Search

The aim of the paper is to carry out survival analysis of a novel multi-state model on infectious disease considering various levels of severity using semi-Markov processes. Various levels of severity of the disease over time and transitions between these severity levels have been considered. Transition probabilities and expected waiting times are derived. Expressions for mean survival time, expected total time in home isolation, and expected total time in hospital are obtained. The analysis of the proposed model is carried out through numerical computation and plotting several graphs. Important conclusions are drawn. The modelling framework proposed here can be used to model any infectious disease irrespective of disease states. The study will be helpful in designing effective measures to control the infectious disease and selecting the appropriate intervention policies. [ABSTRACT FROM AUTHOR]

• A CBM-based joint maintenance strategy is proposed for a complex multi-state system where part of the components are monitored and the predicted RUL of the monitored components is known. • The investigated optimization problem is modeled into a Markov Decision Process. • A deep Q-network algorithm is designed to solve the maintenance strategy. • The application of the traction system of a metro train serves to illustrate the effectiveness of the proposed maintenance strategy and model. The utilization of prognostic information in practical engineering is increasing with the development of technology and predictive modeling. Current research on maintenance strategies for complex multi-state systems often neglects prognostic information or assumes complete availability of all component information. This paper investigates the joint maintenance strategies based on condition-based maintenance for complex multi-state systems, in which the predicted remaining useful life of some components is known. Firstly, a maintenance strategy framework is developed and the joint maintenance strategy is proposed for the studied problem. Then the deterioration process of the component, the imperfect maintenance, and prediction error models are constructed. The optimization problem is modeled as a Markov Decision Process to minimize the maintenance cost, and the system reliability constraints are established by using the universal generating function method. In addition, a deep Q-network is designed to solve the optimal maintenance policy. Finally, the traction system of a metro train is taken as an example to verify the applicability of the model and algorithm. The results show that the proposed maintenance strategy reduces the maintenance cost compared to the current maintenance strategy for both fixed maintenance intervals and dynamic maintenance intervals. [ABSTRACT FROM AUTHOR]

A real-life bleaching system is proposed, and its reliability function is estimated using universal generating function technique. The units are connected to each other in series and parallel arrangements. Further, a 2-out-of-4 system is described and taken as an example where out of 4 units, if 2 are working the system will perform its task. Universal Generating Function technique is applied to both the systems and by using Owen’s and Boland method signature, tail signature, Barlow-Proschan Index and expected time of the proposed complex system is calculated. [ABSTRACT FROM AUTHOR]

For the multi-state system, the reliability problem of the common component failure group overlaps under the uncertainty of the component state probability information. Combined with evidence theory, evidence generalized generation function (EUGF) method, and common cause failure theory, the EUGF analysis method for multi-state systems when the common cause failure component groups overlap each other is proposed. The reliability problem of the multi-state system when the common cause failure component group overlaps and the component state probability information has uncertainty is solved. Integrate uncertainty information and common cause failure information in a multi-state system. The reliability of the system and the multi-state system in the case of independent failure of the component is compared. The reliability of the multi-state system in the failure of the common component failure component is lower than the reliability of the component independent failure. The reliability analysis of the multi-state system in the case of the uncertainty of the component state probability information is more in line with the actual engineering situation due to the overlapping of the failed component groups. [ABSTRACT FROM AUTHOR]

Space-time prism (STP), which envelops the spatial and temporal opportunities for travel and activity participation within a time frame, is a fundamental concept in time geography. Despite many variants, STPs have been mostly modeled for one flexible activity between two anchor points. This study proposes a systemic approach to construct the STP bounds of activity programs that usually include various possible realizations of activity chains. To that effect, multi-state supernetworks are applied to represent the relevant path sets of multi-activity travel patterns. A goal-directed search method in multi-state supernetworks is developed to delineate the potential space-time path areas satisfying the space-time constraints. Particularly, the approximate lower and upper STP bounds are obtained by manipulating the goal-directed search procedure utilizing landmark-based triangular inequalities and spatial characteristics. The suggested approach can in an efficient fashion find the activity state dependent bounds of STP and potential path area. The formalism of goal-directed search through multi-state supernetworks addresses the fundamental shift from constructing STPs for single flexible activities to activity programs of flexible activity chains. [ABSTRACT FROM AUTHOR]

