False Events Research Articles

Suppressing Coda Events with a Bayesian Model of Global Scale Seismology

AbstractLarge seismic events often trigger a wave train of slow decaying energy known as the coda that can mislead signal detectors into forming coda detections that appear to look like regular phase detections. These coda detections can confuse event formation algorithms into building false events known as coda events. Naive solutions to this problem by dropping any detection that looks like a coda detection can have the negative consequence of missing real events. We propose to address this issue by extending an existing Bayesian approach, NET-VISA that has been designed to build event bulletins using a generative model of global-scale seismology. Our extensions significantly boost the existing work by reducing the total number of false events by nearly half and virtually eliminating coda events without changing the number of real events.

Relation between Deese-Roediger-Mcdermott recall measures of false memory and the fading affect bias

The fading affect bias (FAB) is the faster fading of unpleasant affect than pleasant affect for autobiographical event memories, and it is considered a healthy coping mechanism because it is positively related to healthy measures (e.g., self-esteem and positive PANAS), whereas it is negatively related to unhealthy measures (e.g., psychological distress and negative PANAS). Some researchers suggest that Deese-Roediger McDermott (DRM) critical lure false memories for words are conceptually equivalent to false memories for autobiographical event memories, which has not been examined. Based on the finding that false autobiographical event memories negatively predict FAB, the current study tested if false DRM word memories would negatively predict FAB for autobiographical event memories, which would demonstrate support for the conceptual equivalence of DRM memories and autobiographical event memories. We found that three measures of false word recall positively predicted FAB, which is a result that is contrary to prior findings and the contention that DRM false memories for words are conceptually the same as autobiographical event memories.

False spring events in the eastern part of the Baltic Sea region

False spring events in the eastern part of the Baltic Sea region

Paper chip-based colorimetric biosensor for glucose by using CuFe2O4@GO as nanozyme

The development of nanomaterials with notable enzyme-like activity is of great significance to the field of bioanalysis. Herein, a nanomaterial composed of copper ferrite nanoparticles (CuFe2O4 NPs) and graphene oxide (GO) was successfully prepared (CuFe2O4@GO). The obtained CuFe2O4@GO was proved to have peroxidase-like (POD-like) activity, which could catalyze the oxidation of 3,3′,5,5′-tetramethylbenzidine (TMB) to generate oxidized TMB (ox-TMB) in the presence of H2O2 with an obvious color change. On this basis, a colorimetric biosensor using a UV–vis spectrometer as readout and a paper chip-based colorimetric biosensor using a smartphone as readout was constructed for glucose detection, respectively, as glucose oxidase (GOD) could oxidize glucose to form H2O2. The UV–vis spectrometer-based approach exhibits a linear range of 0 to 1000 μM and a limit of detection (LOD) of 0.79 μM for glucose. The smartphone-based method shows a linear range of 0.1 to 50 mM and a LOD of 0.04 mM for glucose. Moreover, the fabricated colorimetric biosensors have good selectivity towards glucose and the sample matrixes cannot affect the detection of glucose. Finally, the concentration of glucose in human serum samples was successfully detected using two different ways, and the results are close to the clinical values. The proposed method embraces several good merits. Firstly, the colorimetric biosensors combining UV–vis spectrometer and smartphone offer precise results, which could effectively prevent the occurrence of false events. Secondly, paper-based microfluidic analytical device (μPAD) as analytical platform and smartphone as readout provides a versatile approach for constructing sensitive, cost-effective, and simple colorimetric biosensor for the visual monitoring of glucose. Lastly, except for glucose detection, this work also shows great application prospects for other small molecules detection that could generate H2O2.

Analyzing false turn-on events with varying gate drive parameters in high voltage GaN devices

In this paper, we address the problem of false turn-on effects in a half-bridge GaN power converter in terms of circuit and device parameters. The model shows that the inherent false turn-on problem is caused by slew rates dvdsdt and dvgsdtduring the switching transients occurring at the turn-on and off phases. A higher slew rate propagates the gate driver voltage to overshoot beyond the threshold voltage causing it to accidentally turn on This study shows that dvdsdt is dependent on the internal device parameters such as gfs (transconductance) and Coss(output capacitance). From the CV characteristics, it is pretty much evident that the internal capacitances Cossand Crss (reverse transfer capacitance) are reduced with higher drain voltage enabling higher slew rates which increases the probability of false turn-on problems. Experimental results at numerous operating points at 400 V with the variation in different gate drive parameters support the analysis.

