Time-varying Analysis Research Articles

A Single-Loop Method for Time-Variant Reliability Sensitivity Analysis of Motion Mechanisms Using Bayes’ Theorem

Abstract Time-variant reliability sensitivity (TRS) analysis can measure the effect of input factors on the structure/mechanism failure. The traditional method for TRS analysis employs a nested sampling procedure, with computational cost depending on the number of input factors. To address the above weaknesses, a single-loop method is developed for TRS analysis. Based on Bayes’ theorem, the sensitivity measure is derived and expressed by the difference between the probability density function (PDF) and the failure-conditional PDF. This derivation allows for TRS analysis to be performed with just one set of samples, where the computational complexity does not depend on the number of inputs. Then, the procedures for Monte Carlo simulation (MCS) are listed based on the innovative estimation of the sensitivity index. Three examples involving numerical and engineering problems are employed to validate the proposed strategy, with the direct MCS introduced for comparison. The results reveal that the proposed strategy provides satisfactory TRS analysis while significantly saving computational resources.

Effects of the Nonsteroidal MRA Finerenone With and Without Concomitant SGLT2 Inhibitor Use in Heart Failure.

Patients with heart failure (HF) with mildly reduced or preserved ejection fraction face heightened long-term risks of morbidity and mortality. Sodium-glucose cotransporter-2 inhibitors (SGLT2i) and the nonsteroidal mineralocorticoid receptor antagonist finerenone have both been shown to reduce the risk of cardiovascular events in this population, but the effects of their combined use are not known. FINEARTS-HF (Finerenone Trial to Investigate Efficacy and Safety Superior to Placebo in Patients With Heart Failure) was a randomized, double-blind, placebo-controlled trial of finerenone in patients with HF and left ventricular ejection fraction ≥40%. Baseline SGLT2i use was a prespecified subgroup. The primary outcome was a composite of total (first and recurrent) worsening HF events and cardiovascular death. We first assessed for evidence of treatment heterogeneity on the basis of baseline SGLT2i use. We further examined SGLT2i uptake during the trial and evaluated the treatment effects of finerenone accounting for baseline and during-trial use of SGLT2i in time-varying analyses. Among 6001 participants, 817 (13.6%) were treated with an SGLT2i at baseline. During 2.6 years median follow-up, treatment with finerenone similarly reduced the risk of the primary outcome in participants treated with an SGLT2i (rate ratio, 0.83 [95% CI, 0.60-1.16]) and without an SGLT2i at baseline (rate ratio, 0.85 [95% CI, 0.74-0.98]; Pinteraction=0.76). In follow-up, 980 participants initiated SGLT2i, which was less frequent in the finerenone arm compared with placebo (17.7% versus 20.1%; hazard ratio, 0.86 [95% CI, 0.76-0.97]). Time-updated analyses accounting for baseline and subsequent use of SGLT2i did not meaningfully alter the treatment effects of finerenone on the primary end point. The treatment benefits of the nonsteroidal mineralocorticoid receptor antagonist finerenone were observed irrespective of concomitant use of an SGLT2i. These data suggest that the combined use of SGLT2i and a nonsteroidal mineralocorticoid receptor antagonist may provide additive protection against cardiovascular events in patients with HF with mildly reduced or preserved ejection fraction. URL: https://www.clinicaltrials.gov; Unique identifier: NCT04435626.

Economic policy uncertainty and US sector REITs: what can we learn from spillovers?