Highlights • Efficient management of airport arrival operations is fundamental for reliable air traffic. • A Bayesian Network approach is adopted to evaluate and predict arrival congestion and delay. • A Markov chain technique is used to develop a reliability analysis based on Multi-State System. • The model provides a predictive and adaptive approach to arrival operational performance. • Results can be applied to derive operational strategies and facilitate decision making in the event of uncertainty. Abstract Air traffic networks are highly dependent on airport arrival processes, which are common triggers for capacity constraints and delay propagation. Arrival Manager tools aim to improve arrival flows at airports. To do so they need reliable, accurate assessments of potential congestion and delay issues. This paper sets out a methodology for predicting and evaluating the operational state of the airport arrival system. This methodology is structured in two steps: the prediction stage and the reliability stage. The prediction model is based on a Bayesian Network approach, which reflects the stochastic and time-varying nature of airport operations. It also provides insights into the interdependencies between factors contributing to airport performance. The reliability model uses a Multi-State System structure, as the airport arrival system has a large number of performance levels. It is developed via a Markov process technique. By combining these prediction and reliability models we can assess the characteristics of the airport arrival system: stationary state, availability, performance and degradation. The methodology is applied to a case study at a busy European airport, using real data from peak traffic months. Results for the scenarios analyzed show that the factors that have a greatest impact on delay and congestion are the level of saturation at arrival processes, the time frame of the day (which determines the arrival declared capacity) and the meteorological conditions. Moreover, arrival states of congestion reduce the airport's ability to maintain optimal performance rates. The model represents an evolution from the traditional corrective and binary vision of performance analysis towards a predictive and multi-state approach. The results can be applied to derive operational strategies and draw conclusions regarding arrival performance and management. [ABSTRACT FROM AUTHOR]

This article studies reliability assessment for Multi-State Systems (MSSs) with components states that are uncertain in both probability and performance realizations. First, we propose a model of (discrete) Hybrid Uncertainty Variable (HUV) for modeling the hybrid uncertainty of the MSS, in which both state performance levels and associated probability level are described by uncertain values. The HUV can be regarded as a generalization of random variable whose realizations and corresponding probabilities are both uncertain values. Especially the uncertain probabilities are controlled by the probability law. Leveraging the HUV-based hybrid uncertainty model, the primitive probability law is considered throughout the whole process from modeling component state probabilities, through the resulting system state probabilities, to the final reliability computations. Therefore, the information loss is reduced to a minimum. Furthermore, we develop a framework for assessing the reliability of the MSS with hybrid uncertainty. In particular, due to hybrid uncertainty considered together with the primitive probability law constraints, the reliability bound computations essentially require solving a pair of multi-linear optimization problems, which in general are non-convex and non-concave and therefore belong to a class of difficult optimization problems. Therefore, we develop a linear programming‐based cut-generation approach for solving the reliability bound assessment problem which achieves a computationally attractive approximation. Finally, the effectiveness of our approaches is validated in the case study with the comparisons to the published results. [ABSTRACT FROM AUTHOR]

Optimal power in wind farms turns to be a modern problem for investors and decision makers; onshore wind farms are subject to performance and economic and environmental constraints. The aim of this work is to define the best installed capacity (best topology) with maximum performance and profits and consider environmental impacts as well. In this article, we continue the work recently done on wind farm topology-finding algorithm. The proposed resolution technique is based on finding the best topology of the system that maximizes the wind farm performance (availability) under the constraints of costs and capital investments. Global warming potential of wind farm is calculated and taken into account in the results. A case study is done using data and constraints similar to those collected from wind farm constructors, managers, and maintainers. Multi-state systems (MSS), universal generating function (UGF), wind, and load charge functions are applied. An economic study was conducted to assess the wind farm investment. Net present value (NPV) and levelized cost of energy (LCOE) were calculated for best topologies found. [ABSTRACT FROM AUTHOR]

In industrial engineering, the components of a critical system are capable of being in partial failure modes, except for “perfect state” and “complete failure”, and the failure behavior of those usually manifests as dynamicity and dependence. However, traditional dynamic fault trees (DFTs), which represent an event as a dichotomous variable, and the extended ones in probability risk assessment cannot actually grasp the dynamic properties of some multi-state systems (MSSs). For these issues, this article further extends the classical DFT language for sequential, prior and trigger-dependent MSSs and presents a unified framework of probability risk analysis based on the dynamic Bayesian net (DBN). First, three types of multi-state dynamic gates (MSDGs) for representation of the above-mentioned failure behavior were defined, and the algorithm for mapping MSDGs to DBNs was proposed. Next, this paper employs the classic Markov chain based on the improved approach of Kronecker algebra to verify these models. Finally, combining a specific example of a shield excavation system, we discuss how the MSDGs can be adopted as a compact modeling language and analyze the dynamic probability risk of the system by compiling the model into a DBN. [ABSTRACT FROM AUTHOR]

Cooperative relay (CR) is a favorable technique to provide better spectral efficiency and enhance the cell coverage area in a cost-effective manner. However, several undesired issues, such as high-power consumption, arise due to use of a high number of relay nodes (RNs) in the network, Moreover, in a dynamic environment, full channel state information (CSI) is not always available. Therefore, all RNs need to calculate the abrupt changes in the channel; otherwise, the overhead delay will increase and the RNs will consume higher network power. To address this, an optimized modified genetic algorithm (Modified GA) based scheme is proposed that utilizes probabilistic selection rules and fitness scores. The results are presented in terms of symbol error rate (SER), network capacity, power consumption, and power improvement in order to analyze the performance of the proposed scheme. The results show that the proposed scheme provides better overall performance as compared to conventional optimization approaches. [ABSTRACT FROM AUTHOR]

