Multi-station Joint Research Articles

Sex differences in cardiac troponin trajectories over the life course

Abstract Background Cardiac troponin is a promising biomarker that may improve cardiovascular risk prediction. Previously, important differences have been demonstrated between cardiac troponin concentration in women and men. At this point, it is unknown whether changes in cardiac troponin concentration over the life course differ in women and men and whether this impacts cardiovascular risk prediction. Purpose Insights into sex-specific cardiac troponin trajectories and how these are influenced by risk factors are necessary to ensure approaches to the prevention of cardiovascular disease in women and men are equitable. Therefore, we examined whether age- and risk factor-related changes in cardiac troponin over the life course differ by sex and if the trajectory of cardiac troponin was informative in respect of cardiovascular outcomes in women and men in the general population. Methods In the Whitehall II cohort, high-sensitivity cardiac troponin I concentrations were measured on three occasions over a 15-year period. Using linear mixed-effects models, the sex-specific trajectories of cardiac troponin were evaluated, and the relationship with conventional cardiovascular risk factors determined. Using multistate joint models, the association between sex-specific trajectories of cardiac troponin and a composite outcome of non-fatal myocardial infarction, non-fatal stroke, or cardiovascular death was evaluated. Results In 2,142 women and 5,151 men (mean 58±7 and 57±7 years, respectively) there were 177 (8.3%) and 520 (10.1%) outcome events, respectively, during a median follow-up of 20.9 [25th to 75th percentile, 15.8 to 21.3] years. Cardiac troponin concentrations were persistently lower in women than in men (median baseline concentration: 2.4 [25th to 75th percentile, 1.7 to 3.6] ng/L versus 3.7 [25th to 75th percentile, 2.6 to 5.8] ng/L, respectively, P<0.001, Figure 1A), with women exhibiting a relatively larger increase with advancing age as compared with men (Pinteraction<0.001, Figure 1B). Apart from age, a significant and divergent interaction with sex was found for the association between cardiac troponin and body mass index (Pinteraction=0.008) and diabetes (Pinteraction=0.003). During follow-up, cardiac troponin concentrations were associated to the outcome in both women and men (adjusted HR per two-fold difference [95% CI], 1.34 [1.17-1.52] and 1.30 [1.21-1.40], respectively, Pinteraction=0.752). The slope of cardiac troponin was significantly associated with the outcome in women, but not in men (adjusted HR [95% CI], 2.70 [1.01-7.33] and 1.31 [0.62-2.75], respectively, Pinteraction=0.250). Conclusion Trajectories of cardiac troponin differ between women and men in the general population, with differing associations to conventional risk factors and cardiovascular outcomes. Our findings highlight the importance of a sex-specific approach when serial cardiac troponin testing is applied for cardiovascular risk prediction.

Study on the variation characteristics of ionospheric F2 layer in Qinghai-Tibet plateau and surrounding areas

The Qinghai-Tibet Plateau (QTP) region is the largest plateau in China and the highest altitude in the world, with a unique geographical environment. Based on the observation data of the 24th solar cycle from six sites on the QTP and its surrounding areas, the multi-station joint detection method is adopted to analyze the temporal variation and spatial distribution characteristics of the critical frequency of the F2 layer(foF2) and the bottom height of the F2 layer (h'F2). The results indicate that the critical frequency of the ionospheric F layer in the QTP is controlled by solar illumination conditions, and conforms to the ionospheric variation law of middle and low latitudes. The height of the F layer has significant regional characteristics, and the F layer height variation in Kunming and Chongqing is particularly drastic, and the average height of the F layer in Chongqing is significantly higher than that in Kunming, which is at a lower latitude, and Lhasa, which is at the same latitude as Chongqing. This phenomenon may be attributed to the unique geographical environment of the QTP, which has an influential effect on the motion of the lower atmosphere, and through the coupling mechanism between the lower and upper atmosphere, has a substantial impact on the F2 layer. The research findings of this article are of great interest to the study of the coupling mechanism between the upper and lower atmosphere.