PurposeThis study examines the impact of Economic Policy Uncertainty (EPU) on US Real Estate Investment Trust (REIT) sectors from 1994 to 2024, focusing on dynamic spillover effects across varying economic environments.Design/methodology/approachEmploying a time-varying parameter vector autoregression (TVP-VAR) model, the research analyzes sectoral interconnectedness and spillover dynamics, capturing the evolving relationships between REIT subsectors and EPU over time.FindingsThe analysis reveals that US REIT subsectors are highly interconnected, indicating substantial internal spillovers. Retail, regional malls and shopping centres are primary shock transmitters, while self-storage, manufactured homes and industrial REITs act as net receivers, demonstrating resilience. Over the study period, Economic Policy Uncertainty (EPU) generally functions as a net receiver of spillovers from REITs. However, pairwise time-varying analysis shows that EPU transmitted shocks during the economic uncertainties of the 1990s but became a net receiver during the 2008 financial crisis and the COVID-19 pandemic, suggesting that disruptions in REIT markets contributed to policy uncertainties during crises. Additionally, the sectors driving volatility shifted over time: residential and apartment REITs were predominant during the 2008 crisis, while disruptions in the office sector were significant during the pandemic.Practical implicationsPolicymakers ought to design targeted interventions to stabilize the sectors that are primary transmitters of shocks during economic downturns and monitor closely economic indicators and adopt policies to mitigate adverse spillover effects.Originality/valueThis study offers novel insights into the time-varying interconnections between REIT sectors and economic uncertainty, illustrating how these relationships evolve with economic conditions and enhancing understanding of REIT resilience under uncertainty.

Time-varying analyses of survival and outcomes in patients with HeartMate 3 left ventricular assist devices.

As patients experience longer survival on HeartMate 3 left ventricular assist devices, there is a need to characterize long-term risks of adverse outcomes more precisely. This study characterized temporal variations in risks of mortality and adverse outcomes in patients with a HeartMate 3. From October 2015 to January 2023, 431 HeartMate 3 devices were implanted at Cleveland Clinic. Survival was estimated to 5 years post-implant. Time-varying risks of death, neurological events, gastrointestinal bleeding, device-related infections, and other adverse events were characterized using multiphase hazard modelling. Survival on HeartMate 3 at 1 and 5 years was 88% and 58%, respectively. Risk of death peaked in the first postoperative month before declining rapidly to a constant, lower hazard. Cumulative number of neurological events/patient at 1 year and 5 years was 0.13 and 0.29, respectively; risk was highest within the first postoperative week, then rapidly declined by 1 month. Cumulative number of gastrointestinal bleeding events/patient at 1 year and 5 years was 0.32 and 0.78, respectively; risk was highest within 1 week postoperatively and gradually declined to a constant risk over the first year. Device-related infections developed in 136 patients. One- and 5-year freedom from device-related infection was 77% and 45%, respectively; risk was initially low before peaking at 6 months postoperatively and then gradually declining to a steady hazard. Long-term survival on HeartMate 3 support was favourable in a large single-centre cohort. Strategies to reduce early postoperative risk of neurological events and late risks of gastrointestinal bleeding, infections and other adverse events are needed.

Do Supply Chain Pressures Affect Consumer Prices in Major Economies? New Evidence from Time-Varying Causality Analysis

This study explores the dynamic causality between the consumer price index and supply chain pressure index in two large economies. Despite extensive research into the role of supply chains at the firm level, literature on their impact on time-varying inflation and macroeconomic stability is scarce. Using data from the United States and the European Union from January 2000 to December 2021, we employ novel time-varying causality techniques to examine the evolving relationships among inflation, supply chains, real gross domestic product, interest rates, and oil prices. Our findings show that supply chains have a significant dynamic relationship with consumer prices, particularly during economic crises like the 2008 global financial crisis and the COVID-19 pandemic. Robustness tests including impulse response functions and the autoregressive distributed lag model confirm this causality. These results are critical for policymakers, emphasizing the importance of supply chains in controlling inflation and achieving global economic resilience and stability.

Evaluating the Effectiveness of mRNA-1273.815 Against COVID-19 Hospitalization Among Adults Aged ≥ 18Years in the United States.

