Novel models and algorithms for systems reliability modeling and optimization

Recent growth in the scale and complexity of products and technologies in the defense and other industries is challenging product development, realization, and sustainment costs. Uncontrolled costs and routine budget overruns are causing all parties involved to seek lean product development processes and treatment of reliability, availability, and maintainability of the system as a true design parameter . To this effect, accurate estimation and management of the system reliability of a design during the earliest stages of new product development is not only critical for managing product development and manufacturing costs but also to control life cycle costs (LCC). In this regard, the overall objective of this research study is to develop an integrated framework for design for reliability (DFR) during upfront product development by treating reliability as a design parameter. The aim here is to develop the theory, methods, and tools necessary for: 1) accurate assessment of system reliability and availability and 2) optimization of the design to meet system reliability targets. In modeling the system reliability and availability, we aim to address the limitations of existing methods, in particular the Markov chains method and the Dynamic Bayesian Network approach, by incorporating a Continuous Time Bayesian Network framework for more effective modeling of sub-system/component interactions, dependencies, and various repair policies. We also propose a multi-object optimization scheme to aid the designer in obtaining optimal design(s) with respect to system reliability/availability targets and other system design requirements. In particular, the optimization scheme would entail optimal selection of sub-system and component alternatives. The theory, methods, and tools to be developed will be extensively tested and validated using simulation test-bed data and actual case studies from our industry partners

Approximation methods for hybrid diffusion systems with state-dependent switching processes : numerical algorithms and existence and uniqueness of solutions

By focusing on hybrid diffusions in which continuous dynamics and discrete events coexist, this work is concerned with approximation of solutions for hybrid stochastic differential equations with a state-dependent switching process. Iterative algorithms are developed. The continuous-state dependent switching process presents added difficulties in analyzing the numerical procedures. Weak convergence of the algorithms is established by a martingale problem formulation first. This weak convergence result is then used as a bridge to obtain strong convergence. In this process, the existence and uniqueness of the solution of the switching diffusions with continuous-state-dependent switching are obtained. Different from the existing results of solutions of stochastic differential equations in which the Picard iterations are utilized, Euler's numerical schemes are considered here. Moreover, decreasing stepsize algorithms together with their weak convergence are given. Numerical experiments are also provided for demonstration

Bone marrow mesenchymal stem cell-derived exosomal lncRNA KLF3-AS1 stabilizes Sirt1 protein to improve cerebral ischemia/reperfusion injury via miR-206/USP22 axis

Abstract Background Cerebral ischemia/reperfusion (I/R) is a pathological process that occurs in ischemic stroke. Bone marrow mesenchymal stem cell-derived exosomes (BMSC-Exos) have been verified to relieve cerebral I/R-induced inflammatory injury. Hence, we intended to clarify the function of BMSC-Exos-delivered lncRNA KLF3-AS1 (BMSC-Exos KLF3-AS1) in neuroprotection and investigated its potential mechanism. Methods To mimic cerebral I/R injury in vivo and in vitro, middle cerebral artery occlusion (MCAO) mice model and oxygen–glucose deprivation (OGD) BV-2 cell model were established. BMSC-Exos KLF3-AS1 were administered in MCAO mice or OGD-exposed cells. The modified neurological severity score (mNSS), shuttle box test, and cresyl violet staining were performed to measure the neuroprotective functions, while cell injury was evaluated with MTT, TUNEL and reactive oxygen species (ROS) assays. Targeted genes and proteins were detected using western blot, qRT-PCR, and immunohistochemistry. The molecular interactions were assessed using RNA immunoprecipitation, co-immunoprecipitation and luciferase assays. Results BMSC-Exos KLF3-AS1 reduced cerebral infarction and improved neurological function in MCAO mice. Similarly, it also promoted cell viability, suppressed apoptosis, inflammatory injury and ROS production in cells exposed to OGD. BMSC-Exos KLF3-AS1 upregulated the decreased Sirt1 induced by cerebral I/R. Mechanistically, KLF3-AS1 inhibited the ubiquitination of Sirt1 protein through inducing USP22. Additionally, KLF3-AS1 sponged miR-206 to upregulate USP22 expression. Overexpression of miR-206 or silencing of Sirt1 abolished KLF3-AS1-mediated protective effects. Conclusion BMSC-Exos KLF3-AS1 promoted the Sirt1 deubiquitinating to ameliorate cerebral I/R-induced inflammatory injury via KLF3-AS1/miR-206/USP22 network