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A new type of variable damping viscous damper is developed to meet the settings of different damping parameter values at different working stages. Its main principle and design structure are introduced, and the two-stage and multi-stage controllable damping methods are proposed.

The theoretical calculation formulas of the damping force of power-law fluid variable damping viscous damper at elongated holes are derived, aiming to provide a theoretical basis for the development and application of variable damping viscous dampers. For the newly developed variable damping viscous damper, the dynamic equations for the seismic reduction system with variable damping viscous dampers under a multi-degree-of-freedom system are established. A feasible calculation and analysis method is proposed to derive the solution process of time history analysis. At the same time, a program is also developed using Matlab. The dynamic full-scale test of a two-stage variable damping viscous damper was conducted, demonstrating that the hysteresis curve is complete and the working condition is stable.

Through the calculation and analysis of examples, the results show that the seismic reduction effect of high and flexible buildings using the seismic reduction system with variable damping viscous dampers is significant. The program developed is used to analyze the seismic response of a broadcasting tower using a variable damping TMD system under large earthquakes. The results indicate that the installation of variable damping viscous dampers can effectively control the maximum inter-story displacement response of TMD water tanks and can effectively consume seismic energy.

This method can provide a guarantee for the safe and effective operation of TMD in wind and vibration control.

Recycled concrete is an economical and environmentally friendly green material. The shear performance of recycled concrete load-bearing masonry is studied, which is great of significance for its promotion and application and also has great significance for the sustainable development of energy materials.

In total, 30 new load-bearing block masonry samples of self-insulating recycled concrete are subjected to pure shear tests, and 42 samples are tested subjected to shear-compression composite shear tests. According to the axial design compression ratio, the test is separated into seven working conditions (0.1–0.8).

According to the test results, the recommended formula for the average shear strength along the joint section of recycled concrete block masonry is given, which can be used as a reference for engineering design. The measured shear-compression correlation curves of recycled concrete block masonry are drawn, and the proposed limits of three shear-compression failure characteristics are given. The recommended formula for the average shear strength of masonry under the theory of shear-friction with variable friction coefficient is given, providing a valuable reference for the formulation of relevant specifications and practical engineering design.

Simulated elastoplastic analysis and finite element modeling on the specimens are performed to verify the test results.

The objective of the study is to measure outbound medical tourists' satisfaction and loyalty based on medical tourists' experience from a developing country's perspective.

The medical tourists taken medical services from India constituted the population of this study. By applying a purposive and snowball sampling techniques, samples were selected. Based on the expectation confirmation theory (ECT) and a modified medical tourism experience model, this study empirically analyzed the research hypotheses by applying the partial least square–structural equation modeling (PLS–SEM) technique.

The empirical result revealed that the experience of service quality, medical tourism infrastructure and experience of medical tourism expenses has a direct impact on medical tourists' satisfaction. Furthermore, destination appeal and culture has a significant moderating effect on the relationship between medical tourists' satisfaction and destination loyalty. Multigroup analysis (MGA) was performed to examine group differences of the model. The MGA results revealed that, based on age, the research model remains undifferentiated between groups. In contrast, based on income, the only relationship, medical tourism infrastructure and medical tourists' satisfaction significantly differ between groups, while others remain identical from each other.

The study results will contribute both theoretically and practically. Theoretically, the study will be a helpful instrument to figure out medical tourists' behavior through the lens of ECT in a developing country’s perspective. Furthermore, practically this study results will assist policymakers and practitioners of medical tourism in formulating strategies and making future decisions effectively.

The study has uniqueness in two aspects. First, the study empirically revealed Bangladeshi medical tourists' experience and behavioral intention to Indian medical tourism destinations. Second, the study’s findings exposed quantifiable insights into the Bangladesh–India medical tourism phenomenon.

This study aims to investigate the spatial distribution of housing prices and identify the affecting factors (independent variable) on the cost of residential units (dependent variable).