Characterization of the starting conditions of the rainy season in Senegal: highlighting the constraints of crop establishment

The start of the rainy season in Senegal is characterized by critical variability, resulting in many crop failures after seed planting when a long dry spell occurs. The objective of this study is to characterize the starting conditions of the rainy season in different areas of Senegal in relation to crop success at the early stage. An analysis of four seasonal components determined from a daily rainfall database of 95 stations from 1950 to 2015. These seasonal components are the sowing date (Sowing), the Onset of the rainy season date (Onset), the length of the longest dry spell (DryMax), and the total rainfall (TotRain) during the 30 days after sowing. Statistical methods of time series homogeneity determination such as the Pettitt test, the Buishand test, the Von Newmann test, and the segmentation method have been applied to determine actual breakpoints and to obtain the most recent and homogeneous period to define the component in each site. The results indicate that these components have not exhibited statistically significant changes since 1950. Indeed, 3% of the stations show breakpoints for the Sowing, 4% for the TotRain, and 2% for the Onset. The start of the season follows a South East-North West gradient. It begins in the extreme South-East part of the country in the second decade of June while the first waves of sowing take place in the South-East center part from the second half of June. The north zone remains exposed to false start events with important seasonality. In the Northern and Central zones, the early or late character of the sowing passes more on the DryMax than the TotRain. It would be interesting to elucidate the effects of the rainfall regime at the early stage on the rainy season profile to gain better control of the pluvial crop yields.

Evaluating Precipitation Events Using GPM IMERG 30-Minute Near-Real-Time Precipitation Estimates

Abstract Accurately detecting and estimating precipitation at near–real time (NRT) is of utmost importance for the early detection and monitoring of hydrometeorological hazards. The precipitation product, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), provides NRT 0.1° and 30-min precipitation estimates across the globe with only a 4-h latency. This study was an evaluation of the GPM IMERG version 6 level-3 early run 30-min precipitation product for precipitation events from 2014 through 2020. The purpose of this research was to identify when, where, and why GPM IMERG misidentified and failed to detect precipitation events in California, Nevada, Arizona, and Utah in the United States. Precipitation events were identified based on 15-min precipitation from gauges and 30-min precipitation from the IMERG multisatellite constellation. False-positive and false-negative precipitation events were identified and analyzed to determine their characteristics. Precipitation events identified by gauges had longer duration and had higher cumulative precipitation than those identified by GPM IMERG. GPM IMERG had many false event detections during the summer months, suggesting possible virga event detection, which is when precipitation falls from a cloud but evaporates before it reaches the ground. The frequency and timing of the merged passive microwave (PMW) product and forward propagation were responsible for IMERG overestimating cumulative precipitation during some precipitation events and underestimating others. This work can inform experts that are using the GPM IMERG NRT product to be mindful of situations where GPM IMERG–estimated precipitation events may not fully resolve the hydrometeorological conditions driving these hazards. Significance Statement Accurately estimating rainfall to detect and monitor a precipitation event at near–real time is of utmost importance for hydrometeorological hazards. We used a state-of-the-art rainfall estimation product called GPM IMERG that uses infrared and passive microwave measurements collected from a constellation of satellites to produce near-real-time rainfall estimates every 30 min worldwide. The purpose of our research was to identify when, where, and why GPM IMERG falsely detected and missed precipitation events. Our results suggest that the frequency and timing of passive microwave precipitation with forward propagation were responsible for IMERG missing events, overestimating total rainfall during some precipitation events, and underestimating total rainfall in other precipitation events. Our future work will further investigate precipitation events using the GPM IMERG version 7 near-real-time product.

The Need of Trustworthy Announcements to Achieve Driving Comfort

An Intelligent Transport System (ITS) is more demanding nowadays and it can be achieved through deploying Vehicular Ad Hoc Networks (VANETs). Vehicles and Roadside Units (RSUs) exchange traffic events. Malicious drivers generate false events. Thus, they need to be identified to maintain trustworthy communication. When an authorised user acts maliciously, the security scheme typically fails. However, a trust model can isolate false messages. In this paper, the significance of trustworthy announcements for VANETs is analysed. To this end, a series of experiments is conducted in Veins to illustrate how the trustworthiness of announcements affects travel time. A traffic scenario is created where vehicles detour to an alternate route with an announcement from the leading vehicle. Both true and false announcements are considered. Results confirm that false announcements and refraining from announcements increase travel time. However, the travel time is reduced with trustworthy announcements. From this analysis, it can be concluded that trustworthy announcements facilitate driver comfort.

