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Trying to better define Bipolar Disorder (BD) progression, different staging models have been conceptualized, each one emphasizing different aspects of illness. In a previous article we retrospectively applied the main staging models to a sample of 100 bipolar patients at four time points over a ten-year observation. In the present study, focusing on Kupka & Hillegers's model, we aimed to assess the transition of the same sample through the different stages of illness and to explore the potential role of clinical variables on the risk of progression. Multistate Model using the mstate package in R and Markov model with stratified hazards were used for statistical analysis. A high hazard of transition from stage 2 to 3 emerged, with a probability of staying in stage 2 decreasing to 14 % after 3 years. BD II was significantly associated with transition from stage 1 to 2, whereas the number of lifetime episodes >3 and the elevated predominant polarity with transition from stage 3 to 4. Our results corroborated the evidence on BD progression and contributed to outline its trajectory over time. Further effort may help to define a standardized staging approach towards ever increasing tailored interventions. • We retrospectively applied Kupka & Hillegers's model to a sample of 100 bipolar patients at 4 time-points over 10 years • We assessed the transition across stages and the role of clinical variables on the risk of progression • A high hazard of transition 2→3 emerged, with a probability of staying in stage 2 decreasing to 14% after 3 years • BD II was associated with transition 1→2; > 3 lifetime episodes and elevated predominant polarity with transition 3→4 [ABSTRACT FROM AUTHOR]

Population-based estimates of excess length of stay after hospital-acquired bacteraemia (HAB) are few and prone to time-dependent bias. We investigated the excess length of stay and readmission after HAB. This population-based cohort study included the North Denmark Region adult population hospitalized for ≥48 hours, from 2006 to 2018. Using a multi-state model with 45 days of follow-up, we estimated adjusted hazard ratios (aHRs) for end of stay and discharge alive. The excess length of stay was defined as the difference in residual length of stay between infected and uninfected patients, estimated using a non-parametric approach with HAB as time-dependent exposure. Confounder effects were estimated using pseudo-value regression. Readmission after HAB was investigated using the Cox regression. We identified 3457 episodes of HAB in 484 291 admissions in 205 962 unique patients. Following HAB, excess length of stay was 6.6 days (95% CI, 6.2–7.1 days) compared with patients at risk. HAB was associated with decreased probability of end of hospital stay (aHR, 0.60; 95% CI, 0.57–0.62) driven by the decreased hazard for discharge alive; the aHRs ranged from 0.30 (95% CI, 0.23–0.40) for bacteraemia stemming from 'heart and vascular' source to 0.72 (95% CI, 0.69–0.82) for the 'urinary tract'. Despite increased post-discharge mortality (aHR, 2.76; 95% CI, 2.38–3.21), HAB was associated with readmission (aHR, 1.42; 95% CI, 1.31–1.53). HAB was associated with considerably excess length of hospital stay compared with hospitalized patients without bacteraemia. Among patients discharged alive, HAB was associated with increased readmission rates. [ABSTRACT FROM AUTHOR]

• Individuals with higher reserve showed a reduced risk of cognitive decline. • Reserves defined by the residual method were strong predictors of cognitive change. • The estimated stage duration from preclinical to mild AD dementia varied by reserve. The relationship of cognitive reserve and measures of reserve with longitudinal cognitive change and the duration of preclinical, prodromal, and mild Alzheimer disease (AD) dementia remains to be fully characterized. In our study, 660 β-amyloid-positive participants staged with preclinical AD, prodromal AD, and dementia due to AD from the Alzheimer's Disease Neuroimaging Initiative were selected. Cognitive reserve and brain reserve were defined by conventional proxies or the residual method at baseline. We evaluated the utility of these reserves in predicting longitudinal cognitive change by mixed effects models and used a multi-state model to estimate stage duration stratified by reserve groups. Corrected for age, sex, and APOE-ε4 status, reserve was associated with cognitive decline, and the effects changed depending on the specific measures of reserve and the stage of AD. Reserves defined by the residual method were stronger predictors of cognitive change than those defined by conventional proxies. The estimated time from preclinical to mild AD dementia varied from 15–24 years based on the different reserve groups, and we observed a linear trend for the longest duration in individuals with high cognitive reserve/high brain reserve, followed by those with high cognitive reserve/low brain reserve, low cognitive reserve/high brain reserve, and low cognitive reserve/low brain reserve. This study showed a reduced risk of cognitive decline for individuals with higher level of reserve regardless of methods for measuring reserve. Interindividual differences in reserve may be important for clinical practice and trial design. [ABSTRACT FROM AUTHOR]

