Time-varying Model Research Articles

Time-Varying Reliability Analysis of Multi-Cracked Beams Considering Maintenance Dependence

Time-varying reliability models of multi-cracked beam structures are established in this paper, which provide a theoretical method for the safety evaluation of multi-cracked beam structures. The reliability models proposed in this paper consider the interaction between the complex statistical correlations between system parameters during system operation and maintenance correlations, which is a difficult problem in the time-varying reliability modeling that takes into account work mechanisms and maintenance behavior. In the proposed models, multiple cracked elements are regarded as a dependent series system. The stresses, crack extensions, and multiple failure modes between each element constitute the complex failure dependence of the system. The time-varying reliability models of a multi-cracked beam structure are established via the neural network method and failure dependence analysis. Moreover, the failure dependence coefficient is proposed to quantify the time-varying failure dependence. Based on the working principle of the beam structures and the maintenance mechanism for the cracked state of the beams, a time-varying system reliability mode considering the maintenance dependence is proposed. Furthermore, the maintenance dependence coefficient index is proposed to quantitatively measure the interaction between the maintenance dependence and the failure dependence. Finally, the validity of the model is verified through the Monte Carlo simulation method. In the numerical examples, the relationship between maintenance dependence and failure dependence is illustrated and the influences of the statistical characteristics of the maintenance characteristic parameters on the maintainability and failure dependence are analyzed.

Optimizing oil spill emergency logistics: a time-varying multi-resource collaborative scheduling model.

Emergency resource scheduling is at the heart of the response to an oil spill, as it lays the foundation for all other emergency operations. Extant studies address the dynamicity inherent to these operations primarily by modeling a dynamic network flow with static data, which is not applicable to continuously changing conditions resulting from oil film movement. To enhance the responsiveness and cost-efficiency of the response to oil spills, this paper takes a novel approach and formulates a multi-objective location-routing model for multi-resource collaborative scheduling, namely, harnessing time-varying parameters rather than static data to model real-time changes in the demand for emergency resources and the transportation network. Additionally, the model considers various operational factors, including the transportation of multiple resources in the order of operating procedures; the coordination of split delivery with the consumption of emergency resources; and the matching of multiple resources with suitable vehicles. To solve the proposed model, a hybrid heuristic algorithm of PSO-PGSA is developed, which utilizes particle swarm optimization (PSO) to search widely for non-dominated solutions. The algorithm then makes use of the plant growth simulation algorithm (PGSA) to find the more effective vehicle routes based on the obtained solutions. Finally, a numerical analysis is used to illustrate the practical capabilities of the developed model and solution strategies. Most significantly, our work not only validates the methodology proposed here but also underlines the importance of incorporating the features of an oil spill emergency response into emergency logistics in general.

Time-varying traffic load model of expressway based on long-term monitored data from multiple regions — A case in central and eastern China

Traffic load is one of the main loads borne by highway transportation infrastructure, which seriously impacts the safe operation of the bridge and road. Due to the change in the external environment and government policy, the traffic load has obvious time-varying characteristics, which is the key to establishing the real traffic load model. The two main time-varying factors of traffic load are the growth of traffic volume and the evolution of vehicle weight. In the long-term research work, there is a lack of research based on continuous monitoring data for the above two factors. Therefore, this paper carries out a study on the time-varying traffic load model of expressways based on long-term monitored data from multiple regions. Firstly, the data sources of monitored 18 expressways in central and eastern China are introduced. Secondly, the Logistic model is selected as the basic model to study the growth laws of expressways from different provinces; meanwhile, the evolution of vehicle weight distribution is also analyzed and a simplified model is established accordingly. Finally, a case study of a newly opened eight-lane expressway is operated to characterize the influence of time-varying vehicles. The results show that the traffic volume and vehicle weight distribution change significantly with the increase of service time. The Logistic model and liner model are appropriate to characterize the evolution model of the traffic volume and vehicle weight distribution separately. The time-varying characteristics of traffic load have a significant impact on the standard fatigue traffic load model and the cumulative equivalent axle loads. The research results could provide a reference for the establishment of time-varying traffic load standard models for highways and the revision of related standards. At the same time, it could provide technical support for the full life-cycle investigation and design of the road and bridge infrastructure.

