Cost Effectiveness of Definitive Treatment Strategies for Autonomously Functioning Thyroid Nodules.

OBJECTIVE: Autonomously functioning thyroid nodules (AFTN) can be treated with antithyroid drugs, radioactive iodine (RAI), thyroid lobectomy or radiofrequency ablation (RFA). Although surgery is most definitive, some patients require lifelong hormone supplementation. RFA avoids this sequela, but its efficacy depends on nodule size. This study aims to compare the relative cost-effectiveness of RAI, RFA and lobectomy for treatment of AFTNs. STUDY DESIGN: A Markov analysis model was created to simulate clinical outcomes, costs and utilities for three AFTN treatments: (1) thyroid lobectomy, (2) RAI, and (3) RFA. PATIENTS: This mathematical model was created using published literature and modeling. MEASUREMENTS: Transition probabilities, utilities and costs were extracted from published literature, Medicare, and RedBook. The willingness to pay threshold was set to $100,000 per quality-adjusted life year. The model simulated 2-year outcomes, reflecting RFA literature. Sensitivity analyses were conducted to account for uncertainty in model variables. RESULTS: In the base model, RAI dominated both lobectomy and RFA, with lower estimated cost ($2000 vs. $9452 and $10,087) and higher cumulative utility (1.89 vs. 1.82 and 1.78 quality-adjusted life years). One-way sensitivity analyses demonstrated that relative cost-effectiveness between surgery and RFA was driven by the probability of euthyroidism after RFA and hypothyroidism after lobectomy. RFA becomes more cost-effective than surgery if the rate of euthyroidism after ablation is higher than 69% (baseline 54%). CONCLUSION: Based on published data, RAI is most cost-effective in treating most AFTN. Surgery is more cost-effective than RFA in most scenarios, but RFA may be more resource-efficient for smaller nodules with a high likelihood of complete treatment.

Stability of a stochastic brucellosis model with semi-Markovian switching and diffusion.

To explore the influence of state changes on brucellosis, a stochastic brucellosis model with semi-Markovian switchings and diffusion is proposed in this paper. When there is no switching, we introduce a critical value R s and obtain the exponential stability in mean square when R s < 1 by using the stochastic Lyapunov function method. Sudden climate changes can drive changes in transmission rate of brucellosis, which can be modelled by a semi-Markov process. We study the influence of stationary distribution of semi-Markov process on extinction of brucellosis in switching environment including both stable states, during which brucellosis dies out, and unstable states, during which brucellosis persists. The results show that increasing the frequencies and average dwell times in stable states to certain extent can ensure the extinction of brucellosis. Finally, numerical simulations are given to illustrate the analytical results. We also suggest that herdsmen should reduce the densities of animal habitation to decrease the contact rate, increase slaughter rate of animals and apply disinfection measures to kill brucella.

Integration of thermal energy storage for sustainable energy hubs in the forest industry: A comprehensive analysis of cost, thermodynamic efficiency, and availability.

Thermal energy storage (TES) offers a practical solution for reducing industrial operation costs by load-shifting heat demands within industrial processes. In the integrated Thermomechanical pulping process, TES systems within the Energy Hub can provide heat for the paper machine, aiming to minimize electricity costs during peak hours. This strategic use of TES technology ensures more cost-effective and efficient energy consumption management, leading to overall operational savings. This research presents a novel method for optimizing the design and operation of an Energy Hub with TES in the forest industry. The proposed approach for the optimal design involves a comprehensive analysis of the dynamic efficiency, reliability, and availability of system components. The Energy Hub comprises energy conversion technologies such as an electric boiler and a steam generator heat pump. The study examines how the reliability of the industrial Energy Hub system affects operational costs and analyzes the impact of the maximum capacities of its components on system reliability. The method identifies the optimal design point for maximizing system reliability benefits. To optimize the TES system's charging/discharging schedule, an advanced predictive method using time series prediction models, including LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit), has been developed to forecast average daily electricity prices. The results highlight significant benefits from the optimal operation of TES integrated with Energy Hubs, demonstrating a 4.5-6 percent reduction in system operation costs depending on the reference year. Optimizing the Energy Hub design improves system availability, reducing operation costs due to unsupplied demand penalty costs. The system's peak availability can reach 98 %, with a maximum heat pump capacity of 2 MW and an electric boiler capacity of 3.4 MW. The GRU method showed superior accuracy in predicting electricity prices compared to LSTM, indicating its potential as a reliable electricity price predictor within the system.

