Multi-state System Research Articles

Diagnosis of Hepatopulmonary Syndrome in a Large Integrated Health System

Data on the accuracy of the diagnosis of hepatopulmonary syndrome (HPS) in cirrhosis is limited. We evaluated the clinical characteristics of patients with International Classification of Diseases (ICD) codes for hepatopulmonary syndrome (HPS) in a large integrated health system. A retrospective review of encounters was performed of all patients with ICD-9-CM and/or ICD-10-CM diagnosis of cirrhosis and HPS from 2014-2019 in a multi-state health system. Demographics and cardiopulmonary testing closest to the time of HPS diagnosis were recorded. HPS was defined using standard criteria. A total of 42,749 unique individuals with cirrhosis were identified. An ICD diagnosis of HPS was found in 194 patients (0.45%), of which 182 had clinically confirmed cirrhosis. 143 (78.5%) underwent contrast-enhanced transthoracic echocardiography, and 98 (54%) had delayed shunting. Among them, 61 patients had a documented arterial blood gas, with 53 showing abnormal oxygenation (A-a gradient of >15 mm Hg). 12 were excluded due to significant pulmonary function test abnormalities and abnormal oxygenation from other cardiopulmonary diseases. Ultimately, 41 (22.5%) fulfilled the criteria for HPS. When stratifying those with an ICD code diagnosis of HPS into HPS, no HPS and indeterminate HPS groups, based on standard diagnostic criteria for HPS, we found that the confirmed HPS patients had similar complications except for less portopulmonary hypertension, worse gas exchange, less cardiopulmonary disease and were more often diagnosed in transplant centers. The diagnosis of HPS by ICD code is made in an extremely small subset of a sizeable cirrhotic cohort. When made, only a minority of these patients meet diagnostic criteria. Our findings highlight the need for improved education and more effective screening algorithms.

Modeling of Repairable Multi-State Systems with time-varying failure and repair rates using Equivalent Systems Dynamics Models

This paper treats with the reliability assessment of a Repairable Multi-State System (RMSS) by means of a Nonhomogeneous Continuous-Time Markov Chain (NH-CTMC). A RMSS run on different operating conditions that may be considered acceptable or unacceptable according to a defined demand level. In these cases, the commonly used technique is Homogeneous Continuous-Time Markov Chain (H-CTMC), since its solution is mathematically tractable. However, the H-CMTC involve that the time between state transitions is exponentially distributed, and the failure and repair rates are constants. It's certainly not true if the system components age with the operation or if the repair activities depend on the instant of time when the failure occurred. In these cases, the failure and repair rates are time-varying and the NH-CTMC is needed to be considered. Nevertheless, for these models the analytical solution may not exist and the use of others techniques is required. This paper proposes the use of an Equivalent Systems Dynamics Model (ESDM) to model a NH-CTMC. A ESDM represent the Markov Model (MM) by means of the language and the tools of the Systems Dynamics (SD), and the results are obtained by simulation. As an example, an RMSS with three components, failure rates associated with the Weibull distribution and repair rates associated with the Log-logistic distribution is developed. This example serves to identify the advantages and disadvantages of a ESDM to make model a RMSS and evaluate some reliability measures.

A fuzzy universal generating function-based method for the reliability evaluation of series systems with performance sharing between adjacent units under parametric uncertainty

This paper considers fuzzy multi-state systems (MSSs) with performance sharing between adjacent units, in which each fuzzy multi-state unit has a random performance and a random demand. If the performance of a unit exceeds its individual demand (performance > demand), its surplus performance can be transmitted to its adjacent unit that experiences performance deficiency (performance < demand). The performance/demand of a unit is represented by the performance/demand levels and the corresponding state probabilities. It is usually difficult to estimate the precise state probabilities due to the inaccuracy and insufficiency of data, therefore, fuzzy state probabilities are given instead of precise state probabilities. This paper proposes a fuzzy universal generating function (FUGF)-based method to evaluate the fuzzy reliability of series systems with performance sharing between adjacent units considering the parametric uncertainty related to the state probabilities of the performance/demand levels of units. The originality of this work lies in two aspects: i) the surplus performance of a unit can only be shared with its adjacent units which experience deficiency; ii) the parametric uncertainty related to the state probabilities is considered. An illustrative example is provided to validate the proposed method.