Onshore and offshore wind power generation forecasting using a novel flexible time-varying fractional nonlinear grey model

Onshore and offshore wind power generation forecasting using a novel flexible time-varying fractional nonlinear grey model

Association between Noise and Cardiovascular Disease in a Nationwide U.S. Prospective Cohort Study of Women Followed from 1988 to 2018.

Long-term noise exposure is associated with cardiovascular disease (CVD), including acute cardiovascular events such as myocardial infarction and stroke. However, longitudinal cohort studies in the U.S. of long-term noise and CVD are almost exclusively from Europe and few modeled nighttime noise, when an individual is likely at home or asleep, separately from daytime noise. We aimed to examine the prospective association of outdoor long-term nighttime and daytime noise from anthropogenic sources with incident CVD using a U.S.-based, nationwide cohort of women. We linked nighttime and daytime anthropogenic modeled noise estimates from a U.S. National Parks Service model (: sound pressure levels exceeded 50 percent of the time) to geocoded residential addresses of 114,116 participants in the Nurses' Health Study. We used time-varying Cox proportional hazards models to estimate risk of incident CVD, coronary heart disease (CHD), and stroke associated with long-term average (14-y measurement period) noise exposure, adjusted for potential individual- and area-level confounders and CVD risk factors (1988-2018; biennial residential address updates; monthly CVD updates). We assessed effect modification by population density, region, air pollution, vegetation cover, and neighborhood socioeconomic status, and explored mediation by self-reported average nightly sleep duration. Over 2,548,927 person-years, there were 10,331 incident CVD events. In fully adjusted models, the hazard ratios for each interquartile range increase in nighttime noise (3.67 dBA) and daytime noise (4.35 dBA), respectively, were 1.04 (95% CI: 1.02, 1.06) and 1.04 (95% CI: 1.02, 1.07). Associations for total energy-equivalent noise level () measures were stronger than for the anthropogenic statistical noise measures. Similar associations were observed for CHD and stroke. Interaction analyses suggested that associations of nighttime and daytime noise with CVD did not differ by prespecified effect modifiers. We found no evidence that inadequate sleep ( h/night) mediated associations of nighttime noise and CVD. Outdoor anthropogenic nighttime and daytime noise at the residential address was associated with a small increase in CVD risk in a cohort of adult female nurses. https://doi.org/10.1289/EHP12906.

LQG Graphon Mean Field Games: Analysis via Graphon-Invariant Subspaces

This paper studies approximate solutions to large-scale linear quadratic stochastic games with homogeneous nodal dynamics parameters and heterogeneous network couplings within the graphon mean field game framework in [2]-[4]. A graphon time-varying dynamical system model is first formulated to study the finite and then limit problems of linear quadratic Gaussian graphon mean field games (LQG-GMFG). The Nash equilibrium of the limit problem is then characterized by two coupled graphon time-varying dynamical systems. Sufficient conditions are established for the existence of a unique solution to the limit LQG-GMFG problem. For the computation of LQG-GMFG solutions, two methods are established and employed where one is based on fixed point iterations and the other on a decoupling operator Riccati equation; furthermore, two corresponding sets of solutions are established based on spectral decompositions. Finally, a set of numerical simulations on networks associated with different types of graphons are presented.