Contrast Information Dynamics: A Novel Information Measure for Cognitive Modelling.

We present contrast information, a novel application of some specific cases of relative entropy, designed to be useful for the cognitive modelling of the sequential perception of continuous signals. We explain the relevance of entropy in the cognitive modelling of sequential phenomena such as music and language. Then, as a first step to demonstrating the utility of constrast information for this purpose, we empirically show that its discrete case correlates well with existing successful cognitive models in the literature. We explain some interesting properties of constrast information. Finally, we propose future work toward a cognitive architecture that uses it.

Inference on spatiotemporal dynamics for coupled biological populations.

Mathematical models in ecology and epidemiology must be consistent with observed data in order to generate reliable knowledge and evidence-based policy. Metapopulation systems, which consist of a network of connected sub-populations, pose technical challenges in statistical inference owing to nonlinear, stochastic interactions. Numerical difficulties encountered in conducting inference can obstruct the core scientific questions concerning the link between the mathematical models and the data. Recently, an algorithm has been proposed that enables computationally tractable likelihood-based inference for high-dimensional partially observed stochastic dynamic models of metapopulation systems. We use this algorithm to build a statistically principled data analysis workflow for metapopulation systems. Via a case study of COVID-19, we show how this workflow addresses the limitations of previous approaches. The COVID-19 pandemic provides a situation where mathematical models and their policy implications are widely visible, and we revisit an influential metapopulation model used to inform basic epidemiological understanding early in the pandemic. Our methods support self-critical data analysis, enabling us to identify and address model weaknesses, leading to a new model with substantially improved statistical fit and parameter identifiability. Our results suggest that the lockdown initiated on 23 January 2020 in China was more effective than previously thought.

Identifying subtypes in persons, situations and person-situation interactions: Categorical latent state-trait modelling approaches.

The latent state-trait theory posits that a psychological construct may reflect stable influences specific to a person (i.e., trait), ephemeral influences from situations (i.e., state), and interactions between them (i.e., state-trait interactions). Researchers conventionally apply mixture modelling to explore heterogeneity in variables by identifying homogenous classes with respect to the measured variable, yet rarely distinguishing between person- and situation-specific classes. The current study introduces novel categorical latent state-trait models to identify subgroups in states and traits, quantifying the effects of person-specific classes, situation-specific classes, and person-situation interactions. The proposed models are applied to an empirical dataset. We discuss statistical inference, effect size measures, and model visualization for the proposed models. Based on realistic parameter values from the empirical dataset, preliminary simulation studies were conducted to investigate models' performances. Bayesian estimation in the proposed models allows flexible testing of a wide range of hypotheses related to state, trait, and interaction effects. We discuss limitations and future directions.

Stacked probability plots of the extended illness-death model using constant transition hazards - an easy to use shiny app.

BACKGROUND: Extended illness-death models (a specific class of multistate models) are a useful tool to analyse situations like hospital-acquired infections, ventilation-associated pneumonia, and transfers between hospitals. The main components of these models are hazard rates and transition probabilities. Calculation of different measures and their interpretation can be challenging due to their complexity. METHODS: By assuming time-constant hazards, the complexity of these models becomes manageable and closed mathematical forms for transition probabilities can be derived. Using these forms, we created a tool in R to visualize transition probabilities via stacked probability plots. RESULTS: In this article, we present this tool and give some insights into its theoretical background. Using published examples, we give guidelines on how this tool can be used. Our goal is to provide an instrument that helps obtain a deeper understanding of a complex multistate setting. CONCLUSION: While multistate models (in particular extended illness-death models), can be highly complex, this tool can be used in studies to both understand assumptions, which have been made during planning and as a first step in analysing complex data structures. An online version of this tool can be found at https://eidm.imbi.uni-freiburg.de/ .

Investigating the impact of initial parameters on autonomous robot swarm movement strategies for enhanced exploration efficiency: a comprehensive study.