Li-ion battery charging strategy based on multi-state joint estimation model

This paper proposes a fast-charging strategy for lithium-ion batteries based on an intelligent optimization algorithm with multi-physics constraints. First, a thermoelectric coupling model and a battery degradation model have been established. Then an improved particle swarm optimization (PSO) is used to calculate the optimal charging strategy under different weighting factors. Finally, the Pareto boundary is drawn according to the optimization objective, and two charging strategies with different optimization objectives are developed. By comparing in different benchmark functions, the So-improved PSO proposed in this paper improves the convergence of the algorithm compared to the traditional PSO algorithm without increasing the computation time. The simulation results show that the multistage constant-current (MCC) charging strategy proposed in this paper can adjust the magnitude and order of charging current according to the change of weights, and reducing the loss of capacity and energy by only 0.017 % and 35.1 % while improving the charging speed by 24.9 % compared with the constant current-constant voltage (CCCV) charging strategy of 2C. Compared with the traditional CCCV charging strategy, the charging method proposed in this paper can have a broad application prospect.

State of Charge and Temperature Joint Estimation Based on Ultrasonic Reflection Waves for Lithium-Ion Battery Applications

Accurate estimation of the state of charge (SOC) and temperature of batteries is essential to ensure the safety of energy storage systems. However, it is very difficult to obtain multiple states of the battery with fewer sensors. In this paper, a joint estimation method for a lithium iron phosphate battery’s SOC and temperature based on ultrasonic reflection waves is proposed. A piezoelectric transducer is affixed to the surface of the battery for ultrasonic–electric transduction. Ultrasonic signals are excited at the transducer, transmitted through the battery, and transmitted back to the transducer by reaching the underside of the battery. Feature indicator extraction intervals of the battery state are determined by sliding–window matching correlation analysis. Virtual samples are used to expand the data after feature extraction. Finally, a backpropagation (BP) neural network model is applied to the multistate joint estimation of a battery in a wide temperature range. According to the experimental results, the root mean square error (RMSE) of the lithium-ion battery’s SOC and temperature estimation results is 7.42% and 0.40 °C, respectively. The method is nondestructive and easy to apply in battery management systems. Combined with the detection of gas production inside the battery, this method can improve the safety of the battery system.

Multistate joint estimation of ultracapacitor based on trans-scale dual extended Kalman filter

Accurate ultracapacitor state of charge (SOC) estimation directly affects safety, durability and reliability of hybrid energy storage system. For that, this paper presents a multistate joint estimation method of ultracapacitor based on trans-scale dual extended Kalman filter to predict SOC and model parameters in real-time. Firstly, an ultracapacitor model is established by using experimental data. Then, the model is combined with two Kalman filters, one of which is used to estimate SOC, and another one is applied to achieve trans-scale update the model parameters. Finally, the effectiveness of the method is verified in the urban dynamometer driving schedule at different temperatures. The validation results show that the mean absolute errors of voltage, SOC and capacity estimation are within 0.49 mV, 0.63% and 0.04 Ah, respectively. Moreover, compared with the other two traditional SOC estimation methods, whose results show that the proposed algorithm has higher estimation accuracy and more stable convergence ability.

Sex Differences in Cardiac Troponin Trajectories Over the Life Course.