Abstract B056: Inflammatory dietary pattern and risk of multiple myeloma in US Black women

Abstract Background: Multiple myeloma (MM) takes a disproportionate toll on Black Americans compared to non-Hispanic whites, yet factors related to these disparities remain poorly understood. In a prior analysis of two prospective cohort studies examining diet and MM, a dietary inflammatory pattern was significantly associated with MM risk. Here, we investigate the association between dietary inflammatory index (DII) score and MM incidence in a cohort of US Black women. Methods: The Black Women’s Health Study (BWHS) is a prospective cohort study that enrolled 59,000 US self-identified Black women age 21-69 at entry in 1995. Incident cases of MM, identified among BWHS participants followed by biennial questionnaires, were confirmed via medical record review or linkage with cancer registries. We analyzed food frequency questionnaire (FFQ) data collected in 1995, excluding participants with implausible or missing FFQ data. We also excluded women diagnosed with cancer (other than non-melanoma skin cancer) prior to baseline. FFQ data were used to create a dietary inflammatory index (DII) for each person. The DII score includes total fat and cholesterol as pro-inflammatory dietary contributors, and n-3 PUFAs, selenium, and vitamin D as anti-inflammatory contributors. The DII has been previously associated with six inflammatory markers (CRP, IL-1b, IL-4, IL-6, IL-10, TNF-a). We evaluated associations of DII score quintile with MM incidence using Cox proportional hazards regression, adjusted for age, body mass index (BMI), and total energy intake (kcal/day). Results: Among 55,276 BWHS participants eligible for this analysis, we confirmed 313 incident cases of MM over 26 years of follow-up. Median age at baseline was 38 years old and median BMI was 26.6 kg/m2. The multivariable hazard ratio for women in the top quintile of DII (most pro-inflammatory diet) relative to the lowest quintile (most anti-inflammatory diet) was 0.71 (95% confidence interval: 0.43, 1.16). Conclusions: Overall, DII score was not associated with MM incidence in this cohort of US Black women. Ongoing analyses of markers of dietary inflammation and specific food groups may clarify the possible role of dietary components in MM pathogenesis. Cumulative time-varying models of DII score may also offer more robust conclusions in future analyses. Citation Format: Anisha V. Patel, Julie R. Palmer, Jessica L. Petrick, Kimberly A. Bertrand. Inflammatory dietary pattern and risk of multiple myeloma in US Black women [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr B056.

ECG Modeling for Simulation of Arrhythmias in Time-Varying Conditions.

The present article proposes an ECG simulator that advances modeling of arrhythmias and noise by introducing time-varying signal characteristics. The simulator is built around a discrete-time Markov chain model for simulating atrial and ventricular arrhythmias of particular relevance when analyzing atrial fibrillation (AF). Each state is associated with statistical information on episode duration and heartbeat characteristics. Statistical, time-varying modeling of muscle noise, motion artifacts, and the influence of respiration is introduced to increase the complexity of simulated ECGs, making the simulator well suited for data augmentation in machine learning. Modeling of how the PQ and QT intervals depend on heart rate is also introduced. The realism of simulated ECGs is assessed by three experienced doctors, showing that simulated ECGs are difficult to distinguish from real ECGs. Simulator usefulness is illustrated in terms of AF detection performance when either simulated or real ECGs are used to train a neural network for signal quality control. The results show that both types of training lead to similar performance.

Cryptocurrency Environmental Attention, Green Financial Assets, and Information Transmission: Evidence From the COVID-19 Pandemic

This paper explores the connectedness between the cryptocurrency environmental attention index and four major green financial assets using time-varying parameter vector autoregression model from January 2014 to December 2021. Findings reveal that connectedness follows a heterogeneous trajectory over time. Results show evidence of higher volatility transmission during the COVID-19 period relative to the entire sample period. The high volatility transmission is a source of concern to policymakers, green stakeholders, and investors, due to limited diversification options.

Ethnic disparities in cardiovascular and renal responses to canagliflozin between Asian and White patients with type 2 diabetes mellitus: A post hoc analysis of the CANVAS Program.

