Time Points Of Change Research Articles

Exploring the Keyword Network Analysis and Research Trends for Sustainable Development of Paralympic Research: Based on Changes in Classification

This study aims to identify an overall keyword network by drawing on core keywords in Paralympic studies and analyzing the relationships. It also aims to provide information on the changes in Paralympic study trends through a time series analysis to understand change, according to the application of a new classification (evidence-based classification). This study made a bibliographic list of 1613 studies by collecting studies published between 2003 and 2022 using keyword network analysis (KNA). By refining to exclude unsuitable studies, 1404 studies and 6414 keywords were used for the analysis. As a result of analyzing entire Paralympic studies performed between 2003 and 2022, Paralympic studies were revealed to be on the rise. The entire Paralympic studies ascertained that the areas of study were mainstream and centered on the top three keywords: athlete; performance; and classification. Secondly, as a result of analyzing Paralympic study trends in a time series manner, based on 2017 when a new classification was applied, studies related to physical impairment centered on the top two keywords, athlete and performance, were mainstream in Section 1 (2003–2016). Olympic Games, performance, and wheelchairs formed detailed study areas. In the Paralympic studies in Section 2 (2017–2022), the relevant studies centered on the top two keywords, athletes and performance, were mainstream. Parasport, disability, and classification were ascertained to form detailed study areas. Through all this, the changed trend in the keyword Paralympics was confirmed. The results of this study are meaningful in that they can contribute to the expansion of the Paralympic areas of study and invigoration by exploring core keywords and providing relevant information based on the significant change point in time, including entire Paralympic study trends.

Bayesian online reliability estimation based on two-stage wiener process degradation model

Deteriorate process of products with the effect of various degradation mechanism or complex components often shows a multiphase degradation phenomenon. Misspecification of single-phase degradation model will lead to poor performance. To make full use of degradation signals, a two-stage degradation model considering random effects on deteriorate rate and change time point is established to enhance the accuracy of degradation description and reliability assessment. Online parameter updating and reliability estimation are applied to reflect the equipment status and capability. We also analyze the reliability estimation error that a product with two-stage degradation signals may introduce when change point time has not shown off. Simulation study is conducted to verify the proposed method.

Bayesian Paleomagnetic Euler Pole Inversion for Paleogeographic Reconstruction and Analysis

AbstractApparent polar wander paths (APWPs) synthesized from paleomagnetic poles provide the most direct data for reconstructing past paleogeography and plate motions for times earlier than ca. 200 Ma. In this contribution, we describe a new method for APWP synthesis that extends the paleomagnetic Euler pole analysis of Gordon et al. (1984, https://doi.org/10.1029/TC003i005p00499) by placing it within the framework of a Bayesian inverse problem. This approach incorporates uncertainties in pole positions and age that are often ignored in standard treatments. The paleomagnetic Euler poles resulting from the inversions provide estimates for full‐vector plate motion (both latitude and longitude) and associated uncertainty. The method allows for inverting for one or more Euler poles with the timing of changepoints being solved as part of the inversion. In addition, the method allows the incorporation of true polar wander rotations, thus providing an avenue for probabilistic partitioning of plate tectonic motion and true polar wander based on paleomagnetic poles. We show example inversions on synthetic data to demonstrate the method's capabilities. We illustrate application of the method to Cenozoic Australia paleomagnetic poles which can be compared to independent plate reconstructions. A two‐Euler pole inversion for the Australian record recovers northward acceleration of Australia in the Eocene with rates that are consistent with plate reconstructions. We also apply the method to constrain rapid rates of motion for cratonic North America associated with the Keweenawan Track of late Mesoproterozoic paleomagnetic poles. The application of Markov chain Monte Carlo methods to estimate paleomagnetic Euler poles can open new directions in quantitative paleogeography.

Secular changes in metamorphism and metamorphic cooling rates track the evolving plate-tectonic regime on Earth

When disturbed, dynamic emergent systems, such as tectonics on Earth, may transition from one stable state to another if the perturbation is sufficiently large. Here, we identify such state shifts through examination of statistically significant change points in the time series of metamorphic pressure–temperature and cooling rate data. Change points occur in the mid-Paleoproterozoic, the Mesoproterozoic, the early and late Paleozoic and the Cenozoic. To compare the timing of change points with mantle geodynamics, we interrogate the time series of calculated mantle potential temperature, which yields a statistically significant drop of >130°C in the mid-Paleoproterozoic, indicating a change in the mechanism of mantle cooling. We interpret changes in the mid-Paleoproterozoic to relate to the global emergence of stable subduction, orogenesis associated with the formation of the Nuna megacontinent, and slab breakoff, consistent with the operation of a distinctive style of Proterozoic plate tectonics. By contrast, changes from the dawn of the Cambrian onward may relate to the large volume of sediments supplied to trenches following multiple Cryogenian and Paleozoic glaciations. Sediments acted as a lubricant, allowing deeper subduction, transport of lower continental plates to mantle depths and faster metamorphic cooling rates, features characteristic of modern plate tectonics. Supplementary material: A dataset of metamorphic cooling rates is available at https://doi.org/10.6084/m9.figshare.c.5943418

After-hours esophagectomy may pose additional risk to patients with esophageal cancer.