Swarm robots are frequently preferred for the exploration of harsh environments and search and rescue operations. This study explores the factors that influence the movement strategies of autonomous robot swarms and their impact on swarm distribution in the field, employing simulation-based analysis. The research consists of two parts: initially, robots undergo free-fall as passive entities, followed by a phase where they employ predefined movement strategies from their fall positions. The study aims to investigate how the initial position and related parameters affect movement characteristics and the ultimate swarm distribution. To achieve this objective, four parameters-radius, height, mass, and the Coefficient of Restitution-were identified, each assigned three different values. The study observes the effects of these parameters on robot motion, considering motion strategies such as Random Walk, Levy Walk, Markov Process, and Brownian Motion. Results indicate that increasing parameter values induce changes in the position values of the free-falling swarm in the first part, which is the initial position for the second part, influencing movement strategies in diverse ways. The outcomes are analyzed concerning the radial and angular spread of the robots. Radial spread measures how far swarm elements spread from their initial positions, while angular spread indicates how homogeneously the robots are distributed according to the polar angle. The study comprehensively investigates how the movement strategies of autonomous robot swarms are impacted by parameters and how these effects manifest in the results. The findings are anticipated to enhance the effective utilization of autonomous robot swarms in exploration missions.

Invariable distribution of co-evolutionary complex adaptive systems with agent's behavior and local topological configuration.

In this study, we developed a dynamical Multi-Local-Worlds (MLW) complex adaptive system with co-evolution of agent's behavior and local topological configuration to predict whether agents' behavior would converge to a certain invariable distribution and derive the conditions that should be satisfied by the invariable distribution of the optimal strategies in a dynamical system structure. To this end, a Markov process controlled by agent's behavior and local graphic topology configuration was constructed to describe the dynamic case's interaction property. After analysis, the invariable distribution of the system was obtained using the stochastic process method. Then, three kinds of agent's behavior (smart, normal, and irrational) coupled with corresponding behaviors, were introduced as an example to prove that their strategies converge to a certain invariable distribution. The results showed that an agent selected his/her behavior according to the evolution of random complex networks driven by preferential attachment and a volatility mechanism with its payment, which made the complex adaptive system evolve. We conclude that the corresponding invariable distribution was determined by agent's behavior, the system's topology configuration, the agent's behavior noise, and the system population. The invariable distribution with agent's behavior noise tending to zero differed from that with the population tending to infinity. The universal conclusion, corresponding to the properties of both dynamical MLW complex adaptive system and cooperative/non-cooperative game that are much closer to the common property of actual economic and management events that have not been analyzed before, is instrumental in substantiating managers' decision-making in the development of traffic systems, urban models, industrial clusters, technology innovation centers, and other applications.

Use of DNA forceps to measure receptor-ligand dissociation equilibrium constants in a single-molecule competition assay.

The ability of biophysicists to decipher the behavior of individual biomolecules has steadily improved over the past thirty years. However, it still remains unclear how an ensemble of data acquired at the single-molecule level compares with the data acquired on an ensemble of the same molecules. We here propose an assay to tackle this question in the context of dissociation equilibrium constant measurements. A sensor is built by engrafting a receptor and a ligand onto a flexible dsDNA scaffold and mounting this assembly on magnetic tweezers. This way, looking at the position of the magnetic bead enables one to determine in real-time if the two molecular partners are associated or not. Next, to quantify the affinity of the scrutinized single-receptor for a given competitor, various amounts of the latter molecule are introduced in solution and the equilibrium response of the sensor is monitored throughout the titration protocol. Proofs of concept are established for the binding of three rapamycin analogs to the FKBP12 cis-trans prolyl isomerase. For each of these drugs the mean affinity constant obtained on a ten of individual receptors agrees with the one previously determined in a bulk assay. Furthermore, experimental contingencies are sufficient to explain the dispersion observed over the single-molecule values.

Dynamic optimisation of unbalanced distribution network management by model predictive control with Markov reward processes.