• Development of generalized probabilistic model for wind speed intermittency by utilizing real-time acquired cloud-based IoT wind speed data for wind energy system in a SA paradigm. • Simplified reliability analysis through multi-state modeling and probability distribution function for wind-integrated systems, departing from conventional convolution-based approaches. • Forecasting of wind speed and wind power generation for two days in advance through a time-series non-linear autoregressive model with exogenous inputs (NARX), and estimation of the operational reliability using the forecasted wind speed and wind power data. The extensive implementation of wind power generation (WPG) challenges power systems' flexibility and reliability during operations. Fluctuating wind speeds lead to variability in generation within wind energy systems, directly impacting operational reliability. Therefore, evaluating wind energy systems' situational awareness (SA) is imperative to forecast two-day-ahead operational reliability, facilitating effective power system planning. The foundation of SA for wind energy systems lies in monitoring the wind profile. This involves gaining insights into the system's state through perceiving wind speed and WPG, comprehending the dynamics of its state, and subsequently predicting operational reliability. To achieve this, a Simulink model of the wind system has been developed and integrated with dSPACE hardware through a real-time interface for real-time validation and implementation of a cloud-based IoT platform. This IoT platform aids in acquiring real-time data on wind profiles, enhancing the assessment of SA. The representation of the wind energy system utilizes a multi-state wind speed model, incorporating the inherent randomness of wind energy generation. A time-series-based non-linear autoregressive with exogenous input (NARX) artificial neural network (ANN) model is employed to predict wind speed and WPG for SA-based operational reliability. This model proves instrumental in forecasting the dynamic nature of wind speed and WPG. The proposed approach for operational planning is validated by implementing the wind energy system in real-time on the dSPACE platform and integrating it with IoT. This approach considers the uncertainties associated with wind generation, providing a comprehensive framework for assessing SA and optimizing power system operations. [ABSTRACT FROM AUTHOR]

• Advanced situational awareness for operational reliability for wind energy systems (WES). • Comprehensive uncertainty analysis of wind speed and the development of a multi-state model for accurately capturing the variability of wind generator power output. • Time-series forecasting of wind speed and power generation for two days ahead employing a non-linear autoregressive with exogenous input ANN (NARX-ANN) model to project operational reliability. • Implementation of an IoT-based real-time monitoring framework to enhance the projection and management of WES operational reliability. Wind power fluctuations impact power system flexibility and reliability, emphasizing situational awareness (SA) in wind power integration. Monitoring wind speed and power generation forms the core of SA, enabling insight into wind system conditions. An Integrated Development Environment (IDE) becomes pivotal in assessing the SA of wind-integrated systems, which is vital for forecasting operational reliability in power system planning. The IDE facilitates Simulink-based wind model development and is linked to dSPACE hardware through a real-time interface (RTI) for validation. This IDE-driven integration utilizes cloud-based Internet of Things (IoT) capabilities, acquiring wind speed and power generation data for precise SA evaluations. A multi-state wind speed model is employed to incorporate energy generation fluctuations. To improve this, the artificial neural network (ANN) model, i.e., a time series-based non-linear autoregressive with exogenous input (NARX) ANN, is utilized to predict future wind speed and power generation, strengthening SA-based operational reliability. The study investigates the effectiveness of a suggested operational planning strategy under wind generation uncertainties. This assessment is carried out within a real-time deployment of wind energy systems seamlessly integrated with IoT on the dSPACE platform. The primary objective is to evaluate the system's ability to maintain SA effectively. [ABSTRACT FROM AUTHOR]