Cardiac troponin concentrations are lower in women than men. We examined whether age- and risk factor-related changes in cardiac troponin over the life course differ by sex and if the trajectory of cardiac troponin was informative in respect of cardiovascular outcomes in women and men in the general population. In the Whitehall II cohort, high-sensitivity cardiac troponin I concentrations were measured on 3 occasions over a 15-year period. Using linear mixed-effects models, the sex-specific trajectories of cardiac troponin were evaluated, and the relationship with conventional cardiovascular risk factors determined. Using multistate joint models, the association between sex-specific trajectories of cardiac troponin and a composite outcome of nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death was evaluated. In 2142 women and 5151 men (mean, 58±7 and 57±7 years of age, respectively), there were 177 (8.3%) and 520 (10.1%) outcome events, respectively, during a median follow-up of 20.9 (25th to 75th percentile, 15.8-21.3) years. Cardiac troponin concentrations were persistently lower in women than in men (median baseline concentration: 2.4 [25th to 75th percentile, 1.7-3.6] ng/L versus 3.7 [25th to 75th percentile, 2.6-5.8] ng/L, respectively, P<0.001), with women exhibiting a relatively larger increase with advancing age as compared with men (Pinteraction<0.001). Apart from age, a significant and divergent interaction with sex was found for the association between cardiac troponin and body mass index (BMI) (Pinteraction=0.008) and diabetes (Pinteraction=0.003). During follow-up, cardiac troponin concentrations were associated to the outcome in both women and men (adjusted hazard ratio per 2-fold difference [95% CI, 1.34 (1.17-1.52) and 1.30 (1.21-1.40), respectively], Pinteraction=0.752). The slope of cardiac troponin was significantly associated with the outcome in women, but not in men (adjusted hazard ratio [95% CI, 2.70 (1.01-7.33) and 1.31 (0.62-2.75), respectively], Pinteraction=0.250). Trajectories of cardiac troponin differ between women and men in the general population, with differing associations to conventional risk factors and cardiovascular outcomes. Our findings highlight the importance of a sex-specific approach when serial cardiac troponin testing is applied for cardiovascular risk prediction.

Multi-State Joint Survival Signature for Multi-State Systems with Shared Multi-State Components

Multi-State Joint Survival Signature for Multi-State Systems with Shared Multi-State Components

Joint TDOA, FDOA and PDOA Localization Approaches and Performance Analysis

Multi-station joint localization has important practical significance. In this paper, phase difference of arrival (PDOA) information is introduced into the joint time difference of arrival (TDOA) and frequency difference of arrival (FDOA) localization method to improve the target localization accuracy. First, the Cramer–Rao lower bound (CRLB) of the joint TDOA, FDOA and PDOA localization approach with multi-station precise phase synchronization is derived. Then, the CRLB of the joint TDOA, FDOA and differential PDOA (dPDOA) localization method for the case of phase asynchronization between observation stations is also presented. Furthermore, the authors analyze the influence of the phase wrapping problem on localization accuracy and propose solutions to solve the phase wrapping problem based on cost functions of grid search. Finally, iterative localization algorithms based on maximum likelihood (ML) are proposed for both TDOA/FDOA/PDOA and TDOA/FDOA/dPDOA scenarios, respectively. Simulation results demonstrate the localization performance of the proposed approaches.

Multi-state joint estimation of series battery pack based on multi-model fusion

Aiming at the problem of on-line real-time estimation of the state of charge (SOC) and capacity of the battery pack, a new mean-difference model (MDM) for series battery pack is proposed. The model realizes the fusion of data-driven Autoregression (AR) model and equivalent circuit model (ECM) to improve the dynamic characteristics of the battery pack model under complex operating conditions. For the model parameters of the new MDM, the differentiated updating strategy is proposed. The differentiated updating strategy proposed can timely update the new MDM parameters online according to their changing characteristics and changing rate. Based on the new MDM, new mean-difference space equations expressing the coupling relationship between battery pack SOC and capacity, as well as a multi-timescale estimation framework based on mean state / difference state changing speed, are proposed. Finally, under this multi-timescale estimation framework, the multi-timescale H infinity filter (Mts-HIF) is used to realize the joint estimation of SOC and capacity of series battery packs. Based on 3 operating conditions, we have verified the proposed new MDM under noiseless and colored noise environment and compared it with the MDM based on n-order RC-ECM (nRC-ECM). The Akaike information criterion (AIC) indicates that the 3-order AR mean model (AR-MM) has the optimal tradeoff between accuracy and complexity under noiseless environment, while the 4-order AR-MM has the optimal tradeoff under colored noise environment and its root mean square error (RMSE) is less than 14.5 mV. Compared with the multi-timescale extended Kalman filter (Mts-EKF) joint estimation algorithm, the Mts-HIF proposed has better robustness under colored noise environment. For the capacity estimation of each cell of battery pack, the RMSE of the Mts-HIF does not exceed 2%, while the RMSE of the Mts-EKF exceeds 4%. For the SOC estimation, the RMSE of the Mts-HIF does not exceed 1.2%, while the RMSE of the Mts-EKF is close to 3%.

