DNA-activated protein kinase functions in a newly observed S phase checkpoint that links histone mRNA abundance with DNA replication

DNA and histone synthesis are coupled and ongoing replication is required to maintain histone gene expression. Here, we expose S phase–arrested cells to the kinase inhibitors caffeine and LY294002. This uncouples DNA replication from histone messenger RNA (mRNA) abundance, altering the efficiency of replication stress–induced histone mRNA down-regulation. Interference with caffeine-sensitive checkpoint kinases ataxia telangiectasia and Rad3 related (ATR)/ataxia telangiectasia mutated (ATM) does not affect histone mRNA down- regulation, which indicates that ATR/ATM alone cannot account for such coupling. LY294002 potentiates caffeine's ability to uncouple histone mRNA stabilization from replication only in cells containing functional DNA-activated protein kinase (DNA-PK), which indicates that DNA-PK is the target of LY294002. DNA-PK is activated during replication stress and DNA-PK signaling is enhanced when ATR/ATM signaling is abrogated. Histone mRNA decay does not require Chk1/Chk2. Replication stress induces phosphorylation of UPF1 but not hairpin-binding protein/stem-loop binding protein at S/TQ sites, which are preferred substrate recognition motifs of phosphatidylinositol 3-kinase–like kinases, which indicates that histone mRNA stability may be directly controlled by ATR/ATM- and DNA-PK–mediated phosphorylation of UPF1

A performance-based warranty for products subject to competing hard and soft failures

This article studies a performance-based warranty for products subject to competing hard and soft failures. The two failure modes are competing in the sense that either one, on a "whichever-comes-first" basis, can cause the product to fail. A performance-based warranty not only covers the repair or replacement of any defect, but also guarantees the minimum performance level throughout the warranty period. In this article, we propose three compensation policies—that is, free replacement, penalty, and full refund, when a product's performance fails to meet the guaranteed level. The expected warranty servicing costs for the three policies are derived, based on the competing risks concept. A warranty design problem is further formulated to simultaneously determine the optimal product price, warranty length, and performance guarantee level so as to maximize the manufacturer's total profit. Numerical studies are conducted to demonstrate and compare the three performance-based compensation policies. It is shown that the full refund policy always leads to the lowest total profit, whereas neither of the other two policies can dominate each other in all scenarios. In particular, the free replacement policy results in a higher total profit than the penalty policy when the replacement cost is low, the penalty cost coefficient is high, and/or the product reliability is high

Reliability modeling and analysis of load-sharing systems with continuously degrading components

This paper presents a reliability modeling and analysis framework for load-sharing systems with identical components subject to continuous degradation. It is assumed that the components in the system suffer from degradation through an additive impact under increased workload caused by consecutive failures. A log-linear link function is used to describe the relationship between the degradation rate and load stress levels. By assuming that the component degradation is well modeled by a step-wise drifted Wiener process, we construct maximum likelihood estimates (MLEs) for unknown parameters and related reliability characteristics by combining analytical and numerical methods. Approximate initial guesses are proposed to lessen the computational burden in numerical estimation. The estimated distribution of MLE is given in the form of multivariate normal distribution with the aid of Fisher information. Alternative confidence intervals are provided by bootstrapping methods. A simulation study with various sample sizes and inspection intervals is presented to analyze the estimation accuracy. Finally, the proposed approach is illustrated by track degradation data from an application example

Accelerated degradation tests planning with competing failure modes

Accelerated degradation tests (ADT) have been widely used to assess the reliability of products with long lifetime. For many products, environmental stress not only accelerates their degradation rate but also elevates the probability of traumatic shocks. When random traumatic shocks occur during an ADT, it is possible that the degradation measurements cannot be taken afterward, which brings challenges to reliability assessment. In this paper, we propose an ADT optimization approach for products suffering from both degradation failures and random shock failures. The degradation path is modeled by a Wiener process. Under various stress levels, the arrival process of random shocks is assumed to follow a nonhomogeneous Poisson process. Parameters of acceleration models for both failure modes need to be estimated from the ADT. Three common optimality criteria based on the Fisher information are considered and compared to optimize the ADT plan under a given number of test units and a predetermined test duration. Optimal two- and three-level optimal ADT plans are obtained by numerical methods. We use the general equivalence theorems to verify the global optimality of ADT plans. A numerical example is presented to illustrate the proposed methods. The result shows that the optimal ADT plans in the presence of random shocks differ significantly from the traditional ADT plans. Sensitivity analysis is carried out to study the robustness of optimal ADT plans with respect to the changes in planning input

A mixture of variational canonical correlation analysis for nonlinear and quality-relevant process monitoring