A joint model framework for estimating population sizes over time and survival probabilities while considering inbreeding and age as covariates in the survival function is elaborated. This methods is tested with data simulated over two small (N ≈ 150) close to extinction open populations, that aims to imitate wild individuals under decline and bottleneck dynamics. A Hidden Markov Model (HMM) perspective with a multi-state formulation that considers young and adult individuals is applied with data augmentation to account for non-seen individuals. The transition probabilities are estimated, and different treatments of the covariates and levels of data seen are compared. Our results suggest that the model framework correctly estimates different population size trends but the parameter estimation is challenging in some cases. The proposed model presents a new way to use the inbreeding coefficient, and further implementations on a real conservation data of wild species can help the decision-making process for the management of small populations. • Simulation of a small population where age and inbreeding affect survival and reproduction. • Estimation of a population size via data augmentation under different levels of sampling. • Use of inbreeding and age as covariates to model the survival probability at an individual level. • Developing and comparing different scenarios and assumptions. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common cancer and the leading cause of cancer-related death among women. However, evidence concerning hematological and biochemical markers influencing the natural history of breast cancer from in situ breast cancer to mortality is limited. In the UK Biobank cohort, 260,079 women were enrolled during 2006–2010 and were followed up until 2019 to test the 59 hematological and biochemical markers associated with breast cancer risk and mortality. The strengths of these associations were evaluated using the multivariable Cox regression models. To understand the natural history of breast cancer, multi-state survival models were further applied to examine the effects of biomarkers on transitions between different states of breast cancer. Eleven biomarkers were found to be significantly associated with the risk of invasive breast cancer, including mainly inflammatory-related biomarkers and endogenous hormones, while serum testosterone was also associated with the risk of in-situ breast cancer. Among them, C-reactive protein (CRP) was more likely to be associated with invasive breast cancer and its transition to death from breast cancer (HR for the highest quartile = 1.46, 95 % CI = 1.07–1.97), while testosterone and insulin-like growth factor-1 (IGF-1) were more likely to impact the early state of breast cancer development (Testosterone: HR for the highest quartile = 1.31, 95 % CI = 1.12–1.53; IGF-1: HR for the highest quartile = 1.17, 95 % CI = 1.00–1.38). Serum CRP, testosterone, and IGF-1 have different impacts on the transitions of different breast cancer states, confirming the role of chronic inflammation and endogenous hormones in breast cancer progression. This study further highlights the need of closer surveillance for these biomarkers during the breast cancer development course. • Higher serum CRP, testosterone, and IGF-1 levels were associated with both the elevated breast cancer risk and mortality. • Serum CRP was more likely to be associated with invasive breast cancer and its transition to death from breast cancer. • Serum testosterone and IGF-1 were more likely to impact the early state of breast cancer development. [ABSTRACT FROM AUTHOR]

Whether risk-reducing salpingo-oophorectomy (RRSO) in BRCA1/2 carriers reduces the breast cancer (BC) risk is conflicting, potentially due to methodological issues of prior analysis. We analysed the association between premenopausal RRSO and BC risk in BRCA1/2 carriers after adjusting for potential biases. We analysed data from 444 BRCA1 and 409 BRCA2 carriers under age 51 with no cancer prior to genetic testing or during first 6 months of surveillance (to avoid cancer-induced testing bias and prevalent-cancer bias). Observation started 6 months after genetic testing (to avoid event-free time bias), until BC diagnosis, risk-reducing mastectomy (RRM) or death. A multistate model with four states (non-RRSO, RRSO, RRM and BC) and five transitions was fitted to characterise outcomes and to calculate the BC risk reduction after premenopausal RRSO (before age 51). A systematic review was performed to assess the association between premenopausal RRSO and BC. During a mean follow-up of 4.3 years, 96 women (11.3%) developed BC (54 BRCA1 , 42 BRCA2). The risk of BC after premenopausal RRSO decreased significantly in BRCA1 carriers (hazard ratio (HR) = 0.45 [95% confidence interval (CI):0.22–0.92]), but was not conclusive in BRCA2 carriers (HR = 0.77 [95%CI:0.35–1.67]). The systematic review suggested that premenopausal RRSO is associated with a decrease of BC risk in both BRCA1 and BRCA2 carriers. Premenopausal RRSO was associated with BC risk reduction in BRCA1 carriers, which can help guide cancer risk-reducing strategies in this population. Longer follow-up and larger sample size may be needed to estimate the potential benefit in BRCA2 carriers. • Germline BRCA1/2 carriers have an increased risk of breast and ovarian cancer. • Bilateral salpingo-oophorectomy reduces ovarian cancer risk in BRCA1/2 carriers. • Whether bilateral salpingo-oophorectomy reduces breast cancer risk is controversial. [ABSTRACT FROM AUTHOR]

Multi-state processes (Webster, 2019) are commonly used to model the complex clinical evolution of diseases where patients progress through different states. In recent years, machine learning and deep learning algorithms have been proposed to improve the accuracy of these models' predictions (Wang et al., 2019). However, acceptability by patients and clinicians, as well as for regulatory compliance, require interpretability of these algorithms's predictions. Existing methods, such as the Permutation Feature Importance algorithm, have been adapted for interpreting predictions in black-box models for 2-state processes (corresponding to survival analysis). For generalizing these methods to multi-state models, we introduce a novel model-agnostic interpretability algorithm called Multi-State Counterfactual Perturbation Feature Importance (MS-CPFI) that computes feature importance scores for each transition of a general multi-state model, including survival, competing-risks, and illness-death models. MS-CPFI uses a new counterfactual perturbation method that allows interpreting feature effects while capturing the non-linear effects and potentially capturing time-dependent effects. Experimental results on simulations show that MS-CPFI increases model interpretability in the case of non-linear effects. Additionally, results on a real-world dataset for patients with breast cancer confirm that MS-CPFI can detect clinically important features and provide information on the disease progression by displaying features that are protective factors versus features that are risk factors for each stage of the disease. Overall, MS-CPFI is a promising model-agnostic interpretability algorithm for multi-state models, which can improve the interpretability of machine learning and deep learning algorithms in healthcare. • Multi-state processes can model the complex evolution of diseases. • Deep learning algorithms improve the accuracy of these models' predictions. • Acceptability for regulatory compliance requires interpretability of these algorithms. • Permutation Feature Importance algorithm must be extended for this purpose. • Our new model-agnostic method detect clinically important features of disease progression. [ABSTRACT FROM AUTHOR]

