Transition Model Research Articles

Uniformization and bounded Taylor series in Newton-Raphsonmethod improves computational performance for a multistate transition model estimation and inference.

Multistate transition models (MSTMs) are valuable tools depicting disease progression. However, due to the complexity of MSTMs, larger sample size and longer follow-up time in real-world data, the computation of statistical estimation and inference for MSTMs becomes challenging. A bounded Taylor series in Newton-Raphson procedure is proposed which leverages the uniformization technique to derive maximum likelihood estimates and corresponding covariance matrix. The proposed method, namely uniformization Taylor-bounded Newton-Raphson, is validated in three simulation studies, which demonstrate the accuracy in parameter estimation, the efficiency in computation time and robustness in terms of different situations. This method is also illustrated using a large electronic medical record data related to statin-induced side effects and discontinuation.

Reliability Evaluation of MMC‐MTDC System Considering Device Failure Rates and Multi‐State Transition

ABSTRACTThe modular multi‐level converter based multi‐terminal high voltage direct current (MMC‐MTDC) system, with its numerous power devices and diverse operating states, poses a significant challenge for reliability evaluation. The paper introduces a method for modeling and assessing the reliability of MMC‐MTDC, considering both power device failure rates and multi‐state transitions of multiple terminals. To determine the weights of influence, the analytic hierarchy process (AHP) is employed to evaluate the impact of sub‐modules (SMs), the controller system, and the water cooling systems of an MMC. Subsequently, a multi‐state transition model is established to describe the changes in the operation mode of the MMC‐MTDC. On this basis, the reliability evaluation via semi‐Markov processes is implemented. A case study is performed on the reliability of a ±200 kV five‐terminal system and the results are compared with practical operation data.

Validation and Comparison of Non-stationary Cognitive Models: A Diffusion Model Application

AbstractCognitive processes undergo various fluctuations and transient states across different temporal scales. Superstatistics are emerging as a flexible framework for incorporating such non-stationary dynamics into existing cognitive model classes. In this work, we provide the first experimental validation of superstatistics and formal comparison of four non-stationary diffusion decision models in a specifically designed perceptual decision-making task. Task difficulty and speed-accuracy trade-off were systematically manipulated to induce expected changes in model parameters. To validate our models, we assess whether the inferred parameter trajectories align with the patterns and sequences of the experimental manipulations. To address computational challenges, we present novel deep learning techniques for amortized Bayesian estimation and comparison of models with time-varying parameters. Our findings indicate that transition models incorporating both gradual and abrupt parameter shifts provide the best fit to the empirical data. Moreover, we find that the inferred parameter trajectories closely mirror the sequence of experimental manipulations. Posterior re-simulations further underscore the ability of the models to faithfully reproduce critical data patterns. Accordingly, our results suggest that the inferred non-stationary dynamics may reflect actual changes in the targeted psychological constructs. We argue that our initial experimental validation paves the way for the widespread application of superstatistics in cognitive modeling and beyond.

A numerical study of laminar/transitional shock–boundary layer interaction on a hypersonic double wedge using a modified $$\gamma $$-transition model

A numerical study of laminar/transitional shock–boundary layer interaction on a hypersonic double wedge using a modified $$\gamma $$-transition model

Investigation on transition characteristics of hydrofoil boundary layer based on algebraic local-correlation-based transition modeling model

Hydrofoil shapes are used for the marine turbine blades to capture kinetic energy from water currents effectively. Predicting transitions is a critical concern when studying the hydrofoil boundary layer. This paper analyzed the transitional behavior of the boundary layer in the National Advisory Committee of Aeronautics (NACA) hydrofoil, NACA0009, with a blunt trailing edge using the Algebraic Local-Correlation-based Transition Modeling (Algebraic LCTM) model. First, through sensitivity analysis, the effects of the maximum y+ (the dimensionless distance y to the wall), grid expansion ratio, number of normal and streamlined grids, and timescale on transition prediction were studied. The results indicate that finer y+ value and appropriate grid expansion ratios can improve the accuracy of transition prediction, while the influence of timescale on the prediction results is relatively small within the range of Courant number theory values. Second, further analysis was conducted on the transition prediction performance under different Reynolds numbers. It was found that the model predictions were consistent with experimental values at low Reynolds numbers, but the predicted transition position was advanced at high Reynolds numbers, mainly because of the significant disparity in eddy viscosity coefficients within the free flow field. In the study of leading-edge roughness bands' impact on boundary layer transition for hydrofoil, the introduction of roughness significantly expedited the transition process. The Algebraic LCTM model outperformed the gamma (γ) transition model, reducing prediction errors by 5–40% for boundary layer parameters and maintaining errors between 0.005 and 4% for wake vortex shedding frequency, as opposed to the γ model's 0–23%. The results of this study provide a theoretical basis for hydrofoil design.