In September 2023 the Food and Drug Administration (FDA) approved an updated mRNA COVID-19 vaccine targeting the XBB.1.5 sublineage. This study evaluates the effectiveness of mRNA-1273.815, a 2023-2024 Omicron XBB.1.5-containing mRNA COVID-19 vaccine in preventing COVID-19-related hospitalizations and medically attended COVID-19 in US adults aged ≥ 18 years. This observational, matched cohort study used medical and pharmacy claims data from HealthVerity. Adults vaccinated with mRNA-1273.815 between September 12, 2023, and December 31, 2023, were followed through January 26, 2024. Vaccinated individuals were matched with individuals unvaccinated with any 2023-2024 COVID-19 vaccine on demographic and clinical characteristics. The primary and secondary outcomes were COVID-19 hospitalization and medically attended COVID-19, respectively. Inverse probability of treatment weighting and Cox proportional hazards regression were utilized to estimate vaccine effectiveness (VE). The study included 1,272,161 vaccinated individuals matched 1:1 with unvaccinated individuals, with a maximum follow-up of 128 (median 84)days. The VE against COVID-19 hospitalization was 51% (95% confidence interval [CI]: 48-54%). Subgroup analyses showed a VE of 56% (95% CI 51-61%) among adults ≥ 65 years and 46% (95% CI 39-52%) in immunocompromised adults. For medically attended COVID-19, the VE was 25% (95% CI 24-27%). Time-varying analyses showed that while VE declined over time, VE remained significant. During the 2023-2024 respiratory season, the mRNA-1273.815 vaccine significantly protected against COVID-19-related hospitalizations and medically attended COVID-19 across diverse adult populations and demonstrated durability of the effect. These results support the continued use of updated COVID-19 vaccines to mitigate severe outcomes and maintain public health safety.

Impact of Global Supply Chain Pressure on International Trade: Evidence from Turkey’s Container Traffic

I aimed to econometrically determine the reflection of pressure-increasing events in the global supply chain on the container traffic in Turkish ports, since any disruption or pressure can have negative consequences such as shortages and high prices for consumers, a decline in profitability for companies, reduced job opportunities for workers, a slowdown in economic growth for countries, and a slowdown in trade for the world. I used the Global Supply Chain Pressure Index (GSCPI) to model the impact of events occurring in the global supply chain, and the export and import container traffic at Turkish ports. Instead of examining the relationship between the variables over the whole sample, I used the time-varying causality analysis, which makes it possible to detect the relationship at different periods in time by analyzing it with the rolling window approach. The dataset includes 230 monthly observations and covers the period between January 2004 and February 2023. The pressure in the supply chain differs according to export and import container loads. The Russia–Ukraine war, the Suez Canal closure, economic sanctions between the U.S. and China, economic crisis, geopolitical conflicts, hurricanes, and port congestion in the U.S. all affected the traffic in Turkish ports. Since this research was conducted with a time-varying approach and the GSCPI variable successfully represented many global supply chain events, it was possible to examine which events affected international trade through a single variable. Thus, event-based assessments become possible instead of just saying that supply chain pressure affects international trade.

Differential long-term tamoxifen therapy benefit by menopausal status in breast cancer patients: secondary analysis of a controlled randomized clinical trial.

Estrogen receptor-positive breast cancer patients have a long-term risk of distant metastatic disease, and premenopausal patients have a higher risk. Randomized studies with long-term follow-up are essential to understand treatment benefit. We elucidated the long-term tamoxifen therapy benefit by menopausal status in the Stockholm tamoxifen trials with 20 years complete follow-up. Secondary analysis of 1242 estrogen receptor-positive and HER2-negative patients that were randomly assigned to 2-5 years of 40 mg adjuvant tamoxifen or no endocrine therapy. Distant recurrence-free interval in tamoxifen-treated vs endocrine untreated patients was assessed by Kaplan-Meier, Cox proportional hazards regression, and time-varying analyses. In premenopausal patients, a statistically significant tamoxifen benefit was observed for lymph node-negative (adjusted hazard ratio [HR] = 0.46, 95% confidence interval [CI] = 0.24 to 0.87), progesterone receptor-positive (adjusted HR = 0.61, 95% CI = 0.41 to 0.91), and genomic low-risk tumors (adjusted HR = 0.47, 95% CI = 0.26 to 0.85) but only lasted beyond 10 years for genomic low-risk tumors. Postmenopausal patients showed long-term benefit for all good-prognosis markers including low-grade (adjusted HR = 0.55, 95% CI = 0.41 to 0.73), lymph node-negative (adjusted HR = 0.44, 95% CI = 0.30 to 0.64), progesterone receptor-positive (adjusted HR = 0.60, 95% CI = 0.44 to 0.80), Ki-67 low (adjusted HR = 0.51, 95% CI = 0.38 to 0.68), and genomic low-risk tumors (adjusted HR = 0.53, 95% CI = 0.37 to 0.74), and regardless of tumor size (≤20 mm: adjusted HR = 0.55, 95% CI = 0.39 to 0.77; >20 mm: adjusted HR = 0.64, 95% CI = 0.44 to 0.94). Premenopausal patients with no poor-prognosis tumor characteristics (clinical marker score = 0) showed early benefit and postmenopausal long-term benefit. Our study suggests differential tamoxifen benefit by menopausal status. Improved long-term endocrine therapy prediction in premenopausal patients is needed and could involve molecular markers because standard tumor characteristics cannot predict benefit beyond 10 years.