In this study, the method proposed by St-Amant et al. based on ionic chromatography was modified to rapidly quantify 226Ra and 228Ra by inorganic mass spectrometry in a broad variety of matrices at environmental levels. The sample volume loaded on the cationic resin AG50Wx8 was varied to achieve the highest possible adsorption without any significant loss of retention for radium. The pH and volume for the elution steps were optimized to obtain the highest pre-concentration factor and compatibility with plasma-based instruments and to eliminate interference to the maximum extent possible. Finally, inductively coupled plasma mass spectrometry (ICP-MS) instrumental conditions were investigated and optimized for the quantification of radium. An instrumental detection limit of 0.53 pg L−1 in 226Ra and 0.47 pg L−1 in 228Ra were achieved. When combined with the cationic separation procedure for interference removal, the overall method detection limit decreases to 10 fg L−1 in less than 2 hours. [ABSTRACT FROM AUTHOR]

A multimode sample introduction system (MSIS) was coupled to a pre-evaporation tube, which was connected to the base of the torch in inductively coupled plasma optical emission spectrometry (ICPOES). The pre-evaporation tube and bottom 7 cm (for ARCOS instrument) or 3 cm (for Agilent instrument) of the torch were surrounded by an infrared-heated ceramic rope, which was heated to 80 °C in nebulisation mode and to 100 °C or 175 °C in hydride generation and dual modes. This resulted in improved sensitivity and detection limit for many elements, especially hydride-forming ones, compared to what is achieved with pneumatic nebulisation, but in some degradation of precision. The applicability of the approach was demonstrated through the accurate determination of several elements in two standard reference materials (waste water and corn bran) with matrix matching but without internal standardization. With infrared heating and operating MSIS in hydride generation (HG) mode, more elements than expected could be accurately determined, including Be, Co, Cr, Mg, Mn, etc., whose determination had not been reported in this mode before. [ABSTRACT FROM AUTHOR]

In this study, three discrete-time multi-state complex systems subject to multiple events are modeled, in a well structured form, as Markovian arrival processes with marked arrivals. The systems, ranked by the number of events affecting the online unit, have multiple and variable repairpersons, and the online unit are partitioned into performance stages. The first system is subject only to internal failures. The second, additionally, considers external shocks, which can produce any of three consequences; extreme failure, degradation of the internal performance of the online unit or cumulative damage. Failure may be repairable or non-repairable. The repair facility is composed of an indeterminate number of repairpersons. When a non-repairable failure occurs, the number of repairpersons may be modified. Finally, the third system includes preventive maintenance in combination with random inspections. Various measures are incorporated, in an algorithmic and computational form, in transient and stationary regimes. Costs and rewards are included in the model to optimize the system from different standpoints. The results of this study are implemented computationally with Matlab, and a numerical example shows the versatility of the modeling. [ABSTRACT FROM AUTHOR]

With the increasing complexity and size of modern advanced engineering systems, the traditional reliability theory cannot characterize and quantify the complex characteristics of complex systems, such as multi-state properties, epistemic uncertainties, common cause failures (CCFs). This paper focuses on the reliability analysis of complex multi-state system (MSS) with epistemic uncertainty and CCFs. Based on the Bayesian network (BN) method for reliability analysis of MSS, the Dempster-Shafer (DS) evidence theory is used to express the epistemic uncertainty in system through the state space reconstruction of MSS, and an uncertain state used to express the epistemic uncertainty is introduced in the new state space. The integration of evidence theory with BN which called evidential network (EN) is achieved by adapting and updating the conditional probability tables (CPTs) into conditional mass tables (CMTs). When multiple CCF groups (CCFGs) are considered in complex redundant system, a modified β factor parametric model is introduced to model the CCF in system. An EN method is proposed for the reliability analysis and evaluation of complex MSSs in this paper. The reliability analysis of servo feeding control system for CNC heavy-duty horizontal lathes (HDHLs) by this proposed method has shown that CCFs have considerable impact on system reliability. The presented method has high computational efficiency, and the computational accuracy is also verified. [ABSTRACT FROM AUTHOR]

In most real life situations, the system’s components contribute differently in different performance levels. Such a situation can be modeled by systems with multi-state components having more than one working status, e.g. perfect functioning, and partial working. In this paper, a multi-state system that consists of two types of three-state components is defined and studied. An explicit formula for the probability that the performance of the system is at least a given level is obtained for the most general case when the components are statistically dependent. The model is applied to evaluate the wind power system that consists of two wind plants in different regions. An optimization problem is formulated to find the optimal number of wind turbines that must be installed in the wind plants by minimizing the total cost under specific power production. [ABSTRACT FROM AUTHOR]

