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Economic damages of hurricanes and tropical cyclones are increasing faster than the populations and wealth of many coastal areas. There is urgency to update priorities of agencies engaged with risk assessment, risk mitigation, and risk communication across hundreds or thousands of water basins. This paper evaluates hydrology and social vulnerability factors to develop a risk register at a subbasin scale for which the priorities of agencies vary by storm scenario using publicly available satellite-based Earth observations. The novelty and innovation of this approach is the quantification and mapping of risk as a disruption of system order, while using social vulnerability indices and sensor data from disparate sources. The results assist with allocating resources across basins under several scenarios of hydrology and social vulnerability. The approach is in several parts as follows: first, a baseline order of basins is defined using the CDC/ATSDR social vulnerability index (SVI). Next, a set of storm scenarios is defined using Earth Observations and modeled data. Next, a swing-weight technique is used to update factor weights under each scenario. Lastly, the importance order of basins relative to the baseline order is used to compare the risk of scenarios across the study area. The risk is thus quantified (by least squares difference of order) as a disruption to the ordering of basins by social and hydrologic factors (i.e., SVI, precipitation, wind speed, and soil moisture), with attention to the most disruptive scenarios. An application is described with extensive mapping of hydrologic basins for Hurricane Ian to demonstrate a versatile method to address uncertainty of scenarios of storm nature and extent across coastal mega-regions. 

The supply chains of semiconductors and integrated devices supports industry across all economic sectors. Globally, the supply chain is experiencing a variety of stressors and disruptions, with effects that cascade across the economy, causing product delays and enterprise losses. However, quantitative models that support an understanding of how stressors influence supply chain performance are needed. Here we show how stress testing can be used for assessing the impact of disruptions on supply chain performance metrics and for characterizing system resilience. We demonstrate a framework that utilizes discrete event simulation for stress testing the resilience of a semiconductor supply chain. Our results include a comparison of resilience curves with and without risk management countermeasures, showing the resilience-enhancing benefits of various supply chain management strategies such as maintaining safety stock and sourcing from multiple suppliers. Supply chain managers can utilize stress testing principles and methodologies to configure their supply chain and engage in practices that contribute to system resilience. 

Reverse engineering (RE) is a widespread practice within engineering, and it is particularly relevant for discovering maliciousfunctionality in digital hardware components. In this paper, we discuss bitstream or firmware RE for field programable gate arrays (FPGAs). A bitstream establishes the configuration of the FPGA device fabric. Complete knowledge of both the physical device fabric and a specific bitstream should be sufficient to determine the complete configuration of the programmed FPGA. However, a significant challenge to bitstream RE arises because information about the FPGA fabric and interpretation of the bitstream is typically incomplete. The uncertainties limit the confidence in the correctness of any configuration determined through the RE process. This paper identifies representative sources of uncertainty in bitstream RE of FPGA devices. 

Purpose In the buyer-supplier relationship of a high-technology enterprise, the concepts of trust and risk are closely intertwined. Entering into a buyer-supplier relationship inherently involves a degree of risk, since there is always an opportunity for one of the parties to act opportunistically. Purchasing and supply managers play an important role in reducing the firm's risk profile, and must make decisions about whether or not to enter into, or remain in, a relationship with a supplier based on a subjective assessment of trust and risk. Design/methodology/approach In this paper, the authors seek to explore how trust in the buyer-supplier relationship can be quantitatively modeled in the presence of risk. The authors develop a model of trust between a buyer and supplier as a risk-based decision, in which a buyer decides to place trust in a supplier, who may either act cooperatively or opportunistically. The authors use a case study of intellectual property (IP) piracy in the electronics industry to illustrate the conceptual discussion and model development. Findings The authors produce a generalizable model that can be used to aid in decision-making and risk analysis for potential supply-chain partnerships, and is both a theoretical and practical innovation. However, the model can benefit a variety of high-technology enterprises. Originality/value While the topic of trust is widely discussed, few studies have attempted to derive a quantitative model to support trust-based decision making. This paper advanced the field of supply chain management by developing a model which relates risk and trust in the buyer-supplier relationship.