To assess the potential heterogeneity in cardiovascular (CV), renal and safety outcomes of canagliflozin between Whites and Asians, as well as these outcomes in each subgroup. The CANVAS Program enrolled 10 142 patients with type 2 diabetes, comprising 78.34% Whites and 12.66% Asians. CV, renal and safety outcomes were comprehensively analysed using Cox regression models, while intermediate markers were assessed using time-varying mixed-effects models. Racial heterogeneity was evaluated by adding a treatment-race interacion term. Canagliflozin showed no significant racial disparities in the majority of the CV, renal and safety outcomes. The heterogeneity (p = .04) was observed on all-cause mortality, with reduced risk in Whites (hazard ratio 0.84; 95% confidence interval 0.71-0.99) and a statistically non-significant increased risk in Asians (hazard ratio 1.64; 95% confidence interval 0.94-2.90). There was a significant racial difference in acute kidney injury (p = .04) and a marginally significant racial heterogeneity for the composite of hospitalization for heart failure and CV death (p = .06) and serious renal-related adverse events (p = .07). Canagliflozin reduced CV and renal risks similarly in Whites and Asians; however, there was a significant racial discrepancy in all-cause mortality. This distinction may be attributed to the fact that Asian patients exhibited diminished CV protection effects and more renal adverse events with canagliflozin, potentially resulting from the smaller reductions in weight and uric acid. These findings highlight the importance of investigating the impact of race on treatment response to sodium-glucose cotransporter-2 inhibitors and provide more precise treatment strategies.

Carbon-embedded energy coordination strategy in park-level integrated energy system considering time-varying carbon emission measurement

Energy efficiency and carbon mitigation are important issues in modern energy systems research. Considering the couplings between energy and carbon, this paper investigates a carbon-embedded energy coordination problem in a park-level integrated energy system (PIES). Firstly, based on energy hub, a multilateral interactive transaction framework is introduced, consisting of energy hub operator (EHO), building users with photovoltaics, and an electric vehicle (EV) charging agent. Secondly, a time-varying carbon emission measurement model for the outsourced electricity is designed, taking into account the dynamic composition of the supply-side generators. Based on the time-varying carbon factor and virtual carbon emission flow, an energy-carbon pricing strategy is proposed to balance the carbon responsibility among different participants. Then, a multi-agent interactive trading model in the PIES is constructed, where the energy consumption plans of building users and EVs are guided by the dynamic energy-carbon integrated prices. Through the iterative interaction among different agents, the optimal trading results are finally obtained. Simulation results illustrate the effectiveness of the proposed energy-carbon integrated pricing method in reducing carbon emissions and promoting energy sharing.

The impact of long-term PM1 exposure on all-cause mortality and its interaction with BMI: A nationwide prospective cohort study in China

BackgroundChina has a serious air pollution problem and a high prevalence of obesity. The interaction between the two and its impact on all-cause mortality is a public health issue of great concern. ObjectivesThis study aimed to investigate the association between long-term exposure to particulate matter with aerodynamic diameter ≤ 1 μm (PM1) and all-cause mortality, as well as the interaction effect of body mass index (BMI) in the association. MethodsA total of 33,087 participants from 162 counties in 25 provinces in China were included, with annual average PM1 exposure being estimated based on the county address. The PM1-mortality relation was evaluated using the time-varying Cox proportional hazards models, with the dose-response relationship being fitted using the penalized splines. Besides, the potential interaction effect of BMI in the PM1-mortality relation was evaluated. ResultsThe incidence of all-cause deaths was 76.99 per 10,000 person-years over a median of 8.2 years of follow-up. After controlling for potential confounders, the PM1-mortality relation was approximately J-shaped. The full-adjustment analysis observed the hazard ratio (HR) of all-cause mortality was 1.114 [95 % confidence interval (CI): 1.017–1.220] corresponding to a 10 μg/m3 rise in PM1 concentration. Further stratified analyses suggested the adverse effects of PM1 might be more pronounced among the underweight. DiscussionHigher PM1 concentrations were associated with an increase in all-cause mortality. The BMI might further alter the relation, and the underweight population was the sensitive subgroup of the population that needed to be protected.