Highlights • Nodal market power assessment method of FDRs is proposed. • Normal and contingency system states are considered in the assessment model. • A set of indices including NMUQ, NMCQ, ENMUQ, and ENMCQ are invented. • Key FDR nodes are identified and specific regulations are implemented. Abstract With the incorporation of higher shares of intermittent renewable energies (RES), more flexible resources are required in power systems to keep load balance. Under some extreme circumstances, the flexible demand resources (FDRs) may have the potential to dominate and obtain excess benefits, preventing other FDRs from participating in the electricity markets. Therefore, it is of great significance to identify the key FDR market power locations and implement some corresponding regulations. However, the relevant researches in power systems focused on the supply side, rather than the demand side. In this paper, a novel nodal market power analysis method is proposed to evaluate the potential influence of FDRs on electricity markets. Firstly, a multi-state model is established to present the multiple power system operation states including the random failures of system components. Then, the nodal market power assessment model is established under each specific state and new indices are proposed to evaluate the nodal market power of FDRs quantitatively. Furthermore, the key FDR nodes in demand side with stronger power in capturing excess revenue are identified. The 24-bus IEEE Reliability Test System is modified to demonstrate the feasibility of the proposed method. The numerical results of the proposed method are capable to display the existence of market power in demand side, and provide some valuable guidance for classification and operation of electricity markets. [ABSTRACT FROM AUTHOR]

Highlights • Time-varying variables are modeled in terms of amplitude and profile. • Probabilistic adaption of initial plans to multiple states is considered. • The MINLP-based DSEP is solved by an integrated CE and DE algorithm. • A three-hierarchy parallel platform reduces CPU time of DSEP. • Flexibility of DSEP is enhanced and extra adaption costs are reduced. Abstract With the development of smart grid and electricity market, the uncertainty for power flow is greatly aggravated, and thus leads to a great challenge on the traditional expansion methods for distribution systems to satisfy the future demands. In this paper, a data-driven multi-state distribution system expansion planning (DSEP) model is explored. Innovatively, amplitude values and profiles of uncertain factors in distribution systems are considered separately. Based on the massive historical measurement data, kernel density estimation and adaptive clustering are utilized to aggregate the typical amplitudes and profiles of time-varying variables respectively without prior knowledge. Consolidating all the uncertain factors, a multi-state model is established which extends DSEP into the deterministic initial planning and the probabilistic re-planning. The minimization of the overall planning cost is considered as the objective, which takes the initial planning costs and the expected costs of the initial plans being adapted to other future states into account. In this way, the flexibility of DSEP can be greatly enhanced and extra investments caused by frequent adjustments of plans are reduced. To avoid the rapid growth of CPU time due to multi-state model utilization, an integrated differential evolution and cross entropy algorithm implemented on a three-hierarchy parallel platform is proposed. The feasibilities of the proposed data-driven multi-state DSEP model and the parallel integrated solution method are demonstrated by numerical studies on a realistic 61-bus distribution system. [ABSTRACT FROM AUTHOR]

Pollution contributes to the degraded state of continental aquatic ecosystems and biodiversity. Some species appear to be tolerant to aquatic pollution, yet little is known about the effects of such pollution on population structure and dynamics. Here, we investigated how wastewater treatment plant (WWTP) effluents of the Cabestany City, in southern France, contribute to the pollution levels of the Fosseille River, and we tested how they could affect population structure and medium-term dynamics of the native freshwater turtle, the Mediterranean Pond Turtle Mauremys leprosa (Schweigger, 1812). Amongst the 68 pesticides surveyed from water samples collected along the river in 2018 and 2021, a total of 16 pesticides were detected, among which eight were found in the upstream section of the river, 15 in the river section located downstream of the WWTP, and 14 in the outfall of the WWTP, exhibiting the contribution of effluents to the river pollution. From 2013 to 2018 and in 2021, capture-mark-recapture protocols were carried out on the freshwater turtle population living in the river. Using robust design and multi-state models, we showed a stable population throughout the study period, with high year-dependent seniority, and a bidirectional transition occurring primarily from the upstream to the downstream river sections of the WWTP. The freshwater turtle population consisted mostly of adults, with a male biased sex ratio detected downstream of the WWTP neither related to sex-dependent survival, recruitment, nor transition, suggesting a male bias in the hatchlings or primary sex ratio. Also, the largest immatures and females were captured downstream of the WWTP, with females having the highest body condition, whereas no such differences were observed in males. This study highlights that population functioning of M. leprosa is driven primarily by effluents induced resources, at least over the medium-term. [Display omitted] • Monitoring of the Mediterranean Pond Turtle between 2013 and 2021 in a polluted river. • Investigation of population dynamics using robust design and multi-state models. • Detection of a male biased adult sex ratio downstream, but not upstream, of a wastewater treatment plant (WWTP). • Detection of bi-directional transitions, mainly directed towards downstream of the WWTP. • Positive covariation of immatures and adult females' body size with habitat pollution level. [ABSTRACT FROM AUTHOR]

