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This paper aims to assess the economic impact of uniform COVID-controlling policies that were implemented by the US government in 2020 and compare it with hypothetical targeted policies that consider the heterogenous effect of COVID-19 on different age groups.

The author began by showing that the adjusted SEQIHR model is a good fit to the US COVID-induced daily death data in that it can capture the nonlinearities of the data very well. Then, he used this model with extra parameters to evaluate the economic effects of COVID-19 through its impact on the job market.

The results show that targeted COVID-controlling policies could reduce the US death rate and GDP loss to 0.03% and 2%, respectively. By comparing these results with uniform COVID-controlling policies, which led to a 0.1% death rate and 3.5% GDP loss, we could conclude that the death rate reduction is 0.07%. Approximately 378,000 Americans died because of COVID-19 during 2020, therefore, reducing the death rate to 0.03% means saving a significant proportion of the COVID-19 casualties, around 280,000 lives.

To the best of the author's knowledge, this paper is the first study to assess the economic impacts of COVID-controlling policies by using the multirisk SEQIHR model.

This paper investigates the joint impact of COVID-19, alliances and digital strategies on bank lending. Additionally, this study examines whether the effect of COVID-19, alliances and digital strategies on bank loans depends on the types of banks.

Using a sample of 92 commercial banks in Indonesia from March 2020 to September 2021, a fixed-effects model (FEM) was used to analyze data.

This study provides robust results regarding the negative impact of the COVID-19 pandemic on bank loans in Indonesian banking. Furthermore, it reveals that collaboration between banks and FinTech does not substantially influence bank lending, despite the rise in proven cases tending to reduce credit expansion. It emphasizes the importance of the development of mobile banking as part of digitalization in boosting loan bank expansion, and this finding is more noticeable in private and small banks.

This study highlights some policy recommendations to improve bank lending during the COVID-19 period, particularly the role of new alliances and digital strategy in involving COVID-19 pandemic mitigation within a novel financial ecosystem.

This study offers a significant contribution to the empirical literature that specifically explores the joint impact of the COVID-19 pandemic, alliances and digital strategies on bank lending in banking.

This paper aims to uncover the key enablers of an agile supply chain in the manufacturing sector amidst disruptions such as pandemics, trade wars and cross-border challenges. The study aims to assess the applicability of existing literature to manufacturing and identify additional industry-specific enablers contributing to the field of supply chain management.

The research methodology is comprehensively described, detailing the utilization of extent literature and semistructured interviews with mid- and top-level executives in a supply chain. The authors ensure the robustness of the data collection process and results interpretation.

The study identifies six essential dimensions of an agile supply chain: information availability, design robustness, external resource planning, quickness and speed, public policy influencing skills and cash flow management. The study provides valuable insights for industry professionals to develop agile supply chains capable of responding to disruptions in a rapidly changing world.

This study is limited by its focus on the manufacturing sector, and future research may explore the applicability of these findings to other industries. By focusing on these essential dimensions identified in the study, managers can develop strategies to improve the agility and responsiveness of their supply chains. In addition, further research may investigate how these enablers may vary in different regions or contexts.

The COVID-19 pandemic has forced executives to reconsider their sourcing strategies and reduce dependence on suppliers from specific geographies. To ensure business continuity, companies should assess the risk associated with their suppliers and develop a business continuity plan that includes multisourcing their strategic materials. Digital transformation will revolutionize the supply chain industry, allowing for end-to-end visibility, real time insights and seamless integration of business and processes. Companies should also focus on creating a collaborative workforce ecosystem that prioritizes worker health and well-being. Maintaining trust with stakeholders is crucial, and firms must revisit their relationship management strategies. Finally, to maintain business leadership and competitiveness during volatile periods, the product portfolio needs to be diversified and marketing and sales teams must work in tandem with product teams to position new products accordingly.

This work contributes substantially to the literature on supply chain agility (SCA) by adding several new factors. The findings result in a more efficient and cost-effective supply chain during a stable situation and high service levels in a volatile situation. A less complex methodology for understanding SCA provides factors with a more straightforward method for identifying well-springs of related drivers. First, the study contributes to reestablish the factors such as quickness, responsiveness, competency, flexibility, proactiveness, collaboration and partnership, customer focus, velocity and speed, visibility, robustness, cost-effectiveness, alertness accessibility to information and decisiveness as applicable factors for SCA. Second, the study suggests a few more factors, such as liquidity management, Vendors’ economic assessment and economic diversity, that are the study’s unique contributions in extending the enablers of SCA. Finally, public policy influencing skills, local administration connects and maintaining capable vendors are the areas that were never considered essential for SCA. These factors have emerged as a vital operational factor during the lockdown, and academicians may consider these factors in the future to assess their applicability.