A Novel Model-Free Adaptive Proportional–Integral–Derivative Control Method for Speed-Tracking Systems of Electric Balanced Forklifts

Similar to many complex systems, the operation process of electric balanced forklifts has characteristics such as time-varying model parameters and nonlinearity. Establishing an accurate mathematical model becomes challenging, making it difficult to apply model-based control methods in engineering practice. Aiming at the longitudinal control system of electric forklifts containing external disturbances, this paper proposes an improved full-format dynamic linearization model-free adaptive PID control (iFFDL-MFA-PID) method. Firstly, the full-format dynamic linearization (FFDL) method is employed to transform the operating system of the electric balanced forklift into a virtual equivalent linear data model. Secondly, the nonlinear residual term and pseudo-gradient (PG) of the data model are estimated using the difference estimation algorithm and the optimal criterion function, respectively. Furthermore, in order to enhance the robustness of the system, the idea of intelligent PID (iPID) is introduced and the principle of equivalent feedback is utilized to derive the iFFDL-MFA-PID control scheme. The design process of this scheme only requires the use of the input and output data of the system, without relying on the mathematical model of the system. Finally, the iFFDL-MFA-PID method proposed in this paper is simulated and tested with the EFG-BC/320 counterbalanced forklift equipped in the Special Equipment Testing Center and compared with the model-free adaptive control method (FFDL-MFAC) and the PID control method. Simulation results show that the speed-tracking error of the electric forklift truck under the action of the iFFDL-MFA-PID algorithm is maintained within ±0.132 m/s throughout the process, achieving higher tracking accuracy and better robustness compared to the MFAC and PID methods.

Inappropriate shock incidence in patients with subcutaneous implantable cardioverter-defibrillators with concomitant cardiac implantable electronic devices: A single-center cohort study.

Subcutaneous implantable cardioverter defibrillators (S-ICDs) are occasionally used in combination with other cardiac implantable electronic devices (CIEDs). However, whether the incidence of inappropriate shock increases in patients with S-ICDs and concomitant CIEDs remains unclear. This study aimed to investigate the association between the concomitant use of CIEDs and the incidence of inappropriate shock in patients with current-generation S-ICDs. A total of 127 consecutive patients received an S-ICD. Patients were assigned to two groups depending on concomitant use of CIEDs at the time of S-ICD implantation: patients without other CIEDs (non-combined group, 106 patients) and patients with other CIEDs (combined group, 21 patients). CIEDs included pacemakers, implantable cardioverter defibrillators, cardiac resynchronization therapy pacemakers, and cardiac resynchronization therapy defibrillators. The primary outcome was inappropriate shock, defined as a shock other than ventricular arrhythmia. Hazard ratios and 95% confidence intervals were calculated using a time-varying Cox proportional hazards model which was adjusted for age because age differed between the groups and could be a confounder. During a median follow-up period of 2.2 years (interquartile range, 1.0-3.4 years), inappropriate shock events occurred in 17 (16%) and five (19%) patients of the non-combined and combined groups, respectively. While the age-adjusted hazard ratio for inappropriate shock was 24% higher in the combined than in the non-combined group (hazard ratio=1.24, 95% confidence interval, 0.39-3.97), this difference was insignificant (p=.71). The incidence of inappropriate shock did not differ between patients with and without concomitant use of CIEDs, suggesting that S-ICDs could potentially be combined with other CIEDs without increasing the number of inappropriate shocks. Further studies are warranted to confirm the safety and feasibility of concomitant use of S-ICDs and CIEDs.