An external factor is an added condition beyond the normal load of the system affecting the system's success. Each external factor has an influence on system components, which may lead to system failure. To decrease the effects of different states of the external factors on system components, multistate protections are established to protect the components from the effects of the external factors. The protection states are dependent on the external factor state. When a protection is at different states, the protection may be damaged. In this paper, considering the joint effect of the external factors and protections on system components, a measure of states of the protections on the system reliability is introduced to identify which protection level has the most important influence on the system success. Based on the importance values of different protections, appropriate actions can be applied in system components to improve the system protection and reliability and reduce the effects of external factors. At last, an example of a computer server system is used to demonstrate the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a general model for multi-state deteriorating systems with condition based preventive maintenance is introduced and analyzed extensively. The system experiences various levels of deterioration and at each stage, an inspection is carried out at constant time intervals in order to identify what kind of preventive maintenance, the system should undergo. When the system fails, despite preventive maintenance, a repair procedure is carried out and the system is restored to its initial fully operational state. The proposed model incorporates also imperfect maintenance, either minimal or major, failed maintenance and sudden failures that may occur mostly due to external factors at any deterioration state as well. Moreover, the sojourn times are assumed to be generally distributed. The main dependability and performance measures of the proposed model are computed while the corresponding transient measures are estimated using Monte Carlo simulation. Our endmost aim is to distinguish inspection and consequently maintenance policies that optimize multi-state deteriorating system's dependability and/or performance. Additionally, multi-objective optimization problems are formulated and solve in order to distinguish preventive maintenance policies that optimize simultaneously both the dependability and performance measures. [ABSTRACT FROM AUTHOR]

Previous research in series systems assumes that each element must satisfy its own demand individually. However, the surplus performance from some elements can be transmitted to other deficient elements in some practical systems such as power generating systems and collaborative computing systems. In this paper, we consider a series system with a performance sharing group of limited size, i.e., the number of elements that can be connected into the performance sharing group is limited. It is assumed that the elements connected into the performance sharing group can change dynamically when the state of the system changes in order to minimize the possible performance deficiency of the system. A reliability evaluation algorithm is proposed for the suggested system and the optimal connection strategy is discussed. Numerical experiments are conducted to illustrate the applications. [ABSTRACT FROM AUTHOR]

We consider a three-state continuous-time semi-Markov process with Weibull-distributed transition times to model the degradation mechanism of an industrial equipment. To build this model, an original combination of techniques is proposed for building a semi-Markov degradation model based on expert knowledge and few field data within the Bayesian statistical framework. The issues addressed are: i) the prior elicitation of the model parameters values from experts, avoiding possible information commitment; ii) the development of a Markov-Chain Monte Carlo algorithm for sampling from the posterior distribution; iii) the posterior inference of the model parameters values and, on this basis, the estimation of the time-dependent state probabilities and the prediction of the equipment remaining useful life. The developed Bayesian model offers the possibility of updating the system reliability estimation every time a new evidence is gathered. The application of the modeling framework is illustrated by way of a real industrial case study concerning the degradation of diaphragms installed in a production line of a biopharmaceutical industry. [ABSTRACT FROM AUTHOR]

It is assumed that information in a traditional multi-state information network (MIN) can be sent anywhere. However, in practical applications, the intensity of information (the capacity of information to be transmitted) is gradually reduced because of the deterioration effect. For example, a Wi-Fi signal decreases if the transmission distance is too great. Hence, a novel MIN model called the deterioration-effect acyclic MIN (AMIN de ) is proposed to meet real-world problems in which the intensity of information decreases by a given amount and transmits to a limited number of nodes. In addition, to counteractsome of the deterioration effect, a novel boost node is introduced toincreaseinformation temporarily. A very straightforward and easily programmed algorithm derived from the universal generating function method (UGFM) is used to evaluate the AMIN de reliability, which is defined as the probability that information can be received by the target node through the AMIN de . The computational complexity of the proposed algorithm is also analyzed. Finally, an example is provided to illustrate how the reliability of the AMIN de is calculated using the proposed algorithm. [ABSTRACT FROM AUTHOR]

The main focus of this paper is on the development of reliability measures for a repairable multi-state system which operates under dynamic regimes under the discrete-time hypothesis. The switching process of regimes is governed by a Markov chain, and the functioning process of the system follows another Markov chain with different transition probability matrices under different regimes. In terms of two chains as above, a new Markov chain is constructed to depict the evolution process of the dynamic system. For the regime consideration, some novel reliability indices are essential and firstly introduced in this paper. By means of hierarchical partitions for the new state space, Ion-Channel modeling theory and discrete-time Markov chain, the traditional and novel reliability and availability functions for the system under random regimes are easily obtained with the closed form solutions, such as two types of system reliabilities, two types of system point availabilities, two types of system multiple-point availabilities and the associated system multi-interval availabilities and so on. In addition, some probability distributions of sojourn times we are interested in are discussed and computed here. Finally, a numerical example is given to illustrate the results obtained in the paper. [ABSTRACT FROM AUTHOR]

A multi-state linear consecutive- k -out-of- n : F system, MLC( k,n ) consists of n components ordered in a line, which fails if at least k consecutive components have failed. It abounds in real-world applications such as wireless sensor networks, microwave station networks, and oil pipeline systems. Performability of an MLC( k,n ) system is concerned with probability that the system performs at a performance state characterized in terms of the largest number of consecutive failed components. This paper proposes a multi-valued decision diagram (MDD)-based approach to model and evaluate performability of an MLC( k,n ) system with heterogeneous components following arbitrary lifetime distributions. The proposed approach encompasses a novel and efficient MDD model generation procedure. Both complexity analysis and illustrative examples are provided to show efficiency of the proposed MDD approach. As demonstrated through examples, the proposed MDD approach is also applicable to MLC( k,n ) systems with non-identical reparable components and component importance analysis. [ABSTRACT FROM AUTHOR]