In this work, a two-level control system is used to minimize the total active power losses of an active distribution system connected to the external grid and composed of a wind turbine, two photovoltaic power sources, and two batteries. At the first control level, a model-based predictive control (MPC) is run, using non-homogeneous Markov reward models for wind power prediction and homogeneous Markov reward models for photovoltaic power. At the second level, an algorithm is run for optimal management of voltage control assets, such as voltage regulating transformers, to minimize losses. Different scenarios have been considered, highlighting the advantages of using an MPC framework. This results in an optimization process that can be influenced by different time horizons depending on whether or not the MPC is applied. The predictions allow considering a long-horizon stepwise optimization process that leads to an increasing number of variables along with the decrease of total active power losses. When the MPC is not applied, a short-horizon analysis is performed with a decrease in both the number of variables and the quality of the results. Different cases are considered in which the nominal power of a photovoltaic unit and the battery capacity are modified.

Statistical inference for complete and incomplete mobility trajectories under the flight-pause model.

We formulate a statistical flight-pause model (FPM) for human mobility, represented by a collection of random objects, called motions, appropriate for mobile phone tracking (MPT) data. We develop the statistical machinery for parameter inference and trajectory imputation under various forms of missing data. We show that common assumptions about the missing data mechanism for MPT are not valid for the mechanism governing the random motions underlying the FPM, representing an understudied missing data phenomenon. We demonstrate the consequences of missing data and our proposed adjustments in both simulations and real data, outlining implications for MPT data collection and design.

Lyme Disease Models of Tick-Mouse Dynamics with Seasonal Variation in Births, Deaths, and Tick Feeding.

Lyme disease is the most common vector-borne disease in the United States impacting the Northeast and Midwest at the highest rates. Recently, it has become established in southeastern and south-central regions of Canada. In these regions, Lyme disease is caused by Borrelia burgdorferi, which is transmitted to humans by an infected Ixodes scapularis tick. Understanding the parasite-host interaction is critical as the white-footed mouse is one of the most competent reservoir for B. burgdorferi. The cycle of infection is driven by tick larvae feeding on infected mice that molt into infected nymphs and then transmit the disease to another susceptible host such as mice or humans. Lyme disease in humans is generally caused by the bite of an infected nymph. The main aim of this investigation is to study how diapause delays and demographic and seasonal variability in tick births, deaths, and feedings impact the infection dynamics of the tick-mouse cycle. We model tick-mouse dynamics with fixed diapause delays and more realistic Erlang distributed delays through delay and ordinary differential equations (ODEs). To account for demographic and seasonal variability, the ODEs are generalized to a continuous-time Markov chain (CTMC). The basic reproduction number and parameter sensitivity analysis are computed for the ODEs. The CTMC is used to investigate the probability of Lyme disease emergence when ticks and mice are introduced, a few of which are infected. The probability of disease emergence is highly dependent on the time and the infected species introduced. Infected mice introduced during the summer season result in the highest probability of disease emergence.

Longitudinal Fluctuations in Treatment Response After OnabotulinumToxinA and Sacral Neuromodulation for Refractory Urgency Incontinence.

PURPOSE: We compared fluctuations in treatment response after onabotulinumtoxinA and sacral neuromodulation for urgency incontinence using Markov models. MATERIALS AND METHODS: We fit data from a randomized trial to Markov models to compare transitions of success/failure over 6 months between 200 U onabotulinumtoxinA and sacral neuromodulation. Objective failure was <50% reduction in urgency incontinence episodes from baseline; subjective failure "strongly disagree" to "neutral" to the Patient Global Symptom Control questionnaire. RESULTS: Of the 357 participants (median baseline daily urgency incontinence episodes 4.7 [IQR 3.7-6.0]) 61% vs 51% and 3.2% vs 6.1% reported persistent states of objective success and failure over 6 months after onabotulinumtoxinA vs sacral neuromodulation. Participants receiving onabotulinumtoxinA vs sacral neuromodulation had lower 30-day transition probabilities from objective and subjective success to failure (10% vs 14%, ratio 0.75 [95% CI 0.55-0.95]; 14% vs 21%, ratio 0.70 [95% CI 0.51-0.89]). The 30-day transition probability from objective and subjective failure to success did not differ between onabotulinumtoxinA and sacral neuromodulation (40% vs 36%, ratio 1.11 [95% CI 0.73-1.50]; 18% vs 17%, ratio 1.14 [95% CI 0.65-1.64]). CONCLUSIONS: Over 6 months after treatment, 2 in 5 women's symptoms fluctuate. Within these initial 6 months, women receiving onabotulinumtoxinA transitioned from success to failure over 30 days less often than sacral neuromodulation. For both treatments, there was an almost 20%-40% probability over 30 days that women returned to subjective and objective success after failure. Markov models add important information to longitudinal models on how symptoms fluctuate after urgency incontinence treatment.