BackgroundThe increase in the incidence of esophageal cancers (ECs) combined with fewer surgeons working at large centers will increase the likelihood of surgery for ECs being performed during later hours. This study aimed to compare esophagectomies’ operative outcomes for EC performed at different surgical starting times.MethodsThis was a single-center, retrospective study. Risk-adjusted cumulative sum curve analysis and Cox regression analysis were used to identify the potential change-point of surgical starting times. The participants were then divided into 2 groups according to the change-point time. Propensity score matching was used to control confounding factors between the 2 groups. We compared the short- and long-term outcomes in both groups.ResultsA total of 702 patients who underwent potentially radical esophagectomy from 7 May 2014 to 31 December 2017 in our institute were included. The 3-year all-cause mortality showed a significant change-point at 16:42, with an increment from 56.5% to 76.9% (P=0.043). Esophagectomy that commenced between 17:00–18:59 was associated with significantly lower overall survival (OS) [multivariate hazard ratio (HR): 2.47; 95% confidence interval (CI): 1.25 to 4.90; P=0.010] and disease-free survival (DFS) (multivariate HR: 2.14; 95% CI: 1.08 to 4.21; P=0.028). The participants were allocated to the during-hours group and the after-hours group according to the change-point of 17:00. A total of 84 participants in the during-hours group were matched to 33 participants in the after-hours group. The median operative time was shorter in the after-hours group [309 (during-hours) vs. 239 (after-hours) minutes, P=0.014); the after-hours group had a greater incidence of respiratory complications (22.63% vs. 45.45%, P=0.023) and 90-day mortality (0 vs. 9.09%, P=0.021). The 5-year OS (P=0.042) and DFS (P=0.030) were significantly higher in the during-hours group.ConclusionsEsophagectomies started during after-hours are correlated with poorer surgical outcomes. It is recommended to cancel selective esophagectomies due to commence after 17:00.

Bayesian Change-Point Joint Models for Multivariate Longitudinal and Time-to-Event Data

Joint modeling of longitudinal and survival data is an active area of statistical research that has received much attention recently. Although it is a common practice to analyze complex longitudinal data using nonlinear mixed-effects (NLME) or nonparametric mixed-effects (NPME) models in literature, the following issues may standout: (i) In practice, the profile of each subject’s longitudinal response may follow a “broken-stick” like trajectory, indicating multiple phases of increase and/or decline in response trajectory. Such multiple phases (with random change-points) may be an important indicator to help quantify treatment effect and clinical diagnosis of a disease. To estimate random change-points, NLME or NPME models become a challenge. (ii) Many studies are often to collect data of multivariate longitudinal variables which may be significantly correlated, ignoring their correlation may lead to biased estimate and reduce efficiency. Moreover, the time-to-event may be dependent of the multivariate longitudinal measures and it is of importance to explore their association. (iii) Missing observations in the longitudinal responses are often encountered. The missing data are likely to be informative (nonignorable) and ignoring this phenomenon may result in inaccurate statistical inference. In this article, under a Bayesian framework we consider a piecewise joint model for multivariate longitudinal and time-to-event data to accurately estimate change rates of longitudinal trajectory patterns and timing of change-point which may be critical indicators to quantify the effect of longitudinal profile on the risk of an event. The proposed models and method are applied to analyze a longitudinal dataset arising from a diabetes study. Simulation studies are conducted to assess the performance of the proposed models under various scenarios.

A Bayesian hierarchical change point model with parameter constraints.