Apolipoprotein A-1 (Apo A-1) plays an important role in lipid transfer and obesity. Chemical unfolding of α-helical Apo A-1 is induced with guanidineHCl and monitored with differential scanning calorimetry (DSC) and CD spectroscopy. The unfolding enthalpy and the midpoint temperature of unfolding decrease linearly with increasing guanidineHCl concentration, caused by the weak binding of denaturant. At room temperature, binding of 50–60 molecules guanidineHCl leads to a complete Apo A-1 unfolding. The entropy of unfolding decreases to a lesser extent than the unfolding enthalpy. Apo A-1 chemical unfolding is a dynamic multi-state equilibrium that is analysed with the Zimm-Bragg theory modified for chemical unfolding. The chemical Zimm-Bragg theory predicts the denaturant binding constant K D and the protein cooperativity σ. Chemical unfolding of Apo A-1 is two orders of magnitude less cooperative than thermal unfolding. The free energy of thermal unfolding is ~0.2 kcal/mol per amino acid residue and ~1.0 kcal/mol for chemical unfolding at room temperature. The Zimm-Bragg theory calculates conformational probabilities and the chemical Zimm-Bragg theory predicts stretches of α-helical segments in dynamic equilibrium, unfolding and refolding independently and fast. [ABSTRACT FROM AUTHOR]

This paper proposes an improved model to perform probabilistic forecasting on PV power output. The uncertainty of weather variability and component availability are modeled separately and then integrated by multiplying the power discount caused by the component failure to the PV daily output curve using clustering method. Based on the PV model, the non-sequential Monte Carlo method is applied to assess the risk of the system, and the DC optimal load model is utilized to calculate the load shedding. Case studies on an IEEE-30 test system validate the rationality of the models and methods. [ABSTRACT FROM AUTHOR]

Multi-state physics systems (MSPS) modeling framework incorporates multi-state models that describes the systems degradation/maintenance process through transitions among discrete states, and physics-based models that describe the degradation process within the states by using physics knowledge and equations. In previous works, piecewise-deterministic Markov process (PDMP) has been adopted to treat the system dynamics and the degradation dependence in MSPS. For reliability assessment, Monte Carlo simulation and finite-volume method are two widely used numerical approaches to solve PDMP. In the present work, a comparative study considering different evaluation criteria of the two approaches is conducted on two representative case studies. We provide clear guidelines for the selection of the two approaches. [ABSTRACT FROM AUTHOR]

Background and purpose To evaluate the quality of life (QoL) of patients with inoperable non-small cell lung cancer treated with conventionally fractionated radiotherapy (CF) vs. continuous hyperfractionated accelerated radiotherapy weekend-less (CHARTWEL). Material and methods The largest monocentric subgroup of the phase III CHARTWEL trial was analyzed up to three years after randomization. QoL was assessed with the European Organization for Research and Treatment of Cancer QoL Core Questionnaire (QLQ-C30) and lung cancer module (QLQ-LC13) and compared using linear mixed models. QoL interrelations with recurrence, metastasis, and death were explored by multi-state modeling. Results 160 patients (98%) provided at least one QoL assessment. Average treatment differences of CF vs. CHARTWEL over three years were −5.4 points (95%CI [−13.6,2.8], p  = 0.19) in global QoL, 11.9 ([2.8,21.0], p  = 0.01) in fatigue, 13.4 ([3.5,23.3], p  = 0.009) in pain, 10.5 ([1.3,19.6], p  = 0.03) in dyspnea, and 5.2 ([−2.7,13.0], p  = 0.19) in dysphagia. At 12 months, the probabilities of being disease-free with good, good or moderate, any global QoL, or alive were 5.1%, 20.3%, 34.2%, 54.4% under CF and 10.4%, 21.0%, 37.5%, 65.3% under CHARTWEL. Conclusions Over three years, QoL was similar or more favorable under CHARTWEL compared to CF. Modeling QoL together with disease states provided additional insight into treatment comparisons. [ABSTRACT FROM AUTHOR]