ABSTRACT In this article, we present an experiential perspective on how a big data analytics course was designed and delivered to students at a major Midwestern university. In reference to the MSIS 2006 Model Curriculum, we designed this course as a level 2 course, with prerequisites in databases, computer programming, statistics, and data mining. Students in the class were mostly seniors or at the graduate level, and had a strong technical and quantitative background. We include details of concepts covered in the course, as well as summaries of four major sample course assignments used. Some of the concepts covered include large-scale data collection and management using the Hadoop ecosystem, stream mining, visual analytics, and social network analytics. Besides Hadoop, the course also introduced various IBM and Teradata big data tools. We show how the course modules align with the intended learning goals and course objectives. A post-course survey indicated that the structure and organization of the course helped students clearly and concisely assimilate the course content. [ABSTRACT FROM AUTHOR]

Utility functions are often used to reflect decision makers' (DMs') preferences. They have the following two merits: one refers to the representation of the DM's utility (satisfaction) level, the other one to the measuring of the consensus level in a negotiation process. Taking the background of China's urban house demolition, a new kind of consensus model is established by using different types of multi-stage fluctuation utility functions, such as concave, convex, S-shaped, reversed S-shaped, reversed U-shaped as well as their combinations, to reveal negotiators' dynamic physiological preferences and consensus level. Moreover, the effects of the decision-making budget and the individual compensation tolerance on the consensus level are also discussed in this paper. Compared with previous research, the proposed model takes both the negotiation cost and DM's preference structure into consideration, and most importantly, it is computational less complex. [ABSTRACT FROM AUTHOR]

We study nonparametric estimation of the illness-death model using left-truncated and right-censored data. The general aim is to estimate the multivariate distribution of a progressive multi-state process. Maximum likelihood estimation under censoring suffers from problems of uniqueness and consistency, so instead we review and extend methods that are based on inverse probability weighting. For univariate left-truncated and right-censored data, nonparametric maximum likelihood estimation can be considerably improved when exploiting knowledge on the truncation distribution. We aim to examine the gain in using such knowledge for inverse probability weighting estimators in the illness-death framework. Additionally, we compare the weights that use truncation variables with the weights that integrate them out, showing, by simulation, that the latter performs more stably and efficiently. We apply the methods to intensive care units data collected in a cross-sectional design, and discuss how the estimators can be easily modified to more general multi-state models. [ABSTRACT FROM AUTHOR]

The notion of signature has been widely applied for the reliability evaluation of technical systems that consist of binary components. Multi-state system modeling is also widely used for representing real life engineering systems whose components can have different performance levels. In this article, the concept of survival signature is generalized to a certain class of unrepairable homogeneous multi-state systems with multi-state components. With such a generalization, a representation for the survival function of the time spent by a system in a specific state or above is obtained. The findings of the article are illustrated for multi-state consecutive- k-out-of- n system which perform its task at three different performance levels. The generalization of the concept of survival signature to a multi-state system with multiple types of components is also presented. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 593-599, 2017 [ABSTRACT FROM AUTHOR]

The evaluation of peritoneal dialysis (PD) programmes requires the use of statistical methods that suit the complexity of such programmes. Multi-state regression models taking competing risks into account are a good example of suitable approaches. In this work, multi-state structured additive regression (STAR) models combined with penalized splines (P-splines) are proposed to evaluate peritoneal dialysis programmes. These models are very flexible since they may consider smooth estimates of baseline transition intensities and the inclusion of time-varying and smooth covariate effects at each transition. A key issue in survival analysis is the quantification of the time-dependent predictive accuracy of a given regression model, which is typically assessed using receiver operating characteristic (ROC)’based methodologies. The main objective of the present study is to adapt the concept of time-dependent ROC curve, and their corresponding area under the curve (AUC), to a multi-state competing risks framework. All statistical methodologies discussed in this work were applied to PD survival data. Using a multi-state competing risks framework, this study explored the effects of major clinical covariates on survival such as age, sex, diabetes and previous renal replacement therapy. Such multi-state model was composed of one transient state (peritonitis) and several absorbing states (death, transfer to haemodialysis and renal transplantation). The application of STAR models combined with time-dependent ROC curves revealed important conclusions not previously reported in the nephrology literature when using standard statistical methodologies. For practical application, all the statistical methods proposed in this article were implemented in R and we wrote and made available a script named as NestedCompRisks. [ABSTRACT FROM AUTHOR]

Fault-tolerant networks are being used to provide more reliable network connections. Development of the fault-tolerant computing system requires accurate reliability modeling. Numerical formulations and complex models are commonly used for obtaining reliability measures. These measures are functions of component failure rates and fault-coverage. Coverage provides information about the fault and error detection, isolation, and system recovery capabilities. This research studied a system consisting three units in parallel with three types of failures namely, catastrophic, human, and unit failures incorporating the coverage factor and evaluated the various reliability measures of the considered multi-state system subject to imperfect fault coverage. [ABSTRACT FROM AUTHOR]