Study of the C-γ-Reθ Transition Model Based on the NNW-HyFLOW Software

Abstract Since the release of hypersonic computational fluid software NNW-HyFLOW in 2023, its computational modules have been continuously expanded for the development needs of hypersonic vehicles. The C-γ-Reθ transition model has been developed based in the framework of the NNW-HyFLOW to expand the prediction of transition in the design of high-speed vehicles. Firstly, the calibration of two basic relations of Re θc and Flength which is originally designed for the low-speed flat plate cases have been realized and expanded to high-speed flows. Secondly, the the empirical formula of Mach number correction has been adjusted by several typical high-speed transition cases. The same correction also has been made to the turbulence Prandtl number correction following the sae procedure. A crossflow transition module has been added with the calibration of the crossflow Reynolds number and validation of the experiment data of the HyTRV lifting body. The numerical tests show that the C-γ-Reθ transition model based on NNW-HyFLOW software has been basically realized with the ability of steamwise transition and crossflow transition prediction.

3D Numerical Investigation of Bubble Upflow Condensation Behaviors During Subcooled Flow Boiling in Mini-Channel with VOSET

In the subcooled flow boiling process, bubble condensation is an inevitable basic phenomenon. This paper studies the condensation phenomenon of the single, double vertical/horizontal saturated bubbles rising in a three-dimensional mini-rectangular channel based on the interface capture method VOSET (coupled volume-of-fluid and level set method) and the phase transition model. Bubble condensation behaviors are investigated at different initial diameters, inlet velocity distributions, subcooling temperatures, bubble gaps, and arrangement for the two-bubble condensing system especially. The effects of these parametric on bubble motion trajectory, shape evolution, volume variation, and condensation rate are presented. The numerical results indicated that the initial bubble size and liquid subcooling play an important role in influencing the shape and volume variation of condensing bubble behaviors significantly, while the inlet velocity distribution only affects bubble motion trajectory. Furthermore, the interaction and coalescence between the bubbles will affect the bubble behaviors and the condensation rate. Finally, the condensation heat transfer coefficients at the bubble surfaces for different cases simulated in this paper are presented, seemingly first in the literature.

Abnormal resistivity behavior of Co–Cu alloys and responsible metastable liquid phase separation

The electrical resistivity of Co-Cu alloys with Cu content of 10, 20, 30, 40, 50, 60, 70, 80, and 90 at.% was measured in the temperature range from 1400 to 2100K. The experiments were performed during of heating and subsequent cooling of the samples. For alloys with Cu content of 30, 40, 60, 70, 80, and 90 at.%, a bend in the cooling curve specific to the liquid-liquid phase separation (LLPS) process was observed. LLPS is shown when the melt is undercooled to the binodal temperature T∗. The phase transition model of LLPS was proposed for to theoretically determine the temperatures T∗. The LLPS model demonstrates the possibility of percolation phenomena occurring in Cu-Co heterogeneous melts rich in cobalt. The LLPS model shows good agreement with experimental data and the abnormal resistivity behavior of Co–Cu alloys.

Long-Term Health Outcomes of Huntington Disease and the Impact of Future Disease-Modifying Treatments: A Decision-Modeling Analysis.