Animals which spend subsequent seasons in different areas connect geographical regions. The connection between breeding and non-breeding grounds is defined as migratory connectivity. The quantification of such connectivity is important, because movements between different locations can have strong consequences for the moving animal as well as the encountered habitats or ecosystems. Connectivity is usually investigated either on the basis of (few unsystematic) re-encounters of (often large numbers of) marked individuals or by observations of a few individuals tracked by remote sensing techniques, i.e. GPS or geolocation. The combination of qualitatively different data sets can reduce the limitations of each type of data and thus improve the accuracy of the estimated connectivity parameters considerably. We formally combine individual tracking data and mark re-encounter data in a probabilistic model framework for quantifying connectivity. In a first example, we quantify migratory connectivity of a long-distance passerine migrant based on ring re-encounter and geolocator data. As a second example, we combine re-encounter data of ear-tagged wild boars with GPS tracking data to estimate the spatial distribution of wild boars during the hunting and the non-hunting seasons. These two examples illustrate the use of the model in two different framework: 1) long-distance migration and, 2) seasonal (e.g. hunting induced) non-migratory movements. Results from the integrated analyses provided more information than the informal comparison of the results from independent analyses on each data set separately. Parameter estimates were more precise in the integrated analyses compared to the separate analyses and stronger conclusions could be drawn. The integration of mark re-encounter and tracking data reduces sampling bias and increases the value of both data sets but the weighting of each data set needs further investigation. [ABSTRACT FROM AUTHOR]

In this paper, we present a methodological work that adopts a system-of-systems (SoS) viewpoint for the evaluation of the robustness of interdependent critical infrastructures (CIs). We propose a Hierarchical Graph representation, where the product flow is dispatched to the demand nodes in consideration of different priorities. We use a multi-state model to describe different degrees of degradation of the individual components, where the transitions between the different states of degradation occur stochastically. The quantitative evaluation of the CIs robustness is performed by Monte Carlo simulation. The methodological approach proposed is illustrated by way of two case studies: the first one concerns small-sized gas and electricity networks and a supervisory control and data acquisition (SCADA) system; the second one considers a moderately large power distribution network, adapted from the IEEE 123 node test feeders. The large size of the second case study requires hierarchical clustering for performing the robustness analysis. [ABSTRACT FROM AUTHOR]

Highlights: [•] We developed an agent-based model to simulate tourism effects on dolphin behavior. [•] The model generated a realistic representation of dolphin and boat dynamics. [•] This predictive tool could be used to support effective management. [•] We investigated model parameterization by fitting the dolphin submodel to data. [•] Traditional visual data were not sufficient to inform such agent-based models. [Copyright &y& Elsevier]

Summary: Objective: The incidence of joint replacements is considered an indicator of symptomatic end-stage osteoarthritis (OA). We analysed data from two national joint replacement registries in order to investigate whether evidence of a pattern of progression of end-stage hip and knee OA could be found in data from large unselected populations. Design: We obtained data on 78,634 hip and 122,096 knee arthroplasties from the Australian Orthopaedic Association National Joint Replacement Registry and 19,786 hip and 12,082 knee arthroplasties from the Norwegian Arthroplasty Register. A multi-state model was developed where individuals were followed from their first recorded hip or knee arthroplasty for OA to receiving subsequent hip and/or knee arthroplasties. We used this model to estimate relative hazard rates and probabilities for each registry separately. Results: The hazard rates of receiving subsequent arthroplasties in non-cognate joints were higher on the contralateral side than on the ipsilateral side to the index arthroplasty, especially if the index was a hip arthroplasty. After 5 years, the estimated probabilities of having received a knee contralateral to the index hip were more than 1.7 times the probabilities of having received a knee ipsilateral to the index hip. Conclusion: The results indicate that there is an association between the side of the first hip arthroplasty and side of subsequent knee arthroplasties. Further studies are needed to investigate whether increased risk of receiving an arthroplasty in the contralateral knee is related to having a hip arthroplasty and/or preoperative factors such as pain and altered gait associated with hip OA. [Copyright &y& Elsevier]