This study provides new insights for decision-makers looking to enhance the resilience and agility of their supply chains. The identification of unique enablers specific to the manufacturing industry contributes to the existing body of literature on agile supply chains in the face of disruptions.

This study investigates a production process that requires N kinds of components for the production of a finished product. The producer orders the various kinds of components from different suppliers and receives the orders in lots at the beginning of each production cycle. Similar to situations often encountered in real life, the lead times are random variables with known probability distributions so that a production cycle starts whenever all N kinds of components become available. Each of the lots received at the start of a production run contains both perfect and imperfect quality components. Once all N kinds of components become available, the producer initiates a screening process to detect the imperfect components. The production of the finished product uses only perfect quality components. The imperfect components are removed from inventory whenever the screening process is completed. The percentage of components of perfect quality present in each lot is a random variable with a known probability distribution.

This production process is described and modeled mathematically and the optimal production/ordering policy is derived based on the mathematical model.

The formulated mathematical model resulted in the determination of the optimal policy consisting of the optimal number of finished items ordered to be produce during each production run, the number of components ordered from each supplier, and the reorder point. The derived closed form expression for the optimal lot size depends on the minimum of the number of perfect quality components in a lot, whereas the reorder point is determined based on the maximum lead time.

The modeling approach and results of this study provide practical implications that may be beneficial to both production and supply chain managers as well as researchers.

This modeling approach that incorporates decision-making related to the logistics of acquiring the components and accounts for the probabilistic nature of the lead times and quality of components addresses a gap in the logistics/production literature.

This paper aims to state the configuration of the proposed antenna which is competent to many networks such as LTE and X band applications. The experimental study encountered the significance of the proposed antenna.

A novel compact Kuznets curve with parabola-shaped quad-band notched antenna is demonstrated in this paper. The presented prototype is ascertained on a composite material composed of woven fiberglass cloth with an epoxy resin binder. The resulting ultra-wideband antenna ranges 3.1–3.54 GHz, 5.17–5.51 GHz, 5.74–6.43 GHz and 6.79–7.60 GHz. To avoid the frequency bands which cause UWB interference,the projected antenna has been incorporated with slotted patch. The proposed antenna design is attained in four steps. The simple circular patch antenna model with defected ground plane is subjected to stepwise progression by including parabola-shaped slot and U shaped slot on the patch to attain four notched bands.

This projected antenna possesses an optimal bond among simulated and measured outcomes,which is more suitable for the quad notched band applications. Substrate analysis is done by varying substrate material, and notch behavior is presented. The proposed method’s optimum performance in metrics such as return loss, voltage standing wave ratio and radiation pattern varies its frequency range from 2.56 to 7.6 GHz.

The antenna adaptation of the defected ground plane has achieved through the quad notched band with operating frequency ranges 2.56 to 7.6 GHz and with eliminated frequency ranges 3.55–5.16 GHz, 5.52–5.73 GHz, 6.44–6.78 GHz and 7.66–10.6 GHz.

The purpose of this paper is to first present the key features of hybrid numerical methods that enable cost-effective simulations of complex thermofluid systems, and then demonstrate the formulation and application of such a method.

A hybrid numerical method is formulated for simulations of a closed-loop thermosyphon operating with slurries of a micro-encapsulated phase-change material suspended in distilled water. The slurries are modeled as homogeneous mixtures, with inputs of effective properties and overall heat-loss coefficients. Combinations of an axisymmetric two-dimensional (2D) control-volume finite-element method and a segmented-quasi-one-dimensional (1D) model are used to achieve cost-effective simulations. Proper matching of the solutions at the interfaces between adjacent axisymmetric 2D and quasi-1D zones is ensured by incorporating and heuristically determining suitable lengths of pre- and post-heating (and also pre- and post-cooling) sections.

In the demonstration problem, which would strictly require full three-dimensional simulations of the fluid flow and heat transfer phenomena, the proposed hybrid 1D/2D numerical method produces results that are in very good agreement with those obtained in a complementary experimental investigation.

The hybrid numerical methods discussed in this paper allow cost-effective computer simulations of complex thermofluid systems. These methods can therefore serve as very useful tools for the design, parametric studies, and optimization of such systems.

