On engineering reliability concepts and biological aging

Some stochastic approaches to biological aging modeling are studied. We assume that an organism acquires a random resource at birth. Death occurs when the accumulated dam-age (wear) exceeds this initial value, modeled by the discrete or continuous random vari-ables. Another source of death of an organism is also taken into account, when it occurs as a consequence of a shock or of a demand for energy, which is a generalization of the Strehler-Mildwan’s model (1960). Biological age based on the observed degradation is also defined. Finally, aging properties of repairable systems are discussed. We show that even in the case of imperfect repair, which is certainly the case for organisms, aging slows down with age and eventually can even fade out. This presents another possible explanation for the human mortality rate plateaus.mortality

Systemic Risk in a Unifying Framework for Cascading Processes on Networks

We introduce a general framework for models of cascade and contagion processes on networks, to identify their commonalities and differences. In particular, models of social and financial cascades, as well as the fiber bundle model, the voter model, and models of epidemic spreading are recovered as special cases. To unify their description, we define the net fragility of a node, which is the difference between its fragility and the threshold that determines its failure. Nodes fail if their net fragility grows above zero and their failure increases the fragility of neighbouring nodes, thus possibly triggering a cascade. In this framework, we identify three classes depending on the way the fragility of a node is increased by the failure of a neighbour. At the microscopic level, we illustrate with specific examples how the failure spreading pattern varies with the node triggering the cascade, depending on its position in the network and its degree. At the macroscopic level, systemic risk is measured as the final fraction of failed nodes, $X^\ast$, and for each of the three classes we derive a recursive equation to compute its value. The phase diagram of $X^\ast$ as a function of the initial conditions, thus allows for a prediction of the systemic risk as well as a comparison of the three different model classes. We could identify which model class lead to a first-order phase transition in systemic risk, i.e. situations where small changes in the initial conditions may lead to a global failure. Eventually, we generalize our framework to encompass stochastic contagion models. This indicates the potential for further generalizations.Comment: 43 pages, 16 multipart figure

A Biased Resistor Network Model for Electromigration Failure and Related Phenomena in Metallic Lines

Electromigration phenomena in metallic lines are studied by using a biased resistor network model. The void formation induced by the electron wind is simulated by a stochastic process of resistor breaking, while the growth of mechanical stress inside the line is described by an antagonist process of recovery of the broken resistors. The model accounts for the existence of temperature gradients due to current crowding and Joule heating. Alloying effects are also accounted for. Monte Carlo simulations allow the study within a unified theoretical framework of a variety of relevant features related to the electromigration. The predictions of the model are in excellent agreement with the experiments and in particular with the degradation towards electrical breakdown of stressed Al-Cu thin metallic lines. Detailed investigations refer to the damage pattern, the distribution of the times to failure (TTFs), the generalized Black's law, the time evolution of the resistance, including the early-stage change due to alloying effects and the electromigration saturation appearing at low current densities or for short line lengths. The dependence of the TTFs on the length and width of the metallic line is also well reproduced. Finally, the model successfully describes the resistance noise properties under steady state conditions.Comment: 39 pages + 17 figure

Propagation of Pericentral Necrosis During Acetaminophen-Induced Liver Injury: Evidence for Early Interhepatocyte Communication and Information Exchange.

Acetaminophen (APAP)-induced liver injury is clinically significant, and APAP overdose in mice often serves as a model for drug-induced liver injury in humans. By specifying that APAP metabolism, reactive metabolite formation, glutathione depletion, and mitigation of mitochondrial damage within individual hepatocytes are functions of intralobular location, an earlier virtual model mechanism provided the first concrete multiattribute explanation for how and why early necrosis occurs close to the central vein (CV). However, two characteristic features could not be simulated consistently: necrosis occurring first adjacent to the CV, and subsequent necrosis occurring primarily adjacent to hepatocytes that have already initiated necrosis. We sought parsimonious model mechanism enhancements that would manage spatiotemporal heterogeneity sufficiently to enable meeting two new target attributes and conducted virtual experiments to explore different ideas for model mechanism improvement at intrahepatocyte and multihepatocyte levels. For the latter, evidence supports intercellular communication via exosomes, gap junctions, and connexin hemichannels playing essential roles in the toxic effects of chemicals, including facilitating or counteracting cell death processes. Logic requiring hepatocytes to obtain current information about whether downstream and lateral neighbors have triggered necrosis enabled virtual hepatocytes to achieve both new target attributes. A virtual hepatocyte that is glutathione-depleted uses that information to determine if it will initiate necrosis. When a less-stressed hepatocyte is flanked by at least two neighbors that have triggered necrosis, it too will initiate necrosis. We hypothesize that the resulting intercellular communication-enabled model mechanism is analogous to the actual explanation for APAP-induced hepatotoxicity at comparable levels of granularity

A Generic Prognostic Framework for Remaining Useful Life Prediction of Complex Engineering Systems

Prognostics and Health Management (PHM) is a general term that encompasses methods used to evaluate system health, predict the onset of failure, and mitigate the risks associated with the degraded behavior. Multitudes of health monitoring techniques facilitating the detection and classification of the onset of failure have been developed for commercial and military applications. PHM system designers are currently focused on developing prognostic techniques and integrating diagnostic/prognostic approaches at the system level. This dissertation introduces a prognostic framework, which integrates several methodologies that are necessary for the general application of PHM to a variety of systems. A method is developed to represent the multidimensional system health status in the form of a scalar quantity called a health indicator. This method is able to indicate the effectiveness of the health indicator in terms of how well or how poorly the health indicator can distinguish healthy and faulty system exemplars. A usefulness criterion was developed which allows the practitioner to evaluate the practicability of using a particular prognostic model along with observed degradation evidence data. The criterion of usefulness is based on comparing the model uncertainty imposed primarily by imperfectness of degradation evidence data against the uncertainty associated with the time-to-failure prediction based on average reliability characteristics of the system. This dissertation identifies the major contributors to prognostic uncertainty and analyzes their effects. Further study of two important contributions resulted in the development of uncertainty management techniques to improve PHM performance. An analysis of uncertainty effects attributed to the random nature of the critical degradation threshold, , was performed. An analysis of uncertainty effects attributed to the presence of unobservable failure mechanisms affecting the system degradation process along with observable failure mechanisms was performed. A method was developed to reduce the effects of uncertainty on a prognostic model. This dissertation provides a method to incorporate prognostic information into optimization techniques aimed at finding an optimal control policy for equipment performing in an uncertain environment