Multi-state System Research Articles

A bi-objective model for reliability analysis of a multi-state system with performance sharing mechanism

A bi-objective model for reliability analysis of a multi-state system with performance sharing mechanism

Mathematical Modelling and Chemical Analysis to Characterise Anthocyanin Self-association Interactions Influencing Colour Expression and Stability in Young Red Wines

AbstractAnthocyanins are phenolic compounds that provide colour to young red wines following extraction from grape skins, and their reaction kinetics are not well understood as they exist within a complex pH-dependent multistate system. For commercial wineries to best control anthocyanin colour expression that is a major determinant for red wine quality, there is a critical need for behaviours of all pH-dependent monomeric and self-associated anthocyanins to be considered together. In response, the current study employed mathematical and experimental techniques to reveal kinetic and steady-state behaviours of all species of a model anthocyanin, malvidin-3-O-β-D-glucopyranoside (M3G), within three Shiraz red wines throughout fermentation. Investigator-developed models represented the following: (i) acid–base reactions that form red, purple, and blue anthocyanin monomers; (ii) hydration reactions that result in monomeric colour loss; and (iii.) physical self-association interactions that temporally stabilise anthocyanin colour. Simulations were validated experimentally using high-performance liquid chromatography with diode array detector (HPLC–DAD) and colourimetric analysis of wine samples. Moreover, a unique predictive modelling framework was developed that takes in measured values for anthocyanin concentration, pH, and temperature at the onset of fermentation, and output data describing corresponding wine colour expression and stability characteristics that would arise after fermentation has progressed. Results of the current study elucidate complex reaction kinetics occurring between anthocyanin species under dynamic red wine conditions and provide meaningful predictions for future wine colour and shelf-life characteristics. Outputs may be used to inform key winemaking decisions influencing red wine quality and to optimise the use of winery resources throughout the fermentation process.

Reliability modeling of multi-state phased mission systems with random phase durations and dynamic combined phases

Random phase durations and dynamic combined phases challenge the application of existing reliability models in the reliability analysis of multistate-phased mission systems (MS-PMSs). To this end, this paper presents a new reliability modeling method for multi-state phased mission systems with random phase durations and dynamic combined phases. Initially, a multi-state multi-valued decision diagram-based (MMDD-based) reliability modeling method is created to efficiently map random phase durations and the dynamic combined phase nature of MS-PMSs into the reliability model. To solve the MMDD-based reliability model, a path probability evaluation method is subsequently constructed with the assistance of the Markov regenerative function. The effectiveness and the superior performance of the proposed MMDD-based reliability model and its solving algorithm are validated by the application to the reliability modeling and analysis of an attitude and orbit control system with multiple modes. Overall, this paper provides the reliability sector with a new reliability model and its solving algorithm to enhance the reliability and safety of multi-state phased mission systems.

Building a Learning Management Framework for Patient Safety: Lessons From a Large Multistate System

Background Embracing a learning, transparent, and improvement culture is crucial for healthcare organizations to effectively learn from human errors and deliver safer, higher-quality patient care. However, the application of organizational learning in healthcare has been relatively narrow, necessitating the establishment of learning systems across healthcare organizations. This paper presents a novel learning management framework in a multistate health system, which is also a patient safety organization (PSO), facilitating a transition into a high reliability organization. We also provide initial results, uniquely contributing to the limited literature on healthcare learning management networks for patient safety. Methods We explore solutions for widespread learning from safety events in a large multistate U.S. healthcare system. We discuss the challenges of culture, technology, measurement systems, and barriers to learning from errors. We present our key driver diagram, critical details of implementing a learning network, and our initial findings, including the positive impact of the good catch program and the culture of safety survey. These initiatives have led to improved communication about errors and a growing willingness to learn from them, demonstrating the effectiveness of our learning management network. We also discuss the importance of data in organizational learning. Results From 2021 to 2023, the overall event reporting rate, which includes all severity levels of harm, increased by 22.41%, from 165.15 to 202.16 events per 10,000 patient days (p-value<0.0001). The reporting rate for near-miss events also rose by 20.50%, from 138.09 to 166.41 events per 10,000 patient days (p-value<0.0001). We attribute these improvements to enhanced data sharing and a culture of transparency the learning management network fosters. The “Communication about error” element in the culture of safety survey, with over 55,000 respondents, improved from 69.5% in 2020 to 70.6% in 2022 (p-value<0.0001), indicating better communication and a growing willingness to learn from errors. Feedback from over 1,000 PSO safe table participants highlighted a respectful and supportive atmosphere, acknowledging areas for improvement without blame. Conclusion In conclusion, we emphasize the comprehensive approach to patient safety, utilizing diverse strategies and evidence-backed methods to spread learning and data sources and reinforcing the commitment to “healing without harm.” Looking ahead, we aim to instill essential learning mindsets, create psychologically safe workplaces, and promote high reliability and safety behaviors. We remain optimistic that the maturation of the learning management network will ultimately contribute to a decrease in harm rates, aligning with our ongoing commitment to patient safety and continuous enhancement. Our future research will focus on further refining and expanding the learning management framework to significantly impact patient safety in healthcare organizations.