Alzheimer's disease is the leading cause of dementia among adults aged 65 or above. Alzheimer's disease is characterized by a change point signaling a sudden and prolonged acceleration in cognitive decline. The timing of this change point is of clinical interest because it can be used to establish optimal treatment regimens and schedules. Here, we present a Bayesian hierarchical change point model with a parameter constraint to characterize the rate and timing of cognitive decline among Alzheimer's disease patients. We allow each patient to have a unique random intercept, random slope before the change point, random change point time, and random slope after the change point. The difference in slope before and after a change point is constrained to be nonpositive, and its parameter space is partitioned into a null region (representing normal aging) and a rejection region (representing accelerated decline). Using the change point time, the estimated slope difference, and the threshold of the null region, we are able to (1) distinguish normal aging patients from those with accelerated cognitive decline, (2) characterize the rate and timing for patients experiencing cognitive decline, and (3) predict personalized risk of progression to dementia due to Alzheimer's disease. We apply the approach to data from the Religious Orders Study, a national cohort study of aging Catholic nuns, priests, and lay brothers.

Anomaly Detection in Activities of Daily Living with Linear Drift

Anomalyq detection in Activities of Daily Living (ADL) plays an important role in e-health applications. An abrupt change in the ADL performed by a subject might indicate that she/he needs some help. Another important issue related with e-health applications is the case where the change in ADL undergoes a linear drift, which occurs in cognitive decline, Alzheimer’s disease or dementia. This work presents a novel method for detecting a linear drift in ADL modelled as circular normal distributions. The method is based on techniques commonly used in Statistical Process Control and, through the selection of a convenient threshold, is able to detect and estimate the change point in time when a linear drift started. Public datasets have been used to assess whether ADL can be modelled by a mixture of circular normal distributions. Exhaustive experimentation was performed on simulated data to assess the validity of the change detection algorithm, the results showing that the difference between the real change point and the estimated change point was \(4.90_{+3.17}^{-1.98}\) days on average. ADL can be modelled using a mixture of circular normal distributions. A new method to detect anomalies following a linear drift is presented. Exhaustive experiments showed that this method is able to estimate the change point in time for processes following a linear drift.

Profiles of Cognitive Change in Preclinical and Prodromal Alzheimer's Disease Using Change-Point Analysis.

Alzheimer's disease (AD) is now understood to have a long preclinical phase in which pathology starts to accumulate in the absence of clinical symptoms. Identifying the temporal stages of accelerated cognitive decline in this phase may help in developing more sensitive neuropsychological tools for early screening of preclinical cognitive decline. Change-point analyses are increasingly used to characterize the temporal stages of accelerated cognitive decline in the preclinical stages of AD. However, statistical comparisons of change-points between specific cognitive measures have not been reported. To characterize and compare the temporal stages of accelerated decline in performance on multiple cognitive tests in a sample of participants from the Baltimore Longitudinal Study on Aging (BLSA) who later developed AD. 165 older adults (baseline age range: 61.1-91.2) from the BLSA developed AD during follow-up. Linear and non-linear mixed models were fit for 11 cognitive measures to determine change-points in rates of decline before AD diagnosis. Bootstrapping was used to compare the timing of change-points across cognitive measures. Change-points followed by accelerated decline ranged from 15.5 years (Standard Error (S.E.) = 1.72) for Card Rotations to 1.9 years (S.E. = 0.68) for the Trail-Making Test Part A before AD diagnosis. Accelerated decline in Card Rotations occurred significantly earlier than all other measures, including learning and memory measures. Results suggest that visuospatial ability, as assessed by Card Rotations, may have the greatest utility as an early predictive tool in identifying preclinical AD.

An optimal control chart for finite matrix sequences at some unknown change point

We present a new measure for evaluating the performance of control charts to detect abrupt changes of finite matrix sequences. The objective is to minimize the probability that the control chart fails to raise the alarm at unknown change point time for a given in-control average run length. We construct and prove the optimal control chart with dynamic control limits in different pre- and post-change distributions. We validate the optimality of the proposed chart by conducting exhaustive experiments on both simulation study and real-world data.

Regional detection of multiple change points and workable application for precipitation by maximum likelihood approach

Precipitation is an essential part of the hydrological cycle. Objective change-point detection plays an important role in the research on extreme climate events and risk assessment in the context of global climate change. The study can automatically identify and extract multiple change points over a lengthy time series and calculate trends over several segmentations by uniting the maximum likelihood approach and the nonparametric Mann-Kendall trend test which is also compared with ordinary least squares (OLS). The observed station precipitation data were compiled over Hebei Province in China from 1961 to 2014. Temporal-spatial characteristics were also investigated by using several indices: the number of change points, standard deviation, timing of change points, minimum and maximum trends for segmentations, and the generalization trend. Obvious change points were generally around 1974, 1981, and 1998, and occurred at few stations before 1974 and after 2000, indicating that precipitation was relatively stable in the study area during the periods 1961–1974 and 2000–2014; the minimum trend for segmentations decreased over all stations; the maximum trend for segmentations increased over all stations except Leting; and generalization trend weakened abrupt changes in specific time sections among the multiple segmentations. Change-point detection followed by trend analysis can detect an obvious increasing or decreasing trend over certain parts of the time series and the proposed method can serve as a management tool with proper measures to deal with climate change. The results for both segmentations and generalization can provide a workable reference for managing regional water resources and implementing strategies to mitigate meteorological risks.