To ensure a series of missions can be completed with only finite breaks, many systems are required to guarantee system safety and mission success. Of these, maintenance decision support is vital. One widely used maintenance strategy has been selective maintenance. Most traditional selective maintenance optimisation research has focused on binary state systems, which are subject to distribution deterioration or failure. However, a majority of systems used in aerospace or industrial applications are multi-state systems with more than two states deteriorating at the same time, meaning that real-time state distribution is needed to provide more timely and effective maintenance. This paper presents a novel integrated system health management-oriented maintenance decision support methodology and framework for a multi-state system based on data mining. An aero-engine system numerical example is given to illustrate the methodology, the results of which demonstrate the significant advantages of using data mining to efficiently obtain state distribution information, and the benefits of using a robust optimal model to choose suitable strategies. This methodology, which is applicable to multi-state systems of varying sizes, has the ability to solve maintenance problems when imperfect maintenance quality is considered. [ABSTRACT FROM AUTHOR]

Engineering systems are designed to carry the load. The performance of the system largely depends on how much load it carries. On the other hand, the failure rate of the system is strongly affected by its load. Besides internal failures, such as fatigue and aging process, systems may also fail due to external impacts such as nature disasters and terrorism. In this paper, we integrate the effect of loading and protection of external impacts on multi-state systems with performance sharing mechanism. The objective of this research is to determine how to balance the load and protection on system elements. An availability evaluation algorithm of the proposed system is suggested and the corresponding optimization problem is solved utilizing genetic algorithms. [ABSTRACT FROM AUTHOR]

One important goal in multi-state modelling is to explore information about conditional transition-type-specific hazard rate functions by estimating influencing effects of explanatory variables. This may be performed using single transition-type-specific models if these covariate effects are assumed to be different across transition-types. To investigate whether this assumption holds or whether one of the effects is equal across several transition-types (cross-transition-type effect), a combined model has to be applied, for instance with the use of a stratified partial likelihood formulation. Here, prior knowledge about the underlying covariate effect mechanisms is often sparse, especially about ineffectivenesses of transition-type-specific or cross-transition-type effects. As a consequence, data-driven variable selection is an important task: a large number of estimable effects has to be taken into account if joint modelling of all transition-types is performed. A related but subsequent task is model choice: is an effect satisfactory estimated assuming linearity, or is the true underlying nature strongly deviating from linearity? This article introduces component-wise Functional Gradient Descent Boosting (short boosting) for multi-state models, an approach performing unsupervised variable selection and model choice simultaneously within a single estimation run. We demonstrate that features and advantages in the application of boosting introduced and illustrated in classical regression scenarios remain present in the transfer to multi-state models. As a consequence, boosting provides an effective means to answer questions about ineffectiveness and non-linearity of single transition-type-specific or cross-transition-type effects. [ABSTRACT FROM AUTHOR]

In the present paper we develop bootstrap tests of hypothesis, based on simulation, for the transition probability matrix arising in the context of a multi-state model. The bootstrap test statistic is based on the paper of Tattar and Vaman (2008), which develops a statistic for the testing problems concerning the transition probability matrix of the non homogeneous Markov process. [ABSTRACT FROM PUBLISHER]

Products are becoming increasingly more complex and intelligent, which requires users to participate in the design process in order to meet customer demands and enhance market competition. Interactive genetic algorithm (IGA) can effectively solve the optimization problem. However, the challenge still remains for IGA to ameliorate user fatigue and reduce the noise in the process of evolution. To address the issue, a multi-stage interactive genetic algorithm (MS-IGA) is proposed, which divides the large population of the traditional interactive genetic algorithm (TIGA) into several stages according to different functional requirements. The proposed MS-IGA is then applied to the car console conceptual design system, to better capture the knowledge of users’ personalized requirements and accomplish the product design. This is especially important in the field of complex product configuration design, such as in cars, personal computers, smart phones and the like. Through the users’ graphic interface, customers separately evaluate product design at every different stage of its evolution, which makes the proposed algorithm more directional than the TIGA. We also introduce genetic sense units, which represent different functional modules, in order to realize the customers’ collaborative design. The extensive experimental results are provided to demonstrate that our proposed algorithm is correct and efficient according to the efficiency test, convergence analysis and fatigue test for application of the product design system, including car interior and other modular product. [ABSTRACT FROM AUTHOR]

A Bayesian system reliability estimation methodology for multiple overlapping uncertain data sets within complex multi-state on-demand and continuous life metric systems is presented in this paper. Data sets are overlapping if they are drawn from the same process at the same time, with reliability data from sensors attached to a system at different functional and physical levels being a prime example. Treating overlapping data as non-overlapping loses or incorrectly infers information on system reliability. Methodologies for system reliability analysis of certain overlapping data sets have previously been proposed. These methodologies, and the approach presented in this paper, are able to incorporate overlapping uncertain evidence from systems with a detailed understanding of the system logic represented using fault-trees, reliability block diagrams or equivalent representations. The method presented here builds on approaches that have already been developed by the authors that allow incorporation of exact or certain data sets. [ABSTRACT FROM AUTHOR]