It Is Clean, But It Still Seems Dirty to Me: Implicit and Explicit Truth of Imagined Contamination as an Explanation of Ego-Dystonic Experience of Obsessions

Individuals suffering from obsessive-compulsive disorder often experience obsessions as ego-dystonic, knowing that a mental event does not reflect reality but acting as if it does. Imagination has been suggested as an important process involved in this mismatch about the actual truth value of obsessions. Imagining false events has been found to impact spontaneous truth evaluations (implicit truth value; ITV), even when people explicitly acknowledged the imagined event as false (explicit truth value; ETV; Shidlovski et al., 2014). The current study examined discrepancies between ITV and ETV along with effects of imagination and their relationship to obsessive-compulsive contamination symptoms. Sixty-two students, recruited across the range of contamination symptoms, participated in this study. First, a table, at which participants were seated, was cleaned in their presence. Next, in a double-categorization autobiographical implicit association test (aIAT), participants classified true or false autobiographical statements together with “clean” or “contaminated” statements regarding the table. Then, they imagined that the table was contaminated prior to the second aIAT administration. ETV was measured by classifying the statements explicitly as true or false. Imagination was associated with reduced ITV, this association did not depend on symptoms, contrary to our predictions. However, symptoms were associated with elevated ETV evaluations of the contaminated statements. Finally, ETV correlated with ITV only for low symptom participants. This correlation was no longer significant following the imagination induction. In conclusion, individuals with contamination symptoms may be more likely to overweigh their explicit evaluations in the presence of contradictory implicit evaluations, creating a discrepancy. This process may account for ego-dystonic experiences reported by OCD patients.

High Temporal Resolution Analyses with GOES-16 Atmospheric Motion Vectors of the Non-Rapid Intensification of Atlantic Pre-Bonnie (2022)

Four-dimensional COAMPS Dynamic Initialization (FCDI) analyses that include high-temporal- and high-spatial-resolution GOES-16 Atmospheric Motion Vector (AMV) datasets are utilized to understand and predict why pre-Bonnie (2022), designated as a Potential Tropical Cyclone (PTC 2), did not undergo rapid intensification (RI) while passing along the coast of Venezuela during late June 2022. A tropical cyclone lifecycle-prediction model based on the ECMWF ensemble indicated that no RI should be expected for the trifurcation southern cluster of tracks along the coast, similar to PTC 2, but would likely occur for two other track clusters farther offshore. Displaying the GOES-16 mesodomain AMVs in 50 mb layers illustrates the outflow burst domes associated with the PTC 2 circulation well. The FCDI analyses forced by thousands of AMVs every 15 min document the 13,910 m wind-mass field responses and the subsequent 540 m wind field adjustments in the PTC 2 circulation. The long-lasting outflow burst domes on both 28 June and 29 June were mainly to the north of PTC 2, and the 13,910 m FCDI analyses document conditions over the PTC 2 which were not favorable for an RI event. The 540 m FCDI analyses demonstrated that the intensity was likely less than 35 kt because of the PTC 2 interactions with land. The FCDI analyses and two model forecasts initialized from the FCDI analyses document how the PTC 2 moved offshore to become Tropical Storm Bonnie; however, they reveal another cyclonic circulation farther west along the Venezuelan coast that has some of the characteristics of a Caribbean False Alarm event.

Online burst detection in water distribution networks based on dynamic shape similarity measure

Monitoring water demand is extremely helpful in the early detection of issues and malfunctions in water distribution networks. Therefore, distinguishing abnormal water meter readings is an important problem in the management process. Although much focus has been given to this problem in the last few years, many of the approaches still lack efficiency and scalability when adapted to (near) real-time settings. To tackle these issues, we propose the Online Curvature Matcher, a real-time algorithm for the detection of pipe burst events in flow readings of District Metering Areas (DMA), which requires only a few weeks of data clean of burst events. In the proposed methodology, libraries of segments of the time series are constructed and then used to find abnormal patterns in consumption. We also propose the incorporation of additional constraints to address the complexities of real-world data (such as the existence of irrigation events, seasonal changes, and discrepancies in behavior due to weekday). We perform widespread testing on data from 32 DMA. The results indicate an increase in detected bursts compared to the state-of-the-art, while a decrease in the number of generated false positive events was achieved. Additionally, it was found that six weeks of history is the minimum amount of data necessary to perform online burst detection, while Dynamic Time Warping (DTW) is a suitable distance metric to measure the similarity between different flow reading segments.