Optimal bilinear control of a reparable multi-state system

This work addresses an optimal bilinear control design representing the corrective maintenance for a reparable multi-state system. The primary interest is to optimise the availability of the system, which is defined as the probability that the system is operating properly when it is requested for use. The system model considered in our current work is governed by coupled transport and integro-differential equations. A corrective maintenance policy is represented by the repair rate, which depends on the distributed repair time. The objective is to determine an optimal repair rate that maximises the availability of the system in good mode over a given system running period. This essentially leads to a bilinear control problem set in a nonreflexive Banach space using -optimisation. A rigorous proof of existence of an optimal controller and the first-order necessary conditions of optimality are addressed. Numerical experiments are conducted to demonstrate the theoretical results.

Balanced Systems by Considering Multi-state Competing Risks Under Degradation Processes

Reliability analysis of balanced system has become a key factor for the safe operation of some important equipment and the perfect completion of some important missions. As some real systems consisting of several subsystems with different levels of performance and under degradation processes when in operations are relatively common, they have received widely concerned in the recent years, but are rarely in the new reliability research field of balanced systems, the researches on the balanced systems by considering multi-state are rare, and even few researches are in combination with degradation processes. In this paper, a new balanced system for the multi-state system under degradation processes by considering new failure criteria is put forth. The system consists of two subsystems and fails when either subsystem enters into absorbing states, a new model is proposed and applied to two special cases by assuming that the thresholds of the sojourn time in upcoming unbalanced subsets are constant and random variables respectively, and this paper is the first to extend balanced systems into competing risks. The related expressions of the reliability indexes are derived by employing the theory of aggregated stochastic processes. Finally, numerical examples are presented and some future research topics are also discussed.

Resilience analysis of multi-state systems with time-dependent behaviors

Most of existing resilience models assume that system performances are continuous. In this paper, we consider resilience modeling and analysis for multi-state systems, whose performances are characterized by discrete, rather than continuous variables. A non-homogeneous Semi-Markov reward process model is developed for resilience analysis of multi-state systems. In the developed model, system performance changes, caused by either disruptive events or system recoveries, are modeled as state transitions, and rewards are used to model financial losses incurred during and after the disruptions. Four resilience metrics are defined to quantify different aspects of resilience. As the developed model is non-homogeneous, it can capture time-dependent system behaviors and their impact on system resilience. An efficient resilience analysis algorithm is also designed based on linear interpolation and implemented using vectorization. The computational benefits of the developed algorithm are demonstrated through two numerical experiments. We apply the developed method on two practical case studies, an oil tank farm and a re-configurable computing system. The results show that the developed methods can quantify resilience of multi-state systems accurately and efficiently.

Risk Factors for Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization: COVID-19-Associated Hospitalization Surveillance Network and Behavioral Risk Factor Surveillance System.