Markov modeling and performance analysis of infectious diseases with asymptomatic patients.

After over three years of COVID-19, it has become clear that infectious diseases are difficult to eradicate, and humans remain vulnerable under their influence in a long period. The presence of presymptomatic and asymptomatic patients is a significant obstacle to preventing and eliminating infectious diseases. However, the long-term transmission of infectious diseases involving asymptomatic patients still remains unclear. To address this issue, this paper develops a novel Markov process for infectious diseases with asymptomatic patients by means of a continuous-time level-dependent quasi-birth-and-death (QBD) process. The model accurately captures the transmission of infectious diseases by specifying several key parameters (or factors). To analyze the role of asymptomatic and symptomatic patients in the infectious disease transmission process, a simple sufficient condition for the stability of the Markov process of infectious diseases is derived using the mean drift technique. Then, the stationary probability vector of the QBD process is obtained by using RG-factorizations. A method of using the stationary probability vector is provided to obtain important performance measures of the model. Finally, some numerical experiments are presented to demonstrate the model's feasibility through analyzing COVID-19 as an example. The impact of key parameters on the system performance evaluation and the infectious disease transmission process are analyzed. The methodology and results of this paper can provide theoretical and technical support for the scientific control of the long-term transmission of infectious diseases, and we believe that they can serve as a foundation for developing more general models of infectious disease transmission.

Experimental Study and Analytical Modeling on Properties of Freeze-Thaw Durability of Coal Gangue Pervious Concrete.

To assess the freeze-thaw (F-T) durability of coal gangue pervious concrete (CGPC) in different F-T cycle media (water, 3.5 wt% NaCl solution), experimental studies on 36 groups of cube specimens and 6 groups of prismatic specimens were carried out, with designed porosity, F-T cycling media, and F-T failure times as variables. The changes in apparent morphology, mass, compressive behavior, relative dynamic elastic modulus, and permeability coefficient have been analyzed in detail. To predict the compressive strength after F-T cycles, a GM (1,1) model based on the grey system theory was developed and further improved into a more accurate grey residual-Markov model. The results reported that the cement slurry and coal gangue aggregates (CGAs) on the specimen surface continued to fall off as F-T cycles increased, and, finally, the weak point was fractured. Meanwhile, the decrease in compressive behavior and relative dynamic elastic modulus was gentle in the early phase of F-T cycles, and they gradually became faster in the later stage, showing a parabolic downward trend. The permeability coefficient increased gradually. When F-T failure occurred, specimen mass dropped precipitously. The F-T failure of CGPC was more likely to occur in 3.5 wt% NaCl solution, and the F-T failure times of samples were 25 times earlier than that of water. This study lays the foundation for an engineering application and provides a basis for the large-scale utilization of CGPC.

The Cost-Effectiveness of Pertuzumab for the Treatment of Metastatic HER2+ Breast Cancer in Czechia: A Semi-Markov Model Using Cost States.

OBJECTIVES: This article estimates the cost-effectiveness of adding pertuzumab to the combination of trastuzumab and docetaxel within the first-line treatment for metastatic breast cancer with the amplification of HER2+. METHODS: Data from Czech clinical practice recorded in the BREAST register are used. A semi-Markov model with states derived based on the treatment phases (first-line medication, no medication, next-line medication, death) is defined to estimate costs from the healthcare payers' perspective. The benefits are estimated as patient survival until death. The Kaplan-Meier estimates are supplemented by the Cox proportional hazard and the accelerated failure time models to control for patient characteristics. Health-related quality-of-life indicators are derived from relevant literature. RESULTS: Based on the used data, adding pertuzumab does not result in statistically significantly longer survival while inducing higher treatment costs (€163 360 compared with €90 112 per patient in 2018 prices). Statistically longer survival was not supported by the log-rank test (P = .97), the Cox proportional hazard model, or the accelerated failure time model using the Gompertz distribution. The incremental cost-effectiveness ratio (€87 200) substantially exceeds the willingness to pay for 1 quality-adjusted life-year (€46 500). CONCLUSIONS: This analysis indicates that adding pertuzumab cannot be considered cost-effective in Czechia. However, the observed phenomenon may be attributed to the limited duration of patient follow-up periods at the time of the study's execution (mean of 20-21 months). Importantly, we find that using states connected to specific treatment phases is appropriate for a retrospective analysis of patient-level clinical data.

