GA model building and implementation GA was limited to stock market prices. Thus, portfolio investment that which to combines another capital market instrument was used.

Investment managers can implement this GA method to solve the usual bottleneck in selecting or determining which stock to advise potential investors to invest in, and also advise on which capital sharing ratio to reduce risk and attain optimal portfolio-mix targeted at achieving an optimal return on investment.

The value proposition of this paper is due to its exhaustiveness in considering the very important measures in the selection of an optimal portfolio such as risk, liquidity ratio, returns, diversification and asset allocation.

The purpose of this paper is to execute the efficiency analysis of the selected metaheuristic algorithms (MAs) based on the investigation of analytical functions and investigation optimization processes for permanent magnet motor.

A comparative performance analysis was conducted for selected MAs. Optimization calculations were performed for as follows: genetic algorithm (GA), particle swarm optimization algorithm (PSO), bat algorithm, cuckoo search algorithm (CS) and only best individual algorithm (OBI). All of the optimization algorithms were developed as computer scripts. Next, all optimization procedures were applied to search the optimal of the line-start permanent magnet synchronous by the use of the multi-objective objective function.

The research results show, that the best statistical efficiency (mean objective function and standard deviation [SD]) is obtained for PSO and CS algorithms. While the best results for several runs are obtained for PSO and GA. The type of the optimization algorithm should be selected taking into account the duration of the single optimization process. In the case of time-consuming processes, algorithms with low SD should be used.

The new proposed simple nondeterministic algorithm can be also applied for simple optimization calculations. On the basis of the presented simulation results, it is possible to determine the quality of the compared MAs.

Cardiac arrest is a severe heart anomaly that results in billions of annual casualties. Smoking is a specific hazard factor for cardiovascular pathology, including coronary heart disease, but data on smoking and heart death not earlier reviewed. The Heart Rate Variability (HRV) parameters used to predict cardiac arrest in smokers using machine learning technique in this paper. Machine learning is a method of computing experience based on automatic learning and enhances performances to increase prognosis. This study intends to compare the performance of logistical regression, decision tree, and random forest model to predict cardiac arrest in smokers. In this paper, a machine learning technique implemented on the dataset received from the data science research group MITU Skillogies Pune, India. To know the patient has a chance of cardiac arrest or not, developed three predictive models as 19 input feature of HRV indices and two output classes. These model evaluated based on their accuracy, precision, sensitivity, specificity, F1 score, and Area under the curve (AUC). The model of logistic regression has achieved an accuracy of 88.50%, precision of 83.11%, the sensitivity of 91.79%, the specificity of 86.03%, F1 score of 0.87, and AUC of 0.88. The decision tree model has arrived with an accuracy of 92.59%, precision of 97.29%, the sensitivity of 90.11%, the specificity of 97.38%, F1 score of 0.93, and AUC of 0.94. The model of the random forest has achieved an accuracy of 93.61%, precision of 94.59%, the sensitivity of 92.11%, the specificity of 95.03%, F1 score of 0.93 and AUC of 0.95. The random forest model achieved the best accuracy classification, followed by the decision tree, and logistic regression shows the lowest classification accuracy.

The purpose of this paper is to exploit a database of pre-existing designs to accelerate parametric optimization of antenna structures is investigated.

The usefulness of pre-existing designs for rapid design of antennas is investigated. The proposed approach exploits the database existing antenna base designs to determine a good starting point for structure optimization and its response sensitivities. The considered method is suitable for handling computationally expensive models, which are evaluated using full-wave electromagnetic (EM) simulations. Numerical case studies are provided demonstrating the feasibility of the framework for the design of real-world structures.

The use of pre-existing designs enables rapid identification of a good starting point for antenna optimization and speeds-up estimation of the structure response sensitivities. The base designs can be arranged into subsets (simplexes) in the objective space and used to represent the target vector, i.e. the starting point for structure design. The base closest base point w.r.t. the initial design can be used to initialize Jacobian for local optimization. Moreover, local optimization costs can be reduced through the use of Broyden formula for Jacobian updates in consecutive iterations.

The study investigates the possibility of reusing pre-existing designs for the acceleration of antenna optimization. The proposed technique enables the identification of a good starting point and reduces the number of expensive EM simulations required to obtain the final design.