BackgroundData on risk factors for COVID-19-associated hospitalization are needed to guide prevention efforts and clinical care. We sought to identify factors independently associated with COVID-19-associated hospitalizationsMethodsU.S. community-dwelling adults (≥18 years) hospitalized with laboratory-confirmed COVID-19 during March 1–June 23, 2020 were identified from the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET), a multi-state surveillance system. To calculate hospitalization rates by age, sex, and race/ethnicity strata, COVID-NET data served as the numerator and Behavioral Risk Factor Surveillance System estimates served as the population denominator for characteristics of interest. Underlying medical conditions examined included hypertension, coronary artery disease, history of stroke, diabetes, obesity [BMI ≥30 kg/m 2], severe obesity [BMI≥40 kg/m 2], chronic kidney disease, asthma, and chronic obstructive pulmonary disease. Generalized Poisson regression models were used to calculate adjusted rate ratios (aRR) for hospitalizationResultsAmong 5,416 adults, hospitalization rates were higher among those with ≥3 underlying conditions (versus without)(aRR: 5.0; 95%CI: 3.9, 6.3), severe obesity (aRR:4.4; 95%CI: 3.4, 5.7), chronic kidney disease (aRR:4.0; 95%CI: 3.0, 5.2), diabetes (aRR:3.2; 95%CI: 2.5, 4.1), obesity (aRR:2.9; 95%CI: 2.3, 3.5), hypertension (aRR:2.8; 95%CI: 2.3, 3.4), and asthma (aRR:1.4; 95%CI: 1.1, 1.7), after adjusting for age, sex, and race/ethnicity. Adjusting for the presence of an individual underlying medical condition, higher hospitalization rates were observed for adults aged ≥65, 45-64 (versus 18-44 years), males (versus females), and non-Hispanic black and other race/ethnicities (versus non-Hispanic whites)ConclusionOur findings elucidate groups with higher hospitalization risk that may benefit from targeted preventive and therapeutic interventions

Experimental investigation of a novel micro gas turbine with flexible switching function for distributed power system

Micro gas turbine (MGT) is widely used in small-scale distributed power systems because of its low emissions and fuel flexibility. However, the under-utilization of its exhaust heat and the low electric efficiency are the main bottlenecks that restrict its application. Additionally, the flexible switching between the power generated by the MGT and the power grid is also a key factor for keeping the secure operation of a distributed power station. Therefore, this paper conducted some experimental investigations of a 30 kW MGT to provide reference solutions for the above issues. This MGT is located at Shanghai Jiao Tong University (SJTU), which is designed by the Gas Turbine Research Institute of SJTU, and is manufactured by a turbo-machinery factory in Chongqing, China. The demonstration prototype is mainly composed of a single stage centrifugal compressor, a radial turbine, a combustor, a high-speed permanent magnet generator, and a control system. The results show that the MGT can achieve steady operation at a low rotational speed from 10000 r/min to 34000 r/min in the case of using oil lubricated bearings, which can greatly reduce the economic cost compared with the use of air bearings. At the same time, the ignition success rate of combustion chamber (CC) reaches 98% at a low rotational speed, and a wide range of stable combustion area can be obtained, because of the novel design method of combustor by referencing the way applied in an axial flow aero-engine. The MGT generating set can achieve functions, such as starting up, ignition, stable operation, loaded operation, grid-connection and stopping. This system also can realize flexibly switching from the start motor mode to the generator mode, and from grid-connected mode to off-grid mode, because the innovative multi-state switching control system is adopted. The above research work can make our state master independent intellectual property rights of micro gas turbine, rather than continue to be subject to the technological monopoly of the developed states, which can provide theoretical and experimental support for the industrialization of MGT in China.

Parameter Estimation for Multi-state Coherent Series and Parallel Systems with Positively Quadrant Dependent Models

A multi-state coherent system consists of multiple components each of which passes through a sequence of states and it is of interest to estimate the distribution of time spent by the components in different states. Although not practical, for mathematical convenience, it is usually assumed that the times spent by the components in various states are independent of each other. This paper considers three-state series and parallel systems and is based on the assumption that times spent by the components in various states are positively quadrant dependent (PQD) and the corresponding dependence is modeled using a Farlie Gumbel Morgenstern (FGM) distribution. To begin with it is shown that even when marginal distributions are assumed exponential, the resulting likelihood function leads to a complicated expression making maximum likelihood (ML) based inference computationally challenging. A generalized method of moment (GMM) estimation is shown to be relatively simpler not only computationally but also the method works for arbitrary marginal distributions. The estimates obtained by GMM are shown to be uniformly consistent under some mild regularity conditions. Finite sample performances of the ML and GMM are illustrated using FGM distribution with various parametric marginal distributions. In case of exponential marginals, it is shown that GMM compares favorably to ML although the former method does not require parametric assumption for the marginals. The proposed methods are also illustrated using a real case study data of a rare type of head and neck cancer.