Disease-modifying treatments (DMTs) such as gene therapy are currently under investigation as a potential treatment for Huntington disease (HD). Our objective was to estimate the long-term natural history of HD progression and explore the potential efficacy impacts and value of a hypothetical DMT using a decision-analytic modeling framework. We developed a health state transition model that separately analyzed 40-year-old individuals with prefunctional decline (PFD, HD Integrated Staging System [HD-ISS] stage <3, total functional score [TFC] 13), active functional decline Shoulson and Fahn category 1 (SF1, HD-ISS stage 3, TFC 13-11), and SF2 (HD-ISS stage 3, TFC 10-7). Three-year outcomes from the TRACK-HD longitudinal study were linearly extrapolated to estimate the long-term health outcomes and costs of each population. For PFD individuals, we used the HD-ISS to predict the onset of functional decline. HD costs and quality-adjusted life years (QALYs) were estimated over a lifetime horizon by applying health state-specific costs and utilities derived from a related HD burden-of-illness study. We then estimated the long-term health impacts of hypothetical DMTs that slowed or delayed onset of functional decline. We conducted sensitivity analyses to assess model uncertainties. The expected life years for 40-year-old PFD, SF1, and SF2 populations were 20.46 (95% credible range [CR]: 19.05-22.30), 13.93 (10.82-19.08), and 10.99 (8.28-22.07), respectively. The expected QALYs for PFD, SF1, and SF2 populations were 15.93 (14.91-17.44), 8.29 (6.36-11.79), and 5.79 (4.14-12.91), respectively. The lifetime costs of HD were $508,200 ($310,300 to $803,700) for the PFD population, $1.15 million ($684,500 to $1.89 million) for SF1 individuals, and $1.07 million ($571,700 to $2.26 million) for SF2 individuals. Although hypothetical DMTs led to cost savings in the PFD population by delaying the cost burdens of functional decline, they increased costs in SF1 and SF2 populations by prolonging time spent in expensive progressive HD states. Our novel HD-modeling framework estimates HD progression over a lifetime and the associated costs and QALYs. Our approach can be used for future cost-effectiveness models as positive DMT clinical trial evidence becomes available.

Cost-effectiveness of sodium-glucose cotransporter-2 inhibitors in the treatment of diabetic nephropathy in Japan.

To assess the cost-effectiveness of diabetic nephropathy treatment with sodium-glucose cotransporter-2 (SGLT2) inhibitors in Japanese clinical practice, considering diabetes-related complications. A population-based Monte Carlo simulation was used to estimate the cost-effectiveness for people with diabetic nephropathy who initiated pharmacotherapy with an SGLT2 inhibitor plus conventional treatment or conventional treatment alone, based on quality-adjusted life-years (QALYs) and healthcare costs. The Canagliflozin and Renal Events in Diabetes with Established Nephropathy Clinical Evaluation study (CREDENCE) and the Japanese Society for Dialysis Research statistical survey were the primary sources of probability and mortality, while Japanese Health Insurance Claims Data were the cost source. The state transition model included diabetic nephropathy, hospitalization due to cardiovascular disease, dialysis, and death. One-way and probabilistic sensitivity analyses were used to explore model uncertainty. Using the threshold of JPY 5 000 000 per QALY, SGLT2 inhibitor plus conventional treatment was more cost-effective than conventional treatment alone, with an incremental cost-effectiveness ratio of JPY 654 309 per QALY. Treating 100 000 people, SGLT2 inhibitor plus conventional treatment prevented 2234 deaths and reduced 5793 fewer heart failure cases, 3967 fewer myocardial infarctions and stroke events. Sensitivity analysis affirmed the robustness of these results for patients aged under 70 years. The SGLT2 inhibitor treatment appeared to be cost-effective for the overall population of our study and particularly for younger patients (<70 years old). For older patients (≥70 years old), the cost-effectiveness was less clear and may require further evaluation. Decision-makers should consider this age-based heterogeneity when making recommendations about SGLT2 inhibitor treatment.