Embedded Distributed Temperature Sensing Enabled Multistate Joint Observation of Smart Lithium-Ion Battery

Accurate monitoring of the internal statuses is highly valuable for the management of the lithium-ion battery (LIB). This article proposes a thermal-model-based method for multistate joint observation, enabled by a novel smart battery design with an embedded and distributed temperature sensor. In particular, a novel smart battery is designed by implanting the distributed fiber optical sensor internally and externally. This promises a real-time distributed measurement of LIB internal and surface temperature with a high space resolution. Following this endeavor, a low-order joint observer is proposed to coestimate the thermal parameters, heat generation rate, state of charge, and maximum capacity. Experimental results disclose that the smart battery has space-resolved self-monitoring capability with high reproducibility. With the new sensing data, the heat generation rate, state of charge, and maximum capacity of LIB can be observed precisely in real time. The proposed method validates to outperform the commonly-used electrical-model-based method regarding the accuracy and the robustness to battery aging.

Online joint estimator of key states for battery based on a new equivalent circuit model

Accurate estimation of the key states of batteries is essential for safe and reliable battery operation. In this paper, a joint estimation method for state of health (SOH), state of charge (SOC) and state of power (SOP) of batteries based on the autoregressive equivalent circuit model (AR-ECM) is proposed. Firstly, considering the coupling relationship existing between these key states of the battery, the state space-coupling model based on AR-ECM is proposed. Then, by analyzing the different characteristics of the model parameters, a differentiated model parameter identification strategy is proposed. Finally, based on the accurate estimation of the model parameters, the square root unscented Kalman filter (SR-UKF) is used to realize the online estimation of SOH and SOC. The SOP estimation under multiple constraints is realized based on the updated state, voltage, and current. Experimental results in noise free and Gaussian white noise environments show that the multi-state joint estimation algorithm has high estimation accuracy and robustness.

On Dependent Multi-State Semi-Coherent Systems Based on Multi-State Joint Signature

On Dependent Multi-State Semi-Coherent Systems Based on Multi-State Joint Signature

Equivalency in joint signatures for binary/multi-state systems of different sizes

The joint signatures of binary-state and multi-state (semi-coherent or mixed) systems with i.i.d. (independent and identically distributed) binary-state components are considered in this work. For the comparison of pairs of binary-state systems of different sizes, transformation formulas of their joint signatures are derived by using the concept of equivalent systems and a generalized triangle rule for order statistics. Similarly, for facilitating the comparison of pairs of multi-state systems of different sizes, transformation formulas of their multi-state joint signatures are also derived. Some examples are finally presented to illustrate and to verify the theoretical results established here.

Multi-state joint estimation for a lithium-ion hybrid capacitor over a wide temperature range

Lithium-ion hybrid capacitors (LIHCs) have the advantages of high energy and power densities and long cycle life. This paper proposes a multi-state joint estimation method for LIHCs. The effects of temperature on the key LIHC parameters are analyzed. A first-order resistor-capacitor (RC) equivalent circuit model is used to characterize the LIHC dynamics. The coupling relationships among multiple states and LIHC parameters are derived. A recursive least squares algorithm with a variable forgetting factor (VFF-RLS) is employed to adaptively update model parameters at different temperatures. Subsequently, the online identified open-circuit voltage (OCV) is corrected by using a long short-term memory (LSTM) artificial neural network model and an ampere-hour integration method. With the corrected OCV curves and the other model parameters, the estimations of the state of charge (SOC), the remaining useful energy (RUE) and the state of energy (SOE) under dynamic operating conditions over a wide temperature range are achieved. The accuracy of the multi-state joint estimation is verified via the Federal Urban Driving Schedule (FUDS) test and the dynamic stress test (DST) at −10 °C, 25 °C and 50 °C. The root-mean-square errors (RMSEs) of the SOC, RUE and SOE estimations are 2.1%, 0.9 Wh, and 2.3%, respectively.