Interstate variation within a five-state health system: Polysomnography prior to tonsillectomy in trisomy 21 patients

ObjectivesSince 2011, the American Academy of Otolaryngology – Head and Neck Surgery Clinical Practice Guidelines have recommended polysomnography (PSG) prior to tonsillectomy in children with Down syndrome (DS). The purpose of this study was to determine adherence to guidelines recommending polysomnography before tonsillectomy for children with DS and sleep-disordered breathing among pediatric otolaryngologists. MethodsA one-year quality assurance retrospective review was conducted at four hospitals within one pediatric health system to identify children with a diagnosis of DS who underwent a tonsillectomy with or without adenoidectomy from January 1, 2018, to December 31, 2018. De-identified data related to age, sex, BMI, procedure type, and preoperative PSG were collected and examined. ResultsThe rate of PSG prior to tonsillectomy was 90.4% (66 of 73) among patients with DS. 51.6% of PSG studies were performed within 90 days before surgery, and 92.2% (59 of 64) of PSG studies were performed within one year before surgery. 33% of patients who did not undergo PSG also were obese or under age two years. The most common reason for not obtaining a PSG prior to tonsillectomy was that either the provider or parent felt the patient would not tolerate it. There was no variance from guidelines by age, sex, and procedure type. ConclusionsPolysomnography for children with DS prior to tonsillectomy is achieved greater than 90% of the time in a multistate pediatric health system. Broader assessment across the nation and future studies regarding the timing of PSG before tonsillectomy are warranted.

Comparisons of Multi-State Systems with Binary Components of Different Sizes

The system signatures of multi-state coherent or mixed systems with i.i.d. (independent and identically distributed) binary-state components are considered in this paper. For any multi-state coherent or mixed system with m i.i.d. components, the signature of its equivalent system of size m + l can be written as a mixture of signatures of several multi-state vector k -out-of- m + l: F systems. This facilitates the comparison of two multi-state systems of different sizes upon transforming the signature of the system of smaller size to the signature of its equivalent system of larger size along the lines of Navarro et al. (2008) for the usual Samaniego signature. Some numerical examples are finally presented to illustrate all the results established here.

Short-term availability and performability analysis for a large-scale multi-state system based on robotic sensors

This paper presents dynamic (short-term) availability and performability analysis for highly responsive large-scale distributed robotic systems. Such systems are intended for the exploration and measurement of different parameters in aggressive environments by using robotic sensors. The typical system, which is considered in this paper, consists of N platforms and m robotic sensors, which initially are installed on each platform. Platforms are distributed on a safe distance from the aggressive (dangerous) environment to be explored. At some time, sensors should be shot from the platform to the environment. Then each platform begins to process all data gathered by its sensors. Each sensor, when it is installed on a platform, and each platform are individually represented as a three-state component – completely available state, minor repairable state, and replacement state. Thus, the entire system and its components are interpreted as a multi-state system. In general, numbers N and m may be enough big. In such cases, the system is large-scale. The problem is to find the short-term availability and the performance for an entire large-scale system which strongly depends on the initial conditions of the system.It has been shown, that there is a great difference between steady-state and short-term availability and performance indices which significantly depend on the initial states of all robotic sensors and platforms. For a highly responsive system, this difference is very important. The main obstacle in finding a solution to the problem is its large multi-dimensionality. To overcome this difficulty, the Lz-transform method was used. This paper suggests a multi-state model for the system and measures for system availability and performance assessment. The paper has also developed an algorithm for an Lz-transform application to evaluate these measures and presents recommendations for the prevention of availability and performance decrease in the initial short-time period.A numerical example is presented to illustrate the proposed method.