Conceptualising climate change vulnerability across the agrarian transition: The example of Egypt

In this study, we explore the relationship between Egypt's agrarian transition and farmers’ perceptions of environmental risks and opportunities for climate change adaptation. Drawing from agrarian studies and rural development pathways in Egypt, we highlight structural challenges in addressing vulnerabilities of households in agrarian communities to environmental change. Our evidence comes from 350 landholders and agricultural labourers in rural Damietta, a governorate in the northeast of the Egyptian Nile Delta Region. We categorize households into three groups based on their reliance on agriculture for income, corresponding to different agrarian transition models. We find that vulnerabilities and capacities for climate change adaptation vary among these groups, with those heavily dependent on agriculture being the most vulnerable, despite having greater awareness of agricultural risks. They exhibit limited capacity to respond effectively – both in and ex situ, indicating a need for targeted support as environmental pressures increase due to climate change.

Building the Bridge: How System Dynamics Models Operationalise Energy Transitions and Contribute towards Creating an Energy Policy Toolbox

The complexity and multi-dimensionality of energy transitions are broadly recognised, and insights from transition research increasingly support policy decision making. Sustainability transition scholars have been developing mostly qualitative socio-technical transition (STT) frameworks, and modelling has been argued to be complementary to these frameworks, for example for policy testing. We systematically evaluate five system dynamics (SD) energy models on their representation of key STT characteristics. Our results demonstrate that (i) the evaluated models incorporate most of the core characteristics of STT, and (ii) the policies tested in the models address different levels and aspects of the multi-level perspective (MLP) framework. In light of the increasing emergence of energy (transition) models, we recommend to systematically map models and their tested policy interventions into the MLP framework or other sustainability transition frameworks, creating an overview of tested policies (a “policy navigator”). This navigator supports policy makers and modellers alike, facilitating them to find previously tested policy options and related models for particular policy objectives.

The Logistic Function in Glass Transition Models of Amorphous Polymers: A Theoretical Framework for Isobaric Cooling Processes

AbstractStudying the macroscopic‐phenomenological behavior of amorphous polymers at glass transition is often subject to limitations because the ordinary differential equations that describe the material behavior require numerical solution. To avoid these limitations, ad‐hoc‐formulated models of the glass transition have been proposed. However, their scope of application is expected to be limited due to insufficient theoretical foundation. This work establishes a theoretical framework for models that use the logistic function to approximate the macroscopic‐phenomenological behavior of amorphous polymers at glass transition. For this purpose, an exactly‐solvable Riccati equation is derived within thermodynamics with internal state variables. A closed‐form expression in terms of mathematical functions for the temperature derivative of a single internal state variable is the result. This closed‐form expression contains the logistic function thus featuring a continuous transition region centered around a pressure and cooling‐rate dependent transition temperature. Based on comparison of existing models with the exact solution derived from the Riccati equation, generalized models that approximate the thermal expansion coefficient and heat capacity at glass transition are proposed. This work thus demonstrates the validity of the logistic function in glass transition models of amorphous polymers and provides suggestions as to how existing models can be extended in their applicability.

CGS-21680 defers cisplatin-induced AKI-CKD transition in C57/BL6 mice

Acute kidney injury (AKI), with a high mortality and morbidity, is known as a risk factor for developing progressive chronic kidney disease (CKD). Targeting transition of AKI to CKD displays an excellent therapeutic potential. This study aims at investigating the role of CGS-21680, selective A2AR agonist, in deferring Cis-induced AKI-CKD transition. The AKI-CKD transition model was induced in C57/BL6 mice by repeated low doses of Cis (2.5 mg/kg i.p for 5 consecutive days in two cycles with a recovery phase of 16 days between two cycles). CGS-21680 was administered daily for 6 weeks (0.1 mg/kg, i.p). Urine, blood, and kidney were collected at three different time points to track the disease progression. CGS-21680 administration preserved kidney function and attenuated tubular damage as evidenced by hematoxylin-eosin (H&E) histopathology. CGS-21680 significantly restored oxidative status as reflected by reduced malondialdehyde (MDA) content and increased total antioxidant capacity (TAC). CGS-21680 showed anti-inflammatory effect as indicated by decreased TNF-α and iNOS. Additionally, CGS-21680 ameliorated endothelial dysfunction and enhanced renal vasodilation as evidenced by upregulation of endothelial nitric oxide synthase (eNOS) and nitric oxide (NO) expression and down regulation of endothelin-1 (ET-1) and its receptor endothelin-A (ET-A) receptor expression. CGS-21680 also attenuated renal fibrosis as reflected by the reduction of percentage of fibrosis in Masson's trichome-stained renal sections and down regulation of transforming growth factor beta1 (TGF-β1) protein expression in IHC-stained renal sections. In conclusion, CGS-21680 could defer Cis-induced AKI-CKD transition via its vasodilatory, antioxidant, anti-inflammatory, and anti-fibrotic effects.