A multistate joint model for interval-censored event-history data subject to within-unit clustering and informative missingness, with application to neurocysticercosis research.

We propose a multistate joint model to analyze interval-censored event-history data subject to within-unit clustering and nonignorable missing data. The model is motivated by a study of the neurocysticercosis (NC) cyst evolution at the cyst-level, taking into account the multiple cysts phases with intermittent missing data and loss to follow-up, as well as the intra-brain clustering of observations made on a predefined data collection schedule. Of particular interest in this study is the description of the process leading to cyst resolution, and whether this process varies by antiparasitic treatment. The model uses shared random effects to account for within-brain correlation and to explain the hidden heterogeneity governing the missing data mechanism. We developed a likelihood-based method using a Monte Carlo EM algorithm for the inference. The practical utility of the methods is illustrated using data from a randomized controlled trial on the effect of antiparasitic treatment with albendazole on NC cysts among patients from six hospitals in Ecuador. Simulation results demonstrate that the proposed methods perform well in the finite sample and misspecified models that ignore the data complexities could lead to substantial biases.

A Systematic Review of Unsystematic Total Ankle Replacement Wear Evaluations.

Numerous studies have reported the use of laboratory multistation joint simulators to successfully predict wear performance and functionality of hip and knee replacements. In contrast, few studies in the peer-reviewed literature have used joint simulation to quantify the wear performance and functionality of ankle replacements. We performed a systematic review of the literature on joint simulator studies that quantified polyethylene wear in total ankle arthroplasty. In addition to the quantified wear results, the load and motion parameters were identified and compared among the studies. A search was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to identify articles reporting total ankle replacement polyethylene wear using joint simulators. Nine studies that used joint simulators and 1 study that used a computer simulation were found. Although all studies used physiological multidirectional motions (i.e., internal/external rotation, plantar flexion/dorsiflexion, anterior/posterior translation), there was large variability among the studies in the magnitudes of these motions. Among these studies, mean non-cross-linked polyethylene wear ranged from 3.3 ± 0.4 to 25.8 ± 3.1 mm per million cycles. In contrast, mean highly cross-linked polyethylene wear ranged from 2.1 ± 0.3 to 3.3 ± 0.4 mm per million cycles. The wide distribution in wear rates was attributable to the highly inconsistent kinematic parameters and loads applied as well as differences in implant design and materials. There is a severe lack of clinically applicable data on wear performance of total ankle replacements in the peer-reviewed literature. No universal set of kinematic load parameters has been established. Furthermore, only 2 of the published studies have validated their findings using independently derived data, such as retrieval analysis. These shortcomings make it difficult to compare findings as a function of design parameters and materials, or to draw clinically relevant conclusions from these simulations. More work is required to enhance the predictive capability of in vitro simulations of total ankle replacements. The results of joint wear simulator studies may not accurately represent in vivo wear of total ankle replacements. Joint simulator studies should establish that they are accurately replicating in vivo wear, thus enabling use of their predictive capabilities for new materials and designs.