Enhanced reliability analysis method for multistate systems with epistemic uncertainty based on evidential network

AbstractEvidential network is considered to have superiority in conducting reliability analysis for complex engineering systems with epistemic uncertainty. However, existing methods tend to result in combinational explosion when multistate systems are involved in the reliability analysis, which means the reliability analysis cost increases exponentially with the number of components and that of functioning states. Therefore, an enhanced reliability analysis method is proposed in this paper for reliability analysis and performance evaluation of multistate systems with epistemic uncertainty, through which the combination explosion can be significantly alleviated. Firstly, the functioning states of each component are sequenced according to utility functions. Secondly, the basic belief assignment (BBA) of each component is reassigned in terms of commonality function, through which the BBA defined in the power set space is represented by two extreme BBA distributions defined in the frame of discernment. Thirdly, the reliability intervals of the system states are calculated through evidential network, and the system performance level is computed. Two multistate system numerical examples are investigated to demonstrate the effectiveness and efficiency of the proposed method.

Numerical verification of the exact WKB formula for the generalized Landau-Zener-Stueckelberg problem

The WKB analysis for higher order differential equations had not been developed until recent progress of the so-called exact WKB method, so it was difficult to find WKB solutions for multistate nonadiabatic models in general, whereas enormous efforts were made especially for the study of generalized Landau-Zener models. Under such circumstances Aoki-Kawai-Takei provided a closed expression for the $S$ matrix of a time-dependent multistate nonadiabatic system, in which diabatic potential curves are given as polynomial functions of time and they all intersect with each other. Their result is completely explicit, and the model covers much wider classes of systems than multistate nonadiabatic models studied so far. Here we numerically show how their exact WKB formula works as long as crossing points of diabatic potential curves are well separated. The model studied includes the cases where diabatic potential functions are nonlinear, not like generalized Landau-Zener models. We also discuss the situations where virtual turning points and new Stokes curves, new components in the Stokes geometry appearing only for higher order differential equations, come into play. In particular, we examine how the presence of new Stokes curves affects final transition probabilities using a couple of concrete examples.

Improving S-MSCKF With Variational Bayesian Adaptive Nonlinear Filter

In recent years, multi-sensor fusion has been paid a great attention and is widely applied to simultaneous localization and mapping (SLAM), e.g. Visual-Inertial Odometry (VIO). However, in the most of previous works the observation noise of sensor is assumed to be Gaussian with invariant constant mean and covariance, which is not the case actually. In this paper, a variational Bayesian adaptive algorithm is proposed to improve the performance of visual-inertial odometry, where covariance of observation noise is modeled as inverse-Wishart distribution with harmonic mean so that observation noise is non-Gaussian. Besides, unscented transformation is utilized to handle the nonlinear system problem. The proposed algorithm is applied to the framework of stereo multi-state constraint Kalman filter system (S-MSCKF), which is a state-of-the-art VIO system with low computational cost and good robustness. Extensive experiments are performed on simulation data and open source datasets. The results demonstrate that our proposed algorithm is more robust and accurate compared with S-MSCKF.

Optimal element allocation and sequencing of multi-state series systems with two levels of performance sharing

Motivated by real-world complex engineering systems, this research proposes a multi-state system with two levels of performance sharing. In this system, there exists a unique performance transmitter between any two adjacent subsystems and any two adjacent elements within each subsystem. Surplus performance of a subsystem or an element can only be transmitted to its adjacent subsystems or elements. We build the reliability model of the proposed system and suggest a corresponding reliability evaluation algorithm by extending the existing universal generating function technique. Since the performance sharing is only allowed between adjacent subsystems and elements, the element allocation and sequencing will affect the system reliability. Due to the complexity of the combinatorial optimization problem, we use the genetic algorithm to find the optimal allocation and sequencing of the elements. An analytical example is provided to illustrate the reliability evaluation algorithm. Numerical experiments are carried out to demonstrate how the optimal allocation and sequencing as well as the capacities of the performance transmitters affect the system reliability.