Abstract: With the increasing burden of chronic diseases on the health care system, Markov-type models are becoming popular to predict the long-term outcomes of early intervention and to guide disease management. However, statisticians have not been actively involved in the development of these models. Typically, the models are developed by using secondary data analysis to find a single “best” study to estimate each transition in the model. However, due to the nature of secondary data analysis, there frequently are discrepancies between the theoretical model and the design of the studies being used. This paper illustrates a likelihood approach to correctly model the design of clinical studies under the conditions where (1) the theoretical model may include an instantaneous state of distinct interest to the researchers and (2) the study design may be such that study data cannot be used to estimate a single parameter in the theoretical model of interest. For example, a study may ignore intermediary stages of disease. Using our approach, not only can we accommodate the two conditions above, but more than one study may be used to estimate model parameters. In the spirit of “If life gives you lemon, make lemonade”, we call this method “Lemonade Method”. Simulation studies are carried out to evaluate the finite sample property of this method. In addition, the method is demonstrated through application to a model of heart disease in diabetes. [Copyright &y& Elsevier]

Abstract: Many seabird species are experiencing population declines, with key factors being high adult mortality caused by fishery by-catch and predation by introduced predators on nesting islands. In the Mediterranean, both of these pressures are intensive and widespread. We studied the adult survival of an endemic Mediterranean seabird, the Yelkouan shearwater (Puffinus yelkouan), between 1969–1994 and 2007–2010 in Malta and between 2004–2010 in France using mark–recapture methods. Mean annual survival probabilities for breeding adults were below 0.9 for all colonies and periods. Between 1969–1994, annual survival for adults of unknown breeding status was on average 0.74 (95% confidence interval: 0.69–0.80) in Malta, possibly as a result of various human disturbances (including illegal shooting), light pollution and fisheries by-catch. Over the period 2004–2010, we found strong support for variation in adult survival probabilities between breeders and non-breeders, and islands with and without introduced predators in France. Survival probabilities for non-breeders (0.95, 0.81–1.0) appeared to be higher than for breeders (0.82, 0.70–0.94), but were imprecise partly due to low recapture probabilities. In Malta, we found evidence for heterogeneity in survival probabilities between two unknown groups (probably breeders and non-breeders), and seasonal variation in survival probability. Birds were more likely to survive the period including the peak breeding season than an equally long period during which they roam widely at sea. Although annual adult survival probability was still low (0.85, 0.58–1.0), colony protection measures appear to have reduced mortality at nesting cliffs. A population model indicated that colonies in France and Malta would currently require continuous immigration of 5–12 pairs per year to maintain stable populations. Our estimates of adult survival probabilities over the past four decades are consistent with overall population declines. Threats to Yelkouan shearwaters require immediate management actions to avoid ongoing population declines in the western Mediterranean. [Copyright &y& Elsevier]

The aim of this paper is to present an R library, called tdc. Msm, developed to analyze multi-state survival data. In this library, the time-dependent regression model and multi-state models are included as two possible approaches for such data. For the multi-state modelling five different models are considered, allowing the user to choose between Markov and semi-Markov property, as well as to use homogeneous or non-homogeneous models. Specifically, the following multi-state models in continuous time were implemented: Cox Markov model; Cox semi-Markov model; homogeneous Markov model; non-homogeneous piecewise model and non-parametric Markov model. This software can be used to fit multi-state models with one initial state (e.g., illness diagnosis), a finite number of intermediate states, representing, for example, a change of treatment, and one absorbing state corresponding to a terminal event of interest. Graphical output includes survival estimates, transition probabilities estimates and smooth log hazard for continuous covariates. [Copyright &y& Elsevier]

Abstract: In studies of chronic disease the outcome measure may be based upon the duration of recurring illness. Our example is the UK700 trial of psychiatric case management, where the total number of days spent in hospital over a 2-year follow-up was the primary outcome. Investigations of treatment effect modifiers were undertaken using an analysis of that primary outcome, a comparison of length of hospitalizations, and also a multi-state modeling approach. The days in hospital outcome was relatively straightforward to analyze, and allowed the complete randomized treatment groups to be compared. In contrast, the comparison of length of hospitalizations included only hospitalized patients, with censored observations not being well accommodated. The multi-state model provided separate treatment effect estimates for admission and discharge, this being more informative about how any reduction in days spent in hospital is achieved. Estimation of the treatment effects through the use of proportional hazards regression allowed appropriate incorporation of censored observations. However, with the multi-state model approach treatment effect estimates are not based upon comparisons of complete randomized treatment groups, as individuals are removed from the risk set for admission whilst at risk for discharge, and vice versa. We conclude that total duration is an appropriate primary outcome for clinical trials, but that multi-state models deserve greater use as an informative secondary analysis. [Copyright &y& Elsevier]