CoOMBE: A suite of open-source programs for the integration of the optical Bloch equations and Maxwell-Bloch equations

The programs described in this article and distributed with it aim (1) at integrating the optical Bloch equations governing the time evolution of the density matrix representing the quantum state of an atomic system driven by laser or microwave fields, and (2) at integrating the 1D Maxwell-Bloch equations for one or two laser fields co-propagating in an atomic vapour. The rotating wave approximation is assumed. These programs can also be used for more general quantum dynamical systems governed by the Lindblad master equation. They are written in Fortran 90; however, their use does not require any knowledge of Fortran programming. Methods for solving the optical Bloch equations in the rate equations limit, for calculating the steady-state density matrix and for formulating the optical Bloch equations in the weak probe approximation are also described. Program summaryProgram Title: CoOMBECPC Library link to program files:https://doi.org/10.17632/5wsg9d52dk.1Developers' repository link:https://github.com/durham-qlm/CoOMBELicensing provisions: GPLv3Programming language: Fortran 90Nature of problem: The present programs can be used for the following operations: (1) Integrating the optical-Bloch equations within the rotating wave approximation for a multi-state atomic system. At the choice of the user, the calculation will return either the time-dependent density matrix at given times or the density matrix in the long time limit if the system evolves into a steady state in that limit. The calculation can be done with or without averaging over the thermal velocity distribution of the atoms. The number of atomic states which can be included in the calculation is limited only by the CPU time available and possibly by memory requirements. An arbitrarily large number of laser or microwave fields can be included in the calculation if these fields are all CW. This number is currently limited to one or two for fields that are not all CW. The calculation can be done in the weak probe approximation, or in the rate equations approximation, or without assuming either of these two approximations. Calculating refractive indexes, absorption coefficients and complex susceptibilities is also possible. (2) Integrating the 1D Maxwell-Bloch equations in the slowly varying envelope approximation for one or two fields co-propagating in a single-species atomic vapour. Although geared towards the case of atoms interacting with laser fields, this code can also be used for more general quantum systems with similar equations of motion (e.g., molecular systems, spin systems, etc.).Solution method: The Lindblad master equation is expressed as a system of homogeneous first order linear differential equations, which are transformed as required and solved to obtain the density matrix representing the state of the atomic system. A variety of methods are offered to this end. The same approach is also used in the calculation of the polarisation of the medium when integrating the Maxwell-Bloch equations. The latter are integrated over space using predictor-corrector methods. The library includes a general driving program making it possible to use these codes without additional program development. The distribution also includes examples of the use of a container for running these programs without a pre-installed Fortran compiler.

Biobjective Maintenance Strategy Optimization for Complex Multistate Systems Based on PH Distribution

Biobjective Maintenance Strategy Optimization for Complex Multistate Systems Based on PH Distribution

Enhancing safety and reliability in multistory construction: A multi-state system assessment of shoring/reshoring operations using interval-valued belief functions

