Body sensor networks (BSNs) are playing a crucial role in tackling arising challenges during the COVID-19 pandemic. This work contributes by modeling and analyzing the BSN reliability considering the effects of correlated functional dependence (FDEP) and random isolation time behavior. Particularly, the FDEP exists in BSNs where a relay is utilized to assist the communication between some biosensors and the sink device. When the relay malfunctions, the dependent biosensors may communicate directly with the sink for a limited, uncertain time. These biosensors then become isolated from the rest of the BSN when their remaining power depletes to the level insufficient to support the direct communication. Moreover, multiple biosensors sharing the same relay and a biosensor communicating with the sink via several alternative relays create correlations among different FDEP groups. In addition, the competition in the time domain exists between the local failure of the relay and the propagated failures of dependent biosensors. Both the correlation and competition complicate the reliability modeling and analysis of BSNs. This work proposes a combinatorial and analytical methodology to address both effects in the BSN reliability analysis. The proposed method is demonstrated using a detailed case study and verified using a continuous-time Markov chain method.

To address the degradation of rechargeable batteries, this paper presents a two-phase gamma process model with a fixed change-point for modeling the voltage-discharge curves of battery cycle aging under a constant current. The model can be applied to estimate the state of charge (SOC) and the remaining useful discharge time (RUT) in a cycle with consideration of the effect of cycle aging, and can also be applied to estimate the state of life (SOL) and the remaining useful life (RUL) across cycles. The applications of the proposed model are demonstrated using the experimental cycle aging data of a lithium iron phosphate battery. A comparison shows that the proposed model generates a more accurate prediction than the conventional two-term exponential model with capacity data under a particle filter framework, and this reveals the superiority of modeling with voltage over modeling with capacity. The analytical expression of mean useful discharge time in a cycle (or mean time to failure) is developed with approximation by a Taylor expansion and the Birnbaum-Saunders distribution, and the result is shown to be in good agreement with the true mean of a gamma process.

This paper intends to contribute with a multi-criteria decision-making (MCDM) framework to support risk evaluation for maintenance activities carried out on critical systems in industry. We propose to first select the best maintenance strategy tailored to companies' requirements and systems' features, and second to perform a risk prioritisation aimed at highlighting priorities of intervention. The Analytic Network Process (ANP) is suggested to select the maintenance policy representing the best trade-off considering the complex and varied interdependencies amongst a diversity of clustered elements characterising the system. Then, the main risks related to the interventions associated to the selected maintenance policy are ranked using the ELimination Et Choix Traduisant la REalité III (ELECTRE III) method, using the same criteria weighted by the previous ANP application. This hybrid MCDM framework is applied to a core subsystem of a real-world marine salt manufacture firm.

In the last 20-30 years, technological innovation has enabled the advancement of industry at a global scale, giving rise to a truly global society, resting on an interdependent web of transnational technical, economic and social systems. These systems are exposed to scenarios of cascading outbreaks, whose impacts can ripple to very large scales through their strong interdependencies, as recently shown by the pandemic spreading of the Coronavirus. Considerable work has been conducted in recent years to develop frameworks to support the assessment, communication, management and governance of this type of risk, building on concepts like systemic risks, complexity theory, deep uncertainties, resilience engineering, adaptive management and black swans. Yet contemporary risk analysis struggles to provide authoritative societal guidance for adequately handling these types of risks, as clearly illustrated by the Coronavirus case. In this paper, we reflect on this situation. We aim to identify critical challenges in current frameworks of risk assessment and management and point to ways to strengthen these, to be better able to confront threats like the Coronavirus in the future. A set of principles and theses are established, which have the potential to support a common foundation for the many different scientific perspectives and 'schools' currently dealing with risk handling issues.

Increasingly prevalent extreme weather events have caused resilience to become an essential sustainable development component for resource and infrastructure networks. Existing resilience metrics require detailed knowledge of the system and potential disruptions, which is not available in the early design stage. The lack of quantitative tools to guide the early stages of design for resilience, forces engineers to rely on heuristics (use physical redundancy, localized capacity, etc.). This research asserts that the required quantitative guidelines can be developed using the architecting principles of biological ecosystems, which maintain a unique balance between pathway redundancy and efficiency, enabling them to be both productive under normal circumstances and survive disruptions. Ecologists quantify this network characteristic using the ecological fitness function. This paper presents the required reformulation required to enable the use of this metric in the design and analysis of resource and infrastructure networks with multiple distinct, but interdependent, interactions. The proposed framework is validated by comparing the resilience characteristics of two notional supply chain designs: one designed for minimum shipping cost and the other designed using the proposed bio-inspired framework. The results support using the proposed bio-inspired framework to guide designers in creating resilient and sustainable resource and infrastructure networks.

Quantification of hazard-induced losses plays a significant role in risk assessment and management of civil infrastructure subjected to hazards in a life-cycle context. A rational approach to assess long-term loss is of vital importance. The loss assessment associated with stationary hazard models and low-order moments (i.e., expectation and variance) has been widely investigated in previous studies. This paper proposes a novel approach for the higher-order analysis of long-term loss under both stationary and nonstationary hazards. An analytical approach based on the moment generating function is developed to assess the first four statistical moments of long-term loss under different stochastic models (e.g., homogeneous Poisson process, non-homogeneous Poisson process, renewal process). Based on the law of total expectation, the developed approach expands the application scope of the moment generating function to nonstationary models and higher-order moments (i.e., skewness and kurtosis). Furthermore, by employing the convolution technique, the proposed approach effectively addresses the difficulty of assessing higher-order moments in a renewal process. Besides the loss analysis, the mixed Poisson process, a relatively new stochastic model, is introduced to consider uncertainty springing from the stochastic occurrence rate. Two illustrative examples are presented to demonstrate practical implementations of the developed approach. Ultimately, the proposed framework could aid the decision-maker to select the optimal option by incorporating higher-order moments of long-term loss within the decision-making process.

Most of the supply chain literature assumes that product substitution is an effective method to mitigate supply chain disruptions and that all production lines either survive or are disrupted together. Such assumptions, however, may not hold in the real world: (1) when there is a shortfall of all products, product substitution may be inadequate unless it is paired with other strategies such as dual sourcing; and (2) production lines do not survive forever and may fail. To relax such assumptions, this paper therefore investigates the situations that the manufacturer may optimize substitution policy and dual sourcing policy to cope with supply chain disruptions. The paper obtains and compares the optimal policies for both deterministic and stochastic demands. A real-world case is also studied to verify the effectiveness of the proposed model.

A novel approach to surrogate modeling motivated by recent advancements in parameter dimension reduction is proposed. Specifically, the approach aims to speed-up surrogate modeling for mapping multiple input variables to a field quantity of interest. Computational efficiency is accomplished by first identifying principal components (PC) and corresponding features in the output field data. A map from inputs to each feature is considered, and the active subspace (AS) methodology is used to capture their relationship in a low-dimensional subspace in the input domain. Thus, the PCAS method accomplishes dimension reduction in the input as well as the output. The method is demonstrated on a realistic problem pertaining to variability in residual stress in an additively manufactured component due to the stochastic nature of the process variables and material properties. The resulting surrogate model is exploited for uncertainty propagation, and identification of stress hotspots in the part. Additionally, the surrogate model is used for global sensitivity analysis to quantify relative contributions of the uncertain inputs to stress variability. Our findings based on the considered application are indicative of enormous potential for computational gains in such analyses, especially in generating training data, and enabling advancements in control and optimization of additive manufacturing processes.