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This paper addresses a predictive condition-based maintenance approach based on monitoring, modeling, and predicting a system's deterioration. The system's deterioration is considered as a stochastic dynamic process with continuous degrading. Structural time series, coupled with state-space modeling and Kalman filtering methods, is adopted for recursively modeling and forecasting the deterioration state at a future time. The probability of a failure is then predicted based on the forecasted deterioration state and a threshold of a failure. Finally, maintenance decisions are made according to the predicted failure probabilities, associated preventive and corrective maintenance cost, and the profit loss due to system performance deterioration. The approach can be applied on-line to provide economic and preventive maintenance solutions in order to maximize the profit of the ownership of a system.

Several viruses preferentially infect and replicate in cancer cells by usurping pathways that are defective in the tumor cell population. Such viruses have a potential as oncolytic agents. The aim of tumor virotherapy is that after injection of the replicating virus, it propagates in the tumor cell population with amplification. As a result, the oncolytic virus spreads to eradicate the tumor. The outcome of tumor virotherapy is determined by population dynamics and different from standard cancer therapy. Several models have been developed that provided considerable insights on the potential therapeutic scenarios. However, virotherapy is potentially risky since large amounts of a replicating virus are injected in the host with a risk of adverse effects. Therefore, the optimal dose, number of doses, and timing are expected to play an important role on the outcome both for the tumor and the host. In the current work, we combine a model of the dynamics of tumor virotherapy that was validated with experimental data with optimization theory to illustrate how we can improve the outcome of tumor therapy. In this first report, we demonstrate that (i) in most circumstances, anything more than two administrations of a vector is not helpful, (ii) correctly timed delivery of the virus provides superior results compared to regularly scheduled therapy or continuous infusion, (iii) a second dose of virus that is not properly timed leads to a worse outcome compared to a single dose of virus, and (iv) it is less costly to treat larger tumors.

A hierarchical mesoporous carbon foam (ECF) with an interconnected micro-/mesoporous architecture was prepared and used as a binder-free, low-cost, high-performance anode for lithium ion batteries. Due to its high specific surface area (980.6 m2/g), high porosity (99.6%), light weight (5 mg/cm3) and narrow pore size distribution (~2 to 5 nm), the ECF anode exhibited a high reversible specific capacity of 455 mAh/g. Experimental results also demonstrated that the anode thickness significantly influence the specific capacity of the battery. Meanwhile, the ECF anode retained a high rate performance and an excellent cycling performance approaching 100% of its initial capacity over 300 cycles at 0.1 A/g. In addition, no binders, carbon additives or current collectors are added to the ECF based cells that will increase the total weight of devices. The high electrochemical performance was mainly attributed to the combined favorable hierarchical structures which can facilitate the Li+ accessibility and also enable the fast diffusion of electron into the electrode during the charge and discharge process. The synthesis process used to make this elastic carbon foam is readily scalable to industrial applications in energy storage devices such as li-ion battery and supercapacitor.

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The remaining useful life (RUL) prediction of mechanical products has been widely studied for online system performance reliability, device remanufacturing, and product safety (safety awareness and safety improvement). These studies incorporated many different models, algorithms, and techniques for modeling and assessment. In this paper, methods of RUL assessment are summarized and expounded upon using two major methods: physics model based and data driven based methods. The advantages and disadvantages of each of these methods are deliberated and compared as well. Due to the intricacy of failure mechanism in system, and difficulty in physics degradation observation, RUL assessment based on observations of performance variables turns into a science in evaluating the degradation. A modeling method from control systems, the state space model (SSM), as a first order hidden Markov, is presented. In the context of non-linear and non-Gaussian systems, the SSM methodology is capable of performing remaining life assessment by using Bayesian estimation (sequential Monte Carlo). Being effective for non-linear and non-Gaussian dynamics, the methodology can perform the assessment recursively online for applications in CBM (condition based maintenance), PHM (prognostics and health management), remanufacturing, and system performance reliability. Finally, the discussion raises concerns regarding online sensing data for SSM modeling and assessment of RUL.

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