An efficient second-order method based on exponential time differencing approach for solving American options under multi-state regime switching is developed and analysed for stability and convergence. The method is seen to be strongly stable (L-stable) in each regime. The implicit predictor–corrector nature of the method makes it highly efficient in solving nonlinear systems of partial differential equations arising from multi-state regime switching model. Stability and convergence of the method are examined. The impact of regime switching on option prices for different jump rates and volatility is illustrated. A general framework for multi-state regime switching in multi-asset American option has been provided. Numerical experiments are performed on one and two assets to demonstrate the performance of the method with convex as well as non-convex payoffs. The method is compared with some of the existing methods available in the literature and is found to be reliable, accurate and efficient. [ABSTRACT FROM PUBLISHER]

This paper considers a reliability importance evaluation for components in an aging multi-state system. In practical reliability engineering a “curse of dimensionality” (the large number of states that should be analyzed for a multi-state system model) is a main obstacle for importance assessment. In order to challenge the problem, this paper proposes a new method that is based on an L Z -transform of the discrete-state continuous-time Markov process and on Ushakov׳s Universal Generating Operator. The paper shows that the proposed method can drastically reduce a computational burden. In order to illustrate the method, a solution of a real world problem is presented as a numerical example. [ABSTRACT FROM AUTHOR]

An ever increasing need for extra functionality in a single embedded system demands for extra Input/Output (I/O) devices, which are usually connected externally and are expensive in terms of energy consumption. To reduce their energy consumption, these devices are equipped with power saving mechanisms. While I/O device scheduling for real-time (RT) systems with such power saving features has been studied in the past, the use of energy resources by these scheduling algorithms may be improved. Technology enhancements in the semiconductor industry have allowed the hardware vendors to reduce the device transition and energy overheads. The decrease in overhead of sleep transitions has opened new opportunities to further reduce the device energy consumption. In this research effort, we propose an intra-task device scheduling algorithm for real-time systems that wakes up a device on demand and reduces its active time while ensuring system schedulability. This intra-task device scheduling algorithm is extended for devices with multiple sleep states to further minimise the overall device energy consumption of the system. The proposed algorithms have less complexity when compared to the conservative inter-task device scheduling algorithms. The system model used relaxes some of the assumptions commonly made in the state-of-the-art that restrict their practical relevance. Apart from the aforementioned advantages, the proposed algorithms are shown to demonstrate the substantial energy savings. [ABSTRACT FROM AUTHOR]

In this article, we provide an overview--a primer--of the rise of Minority Serving Institutions (MSIs) as context for understanding the contemporary place of these institutions in our broader system of higher education. We also demonstrate how the emergence and the evolution of MSIs stem from our nation's struggle to provide equal educational opportunities to minority communities. Throughout the article, we interweave the shared and individual struggles as well as the successes across these 4 major types of MSIs. Woven throughout this narrative, we explore in-depth (a) the role of the federal government in both suppressing and elevating higher education for minorities, and (b) the impact of various groups and individuals on the growth of MSIs. It is through the historical legacy of MSIs that we showcase how these institutions came to represent the voices and concerns of minority communities to take control and manage their own education. We conclude the article with a snapshot of the place of each of the 4 types of MSIs in contemporary higher education and recommendation for future research. [ABSTRACT FROM AUTHOR]

This research investigates a redundancy-scheduling optimization problem for a multi-state series parallel system. The system is a flow shop manufacturing system with multi-state machines. Each manufacturing machine may have different performance rates including perfect performance, decreased performance and complete failure. Moreover, warm standby redundancy is considered for the redundancy allocation problem. Three objectives are considered for the problem: (1) minimizing system purchasing cost, (2) minimizing makespan, and (3) maximizing system reliability. Universal generating function is employed to evaluate system performance and overall reliability of the system. Since the problem is in the NP -hard class of combinatorial problems, genetic algorithm (GA) is used to find optimal/near optimal solutions. Different test problems are generated to evaluate the effectiveness and efficiency of proposed approach and compared to simulated annealing optimization method. The results show the proposed approach is capable of finding optimal/near optimal solution within a very reasonable time. [ABSTRACT FROM AUTHOR]

In this paper, an instantaneous availability model for repairable multi-state system (MSS) with common bus performance sharing is proposed. The repairable MSS consists of some multi-state units and a common bus performance redistribution system. Each unit in the system has several performance levels and must satisfy its individual random demand. A unit can transmit the surplus performance to other units in real time through the common bus performance redistribution system, if it has a performance that exceeds its demand. The entire system fails if the demand of any unit is not satisfied. A new method based on the combination of the stochastic process method and the universal generating function technique is suggested to evaluate the instantaneous availability and the mean instantaneous performance deficiency of the proposed repairable MSS. Two examples are given for applications in the end. [ABSTRACT FROM AUTHOR]