Monetary shocks and production network in the G7 countries

Understanding the structure and properties of production networks is essential to identify the transmission channels from monetary shocks. While growingly studied, this literature keeps displaying critical caveats from which the investigation of G-7 economies is not spared. To fill this gap, this paper applies a version of Time-Varying Parameters Bayesian Vector-Autoregressions models (TVP-VAR) and investigates the responses of production networks (upstream and downstream dynamics) to endogeneous monetary shocks on key macro-level indicators (GDP, GDP deflator, exchange rate, short-term and long-term interest rates). Two distinct time-lengths are considered: a test (i.e., 2000–2014) and a treated period (i.e., 2007–2009,”the Great Recession”). Prior, key statistical conditions are checked using a stepwise stationary testing framework including the Kwiatkowski–Phillips–Schmidt–Shin (Kapetanios et al. in J Economet 112(2):359–379, 2003—KPSS) and panel Breitung (Nonstationary panels, panel cointegration, and dynamic panels. Emerald Group Publishing Limited, London, 2001) unit root tests; followed by the Pesaran (General diagnostic tests for cross section dependence in panels, 2004) Cross-sectional Dependence (CD) test; and the Im–Pesaran–Shin (Im et al. in J Economet 115(1):53–74, 2003—IPS) test for unit root in the presence of heterogenous slope coefficients. Panel Auto-Regressive Distributed Lag Mean Group estimates (PARDL-MG) offer interesting short- and long-run monetary shocks-production networks response functions, stratified by country and sector. Findings clearly indicate that upstreamness forces dominated downstremness dynamics during the period 2000–2014, whereas the financial sector ermeges as the clear transmission channel through which monetary shocks affected the productive economy during the Great Recession. In general, we conclude that the prioduction structure influences the transmission of monetary shocks in the G-7 economies. Adequate policy implications are supplied, along with a methodological note on the forecasting potential of TVP-VAR methodologies when dealing with series exhibiting structural breaks.

Short-Term Load Forecasting Based on CNN-Bilstm Considering Load Time-Varying Trend Mapping

Background:: Accurate short-term load forecasting is an important guarantee for the safety, stability, economic and efficient operation of power systems. Objective:: In order to effectively improve the forecasting accuracy, this paper proposes a hybrid forecasting model of convolutional neural network (CNN) and bi-directional long short-term memory (BiLSTM) neural network considering load time-varying trend mapping model (M). Methods:: Firstly, the time-varying features of the load curve are analyzed, and a mapping model is established to characterize the load time-varying trend. The features of the load time-varying trend are extracted, and they are quantified into a mathematical model. Secondly, the feature set is reconstructed through data migration. Then, the reconstructed feature set is input into the CNN-BiLSTM hybrid model. In the hybrid model, CNN is used to extract the features from data again to form a new feature vector, and then the feature vector is input into BiLSTM for forecasting. Result:: The power load data set from the New England in United States is used to simulate and verify the correctness and validity of the proposed method. Conclusion:: With the comparison of the forecasting results between different load forecasting models, the results show that the forecasting accuracy of the proposed method is higher and it is verified that the load time-varying trend mapping method proposed can improve the forecasting accuracy of different models in varying degrees.

Fast-varying time lags in the quasi-periodic oscillation in GRS 1915 + 105

ABSTRACT The properties of subsecond time variability of the X-ray emission of the black hole binary GRS 1915 + 105 are very complex and strictly connected to its patterns of variability observed on long time-scales. A key aspect for determining the geometry of the accretion flow is the study of time lags between emission at different energies, as they are associated to key time-scales of the system. In particular, it is important to examine the lags associated to the strong low-frequency quasi-periodic oscillations (QPOs), as the QPOs provide unambiguous special frequencies to sample the variability. We have analysed data from an observation with the AstroSat satellite, in which the frequency of the low-frequency QPO varies smoothly between 2.5 and 6.6 Hz on a time-scale of ∼10 h. The derived phase lags show the same properties and evolution of those observed on time-scales of a few hundred days, indicating that changes in the system geometry can take place on times below one day. We fit selected energy spectra of the source and rms and phase-lag spectra of the QPO with a time-variable Comptonization model, as done previously to RossiXTE data of the same source, and find that indeed the derived parameters match those obtained for variations on much longer time-scales.

