This paper conducts a focused probabilistic risk assessment (PRA) on the reliability of commercial off-the-shelf (COTS) drones deployed for surveillance in areas with diverse radiation levels following a nuclear accident. The study employs the event tree/fault tree digraph approach, integrated with the dual-graph error propagation method (DEPM), to model sequences that could lead to loss of mission (LOM) scenarios due to combined hardware–software failures in the drone’s navigation system. The impact of radiation is simulated by a comparison of the total ionizing dose (TID) with the acceptable limit for each component. Errors are then propagated within the electronic hardware and software blocks to determine the navigation system’s reliability in different radiation zones. If the system is deemed unreliable, a strategy is suggested to identify the minimum radiation-hardening requirement for its subcomponents by reverse-engineering from the desired mission success criteria. The findings of this study can aid in the integration of COTS components into radiation-hardened (RAD-HARD) designs, optimizing the balance between cost, performance, and reliability in drone systems for nuclear-contaminated search and rescue missions.

Wearable devices and fitness trackers have gained popularity in healthcare and telemedicine as tools to reduce hospitalization costs, improve personalized health management, and monitor patients in remote areas. Smartwatches, particularly, offer continuous monitoring capabilities through step counting, heart rate tracking, and activity monitoring. However, despite being recognized as an emerging technology, the adoption of smartwatches in patient monitoring systems is still at an early stage, with limited studies delving beyond their feasibility. Developing healthcare applications for smartwatches faces challenges such as short battery life, wearable comfort, patient compliance, termination of non-native applications, user interaction difficulties, small touch screens, personalized sensor configuration, and connectivity with other devices. This paper presents a case study on designing an Android smartwatch application for remote monitoring of geriatric patients. It highlights obstacles encountered during app development and offers insights into design decisions and implementation details. The aim is to assist programmers in developing more efficient healthcare applications for wearable systems.

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Today, Probabilistic Risk Assessment (PRA) plays a vital role in assuring mission success for robotic and crewed missions alike. Current-day PRA techniques integrate multimodal, often black-box analyses to build comprehensive risk profiles. This paper describes a review and verification study of the “Nuclear Risk Assessment for the Mars 2020 Mission Environmental Impact Statement” (N-PRA)[1]. Sandia National Labs conducted the N-PRA for NASA’s Jet Propulsion Laboratory (JPL). More specifically, we have verified the source term calculations associated with the release of radionuclides from a Multi-Mission Radiothermoelectic Generator (MMRTG) power source for a limited set of accident scenarios in the case of an accidental re-entry into Earth Orbit with an Earth impacting trajectory. We achieve this by using analytical methods[2] historically implemented for the Cassini Mission PRA[3] for a failed planetary swingby gravity-assist. Our results are within 28% to 56% of the referenced study. Limitations in our methodology are attributed to a lack of modern simulation-based tools and deterministic methods for modeling complex physical phenomena. The results are interpreted and compared with the values presented by the initial authors, along with comments for improving our current methodology.

We define supply chains (SCs) as sequences of processes that link the demand and supply of goods or services within a network. SCs are prone to shortages in delivering their output goals due to several factors such as personnel undersupply, inefficient processes, policy failure, equipment malfunction, natural hazards, pandemic outbreaks, power outages, or economic crises. Recent notable supply-chain failures include the 2021 Texas power crisis, personal protection equipment shortages during the COVID-19 pandemic, and regional or global food chain shortages. The consequences of such shortages can range from negligible to devastating. The Texas power crisis resulted in the death of 70 people and left approximately 4.5 billion homes and businesses without power for multiple days. In this paper, we presented a methodology to quantify the failure probability of the throughput of a supply chain. We divided the methodology into two major categories of steps. In the first step, we converted the given or assumed supply chain data into fault trees and quantify them. In the second step, we iterated the quantification of the fault tree to build a supply chain shortage risk profile. We introduced the notion of success criteria for the output from a facility, based on which we included or excluded the facility for quantification. With the inclusion of relevant field data, we believe that our methodology can enable the stakeholders in the supply-chain decision-making process to detect vulnerable facilities and risk-inform prevention and mitigation actions. Applications for this methodology can include construction, inventory stocking, assessing manufacturing quantities, policy changes, personnel allocation, and financial investment for critical industries such as nuclear, pharmaceutical, aviation, etc.