Binary k-out-of- n systems are commonly used reliability models in engineering practice. Many authors have extended the concept of k-out-of- n system to multi-state k-out-of- n systems. This paper proposes a binary decision diagram (BDD) based approach for binary k-out-of- n: G system and a multi-state multi-valued decision diagram (MMDD) based approach for multi-state k-out-of- n: G system. BDD and MMDD have been extensively used for representing and manipulating logic functions in many areas, including reliability modeling and analysis. In this paper, patterns of BDD/MMDD for binary/multi-state k-out-of- n: G system are summarized and proved, a two-step algorithmic process is proposed for modeling the BDD/MMDD and three case studies are implemented to demonstrate the presented methods. Complexity analysis shows that the presented method is more computationally efficient than the traditional algorithms for k-out-of- n: G system. [ABSTRACT FROM AUTHOR]

Multi-state modeling of biomolecules refers to a series of techniques used to represent and compute the behavior of or that can adopt a large number of possible functional states. Biological often rely on complexes of biological that can undergo several functionally significant modifications that are mutually compatible. Thus, they can exist in a very large number of functionally different states. such multi-state systems poses two problems: the problem of how to describe and specify a multi-state system (the “specification problem”) and the problem of how to use a computer to simulate the progress of the system over time (the “computation problem”). To address the specification problem, modelers have in recent years moved away from explicit specification of all possible states and towards formalisms that allow for implicit model specification, including the κ-calculus , BioNetGen –, the Allosteric Network Compiler , and others , . To tackle the computation problem, they have turned to particle-based methods that have in many cases proved more computationally efficient than population-based methods based on , , or the , . Given current computing technology, particle-based methods are sometimes the only possible option. Particle-based simulators fall into two further categories: non simulators, such as StochSim , DYNSTOC , RuleMonkey , , and the Network-Free Stochastic Simulator (NFSim) , and spatial simulators, including Meredys , SRSim , , and MCell –. Modelers can thus choose from a variety of tools, the best choice depending on the particular problem. Development of faster and more powerful methods is ongoing, promising the ability to simulate ever more complex signaling processes in the future. [ABSTRACT FROM AUTHOR]

Abstract: This study introduces a model of individual belief updating of subjective travel times as a function of the provision of different types of travel information. Travel information includes real-time prescriptive or descriptive, and public or personal information. The model is embedded in a start-of-the art multi-state supernetwork representation of individual daily activity-travel scheduling behavior. The belief updating process of subjective travel times under information provision is based on Bayes’ Theorem. The multi-state supernetwork predicts daily activity travel choices based on the minimization of generalized costs related to the full activity-travel pattern. These generalized costs are based on expected travel times across the network. Thus, the simulation model will capture changes in activity-travel scheduling decisions that are made by individuals after updated their beliefs about expected travel times when receiving new travel information. [Copyright &y& Elsevier]

Abstract: Reliability modeling of multi-state hierarchical systems is challenging because of the complex system structures and imbalanced reliability information available at different system levels. This paper proposes a Bayesian multi-level information aggregation approach to model the reliability of multi-level hierarchical systems by utilizing all available reliability information throughout the system. Cascading failure dependency among components and/or sub-systems at the same level is explicitly considered. The proposed methodology can significantly improve the accuracy of system-level reliability modeling. A case study demonstrates the effectiveness of the proposed methodology. [Copyright &y& Elsevier]

There are several exact or approximating approaches that apply d-MinCuts (d-MCs) to compute multistate two-terminal reliability. Searching for and determining d-MCs in a stochastic-flow network are important for computing system reliability. Here, by investigating the existing methods and using our new results, an efficient algorithm is proposed to find all the d-MCs. The complexity of the new algorithm illustrates its efficiency in comparison with other existing algorithms. Two examples are worked out to show how the algorithm determines all the d-MCs in a network flow with unreliable nodes, and in a network flow of moderate size. Moreover, using the d-MCs found by the algorithm, the system reliability of a sample network is computed by the inclusion-exclusion method. Finally, to illustrate the efficacy of using the new presented techniques, computational results on random test problems are provided in the sense of the performance profile introduced by Dolan and Moré. [ABSTRACT FROM PUBLISHER]

Many engineering systems can perform their intended tasks with various levels of performance, which are modeled as multi-state systems (MSS) for system availability and reliability assessment problems. Uncertainty is an unavoidable factor in MSS modeling, and it must be effectively handled. In this work, we extend the traditional universal generating function (UGF) approach for multi-state system (MSS) availability and reliability assessment to account for both aleatory and epistemic uncertainties. First, a theoretical extension, named hybrid UGF (HUGF), is made to introduce the use of random fuzzy variables (RFVs) in the approach. Second, the composition operator of HUGF is defined by considering simultaneously the probabilistic convolution and the fuzzy extension principle. Finally, an efficient algorithm is designed to extract probability boxes (p-boxes) from the system HUGF, which allow quantifying different levels of imprecision in system availability and reliability estimation. The HUGF approach is demonstrated with a numerical example, and applied to study a distributed generation system, with a comparison to the widely used Monte Carlo simulation method. [ABSTRACT FROM PUBLISHER]