Ensuring safety and reliability in multistory concrete construction necessitates rigorous assessment methodologies capable of addressing the inherent uncertainties of the construction process. This paper introduces a novel reliability assessment methodology for shoring/reshoring (S/R) operations, framed within a multi-state parallel-series system (MSS). Our approach innovatively combines concrete slab capacities, forming system strengths, and construction quality, integrating cost and time efficiencies. Central to our method is the use of Interval Universal Generating Functions (IUGF), which leverage Dempster-Shafer (D-S) theory and interval analysis to robustly estimate system reliability under uncertainty. For instance, the model considers concrete strength ranging from 0.72 × 2.1D to 0.96 × 2.1D at different curing stages, producing a system reliability index between 0.63 and 0.76, against a desired threshold of 0.8. This methodology produces interval-valued belief functions that capture a comprehensive range of potential system states and their likelihoods, offering a refined perspective on system safety. An illustrative case study demonstrates the practical application of this methodology in optimizing formwork removal and S/R schedules, significantly enhancing decision-making in construction operations. This paper aligns with the core themes of reliability engineering and system safety by addressing critical safety and reliability issues in complex technological systems through advanced probabilistic safety assessment methods. It significantly enriches the professional discourse by presenting a robust method that can be adapted across various engineering fields facing similar reliability challenges.

Corrective maintenance minimizing expected mission losses in shock exposed multistate production-storage systems

Production-storage systems are often subject to random external shocks such as storm weather and electromagnetic interference. However, most of the existing models failed to take the shock effects into consideration. This paper extends the state of the art by modeling random shocks and optimizing their mitigating corrective maintenance policy (CMP) that defines the condition triggering repair actions to restore the production subsystem to a higher performance level. The CMP can greatly impact the amount of unsupplied product as well as the maintenance and operation costs, all contributing to the expected mission losses (EML). This paper contributes by modeling and optimizing the CMP to minimize the EML. The proposed solution encompasses a new numerical algorithm that evaluates the EML of the considered production-storage system under a given CMP and the genetic algorithm realized for solving the CMP optimization problem. The proposed model is demonstrated using a case study of a multistate power generation system subject to voltage surges due to storm weather. Based on the investigation of effects of several model parameters (shock rate, storage capacity, initial amount of product in the storage, cost of unsupplied product) on the EML and optimization solutions, useful and insightful management recommendations are provided.

Safety of Triptans in Patients Who Have or Are at High Risk for Cardiovascular Disease: A Target Trial Emulation

ObjectiveTo evaluate the safety of triptans in migraine patients with cardiovascular disease or elevated cardiovascular risk. Patients and MethodsWe retrieved data from a multistate US-based health system (January 2000 to August 2022) on adults with migraine and confirmed cardiovascular/cerebrovascular disease, or at least two cardiovascular risk factors. We compared the effect of triptans to nontriptan treatments on major adverse cardiovascular events (MACE) and its components at 60 days of starting treatments. We emulated a target trial and used propensity score matching for analysis. ResultsThe 3518 patients in the triptan group were matched to the 3518 patients in the nontriptan group (median age, 55 years; 80.60% female). At 60 days, 52 patients (1.48%) in the triptan group had MACE, compared with 13 patients (0.37%) in the nontriptan group (relative risk [RR], 4.00; 95% CI, 2.24 to 7.14). Patients treated with triptans also had significantly higher risk of nonfatal myocardial infarction (15 patients (0.43%) vs 0 patients (0.00%)); heart failure (RR, 4.50; 95% CI, 1.91 to 10.61); and nonfatal stroke (RR, 8.00; 95% CI, 1.00 to 63.96). Five patients (0.14%) in each group died. The findings were consistent when analyses were restricted to sumatriptan, oral administration of triptan, patients with chronic migraine, history of cardiovascular disease, or history of cerebrovascular disease. ConclusionTriptans likely increase the risk of MACE; however, the incidence of MACE remains low in migraine patients with cardiovascular disease or elevated cardiovascular risk. Trial RegistrationTreatments of Migraine With Triptans in Individuals With Elevated Cardiovascular Risk and in Pregnant Women. ClinicalTrials.gov Identifier: NCT05854992 (https://classic.clinicaltrials.gov/ct2/show/NCT05854992)

Impact of Point Prevalence Surveillance on Transmission of Candida auris Within an Acute Care Health System