The method of the present study is descriptive-analytical and has an applied purpose. The used statistical population in this study is the residential units’ price in Tehran in 2021. For this purpose, the average per square meter of residential units in the city neighborhoods was entered in the geographical information system. Two techniques of ordinary least squares regression and geographically weighted regression have been used to analyze housing prices and modeling. Then, the results of the ordinary least squares regression and geographically weighted regression models were compared by using the housing price interpolation map predicted in each model and the accurate housing price interpolation map.

Based on the results, the ordinary least squares regression model has poorly modeled housing prices in the study area. The results of the geographically weighted regression model show that the variables (access rate to sports fields, distance from gas station and water station) have a direct and significant effect. Still, the variable (distance from fault) has a non-significant impact on increasing housing prices at a city level. In addition, to identify the affecting variables of housing prices, the results confirm the desirability of the geographically weighted regression technique in terms of accuracy compared to the ordinary least squares regression technique in explaining housing prices. The results of this study indicate that the housing prices in Tehran are affected by the access level to urban services and facilities.

Identifying factors affecting housing prices helps create sustainable housing in Tehran. Building sustainable housing represents spending less energy during the construction process together with the utilization phase, which ultimately provides housing at an acceptable price for all income deciles. In housing construction, the more you consider the sustainable housing principles, the more sustainable housing you provide and you take a step toward sustainable development. Therefore, sustainable housing is an important planning factor for local authorities and developers. As a result, it is necessary to institutionalize an integrated vision based on the concepts of sustainable development in the field of housing in the Tehran metropolis.

This study aims to explore the gap between research and practice on supply chain risks due to COVID-19 by exploring the changes in global emphasis on supply chain risk research.

This work designed a research framework to compare the research of supply chain risks before and during the COVID-19 pandemic based on machining learning and text clustering and using the relevant publications of the web of science database.

The results show that scholars' attention to supply chain crisis has increased in the wake of the COVID-19 outbreak, but there are differences among countries. The United Kingdom, India, Australia, the USA and Italy have greatly increased their emphasis on risk research, while the supply chain risk research growth rate in other countries, including China, has been lower than the global level. Compared with the pre-pandemic period, the research of business finance, telecommunications, agricultural economics policy, business and public environmental occupational health increased significantly during the pandemic. The hotspots of supply chain risk research have changed significantly during the pandemic, focusing on routing problem, organizational performance, food supply chain, dual-channel supply chain, resilient supplier selection, medical service and machine learning.

This study has limitations in using a single database.

This work compared the changes in global and various countries' supply chain risk research before and during the pandemic. On the one hand, it helps to judge the degree of response of scholars to the global supply chain risk brought about by COVID-19. On the other hand, it is beneficial for supply chain practitioners and policymakers to gain an in-depth understanding of the relationship between the COVID-19 pandemic and supply chain risk, which might provide insights into not only addressing the supply chain risk but also the recovery of the supply chain.

The initial exploration of the changing extent of supply chain risk research in the context of COVID-19 provided in this paper is a unique and earlier attempt that extends the findings of the existing literature. Secondly, this research provides a feasible analysis strategy for supply chain risk research, which provides a direction and paradigm for exploring more effective supply chain research to meet the challenges of COVID-19.

The purpose of this paper is to propose a new mixed-integer formulation for the time-dependent multi-skilled resource-constrained project scheduling problem (MSRCPSP/t) considering learning effect. The proposed model extends the basic form of the MSRCPSP by three concepts: workforces have different efficiencies, it is possible for workforces to improve their efficiencies by learning from more efficient workers and the availability of workforces and resource requests of activities are time-dependent. To spread dexterity from more efficient workforces to others, this study has integrated the concept of diffusion maximization in social networks into the proposed model. In this respect, the diffusion of dexterity is formulated based on the linear threshold model for a network of workforces who share common skills. The proposed model is bi-objective, aiming to minimize make-span and total costs of project, simultaneously.

The MSRCPSP is an non-deterministic polynomial-time hard (NP-hard) problem in the strong sense. Therefore, an improved version of the non-dominated sorting genetic algorithm II (IM-NSGA-II) is developed to optimize the make-span and total costs of project, concurrently. For the proposed algorithm, this paper has designed new genetic operators that help to spread dexterity among workforces. To validate the solutions obtained by the IM-NSGA-II, four other evolutionary algorithms – the classical NSGA-II, non-dominated ranked genetic algorithm, Pareto envelope-based selection algorithm II and strength Pareto evolutionary algorithm II – are used. All algorithms are calibrated via the Taguchi method.