Analysis of the impact of central bank digital currency on stock markets: Dynamics and implications

The purpose of the study is to explore the influence of central bank digital currency on stock markets. To realize the purpose, the TVP-VAR model was built, which determines the impact of volatility of the CBDC attention index (CBDCAI) on the volatility of stock market indices. The study uses a time-varying vector autoregressive model that analyzes weekly data from the first week of January 2015 to the first week of July 2023. The endogenous vector to be assessed by VAR contains CBDCAI and stock market indices of different countries (France: CAC 40, The United States of America: S&P 500, Germany: DAX 40, United Kingdom: FTSE 100, China: SSEC, The Netherlands AEX 25, Switzerland: SMI 20, Japan: Nikkei 225, India: NIFTY 50, Brazil: BVSP, South Korea: KOSPI). The results of the TVP-VAR model show that compared to stock market indices, CBDCAI appeared to be relatively independent and isolated. Interdependence and mutual influence between the digital currency market of central banks and stock markets were also revealed. In addition, CBDC functions primarily as a volatility absorber rather than a source of volatility. Despite the overall ability of the CBDC market to absorb fluctuations in volatility, it may also change its function with the widespread adoption of central bank digital currencies in many countries.

Investigation of Effect of the Tooth Profile Modification on the Engagement Characteristics of the Gear Pair

As a vital component in the electric vehicle, the higher requirements for the gear precision are urgently needed in the service environment with high speed and heavy load. The tooth profile modification can greatly improve the accuracy and strength of the gear transmissions. Therefore, accurately obtaining the optimum modification parameters of the gear system under the action of specific loads is the premise of the research of gear vibration and noise reduction. The time-varying mesh stiffness calculation model is established in this paper considering the coupling effect of the tooth profile errors and structural coupling effect. The minimum variations of the loaded static transmission error are selected as the objective function to obtain the optimum modification parameters of gear pair. In addition, the effect of torque, modification amount and modification length on the engagement characteristics of the gear pair are studied. The research can provide some references for the design and optimization for the gear transmission system in the electric vehicle.

Predictive value of functional disability scales among stroke survivors: A long-term mortality evaluation in a Brazilian stroke cohort

ObjectiveTo assess the influence of two functional scales- Modified Rankin Scale (m-RS) and Modified Katz Index (m-Katz Index) on long-term mortality in a stroke cohort. Material and methodsAmong 760 stroke survivors (median age: 66 (IQR:56–75), 56.4 % women) m-Katz Index and m-RS scales applied at 1 and 6 months after stroke, were investigated in relation to 12-years of all-cause mortality. Kaplan–Meier survival curves were computed, and time-varying covariate Cox regression models were fitted to calculate hazard ratios (HRs) with 95 % confidence intervals (CIs) in all sample and by sex. The prognostic ability of the fitted models was computed for each model by six different measures. ResultsAfter 12 years of follow-up (median survival time: 7.3 years), 311 participants died. Overall survival curves show lower survival rates among those with the highest levels of disability/dependence (all log-rank p-values <0.0001). These findings were confirmed in all regression models for both sexes, particularly in men who had higher levels of dependence on Activities of Daily Living (ADLs) by m-Katz Index and severe disability by m-RS and presented the highest HR of dying (HR: 3.34 (95 %CI: 2.27–4.92) and HR: 4.94 (95 % CI: 3.15–7.75), respectively). ConclusionsBoth the m-Katz Index and the m-RS scale were good predictors of long-term mortality, which is of importance for guiding the functional rehabilitation of stroke patients. Besides, high levels of disability and dependence were implicated with high mortality risks, regardless of sex.