Proper monitoring of quality-related variables in industrial processes is nowadays one of the main worldwide challenges with significant safety and efficiency implications.Variational Bayesian mixture of canonical correlation analysis (VBMCCA)-based process monitoring method was proposed in this paper to predict and diagnose these hard-to-measure quality-related variables simultaneously. Use of Student's t-distribution, rather than Gaussian distribution, in the VBMCCA model makes the proposed process monitoring scheme insensitive to disturbances, measurement noises, and model discrepancies. A sequential perturbation (SP) method together with derived parameter distribution of VBMCCA is employed to approach the uncertainty levels, which is able to provide a confidence interval around the predicted values and give additional control line, rather than just a certain absolute control limit, for process monitoring. The proposed process monitoring framework has been validated in a wastewater treatment plant (WWTP) simulated by benchmark simulation model with abrupt changes imposing on a sensor and a real WWTP with filamentous sludge bulking. The results show that the proposed methodology is capable of detecting sensor faults and process faults with satisfactory accuracy

Maintenance optimisation for systems with multi-dimensional degradation and imperfect inspections

In this paper, we develop a maintenance model for systems subjected to multiple correlated degradation processes, where a multivariate stochastic process is used to model the degradation processes, and the covariance matrix is employed to describe the interactions among the processes. The system is considered failed when any of its degradation features hits the pre-specified threshold. Due to the dormancy of degradation-based failures, inspection is implemented to detect the hidden failures. The failed systems are replaced upon inspection. We assume an imperfect inspection, in such a way that a failure can only be detected with a specific probability. Based on the degradation processes, system reliability is evaluated to serve as the foundation, followed by a maintenance model to reduce the economic losses. We provide theoretical boundaries of the cost-optimal inspection intervals, which are then integrated into the optimisation algorithm to relieve the computational burden. Finally, a fatigue crack propagation process is employed as an example to illustrate the effectiveness and robustness of the developed maintenance policy. Numerical results imply that the inspection inaccuracy contributes significantly to the operating cost and it is suggested that more effort should be paid to improve the inspection accuracy

Warranty service contracts design for deteriorating products with maintenance duration commitments

With the increasing diversification of customers’ demand and purchasing behaviors, more and more manufacturers have focused their attention on the warranty service contracts design. The maintenance duration of the sold product, which plays an important role in the normal production and operation process of the user, is frequently taken into consideration in warranty contracts. In this study, we design different warranty contracts with various combinations of maintenance duration and availability requirements. The manufacturer commits to compensate for each overdue repair or failing to satisfy the availability target. The customers’ choice behavior is described by the multinomial logit (MNL) model, and customers often form their own minimum acceptable levels (also referred to as reference points) of maintenance duration and availability when making purchasing decisions, which have an impact on the contract choice. The expected warranty servicing profit is maximized to determine the optimal price, maintenance duration and availability. Finally, the proposed warranty contracts are demonstrated by numerical examples. We find that the maintenance duration affects not only the warranty cost but also the customer choice, which further affects the optimal contract pricing and profits

A Novel Fast and Robust Binary Affine Invariant Descriptor for Image Matching

As the current binary descriptors have disadvantages of high computational complexity, no affine invariance, and the high false matching rate with viewpoint changes, a new binary affine invariant descriptor, called BAND, is proposed. Different from other descriptors, BAND has an irregular pattern, which is based on local affine invariant region surrounding a feature point, and it has five orientations, which are obtained by LBP effectively. Ultimately, a 256 bits binary string is computed by simple random sampling pattern. Experimental results demonstrate that BAND has a good matching result in the conditions of rotating, image zooming, noising, lighting, and small-scale perspective transformation. It has better matching performance compared with current mainstream descriptors, while it costs less time

Multi-level Extensible Synthetic Evaluation of the Quality of Passenger Transport Service of High-speed Rail

Abstract: This study establishes the evaluation index system of the quality of passenger transport service of highspeed rail based on the characteristics of passenger transport and determines the index weights with the Fuzzy Analytic Hierarchy Process. In order to make up for the deficiencies of present quality evaluation method of passenger transport service of high-speed rail, this study develops the multi-level extensible synthetic evaluation model of the quality of passenger transport service of high-speed rail with the core of extension method based on the questionnaire survey to the passengers. This study tests the model with the data surveyed in a passenger station of high-speed rail, which can not only expand extension application areas, but also provide new ideas and means for the evaluation of the quality of passenger transport service of high-speed rail

Online reinforcement learning for condition-based group maintenance using factored Markov decision processes

We investigate a condition-based group maintenance problem for multi-component systems, where the degradation process of a specific component is affected only by its neighbouring ones, leading to a special type of stochastic dependence among components. We formulate the maintenance problem into a factored Markov decision process taking advantage of this dependence property, and develop a factored value iteration algorithm to efficiently approximate the optimal policy. Through both theoretical analyses and numerical experiments, we show that the algorithm can significantly reduce computational burden and improve efficiency in solving the optimization problem. Moreover, since model parameters are unknown a priori in most practical scenarios, we further develop an online reinforcement learning algorithm to simultaneously learn the model parameters and determine an optimal maintenance action upon each inspection. A novel feature of this online learning algorithm is that it is capable of learning both transition probabilities and system structure indicating the stochastic dependence among components. We discuss the error bound and sample complexity of the developed learning algorithm theoretically, and test its performance through numerical experiments. The results reveal that our algorithm can effectively learn the model parameters and approximate the optimal maintenance policy