Writing a computer program for modeling multi-state disease process for cancer or chronic disease is often an arduous and time-consuming task. We have developed a SAS macro program for estimating the transition parameters in such models using SAS IML. The program is very flexible and enables the user to specify homogeneous and non-homogeneous (i.e. Weibull distribution, log–logistic, etc.) Markov models, incorporate covariates using the proportional hazards form, derive transition probabilities, formulate the likelihood function, and calculate the maximum likelihood estimate (MLE) and 95% confidence interval within a SAS subroutine. The program was successfully applied to an example of a three-state disease model for the progression of colorectal cancer from normal (disease free), to adenoma (pre-invasive disease), and finally to invasive carcinoma, with or without adjusting for covariates. This macro program can be generalized to other k-state models with s covariates. [Copyright &y& Elsevier]

• The proposed multi-state model prevents the underestimation of reliability level caused by two-state failure model. • The proposed model can accurately evaluate the reliability index, which better reflects the multiple operation states. • Different TES strategies will lead to different operation decisions and reliability results of CSP plants. • The SF size and the TES capacity are positively correlated with the system reliability level. But the system reliability index tends to be stable when the SF size and the TES capacity are large. This paper proposes a multi-state reliability evaluation model for the concentrated solar power (CSP) plant considering partial function failure. Firstly, the component functions and components' consequences of random failure of CSP plants are analyzed. Core components affecting the output of CSP plants are selected. The CSP plant is divided into three subsystems: the heat collection, the heat exchange, and the power generation subsystems, according to the component function. Each subsystem is equivalent to a two-state component. Thus, an 8-state Markov model of CSP plant is established. The CSP plant can be operated in a partial function failure state as thermal energy storage (TES) or generation, depending on the functions of solar-heat and heat-electricity conversion. On this basis, the 8-state model can be simplified to a 4-state model by combining the partial function failure states of the same effect. Finally, the 4-state model is applied to the sequential Monte Carlo simulation for the reliability evaluation of power systems containing CSP plants. Two TES strategies are considered in the simulation: One is based on solar field (SF) average output, another is designed to satisfy the load preferentially. The proposed model is tested on the modified RBTS/IEEE RTS systems to verify the effectiveness. [ABSTRACT FROM AUTHOR]

• The concepts and features of multi-state systems considering state transition process (MSS-STP) are innovatively proposed. • The state space of the MSS-STP consists of a combination of discrete steady states and continuous transition curves. • The traditional reliability evaluation framework for conventional multi-state systems is extended. • The developed MSS-STP model is applied to natural gas system to demonstrate its effectiveness. Multi-state system (MSS) models have been widely used to represent the reliability of engineering systems that have a finite number of performance levels. The previous studies related to the reliability evaluation of the MSSs generally follow the assumption that the transition among different performance states is instantaneous with no time delay. However, there exist several engineering systems that transit slowly between different states, such as gas supply systems and heating distribution systems. When evaluating the operating reliability of such an engineering system, the neglect of the non-instant state transition process may lead to inaccurate or insufficient evaluation results. To fill the research gap, the traditional MSS model is extended to handle the situation where the non-instant state transition features should be considered. The concepts and features of multi-state systems considering state transition process (MSS-STP) are innovatively proposed. Considering the non-instant state transition process, the traditional reliability evaluation framework for the MSSs is extended to evaluate the operating reliability of MSS-STP. The procedure of transition curve discretization is innovatively proposed to divide the dynamic transition process into a series of discrete states. The developed general MSS-TSP model is applied to the natural gas system (NGS) to demonstrate the effectiveness of the proposed reliability evaluation framework. The proposed technique can provide a useful tool for system operators to accurately evaluate the operating reliability of MSS-STP [ABSTRACT FROM AUTHOR]

• We build a one-day driving pattern of electric ride-hailing vehicles (ERVs). • A multi-state model based method is designed for ERVs' pattern with charging state. • A new micro-trip-based driving cycle design methodology is proposed. • Two typical types of ERV drivers following different patterns are identified. • The representativeness of combined driving cycle and one-day pattern is confirmed. Developing electric ride-hailing vehicles (ERVs) is an integral part of promoting green transportation. To understand how ERVs operate, and in turn to improve their battery systems, we have developed a comprehensive, methodological framework to construct their utilization patterns and driving cycles. The methodology is based on a multi-state model (MSM). It builds one-day activity patterns of electric vehicles that belong in an ERV fleet. The driving patterns outline when (time of occurrence), for how long (duration), and under what battery conditions (state of charge) a driver typically decides to either operate or charge a vehicle. Two driving patterns, pointing toward two typical types of drivers, have been identified. By utilizing operational data in Xiamen, China, obtained from Didi Chuxing, we propose a novel method to build the first driving cycle series of ERVs, which can delineate how ERV drivers drive under the operating state. The cycle series consists of four driving cycles, synthesized by the micro-trip-based method using operational data from four time slices. Analysis against real data has shown that our method significantly outperforms existing methods, demonstrating the representativeness of the cycles. [ABSTRACT FROM AUTHOR]