Comprehensive numerical tests are conducted to evaluate the performance of the IM-NSGA-II in comparison with the other four methods in terms of convergence, diversity and computational time. The computational results reveal that the IM-NSGA-II outperforms the other methods in terms of most of the metrics. Besides, a sensitivity analysis is implemented to investigate the impact of learning on objective function values. The outputs show the significant impact of learning on objective function values.

The proposed model and algorithm can be used for scheduling activities of small- and large-size real-world projects.

Based on the previous studies reviewed in this paper, one of the research gaps is the MSRCPSP with time-dependent resource capacities and requests. Therefore, this paper proposes a multi-objective model for the MSRCPSP with time-dependent resource profiles. Besides, the evaluation of learning effect on efficiency of workforces has not been studied sufficiently in the literature. In this study, the effect of learning on efficiency of workforces has been considered. In the scarce number of proposed models with learning effect, the researchers have assumed that the efficiency of workforces increases as they spend more time on performing a skill. To the best of the authors’ knowledge, the effect of learning from more efficient co-workers has not been studied in the literature of the RCPSP. Therefore, in this research, the effect of learning from more efficient co-workers has been investigated. In addition, a modified version of the NSGA-II algorithm is developed to solve the model.

Supply chains’ (SCs’) sustainability practices and recovery strategies are attaining popularity in academia and industries to improve the resilience of the SCs and to manage large-scale disruptions. The global pandemic caused by the COVID-19 has raised the question of the sustainability of essential health-care products’ SCs of Bangladesh. It is an essential avenue for making the life of people safe and secure. Despite its importance, most of the health-care SCs in Bangladesh are struggling to meet the demand of its nation due to capacity shortage, technological backwardness of the manufacturers, delivery capacity shortages and less advanced forecasting capabilities. Therefore, this study aims to investigate the key performance indicators (KPIs) of a sustainable recovery strategy in the context of health-care SCs considering the COVID-19 pandemic.

This study used a dynamic method named graph theory and matrix approach to evaluate the most critical KPIs of a sustainable recovery strategy in the context of Bangladeshi health-care SCs.

The result revealed that dynamic forecasting and planning with a smooth delivery and distribution support system, production capacity diversification and having alternative or multiple suppliers during extraordinary disruptions may aid in the sustainability of the health-care SCs in Bangladesh.

This study is unique as no previous study has identified and examined the sustainable recovery strategy perspective KPIs considering the COVID-19 pandemic in the context of Bangladeshi health-care SCs. This study will also add value by guiding decision-makers of the health-care SCs of Bangladesh to adopt strategies toward the sustainability of SCs.

The abrupt outbreak of coronavirus disease (COVID-19) hit every nation in 2020–2021, causing a worldwide pandemic. The worldwide COVID-19 epidemic, described as a “black swan”, has severely disrupted manufacturing firms' supply chain. The purpose of this study is to investigate how supply chain data analytics enable the effective deployment of agility, adaptability and alignment (3As) strategies, resulting in improving post-COVID disruption performance. It also analyses the indirect effect of supply chain data analytics on disruption performance through the 3As supply chain strategies.

The hypothesis and theoretical framework were tested using a questionnaire survey. The authors employed structural equation modelling through the SMART PLS version 3.2.7 to analyse data from 163 textile firms located in Pakistan.

The results revealed that the supply chain data analytics contributed positively and significantly to the agility and adaptability, while all 3As supply chain strategies impacted the PPERF substantially. Further, the connection between supply chain data analytics (SCDA) and disruption performance has substantially been influenced through 3As supply chain strategies.

The results imply that in the event of low likelihood, high effect disruptions, managers and decision-makers should focus their efforts on integrating data analytics capabilities with 3As supply chain policies to ensure long-term company success.

This research sheds fresh light on the importance of data analytics in effectively implementing 3As strategies for sustaining company performance amid COVID-19 disruptions.