The proposed design framework proved to be useful for the identification of good initial design and rapid optimization of modern antennas. Identification of the starting point for the design of such structures is extremely challenging when using conventional methods involving parametric studies or repetitive local optimizations. The presented methodology proved to be a useful design and geometry scaling tool when previously obtained designs are available for the same antenna structure.

On-ramp merging areas are typical bottlenecks in the freeway network since merging on-ramp vehicles may cause intensive disturbances on the mainline traffic flow and lead to various negative impacts on traffic efficiency and safety. The connected and autonomous vehicles (CAVs), with their capabilities of real-time communication and precise motion control, hold a great potential to facilitate ramp merging operation through enhanced coordination strategies. This paper aims to present a comprehensive review of the existing ramp merging strategies leveraging CAVs, focusing on the latest trends and developments in the research field.

The review comprehensively covers 44 papers recently published in leading transportation journals. Based on the application context, control strategies are categorized into three categories: merging into sing-lane freeways with total CAVs, merging into sing-lane freeways with mixed traffic flows and merging into multilane freeways.

Relevant literature is reviewed regarding the required technologies, control decision level, applied methods and impacts on traffic performance. More importantly, the authors identify the existing research gaps and provide insightful discussions on the potential and promising directions for future research based on the review, which facilitates further advancement in this research topic.

Many strategies based on the communication and automation capabilities of CAVs have been developed over the past decades, devoted to facilitating the merging/lane-changing maneuvers at freeway on-ramps. Despite the significant progress made, an up-to-date review covering these latest developments is missing to the authors’ best knowledge. This paper conducts a thorough review of the cooperation/coordination strategies that facilitate freeway on-ramp merging using CAVs, focusing on the latest developments in this field. Based on the review, the authors identify the existing research gaps in CAV ramp merging and discuss the potential and promising future research directions to address the gaps.

In response to the pandemic, the Korean government introduced fiscal measures: including the Emergency Disaster Relief Funds which is the first-ever universal benefit in Korea. This paper identifies the effects of the measures on poverty, household income and household consumption expenditure under the disproportionate effect of the pandemic.

This study analysed the Korea Household Income and Expenditure Survey (KHIES) with Changes-in-Changes at five percentiles (5, 25, 50, 75 and 95%) instead of Difference-in-Differences (DD) because the parallel trends assumption of DD cannot be investigated due to the recent KHIES redesign. In addition, it also exmined the effects on vulnerable groups (e.g. female, elderly and young households).

COVID-19 has had prompt and disproportionate effects on the vulnerable, such as low-income, female and elderly households. However, the government measures had a limited effect. First, the measures could not mitigate the initial income reduction and only had temporary positive effects on income and consumption expenditure. Second, young households tended to save the relief instead of present consumption. Lastly, education disparity was observed at 25 and 50%. Therefore, this study suggests that response measures need to be sustainable and concentrated on the vulnerable.

A large literature estimated effects on either household income or household consumption expenditure, and focused on macroeconomic indices (e.g. marginal propensity to consumption). This study analysed both income (poverty) and consumption expenditure and found policy implications for better welfare system in an economic downturn.

A novel framework for expedited antenna optimization with an iterative prediction-correction scheme is proposed. The methodology is comprehensively validated using three real-world antenna structures: narrow-band, dual-band and wideband, optimized under various design scenarios.

The keystone of the proposed approach is to reuse designs pre-optimized for various sets of performance specifications and to encode them into metamodels that render good initial designs, as well as an initial estimate of the antenna response sensitivities. Subsequent design refinement is realized using an iterative prediction-correction loop accommodating the discrepancies between the actual and target design specifications.

The presented framework is capable of yielding optimized antenna designs at the cost of just a few full-wave electromagnetic simulations. The practical importance of the iterative correction procedure has been corroborated by benchmarking against gradient-only refinement. It has been found that the incorporation of problem-specific knowledge into the optimization framework greatly facilitates parameter adjustment and improves its reliability.

The proposed approach can be a viable tool for antenna optimization whenever a certain number of previously obtained designs are available or the designer finds the initial effort of their gathering justifiable by intended re-use of the procedure. The future work will incorporate response features technology for improving the accuracy of the initial approximation of antenna response sensitivities.

The proposed optimization framework has been proved to be a viable tool for cost-efficient and reliable antenna optimization. To the knowledge, this approach to antenna optimization goes beyond the capabilities of available methods, especially in terms of efficient utilization of the existing knowledge, thus enabling reliable parameter tuning over broad ranges of both operating conditions and material parameters of the structure of interest.