Does the inclusion of vehicle impoundment in provincial short-term administrative driver’s license suspension programs reduce total and alcohol-related fatal collisions in Canada?

Objectives: To determine (1) whether the implementation of vehicle impoundment as part of provincial short-term administrative driver licensing suspension (ADLS) programs significantly reduced total and alcohol-related collision fatalities, and (2) if provinces with vehicle impoundment as part of their short-term ADLS programs see greater reductions in total and alcohol-related fatal collisions when compared to provinces without a vehicle impoundment law.Methods: Data on monthly total and alcohol-related fatal collisions from January 2005 to December 2016 are drawn from British Columbia (BC), Alberta (AB), Saskatchewan (SK), Manitoba (MB) and Ontario (ON). Changepoint time series analysis of fatal crashes is employed to detect within-province differences after implementing short-term impoundment programs, and between province differences comparing provinces with short-term impoundment programs (BC, introduced October 2010; AB, introduced July 2012; and SK, introduced July 2014) and those without (ON and MB). Outcome measures are the monthly per capita total and alcohol-related fatal collisions.Results: A significant reduction in per-capita alcohol-related fatal crashes was observed in British Columbia (−47.4%) in the period following the adoption of vehicle impoundment in their short-term ADLS. A significant decrease was also observed in Alberta (−37.5%), though this trend began prior to policy change; no significant effect was observed in per capita alcohol-related fatal crashes rates in Saskatchewan (−6.1%) in the two years following the introduction of vehicle impoundment. Ontario and Manitoba, two provinces without mandatory vehicle impoundment laws, also experienced significant reductions in per-capita alcohol-related fatal crashes between 2005 and 2016 (−36.4% and −35%, respectively).Conclusions: While mandatory vehicle impoundment programs for driving in the “warn” range (0.05%–0.08% BAC) have shown success in reducing fatal crashes in British Columbia, similar reductions in two other provinces with short-term vehicle impoundment were not observed. Moreover, large reductions in fatal crashes were observed in two provinces without vehicle impoundment as part of their short-term ADLS programs. Collectively, these findings suggest that vehicle impoundment, alone, has limited impact on fatal crash rates and that other factors help to explain the observed trends. Further analysis of new vehicle impoundment programs is warranted.

Hydro-Meteorological Trends in the Upper Omo-Ghibe River Basin, Ethiopia

This study aimed to assess trends of hydro-meteorological variables in the Upper Omo-Ghibe river basin, Ethiopia. Data records from eleven rainfall, eight air temperature, and five streamflow stations between 1981 to 2008 were investigated. The trends and change points were evaluated for different periods of time depending on data availability. Mann-Kendall and Pettit tests were used to identify trends and change points at a 5% significance level. The tests were applied to mean annual, monthly and seasonal time scales. Rainfall exhibited statistically decreasing trends at a mean annual time scale, while seasonal rainfall depicted heterogeneous results in both directions. For the majority of the stations, air temperature showed statistically significant increasing trends. The magnitude of change in temperature for mean annual, wet and dry season has increased about 0.48, 0.46, and 0.61 °C per decade for Jimma station. The Pettit test revealed that the late 1980s and 1990s were the change points. There is generally a decreasing trend in streamflow. The decline in annual rainfall and rise in temperature affected the streamflow negatively. Overall, the results indicate that trend sand change point times varied considerably across the stations and catchments. The identified significant trends can help to support planning decisions for water management.

Using a non-homogeneous Poisson model with spatial anisotropy and change-points to study air pollution data

A non-homogeneous Poisson process is used to study the rate at which a pollutant’s concentration exceeds a given threshold of interest. An anisotropic spatial model is imposed on the parameters of the Poisson intensity function. The main contribution here is to allow the presence of change-points in time since the data may behave differently for different time frames in a given observational period. Additionally, spatial anisotropy is also imposed on the vector of change-points in order to account for the possible correlation between different sites. Estimation of the parameters of the model is performed using Bayesian inference via Markov chain Monte Carlo algorithms, in particular, Gibbs sampling and Metropolis-Hastings. The different versions of the model are applied to ozone data from the monitoring network of Mexico City, Mexico. An analysis of the results obtained is also given.