Research on time-varying mesh stiffness analysis method of gears with local defective faults

Research on time-varying mesh stiffness analysis method of gears with local defective faults

Long-term benefit of SGLT2 inhibitors to prevent heart failure hospitalization in patients with diabetes, with potential time-varying benefit.

SGLT2 inhibitors show promise in reducing hospitalization for heart failure in diabetics, but their long-term effects and time-dependency remain unclear. We conducted a retrospective nested case-control study within a large type 2 diabetic cohort (n = 11,209) using electronic health records. Cases (heart failure hospitalization, n = 352) were matched to controls (n = 1372) based on age, sex, cohort entry date, and diabetes duration. Matched-set conditional logistic regression was used to estimate hazard ratios (HRs) for antidiabetic drug class and heart failure hospitalization risk. SGLT2 inhibitors were associated with a significant reduction in heart failure hospitalization risk (adjusted HR 0.56, 95% CI 0.38-0.82, p = 0.028). This protective effect appeared more pronounced with a longer duration of treatment, suggesting a potential cumulative benefit. Time-varying analysis within propensity score-matched cohorts revealed a progressive decrease in hospitalization risk with continued SGLT2 inhibitor use, indicating a strengthening effect over time (greedy nearest neighbor: HR 0.52, CI 0.31-0.87, p = 0.015; optimal matching: HR 0.54, CI 0.34-0.85, p = 0.008). While promising, further investigation with larger datasets is warranted to definitively confirm these findings.

Probabilistic analysis framework for the deterioration of marine tubular steel structures based on random field simulation

Corrosion reduces the effective cross-section, leading to the bearing capacity loss of marine tubular steel structures; the structures’ time-variant performance are of great concern in practices. The paper employs random field simulation to account for corrosion's spatial variability in evaluating the bearing capacity of the axially-loaded steel tubes, and proposes a time-variant reliability analysis framework for marine tubular steel structures, from the member-level to the structure-level. Results are compared with those neglecting the corrosion's spatial variability. The proposed method prevents a significant underestimation of the failure probability comparing to that neglecting spatial variability, and provides a tool for designers to predict the time-variant reliability levels for marine steel structures.

A Study on Nonlinearity in Mixers Using a Time-Varying Volterra-Based Distortion Contribution Analysis Tool

A Study on Nonlinearity in Mixers Using a Time-Varying Volterra-Based Distortion Contribution Analysis Tool

Gap filling between GRACE and GRACE-FO missions: assessment of interpolation techniques

We propose a benchmark for comparing gap-filling techniques used on global time-variable gravity field time-series. The Gravity Recovery and Climate Experiment (GRACE) and the GRACE Follow-On missions provide products to study the Earth’s time-variable gravity field. However, the presence of missing months in the measurements poses challenges for understanding specific Earth processes through the gravity field. We reproduce, adapt, and compare satellite-monitoring and interpolation techniques for filling these missing months in GRACE and GRACE Follow-On products on a global scale. Satellite-monitoring techniques utilize solutions from Swarm and satellite laser ranging, while interpolation techniques rely on GRACE and/or Swarm solutions. We assess a wide range of interpolation techniques, including least-squares fitting, principal component analysis, singular spectrum analysis, multichannel singular spectrum analysis, auto-regressive models, and the incorporation of prior data in these techniques. To inter-compare these techniques, we employ a remove-and-restore approach, removing existing GRACE products and predicting missing months using interpolation techniques. We provide detailed comparisons of the techniques and discuss their strengths and limitations. The auto-regressive interpolation technique delivers the best score according to our evaluation metric. The interpolation based on a least-squares fitting of constant, trend, annual, and semi-annual cycles offers a simple and effective prediction with a good score. Through this assessment, we establish a starting benchmark for gap-filling techniques in Earth’s time-variable gravity field analysis.