Background: Patients infected or colonized with Candida auris can serve as a transmission source for other patients. Screening patients for Candida auris colonization allows facilities to implement infection prevention and control measures and minimize the risk of transmission. The Centers for Disease Control and Prevention (CDC) recommends healthcare facilities perform three types of screening; admission screening in patients with specific risks, close contact screening of patients who overlap a confirmed positive case for 3 or more days or point prevalence surveillance if there is evidence of ongoing transmission within the facility. The CDC further recommends that patients being screened for Candida auris be maintained in transmission-based precautions while awaiting Results: In 2022-2023 there was ongoing transmission of Candida auris occurring in a community served by a large multi-state healthcare system. Close contact and point prevalence surveillance screening for both acute and non-acute healthcare facilities were implemented by the local health jurisdiction. Methods: A composite swab of the bilateral axilla and groin was used to screen close contacts of patients confirmed to be infected or colonized with Candida auris. Close contact was defined as having been on the same unit as the positive patient for 3 or more days while the patient was not in transmission-based precautions. Point prevalence surveillance was performed on all patients currently housed on units where close contact screen-positive patients resided. Potentially exposed patients who had been discharged were not screened. Patients were placed in contact transmission-based precautions until results were received. In 1657 patients in six acute care facilities were identified for Candida auris screening. 161 patients refused or were unable to be screened. Of the 1496 patients screened, 40 screened positive, demonstrating a 2.67% secondary attack rate. Of the 40 screen-positive patients, 5 were identified through point prevalence and 35 through close contact screening. Conclusion: Performing point prevalence surveillance in acute care facilities is operationally challenging and costly with little benefit in the prevention of Candida auris transmission. More robust collection and reporting of screening data is needed to inform surveillance protocols and prevention strategies specific to different healthcare settings. Limitations of this study include the lack of screening completion in discharged patients identified as close contact or point prevalence surveillance eligible. Additionally, some patients had a history of contact with healthcare facilities outside of this healthcare system, with unknown exposure risks or prevention strategies.

Optimal Preventive Maintenance Scheduling of Multi State System. A Comparative Study of Different Meta-heuristic Algorithms

This work presents a comparative study of different meta-heuristic algorithms which are commonly used in the literature, such as Genetic Algorithms, Particle Swarm Optimization, Artificial Bee Colony and Grey Wolf Optimizer. These algorithms are applied to solve a multi-state system maintenance optimization problem considering time and system availability constraints. The objective is to find the optimal inspection and maintenance intervals for each component of the system in order to minimize the preventive maintenance cost of overall the system. The performances of the algorithms are compared using different metrics, regarding the quality and stability of the results and the speed of convergence, in order to determine the most efficient algorithms.

Treatment Readiness in Psychiatric Residential Care for Adolescents.

There are many factors to consider when treating adolescents with psychiatric challenges, including whether they are willing and interested in participating in treatment. This study aimed to explore how treatment readiness impacts treatment experience for adolescents in psychiatric residential care who came into treatment with moderate to severe depression. All participants (N = 1,624; Mage = 15.58, SD = 1.46) were admitted to a large, multi-state psychiatric residential system between January 2020 and March 2022. Patients were 95.6% White, 99% non-Hispanic, and 64.7% identified as female. At intake, all patients were administered an assessment which includes the multi-dimensional Behavioral Health Screen (BHS) that assesses psychopathology and risk factors, a working alliance scale, depression, and well-being measures. Patients were also asked how they were admitted to the program, using a single item, multiple choice question as an informal treatment readiness measure, yielding three readiness groups: precontemplation, contemplation, or preparation. Regression analysis results indicated that patients' readiness level was associated with different baseline characteristics (e.g., age, gender, psychopathology symptoms, risk factors) and week 3 outcomes (e.g., decreased symptoms, well-being, alliance, satisfaction). The clinical implications, as well as limitations and future directions, will be discussed.

Degradation modeling and remaining life prediction of multi-state long-life systems under random environmental influences

Abstract For large-scale systems such as bridges, which have long operating lifetimes, the operating states are usually categorized into multiple levels, and they are also subjected to various random environmental influences during operation. However, due to the significant granularity in the categorization of system states, it is difficult to assess the system state transitions influenced by random environmental factors, which compromises the accuracy of remaining life predictions. In this study, we focus on long-life systems with multiple states and investigate the degradation modeling and remaining life prediction considering the impact of random environmental factors. The system degradation process, based on the semi-Markov process and multi-state modeling, was decomposed into states using the sub-exponential approximation method. A state transition probability model considering exponential environmental influences was constructed. Furthermore, based on the developed model for calculating the distribution of sojourn times in multiple states, a reliability and remaining life prediction model for the system was derived. By taking the bridge deck as a case study, the verification and analysis of remaining life prediction for the bridge deck were conducted under the influences of average daily traffic volume and bridge age. The results indicate that both the average daily traffic volume and bridge age have a significant impact on the degradation of the bridge deck. The relative error of the predicted results considering the above effects falls within the range of 1.77%–12.18%.