Change point analysis of global temperature records

Climate change is the result of complex interactions between a wide array of climatic variables. Over a long period of time, climatic patterns can shift, possibly multiple times. Abrupt shifts that occur over a relatively short period of time are known as change points. During these intervals, different climatic variables may undergo dramatic shifts posing serious consequences for many biological and physical systems. In this paper, we discuss a Bayesian algorithm for detecting the location of change points in time series data. In particular, we utilize our method to analyse five different global surface temperature anomaly data sets, as well as temperature records associated with land, ocean, and different zonal bands in an attempt to identify common features of these data sets. Change points in the five global records were detected between 1902–1917, 1936–1945, and 1963–1976, but do these change points also manifest themselves across other parts of the globe? Our analysis indicates that the timing of change points is consistent from one record to the next, but not all change points appear in all records. In particular, there were more change points in the ocean than on land, and also more change points in southern latitudes than in northern latitudes.

Multiple changepoint detection with partial information on changepoint times

This paper proposes a new minimum description length procedure to detect multiple changepoints in time series data when some times are a priori thought more likely to be changepoints. This scenario arises with temperature time series homogenization pursuits, our focus here. Our Bayesian procedure constructs a natural prior distribution for the situation, and is shown to estimate the changepoint locations consistently, with an optimal convergence rate. Our methods substantially improve changepoint detection power when prior information is available. The methods are also tailored to bivariate data, allowing changes to occur in one or both component series.

Articulation and Dynamics Influence the Perceptual Attack Time of Saxophone Sounds.

Perceptual attack time (PAT) is defined as the moment when the most salient rhythmical feature of a sound is perceived. This paper focuses on the PAT of saxophone sounds, investigating how the location of this point in time changes when a note is played with different characteristics. Nine saxophone sounds that differ in articulation and dynamics were examined. Ground truth for PAT was determined in a synchronization judgment experiment with 40 participants. Articulation (p < 0.001, η2 = 0.316), dynamics (p < 0.001, η2 = 0.098), and their interaction (p < 0.001, η2 = 0.094) affected the placement of the PAT. The onset rise time, which has been used as a predictor for PAT in earlier studies, was only weakly correlated with PAT (r = 0.143, p = 0.006).

Su1235 - What is Appropriate Upper Endoscopy Interval Among Dyspeptic Patients with Previously Normal EGD? A Multicenter Study with Bayesian Time-Change Point Analysis

Su1235 - What is Appropriate Upper Endoscopy Interval Among Dyspeptic Patients with Previously Normal EGD? A Multicenter Study with Bayesian Time-Change Point Analysis

A Bayesian screening approach for hepatocellular carcinoma using multiple longitudinal biomarkers.

Advanced hepatocellular carcinoma (HCC) has limited treatment options and poor survival, therefore early detection is critical to improving the survival of patients with HCC. Current guidelines for high-risk patients include ultrasound screenings every six months, but ultrasounds are operator dependent and not sensitive for early HCC. Serum α-Fetoprotein (AFP) is a widely used diagnostic biomarker, but it has limited sensitivity and is not elevated in all HCC cases so, we incorporate a second blood-based biomarker, des'γ carboxy-prothrombin (DCP), that has shown potential as a screening marker for HCC. The data from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial is a valuable source of data to study biomarker screening for HCC. We assume the trajectories of AFP and DCP follow a joint hierarchical mixture model with random changepoints that allows for distinct changepoint times and subsequent trajectories of each biomarker. The changepoint indicators are jointly modeled with a Markov Random Field distribution to help detect borderline changepoints. Markov chain Monte Carlo methods are used to calculate posterior distributions, which are used in risk calculations among future patients and determine whether a patient has a positive screen. The screening algorithm was compared to alternatives in simulations studies under a range of possible scenarios and in the HALT-C Trial using cross-validation.

An application of Dirichlet process in clustering subjects via variance shift models: A course-evaluation study

In this article, the Dirichlet process (DP) is applied to cluster subjects with longitudinal observations. The basis of clustering is the ability of subjects to adapt themselves to new circumstances. Indeed, the basis of clustering depends on the time of changing response variability. This is done by providing a random change-point time in the variance structure of mixed-effects models. The DP is assumed as a prior for the distribution of the random change point. The discrete nature of the DP is utilized to cluster subjects according to the time of adaption. The proposed model is useful to identify groups of subjects with distinctive time-based progressions or declines. Transition mixed-effects models are also used to account for the serial correlation among observations over time. A joint modelling approach is utilized to handle the bias created in these models. The Gibbs sampling technique is adopted to achieve parameter estimates. Performance of the proposed method is evaluated via conducting a simulation study. The usefulness of the proposed model is assessed on a course-evaluation dataset.