Information linkages across countries around net zero announcements

This study investigates the information linkages around net zero announcements across countries. Relying on rational expectation theory, this study employs the generalized method of moments (GMM) as well as the implied volatility approach to quantify volatility linkages between exchange-traded funds (ETFs) from nine countries and a global ETF (WLD). The GMM analysis reveals that volatility linkages among country ETFs and WLD range from 39.67 % to 71.43 %, while the implied volatility approach indicates that volatility linkages between markets range from 32.31 % to 65.36 %, indicating significant information spillover across countries. A time-varying dynamic analysis further shows that the US Government's net zero announcement increased volatility linkages across markets by 8.7 % to 58.05 %, signaling market approval of the US commitment to net zero targets. Multivariate analysis of the monthly correlation between country ETFs and WLD shows that the US plays a pivotal role. Although net zero announcements by the US, UK, and China individually impacted market correlations, the effect of China's announcement was insignificant when all announcements were considered in the model. Without US participation, efforts by other countries to achieve global net zero goals are unlikely to succeed.

Active galactic nucleus time-variability analysis and its caveats

In this study, we demonstrate some of the caveats in common statistical methods used for analysing astronomical variability timescales. We consider these issues specifically in the context of active galactic nuclei (AGNs) and use a more practical approach compared to mathematics literature, where the number of formulae may sometimes be overwhelming. We conducted a thorough literature review both on the statistical properties of light-curve data, specifically in the context of sampling effects, as well as on the methods used to analyse them. We simulated a wide range of data to test some of the known issues in AGN variability analysis as well as to investigate previously unknown or undocumented caveats. We discovered problems with some commonly used methods and confirmed how challenging it is to identify timescales from observed data. We find that interpolation of a light curve with biased sampling, specifically with bias towards flaring events, affects its measured power spectral density in a different manner than those of simulated light curves. We also find that an algorithm aiming to match the probability density function of a light curve has often been used incorrectly. These new issues appear to have been mostly overlooked and not necessarily addressed before, especially in astronomy literature.

Time-heterogeneous impacts of global temperature cycle on world business cycle

ABSTRACT We study the statistical relationship between the world business cycle and the global temperature cycle. To amplify the signal-to-noise ratio, we estimate a two-state latent dynamic process from the original data using the endogenous regime-switching methodology. Subsequently, we apply a time-varying structural VAR analysis to identify the time-heterogeneous relationship between the extracted latent factors. Our findings provide empirical evidence that the global mean temperature cycle has a negative impact on the world business cycle during super El Niño periods, which are characterized by relatively high temperature variance records given the past information.

Time varying torsional stiffness analysis of RV reducer

The rotary vector reducer (RV reducer) is a key reduction mechanism for industrial robots, and its torsional stiffness directly determines positioning accuracy and dynamic characteristics. Unlike traditional time-varying torsional stiffness analysis methods considering limited factors and statics solution method, a time-varying torsional stiffness analysis method considering output torque variability and dynamic nonlinearity is proposed to assess the time-varying torsional stiffness of the reducer. Firstly, the rigid-flexible coupling multi-body dynamic model was built employing software ADAMS, with crank shaft and cycloidal gears being treated as flexible bodies. Its correctness was verified under standard condition through grey correlation theory and the torsional stiffness under different conditions was obtained, respectively. Secondly, a contact finite element model of the reducer was established, taking contact relationships between different shaft-gear-bearings into account. As a comparison, a rotating-join finite element model was also constructed. And their torsional stiffness under experimental condition was calculated. Finally, the torsional stiffness test was conducted to verify the accuracy of the three models under this condition. The results show that the time-varying torsional stiffness obtained by the proposed method is only 8.83% different from the test results and exhibiting a certain periodic pattern under standard conditions.

Empirical insights into India's energy demand: unveiling elasticity dynamics for strategic policy initiatives.