Exceptional Nexus in Bose-Einstein Condensates with Collective Dissipation.

In multistate non-Hermitian systems, higher-order exceptional points and exotic phenomena with no analogues in two-level systems arise. A paradigm is the exceptional nexus (EX), a third-order EP as the cusp singularity of exceptional arcs (EAs), that has a hybrid topological nature. Using atomic Bose-Einstein condensates to implement a dissipative three-state system, we experimentally realize an EX within a two-parameter space, despite the absence of symmetry. The engineered dissipation exhibits density dependence due to the collective atomic response to resonant light. Based on extensive analysis of the system's decay dynamics, we demonstrate the formation of an EX from the coalescence of two EAs with distinct geometries. These structures arise from the different roles played by dissipation in the strong coupling limit and quantum Zeno regime. Our Letter paves the way for exploring higher-order exceptional physics in the many-body setting of ultracold atoms.

Multi-state system reliability: An emerging paradigm for sophisticated engineered systems

Multi-state system reliability: An emerging paradigm for sophisticated engineered systems

Follow up care for adults with diabetes treated for severe hypoglycemia by emergency medical Services, 2013–2019

AimsTo capture the types and content of healthcare encounters following severe hypoglycemia requiring emergency medical services (EMS) and to correlate their features with subsequent risk of severe hypoglycemia. MethodsA retrospective cohort was obtained by linking data from a multi-state health system and an advanced life support ambulance service. This identified 1977 EMS calls by 1028 adults with diabetes experiencing hypoglycemia between 1/1/2013–12/31/2019. We evaluated the healthcare engagement over the following 7 days to identify rates of discussion of hypoglycemia, change of diabetes medications, glucagon prescribing, and referral for diabetes. ResultsRates of hypoglycemia discussion increased with escalating levels of care, from 11.5 % after EMS calls without emergency department (ED) transport or outpatient clinical encounters to 98 % among hospitalized patients with outpatient follow-up. EMS transport and outpatient follow-up were associated with significantly higher odds of discussion of hypoglycemia (OR 60 and OR 22.1, respectively). Interventions were not impacted by previous severe hypoglycemia within 30 days. Prescription of glucagon was rare among all patients. ConclusionsInterventions to prevent recurrent hypoglycemia increase with escalating levels of care but remain inadequate and inconsistent with clinical guidelines. Greater attention is needed to ensure timely diabetes-related follow-up and treatment modification for patients experiencing severe hypoglycemia.

Primary outcomes of the enhanced, EHR-facilitated cancer symptom control (E2C2) cluster-randomized, stepped wedge, pragmatic trial.

