Ripple effect in the supply chain network: Forward and backward disruption propagation, network health and firm vulnerability.

A local disruption can propagate to forward and downward through the material flow and eventually influence the entire supply chain network (SCN). This phenomenon of ripple effect, immensely existing in practice, has received great interest in recent years. Moreover, forward and backward disruption propagations became major stressors for SCNs during the COVID-19 pandemic triggered by simultaneous and sequential supply and demand disruptions. However, current literature has paid less attention to the different impacts of the directions of disruption propagation. This study examines the disruption propagation through simulating simple interaction rules of firms inside the SCN. Specifically, an agent-based computational model is developed to delineate the supply chain disruption propagation behavior. Then, we conduct multi-level quantitative analysis to explore the effects of forward and backward disruption propagation, moderated by network structure, network-level health and node-level vulnerability. Our results demonstrate that it is practically important to differentiate between forward and backward disruption propagation, as they are distinctive in the associated mitigation strategies and in the effects on network and individual firm performance. Forward disruption propagation generally can be mitigated by substitute and backup supply and has greater impact on firms serving the assembly role and on the supply/assembly networks, whereas backward disruption propagation is normally mitigated by flexible operation and distribution and has bigger impact on firms serving the distribution role and on distribution networks. We further analyze the investment strategies in a dual-focal supply network under disruption propagation. We provide propositions to facilitate decision-making and summarize important managerial implications.

An Easy-to-Implement Hierarchical Standardization for Variable Selection under Strong Heredity Constraint.

For many practical problems, the regression models follow the strong heredity property (also known as the marginality), which means they include parent main effects when a second-order effect is present. Existing methods rely mostly on special penalty functions or algorithms to enforce the strong heredity in variable selection. We propose a novel hierarchical standardization procedure to maintain strong heredity in variable selection. Our method is effortless to implement and is applicable to any variable selection method for any type of regression. The performance of the hierarchical standardization is comparable to that of the regular standardization. We also provide robustness checks and real data analysis to illustrate the merits of our method.

A potentiometric SO2 gas sensor based on the Li3PO4-Li2SiO3 solid electrolyte thin film.

A potentiometric sulfur dioxide (SO2) gas sensor based on the Li3PO4-Li2SiO3 solid electrolyte thin film was developed. The sensor was based on a galvanic cell O2, SO2, Au, Li2SO4-V2O5|Li3PO4-Li2SiO3|Au, SO2, O2. The Li3PO4-Li2SiO3 thin film was deposited on the Al2O3 substrate by RF magnetron sputtering, and the Au patterns were fabricated as the electrodes. The sensing electrode materials of Li2SO4-V2O5 with different dopants TiO2 and MgO were prepared, and as the ratio of Li2SO4:TiO2 is 1:1 (mol) with 5 wt. % V2O5 and 5 wt. % MgO, the sensor showed relatively good response characteristics to SO2 at 450 °C. Then, the sensor was further tested at the working temperature from 400 °C to 500 °C. The results show that the sensor has good response to SO2 at 400-450 °C, and the sensitivity is 90.15-57.19 mV/dec. The response and recovery times are 20-40 s and 2.5-4 min, respectively, when the gas concentration of SO2 increases from 10 ppm to 100 ppm and then decreases to 10 ppm. At 425 °C, the sensitivity is close to the theoretical values, and the final recovery potential of the sensor is almost consistent with the initial potential.