Aerodynamic breakup of emulsion droplets in airflow

AbstractAerodynamic breakup refers to the process where large droplets are fragmented into small droplets by the aerodynamic force in airflow, which plays a vital role in fluid atomization and spray applications. Previous research has primarily concentrated on the aerodynamic breakup of single‐component droplets, but investigations into the breakup of emulsion droplets are limited. This study experimentally investigated the aerodynamic breakup of water‐in‐oil emulsions in airflow, utilizing high‐speed photography to observe the breakup process and digital in‐line holography to measure fragment sizes. Comparative analyses between emulsion droplets and single‐component droplets are conducted to examine the breakup morphology, breakup regime, deformation characteristics, and fragment size distributions. The emulsion droplets exhibit higher apparent viscosity and shorter stretching lengths of the bag film and peripheral rim due to the presence of a dispersed phase. The breakup regime transitions of emulsions are modeled by integrating the viscosity model of emulsions and the transition model of the pure fluid. The fragment sizes of emulsion droplets are larger due to the shorter lengths of the bag film and peripheral rim.

Destination and time-series inference of moving objects via conditionally Markov process

AbstractThis paper presents a destination and time-series inference algorithm for tracking moving targets. The destination of the object is considered the intent, and inference and state estimation are performed in the Bayesian framework. To describe the destination-aware target motion, we construct the state transition model using a conditionally Markov process. We introduce a multiple model to achieve simultaneous intent and time-series inferences. Given finite destination candidates, the maximum a posteriori hypothesis is chosen as the destination. For time-series inference, local estimates obtained from Kalman filters are fused to yield target state estimates. To address unspecified terminal conditions, the proposed algorithm incorporates parameter correction techniques based on relative geometry. Numerical simulations are performed to validate the proposed inference algorithm.

Improvement of Spatio-Temporal Inconsistency of Time Series Land Cover Products

In recent years, time series land cover products have been developed rapidly. However, the traditional classification strategy rarely considers time continuity and spatial consistency, which leads to the existence of unreasonable changes among the multi-period products. In order to solve the existing problems, this paper proposes a matrix decomposition model and an optimized hidden Markov model (HMM) to improve the consistency of the time series land cover maps. It also compares the results with the spatio-temporal window filtering model. The spatial weight information is introduced into the singular value decomposition (SVD) model, and the regression model is constructed by combining the eigenvalues and eigenvectors of the image to predict the unreasonable variable pixels and complete the construction of the matrix decomposition model. To solve the two problems of reliance on expert experience and lack of spatial relationships, this paper optimizes the model and proposes the HMM Land Cover Transition (HMM_LCT) model. The overall accuracy of the matrix decomposition model and the HMM_LCT model is 90.74% and 89.87%, respectively. It is found that the matrix decomposition model has a better effect on consistency adjustment than the HMM_LCT model. The matrix decomposition model can also adjust the land cover trajectory to better express the changing trend of surface objects. After consistent adjustment by the matrix decomposition model, the cumulative proportion of the first 15 types of land cover trajectories reached 99.47%, of which 83.01% were stable land classes that had not changed for three years.