A Novel Temperature–Hysteresis Model for Power Battery of Electric Vehicles with an Adaptive Joint Estimator on State of Charge and Power

The battery state of charge (SOC) and state of power (SOP) are two essential parameters in the battery management system. For power lithium-ion batteries, temperature variation and the hysteresis effect are two of the main negative contributions to the accuracy of model-based SOC and SOP estimation. Thereby, a reliable circuit model is established herein to accurately estimate the working state of batteries. Considering the effect that temperature and hysteresis have on the electrical system, a unique fully-coupled temperature–hysteresis model is proposed to describe the interrelationship among capacity, hysteresis voltage, and temperature comprehensively. The key parameters of the proposed model are identified by experiments operated on lithium-ion batteries under varying ambient temperatures. Then we build a multi-state joint estimator to calculate the SOC and SOP on the basis of the temperature–hysteresis model. The effectiveness of the advanced model is verified by experiments at different temperatures. Moreover, the proposed joint estimator is verified by the improved dynamic stress test. The experimental results indicate that the proposed estimator making use of the temperature–hysteresis model can estimate SOC and SOP accurately and robustly. Our results also prove invaluable in terms of the construction of a flexible battery management system for applications in the actual industrial field.

Path-specific, dispersion-based velocity models and moment tensors of moderate events recorded at few distant stations: Examples from Brazil and Greece

Centroid moment tensor (CMT) determination in intraplate regions like Brazil can be very difficult, because earthquakes are often recorded just at few and distant stations. This paper introduces a methodology for datasets like that. The methodology is based on waveform inversion in which each source-station path has its own velocity model. The 1-D path-specific velocity models are derived from the Rayleigh- and Love-wave dispersion curves. The waveform inversion is accompanied by posterior check of numerous P-wave first-motion polarities. An important innovation is the use of so-called frequency range test. The test basically consists in calculating CMT's for many different frequency ranges to assess the stability and uncertainty of the solution. The method is validated on two Brazilian earthquakes and a well-known Greek event. An offshore event (mb 5.2) in SE Brazil is inverted with four stations, at epicentral distances 300–400 km. The other Brazilian earthquake (mb 4.8 in Central Brazil) is even more challenging – only two broadband stations at 800–1300 km are at disposal for waveform inversion. The paper unambiguously demonstrates that the path-specific velocity models significantly increase the reliability of the CMT's. While standard models (e.g. IASP91) typically allow waveform modeling up to epicentral distances of the order of a few (∼10) minimum shear wavelengths (MSW), using the path-specific velocity models we successfully inverted waveforms up to > 20 MSW. Single-station waveform inversions are thoroughly tested, but multi-station joint inversions are shown to be preferable. The new methodology of this paper, providing a reasonable estimate of focal mechanisms and their uncertainties in case of highly limited waveform data, may find broad applicability in Brazil and elsewhere.

A data-driven adaptive state of charge and power capability joint estimator of lithium-ion polymer battery used in electric vehicles

An accurate SoC (state of charge) and SoP (state of power capability) joint estimator is the most significant techniques for electric vehicles. This paper makes two contributions to the existing literature. (1) A data-driven parameter identification method has been proposed for accurately capturing the real-time characteristic of the battery through the recursive least square algorithm, where the parameter of the battery model is updated with the real-time measurements of battery current and voltage at each sampling interval. (2) An adaptive extended Kalman filter algorithm based multi-state joint estimator has been developed in accordance with the relationship of the battery SoC and its power capability. Note that the SoC and SoP can be predicted accurately against the degradation and various operating environments of the battery through the data-driven parameter identification method. The robustness of the proposed data-driven joint estimator has been verified by different degradation states of lithium-ion polymer battery cells. The result indicates that the estimation errors of voltage and SoC are less than 1% even if given a large erroneous initial state of joint estimator, which makes the SoP estimate more accurate and reliable for the electric vehicles application.

Sufficient statistic likelihood construction for age- and time-dependent multi-state joint recapture and recovery data

Two closed-form likelihoods for multi-state joint recapture and recovery data were proposed in King and Brooks (2003); the first incorporated dependence on time and cohort whilst the second included dependence on age and cohort. However, when multi-state joint recapture and recovery data are modelled, it is likely that dependence on age and time will both be potentially of interest and therefore the most useful model formulation will incorporate both of these parameter structures. Within this article the likelihood function required for such a model is derived in terms of a set of sufficient statistics.