This paper studies the textile supply chain tactical planning under demand fuzziness through considering environmentally friendly and social responsibility. Hence, carbon emission in textile production and transportation is considered along with supply chain profitability.

The authors present a fuzzy multi-objective mathematical optimization model with credibilistic chance constraints to determine the fabric procurement quantities and production plan under uncertainty. The solution procedure makes use of credibility measure and fuzzy aggregation operator to attain compromise solutions.

A trade-off among carbon emissions, social performance and supply chain total profit is conducted. The analyses indicate the importance of transportation costs and carbon emission while determining the supply chain's tactical plan.

The textile supply chain's social sustainability alongside carbon emissions of textile operations is contemplated to provide apparel production and distribution logistics planning under uncertainty. In doing so, the authors propose a hybrid credibility-possibility mathematical optimization model to determine a compromise solution for textile managers.

The purpose of this study is to investigate which of the two carbon allowance allocation methods (CAAMs), i.e. grandfathered system carbon allowance allocation (GCAA) and baseline system carbon allowance allocation (BCAA), is more beneficial to capital-constrained supply chains under the carbon emission allowance repurchase strategy (CEARS).

Adopting CEARS to ease the capital-constrained supply chains, this study develops two-period game models with manufacturers as leaders and retailers as followers from the perspective of profit and social welfare maximization under two CAAMs (GCAA and BCAA), where the first period produces normal products, and the second period produces low-carbon products.

First, higher carbon-saving can better use CEARS and achieve a higher supply chain profit under the two CAAMs. However, the higher the end-of-period carbon price is, the lower the social welfare is. Second, when carbon-saving is small, GCAA achieves both economic and environmental benefits; BCAA reduces carbon emissions at the expense of economic benefit. Third, the supply chain members gain higher profits and social welfare under GCAA, so the government and supply chain members are more inclined to choose GCAA.

By analyzing the profits and total carbon emissions of capital-constrained supply chains under GCAA and BCAA, this study provides theoretical references for retailers and capital-constrained manufacturers. In addition, by comparing the difference in social welfare under GCAA and BCAA, it provides a basis for the government to choose a reasonable CAAM.

The purpose of this paper is to understand business students' intention to behave ethically in general, and in particularly in the business context of a developing country, India.

The paper surveyed 250 final semester MBA students from different business schools in Indore city of Madhya Pradesh in India. The study employed the most popular behavioural theory, the Theory of Planned Behaviour (TPB) to understand the intention of business students to behave ethically. Structural equation modelling was used to analyse direct effects of the constructs on behavioural intention, and the overall model.

Findings revealed that attitude, subjective norm and perceived behavioural control are positively related and have strong influence on ethical behavioural intention of business students. All constructs together explain 67 percent variance in intention. Attitude alone contributes 46 percent in explaining variance in ethical behavioural intention.

Business ethics field can benefit from this study as it provides an empirical explanation of the contribution of each factor that is, attitude, subjective norms and perceived behavioural control, in ethical behavioural intention of business students. This is directly beneficial for business schools and for education policymakers as the information can help policymakers to understand the potential of existing business ethics education. This study is limited to a data set of 250 business students in the context of a single country which cannot be generalized. So, there is need for research of this type in a more collaborative international context.

To the best of my knowledge, this is the first study in the Indian context to predict the intention of business students to behave ethically, using the TPB model. This study contributes valuable knowledge to the domain of business ethics, behavioural studies as well the field of business education, and suggests to explore ways to strengthen the three constructs attitude, subjective norm and perceived behavioural control, as these constructs were found to have a strong influence in forming ethical behavioural intention of business students of business schools in India.

This paper aims to minimize the total carbon emissions and costs and also maximize the total social benefits.

The present study develops a mathematical model for a closed-loop supply chain network of perishable products so that considers the vital aspects of sustainability across the life cycle of the supply chain network. To evaluate carbon emissions, two different regulating policies are studied.

According to the obtained results, increasing the lifetime of the perishable products improves the incorporated objective function (IOF) in both the carbon cap-and-trade model and the model with a strict cap on carbon emission while the solving time increases in both models. Moreover, the computational efficiency of the carbon cap-and-trade model is higher than that of the model with a strict cap, but its value of the IOF is worse. Results indicate that efficient policies for carbon management will support planners to achieve sustainability in a cost-effectively manner.

This research proposes a mathematical model for the sustainable closed-loop supply chain of perishable products that applies the significant aspects of sustainability across the life cycle of the supply chain network. Regional economic value, regional development, unemployment rate and the number of job opportunities created in the regions are considered as the social dimension.