Leveraging a Bayesian network approach to model and analyze supplier vulnerability to severe weather risk: A case study of the U.S. pharmaceutical supply chain following Hurricane Maria.

The United States government has identified the health care sector as part of the critical infrastructure for homeland security to protect citizens against health risks arising from terrorism, natural disasters, and epidemics. Citizens also have expectations about the role that health care plays in enjoying a good quality of life, by providing response systems to handle emergencies and other illness situations adequately. Among the systems required to supportdesired performance levels is a robust and resilient pharmaceutical supply chain that is free of disruption. Shortages of drugs place undue pressure on healthcare providers to devise alternative approaches to administer patient care. With climate change expected to result in increasingly severe weather patterns in the future, it is critical that logistics engineers understand the impact that a catastrophic weather event could have on supply chain disruption to facilitate the design of supply systems that are robust and resilient. This study investigates the main causal and intermediate events that led to risk propagation in, and disruption of, the U.S. pharmaceutical supply chain following Hurricane Maria. A causality Bayesian model is developed to depict linkages between risk events and quantify the associated cumulative risk. The quantification is further examined through different advanced techniques such as predictive inference reasoning and sensitivity analysis. The general interpretation of these analyses suggests that port resilience is imperative to pharmaceutical supply chain performance in the case of Puerto Rico.

The Impact of Participants' Anthropometry on Muscle Activation Levels While Interacting with the Level of Expertise, Task Type, and Single Muscles.

In this research paper, we implemented a mixed factor design in order to investigate the effect of four anthropometries: height, weight, lower-arm dimensions, and upper-arm dimensions on the muscle activation level of participants when interacting with three types of moderators: experiment expertise, task type, and muscle type. The research paper focused on two levels of expertise (novice and expert), two tasks (deck-building and picket installation), and four arm muscles (Brachioradialis (BR), Extensor Carpi Ulnaris (ECU), Flexor Carpi Radialis (FCR), and Flexor Carpi Ulnaris (FCU)), which resulted in 16 (2 × 2 × 4) groups. For each of the 16 groups, the data were analyzed in order to investigate the relationship between the four anthropometries and the four muscle activation levels of the participants. Amos software (IBM, Armonk, NY, USA), along with multiple group structural equation modeling, was used to test a total of 16 direct relationships, as well as the moderation effects in the designed experiment. The results show that the participants' expertise can moderate the relationship between their height and muscle activation levels, the relationship between their weight and muscle activation levels, and the relationship between their lower arm dimensions and muscle activation levels. Moreover, the findings of this research paper demonstrate that the relationship between the lower arm dimensions and muscle activation levels, and the relationship between weight and muscle activation levels are moderated by the type of muscle used by the participants (i.e., BR, ECU, FCR, and FCU).