Policymakers and stakeholders can use insights from price elasticity and income elasticities of energy demand to devise targeted interventions that promote energy efficiency, reduce reliance on fossil fuels, and drive the transition to more sustainable and resilient energy systems. This study seeks to examine how changes in income elasticity and price elasticity affect energy consumption over time using the ARDL model and a time-varying parameter technique, state-space Kalman filter. The study's findings reveal that energy prices have a negative and significant impact on energy consumption, whereas GDP per capita and population growth have apositive and significant impact on energy consumption in both the short and long run. The findings underline the importance of economic growth and increasing population in driving growing energy demand in India, which is consistent with the country's energy-intensive development aspirations. The time-varying analysis shows that the price elasticity of energy consumption follows a U-shaped pattern across time. However, from the onset of the new millennium decade, the elasticity of energy consumption with regard to its price has continually decreased, reaching - 0.54 by 2020. Furthermore, the findings imply that the income elasticity of India's energy demand has stabilized around unity, indicating that the country is currently near the middle of the energy intensity-GDP inverted-U trajectory. Given these evolving elasticities, policymakers should take a flexible and forward-thinking strategy to achieve optimal energy consumption levels.

Risk of Discontinuance in Contraceptive Uptake Among Kenya Women: A Survival Analysis Using the Cox Proportional Hazard Model

A problem of equal importance is the substantial challenge posed by the discontinuance of contraceptive use in family planning programs, particularly for developing countries such as Kenya. Investigating the determinants of contraceptive discontinuation using demographic factors can provide insights to enhance the efficacy of family planning. Using data from the Performance Monitoring for Action (PMA) survey, this study examined the demographic and contraceptive method-related covariates associated with the risk of discontinuation of contraception among Kenyan women. The study employed Cox proportional hazards models to analyze sampled data of Kenyan women from the PMA survey. Two separate models were used: a standard model which included demographic characteristics with a baseline contraceptive variable (Ever use contraceptive) and an extended model showing how specific contraceptive methods impact the likelihood of the risk of discontinuing contraception. Older age (Coefficient of -0.268 with a hazard ratio of 0.765) was associated with a decreased risk of contraceptive discontinuation. At the same time, women with higher educational levels (Coefficient of 0.015, with a hazard ratio of 1.015) were related to an elevated hazard and were more likely to discontinue their current method of contraception. The extended model showed that subcutaneous injectable contraceptives (Coefficient of -2.040 with a hazard ratio of 0.130) and male condoms (Coefficient of -1.137 with a hazard ratio of 0.321) were conditionally related to a significantly decreased hazard of discontinuation. The results highlight the importance of contraception choice and the significance of age and education in maintaining continuous contraception. To support Kenyan women in maintaining continuous contraception in achieving their reproductive health goals comprehensive education on emergency contraception and the provision of long-acting reversible contraceptives should be provided by health and family planning organizations. There is a call for a longitudinal study to offer an understanding of the dynamic behavior of contraception over time and the need for time-varying analyses to capture the evolving nature of contraception and its discontinuation.

High-frequency whole blood donation and its impact on mortality: Evidence from a data linkage study in Australia.

Previous reports suggest that blood donors have a lower mortality risk, which may partially reflect the "healthy donor effect" (HDE). HDE arises in donors due to selection bias and confounding if not appropriately addressed. Using the Sax Institute's 45 and Up Study data linked with blood donation history, we used a "5-year exposure window" method to select donors into regular high-frequency whole blood (WB)donors (at least two donations per exposure year) and low-frequency donors (remaining donors) with an active donation career of 5 years. To further reduce the confounding, we used statistical approaches like the inverse probability weighted (IPW) marginal structural model and the doubly robust targeted minimum loss-based estimator (TMLE), which incorporated machine learning algorithms and time-varying analyses. We selected 4750 (64.7%) low-frequency and 2588 (35.3%) high-frequency donors in the analyses. A total of 69 (1.5%) from the low-frequency and 45 (1.7%) donors from the regular high-frequency group died during the 7-year follow-up period. We did not find any statistically significant association between regular high-frequency blood donation and mortality (IPW RR = 0.98 95% CI 0.68, 1.28). TMLE model also showed similar results to IPW (RR = 0.97 95% CI 0.80, 1.16). Time-varying TMLE did not find any significant association between high-frequency donation and all-cause mortality either (RR = 0.98 95% 0.74, 1.29). We did not find a significant association between regular high-frequency WB donation and all-cause mortality when appropriate methods were employed to minimize the HDE.