Developing Open Science in Africa: Barriers, Solutions and Opportunities

The paper argues for the development of open science in Africa as a means of energising national science systems and their roles in supporting public and private sectors and the general public. It focuses on the complexity of the social and economic challenges created by climate change and the demographic explosion and the difficulty of confronting them in the absence of an adequate digital infrastructure. Although a well-coordinated, federated multi-state open science system would be a means of overcoming this barrier, African science systems largely operate independently of each other, creating siloes of incompatible policies, practices and data sets that are not mutually consistent or inter-operable. Africa’s linguistic chasms of English, French, Portuguese, Spanish and indigenous languages create further barriers. As international science moves towards greater openness and data sharing to address the complexity inherent in major global challenges, Africa’s stance needs radical overhaul. The paper draws on the questionnaire data from 15 African Science Granting Councils and the state-of-the-art Report to them on “Open Science in Research and Innovation for Development in Africa”. It concludes that a well-developed Open Science system for Africa, would develop and enhance collaborations and partnerships among Africans to tackle the challenges that they face and accelerate innovation and development.

Coherent control of dissipative dynamics in a periodically driven lattice array

We find a different mechanism for suppression of decay in an open one-dimensional lattice system, which originates from a dark Floquet state, a sink state to which the system is asymptotically driven, whose overall probability is determined only by the parameters of the periodic driving field. The zero-quasienergy of dark Floquet state has been shown to be not a real zero, but a vanishingly small negative imaginary number which will cause undesirable physical effect in long-time evolution of quantum states, which is extremely different from the conservative counterpart. Another important finding is that the value of the system's effective decay, determined by the size of the non-zero imaginary part of the dark-Floquet-state-related quasienergy, depends not on how many localized lossy sites there are but on which one of the lossy sites is nearest to the driven site. Thus, for specially designed local dissipation, by controlling the driving parameters, it is possible for us to drive the system to a dark Floquet state with a much lower level of overall probability loss as compared to the undriven case and with good stability over enough longer evolution time. These results are applicable to the multisite lattice system with an odd number of sites and may be significant for long-time control of decay in a vast family of multistate physical systems with localized dissipation.

An Accelerated Simulation Approach for Multistate System Mission Reliability and Success Probability under Complex Mission

The mission reliability and success probability estimation of multistate systems under complex mission conditions are studied. The reliability and success probability of multistate phased mission systems (MS-PMS) is difficult to use analytic modeling and solving. An estimation approach for mission reliability and success probability based on Monte Carlo simulation is established. By introducing accelerated sampling methods such as forced transition and failure biasing, the sampling efficiency of small-probability events is improved while ensuring unbiasedness. The ship’s propulsion and power systems are used as applications, and the effectiveness of the method is verified by a numerical example. Under complex missions, such as missions with different mission time and their combinations, and phased-missions, the proposed method is superior in small-probability event sampling than the crude simulation method. The calculation example also studies the influence of mission factors or system reliability and maintainability factors on system availability and mission success probability, and analyzes the relationship between different mission types and system availability and success probability.

State-based mission abort policies for multistate systems

Most of the previous research on optimal mission abort policies was devoted to binary systems that can be only in two states, i.e., operable or failure. However, a wide class of engineering systems is characterized by more than one operable state. Thus, it can be reasonable to terminate a mission and initiate a rescue procedure that can save, e.g., costly or safety-critical systems when a system had deteriorated to a certain extent. A system operates in a random environment modeled by a renewal process of shocks. With each shock, the state of a system can deteriorate with certain probabilities that can eventually result in the total failure. The bivariate constrained probability-wise and cost-wise optimization problems are formulated and the necessary relationships for solving them are derived. The detailed illustrative numerical example is discussed.