Longitudinal needs and cancer knowledge in Swiss childhood cancer survivors transitioning from pediatric to adult follow-up care: results from the ACCS project

PurposeChildhood Cancer Survivors (CCSs) have an increased risk for treatment-related chronic health conditions, but the adherence to long-term follow-up (LTFU) care decreases over time. We therefore assessed the CCSs’ development of cancer knowledge, cancer worries, self-management skills, and expectations for LTFU care in a structured, cancer center-based transition model—a crucial part for maintaining adherence.MethodsUsing questionnaire-based surveys, we compared the CCSs’ cancer knowledge with medical record data and assessed cancer worries (6 questions), self-management skills (15 questions), and expectations (12 questions) longitudinally by validated scales. We used descriptive statistics for presenting our results.ResultsWe analyzed 17 CCSs, 71% were female, had a median age of 8 years at diagnosis and 21 years at study enrollment. The knowledge about late effects increased during the transition process, except for the risk of secondary malignancies. Leukemia survivors had a decrease in cancer worries. At least 75% of the CCSs agreed to 11 of 15 self-management questions before and after transition, with the highest increase over time in less parental involvement. The CCSs expected the most, that physicians know the CCSs’ cancer history, that the visit starts on time, and that physicians can always be called in case of questions.ConclusionsOur transition model improved cancer knowledge, especially the risk for late effects, decreased cancer worries, and identified expectations for LTFU care which should be considered in the future. A structured transition process with evidence-based tools further increases the knowledge of CCS for LTFU through empowerment.

Spectroscopic study of wemple-didomenico empirical formula and taucs model to determine the optical band gap of dye-doped polymer based on chitosan: Common poppy dye as a novel approach to reduce the optical band gap of biopolymer

This study investigates the effect of a natural dye extracted from common poppy (Papaver rhoeas) waste flowers on the optical properties of chitosan (CS) films. The extraction of natural dyes from waste flowers can be considered a new field for research in green chemistry. CS films are flexible and biodegradable but have low optical activity and band gap, limiting their applications in optical devices. The doped CS polymer with different concentrations of Papaver rhoeas dye exhibited enhanced optical properties. Also, 30 % glycerol was added as a plasticizer to omit film brittleness. The FTIR examinations is helpful to propose a mechanism that explains the interaction of the dye with the host polymer. The UV–vis spectroscopic examination establish that the optical characteristics of the films can be modified by adjusting the dye concentration. Furthermore, optical absorption properties are described using the Tauc non-direct transition model, revealing an approximate optical band gap of 1.64 eV. This band gap defines the energy required for electron transitions, elucidating the material’s electronic characteristics. The extinction coefficient (k) and refractive index (n) of the CS-doped films’ shows a dispersion behavior at visible regions of EM radiation. The Wemple-DiDomenico single oscillator model was used to investigate the n dispersion and determine the oscillator energy equivalent to the optical band gap. Additionally, calculations have been performed on optical dielectric properties and optical conductivity. The Urbach energy was measured and used to detect the structure of the films. The findings underscore the potential applications of these natural dye-doped CS films in eco-friendly materials and optical devices.

The conceptualisation of cardiometabolic disease policy model in the UK

BackgroundDecision models are increasingly used to inform policy-making processes, and there is a need to improve their credibility. The estimation of health and economic outcomes generated from decision models is influenced by the development process itself. This paper aims to present the conceptual model development process of cardiometabolic disease (CMD) policy models in the UK setting.MethodsThis conceptual model followed the International Society of Pharmacoeconomics and Outcomes Research-Society of Medical Decision Making (ISPOR-SMDM) Modelling Good Research Practices Task Force-2.ResultsFirst, for the conceptualisation of the problem, the CMD disease staging, progression and current clinical guidelines were summarised, followed by a systematic review of published policy models. We critically appraised policy models such as cardiovascular disease and type 2 diabetes. Key messages from the review emphasised the importance of understanding various determinants influencing model development, including risk factors, model structure, models’ parameters, data utilisation, economic perspective, equality/equity consideration, transparency and validation process. Second, as a sequential process, is model conceptualisation, to determine which modelling types and their attributes best represent the defined problem. Expert opinions, including a clinician and experienced modellers, provided input on the state transition model to ensure the structure is clinically relevant. From this stage, the consideration and agreement to establish a disease state in a state transition model was discussed.ConclusionThis conceptual model serves as a basis for representing the systematic process for structuring a CMD policy model to enhance its transparency and credibility.