Partially Linear Additive Functional Regression

We consider a novel partially linear additive functional regression model in which both a functional predictor and some scalar predictors appear. The functional part has a semiparametric continuously additive form, while the scalar predictors appear in the linear part. The functional part has the optimal convergence rate, and the asymptotic normality of the nonfunctional part is also shown. Simulations and an empirical analysis of a Covid-19 data set demonstrate the performance of the proposed estimator.

Increasing global supply chains' resilience after the COVID-19 pandemic: Empirical results from a Delphi study.

COVID-19 has revealed global supply chains' vulnerability and sparked debate about increasing supply chain resilience (SCRES). Previous SCRES research has primarily focused on near-term responses to large-scale disruptions, neglecting long-term resilience approaches. We address this research gap by presenting empirical evidence from a Delphi study. Based on the resource dependence theory, we developed 10 projections for 2025 on promising supply chain adaptations, which were assessed by 94 international supply chain experts from academia and industry. The results reveal that companies prioritize bridging over buffering approaches as long-term responses for increasing SCRES. Promising measures include increasing risk criteria importance in supplier selection, supply chain collaboration, and supply chain mapping. In contrast, experts ascribe less priority to safety stocks and coopetition. Moreover, we present a stakeholder analysis confirming one of the resource dependence theory's central propositions for the future of global supply chains: companies differently affected by externalities will choose different countermeasures.

A robust numerical two-level second-order explicit approach to predicting the spread of Covid-2019 pandemic with undetected infectious cases.

This paper develops a numerical two-level explicit approach for solving a mathematical model for the spread of Covid-19 pandemic with that includes the undetected infectious cases. The stability and convergence rate of the new numerical method are deeply analyzed in the L∞ -norm. The proposed technique is less time consuming than a broad range of related numerical schemes. Furthermore, the method is stable, and at least second-order accurate and it can serve as a robust tool for the integration of general ODEs systems of initial-value problems. Some numerical experiments are provided which include the pandemic in Cameroon, and discussed.