Batesian mimicry or general food deception? An evolutionary game between plants for pollinator services.

Floral food deception is a well-known phenomenon which is not thoroughly understood. Particularly, it is unclear what drives a plant towards Batesian mimicry or towards generalized food deception. We analysed the evolutionary game between a Model species with nectar-secreting flowers and a Deceiver species that provides no nectar who share pollinators for reproduction. We focused our analysis on the effect of similarity of floral signals between participating plants and on costs of nectar production. We defined payoffs in the game between Models and Deceivers as the stationary visitation frequencies to participating species with different signal similarities and nectar costs. Therefore, fitness payoff of each strategy was a product of complex interactions between plant species composing the community and the pollinators visiting them. Our model provides a unified framework in which consequences of Model species interaction with different deception modes can be compared. Our findings suggest that plant-pollinator systems, like other mutualistic systems, are prone to exploitation, and that exploitation may persist at a large range of conditions. We showed that floral similarity, and thus, pollinators' ability to discriminate between Model and deceptive species, governs the stability of Batesian mimicry, while pollinator switching and sampling behaviour enables the persistence of general food deception.

Statistical Learning of Chord-Transition Regularities in a Novel Equitempered Scale: An MMN Study.

In music and language domains, it has been suggested that patterned transitions of sounds can be acquired implicitly through statistical learning. Previous studies have investigated the statistical learning of auditory regularities by recording early neural responses to a sequence of tones presented at high or low transition probabilities. However, it remains unclear whether the statistical learning of musical chord transitions is reflected in endogenous, regularity-dependent components of the event-related potential (ERP). The present study aimed to record the mismatch negativity (MMN) elicited by chord transitions that deviated from newly learned transitional regularities. Chords were generated in a novel 18 equal temperament pitch class scale to avoid interference from the existing tonal representations of the 12 equal temperament pitch class system. Thirty-six adults without professional musical training listened to a sequence of randomly inverted chords in which certain chords were presented with high (standard) or low (deviant) transition probabilities. An irrelevant timbre change detection task was assigned to make them attend to the sequence during the ERP recording. After that, a familiarity test was administered in which the participants were asked to choose the more familiar chord sequence out of two successive sequences. The results showed that deviant transitions elicited the MMN, although the participants could not recognize the standard transition beyond the level of chance. These findings suggest that humans can statistically learn new transitional regularities of chords in a novel musical scale, even though they did not recognize them explicitly. This study provides further evidence that music-syntactic regularities can be acquired implicitly through statistical learning.

Study design for restricted mean time analysis of recurrent events and death.

The restricted mean time in favor (RMT-IF) of treatment has just been added to the analytic toolbox for composite endpoints of recurrent events and death. To help practitioners design new trials based on this method, we develop tools to calculate the sample size and power. Specifically, we formulate the outcomes as a multistate Markov process with a sequence of transient states for recurrent events and an absorbing state for death. The transition intensities, in this case the instantaneous risks of another nonfatal event or death, are assumed to be time-homogeneous but nonetheless allowed to depend on the number of past events. Using the properties of Coxian distributions, we derive the RMT-IF effect size under the alternative hypothesis as a function of the treatment-to-control intensity ratios along with the baseline intensities, the latter of which can be easily estimated from historical data. We also reduce the variance of the nonparametric RMT-IF estimator to calculable terms under a standard set-up for censoring. Simulation studies show that the resulting formulas provide accurate approximation to the sample size and power in realistic settings. For illustration, a past cardiovascular trial with recurrent-hospitalization and mortality outcomes is analyzed to generate the parameters needed to design a future trial. The procedures are incorporated into the rmt package along with the original methodology on the Comprehensive R Archive Network (CRAN).