LBA12006 Background: Symptom burden and functional decline are prevalent, inconsistently treated, and associated with adverse health outcomes in patients with cancer. Symptom monitoring with electronic patient-reported outcome measures (ePROMs) has yielded mixed results due, in part, to care teams’ variable bandwidth and resourcing for symptom management. The collaborative care model (CCM) offers a validated means to address these issues and potentially improve clinical and health services outcomes. Capturing ePROMS through the electronic health record (EHR) for use in CCM delivery provides a potentially scalable approach to manage symptoms at the population level. Methods: E2C2 is a cluster-randomized, population-level, stepped wedge pragmatic trial that compares a bundled, EHR-facilitated, CCM-based intervention to improve control of SPPADE symptoms (Sleep interference, Pain, impaired Physical function, Anxiety, Depression, and Energy deficit/fatigue) with usual care. All patients, regardless of cancer type or stage, treated in the medical oncology clinics of a multi-state health system, were assigned to one of fifteen clusters. Control and intervention conditions monitored SPPADE symptoms with Epic EHR administered 11-point numerical rating scales (NRSs). Moderate and severe symptoms were defined as 4-6/10 and >7/10, respectively. The intervention added EHR clinician decision support; automated delivery of symptom self-management information; and options to address severe symptoms with a dedicated symptom care manager. The primary outcome was post-baseline SPPADE scores assessed using multivariate regression of six cluster-period mean SPPADE symptom scores against E2C2 exposure, fixed cluster and secular time effects. Results: From March 2019 to January 2023, 50,559 patients were assigned to E2C2 clusters and 40,295 completed at least one ePROM. At first assessment, participants’ mean age was 63.3 years; 58% were female; 26% were rural, and the prevalences of moderate or worse symptoms were fatigue 42%, impaired function 34%, sleep disturbance 34%, anxiety 27%, pain 26%, and depression 23%. The intervention significantly reduced mean composite SPPADE symptom scores, p <0.001, among all patients, as well as those with >1 moderate or worse symptom. All mean symptom scores were lower in the intervention group, with the largest effects detected among patients with fatigue -0.2 (-0.4, -0.07), anxiety -0.14 (-0.2, -0.03), and depression -0.1 (-0.2, -0.002). Conclusions: In this large cluster-randomized trial, an EHR-facilitated, bundled intervention that scaled CCM-based surveillance and management of SPPADE symptoms significantly reduced population-level symptom burden, particularly for fatigue, anxiety, and depression. This study provides real world evidence and the foundation for future national efforts aimed at controlling symptoms in patients with cancer. Clinical trial information: NCT03892967 .

Associations Between Sodium-Glucose Co-transporter 2 Inhibitors and Urologic Diseases: Implications for Lower Urinary Tract Symptoms From a Multi-State Health System Analysis

To analyze the frequency of new urologic visits and urologic diagnoses in patients prescribed sodium-glucose co-transporter-2 inhibitors (SGLT-2is). Records from a multi-state health system between 2014 and 2022 were reviewed to identify patients referred for outpatient urology evaluation within 2 years of diabetes medication prescription. Patients were stratified by the prescription of SGLT-2is or another diabetes medication. Frequency of urology visits within 1-year, urologic diagnoses, and prescriptions to treat lower urinary tract symptoms (LUTS) were compared. Patients were stratified by whether they had achieved HbA1c goal (≥7% or <7%) following treatment as well as by sex. Multivariable logistic regression was performed to determine if SGLT-2 use independently predicted outcomes of interest. 163,827 patients met inclusion criteria. Use of SGLT-2is was associated with a higher frequency of early urologic referral, balanitis/balanoposthitis, overactive bladder, urinary frequency, urgency, and need for LUTS medications in males with HbA1c ≥7%. Females on SGLT-2is with HbA1c ≥7% also had higher rates of urinary incontinence. In those with HbA1c <7%, only balanitis/balanoposthitis and urinary incontinence were higher in the SGLT-2i cohorts for males and females, respectively. Multivariable analysis found SGLT-2i use as predictive of early urology referral, balanitis/balanoposthitis, urinary urgency, frequency, overactive bladder, and need for LUTS medications in males. Multivariable analysis of females demonstrated similar results. SGLT-2is may lead to worse urologic outcomes and increased utilization of urologic care relative to other diabetic medications. Future studies are necessary to identify which patients are at highest risk of adverse urologic outcomes.

Importance Measure for Multilevel Inspections of Multistate Systems: A Value of Information Perspective

Importance Measure for Multilevel Inspections of Multistate Systems: A Value of Information Perspective

Reliability evaluation of a multi-state system with dependent components and imprecise parameters: A structural reliability treatment

Reliability evaluation of a multi-state system (MSS) with dependent components makes much practical sense because the independent identical assumption (i.i.d.) assumption between different components is sometimes impractical in the context of real engineering cases. The task becomes more challenging if imprecision gets involved due to the pervasive uncertainty. The loss of monotony resulting from the introduction of imprecise parameters makes many analytical reliability methods not applied. To address this challenge, in this paper, we develop a survival signature-based reliability framework for an MSS taking into account both dependence and uncertainty. In our framework, the survival function is derived through some unique structural reliability treatments. Vine copula and imprecise probability are integrated and embedded within the framework to address the case that dependence and imprecision simultaneously appear. Implementation-wise, two numerical simulation algorithms are developed to address some complicated cases in which the analytical solution is not available. For demonstration and validation, both the numerical case and application examples are presented. The results show the superiority of the proposed method and its potential in real engineering use.