Unringing the bell: Successful debriefing following a rich false memory study

In rich false memory studies, familial informants often provide information to support researchers in planting vivid memories of events that never occurred. The goal of the current study was to assess how effectively we can retract these false memories via debriefing – i.e., to what extent can we put participants back the way we found them? We aimed to establish (1) what proportion of participants would retain a false memory or false belief following debriefing, and (2) whether richer, more detailed memories would be more difficult to retract. Participants (N = 123) completed a false memory implantation protocol as part of a replication of the “Lost in the Mall” study (Loftus & Pickrell, Psychiatric Annals, 25, 720-725, 1995). By the end of the protocol, 14% of participants self-reported a memory for the fabricated event, and a further 52% believed it had happened. Participants were then fully debriefed, and memory and belief for the false event were assessed again. In a follow-up assessment 3 days post-debriefing, the false memory rate had dropped to 6% and false belief rates also fell precipitously to 7%. Moreover, virtually all persistent false memories were found to be nonbelieved memories, where participants no longer accepted that the fabricated event had occurred. Richer, more detailed memories were more resistant to correction, but were still mostly retracted. This study provides evidence that participants can be “dehoaxed”, and even very convincing false memories can be retracted.

Evaluation of limiting factors for SAR backscatter based cut detection of alpine grasslands

Several studies utilized C-band Synthetic Aperture Radar (SAR) backscatter time series to detect cut events of grasslands. They identified several potential factors hindering the detection: Vegetation characteristics, precipitation, and the timing of salvage of the harvested grass. This study uses a comprehensive in situ database to assess the impact of those factors on the detection rate of cut events by performing a cut detection based on Sentinel-1 backscatter time series and relating the accuracy to the potentially limiting factors. The results can be summarized in the following key findings: (i) The detection rate decreases significantly with grass heights below 35 cm and a biomass of less than 2100 kg/ha. As the grass of the first growth is typically characterized by greater height and higher biomass, first cuts achieved a higher accuracy with 85% compared to re-growth cuts with 65%. (ii) False positive cut events were related to higher precipitation amounts, but adding precipitation data to the model led only to a slight increase of the accuracy of re-growth cuts, but a decrease of the overall accuracy. (iii) No relation was found between the timing of salvage and the backscatter behaviour. These insights contribute to a better utilization of C-band backscatter for vegetation analysis and agricultural applications, including cut detection. Further research with dense in situ measurements, including Vegetation Water Content (VWC) is required to fully understand the behaviour of C-band backscatter over managed grasslands.

Accurate Detection and Tracking of Small-Scale Vehicles in High-Altitude Unmanned Aerial Vehicle Bird-View Imagery

Vehicle detection and tracking from unmanned aerial vehicles (UAVs) aerial images are among the main tasks of intelligent traffic systems. Especially in tasks with long distances, extensive backgrounds, and small objects, it increases the difficulty of localization and regression, which can easily lead to missed detections and false positives. This paper proposes a detection-based small-scale vehicle tracking framework that integrates an improved YOLOX network and the DeepSORT algorithm to address these issues. Based on the original YOLOX network, a shallow feature extraction network, 160 × 160 pixels, is added to enhance the ability to extract small-scale object features. A convolutional block attention module (CBAM) is inserted in front of the neck network to select crucial information for vehicle detection tasks while suppressing noncritical ones. EIoU_Loss is introduced as the bounding box regression loss function in training to speed up their convergence and improve the localization accuracy of the small objects. Furthermore, an image segmentation method is proposed to effectively reduce missed and false detection events. It divides the original high-definition image into multiple subimages, first detected and then reassembled. Finally, the improved YOLOX network is used as the detector of the DeepSORT to perform small-scale vehicle detection and tracking tasks in various traffic scenarios. Experiments show that the proposed method can significantly improve the detection accuracy of the network and effectively solve the problems of missed detection and false positives in small-scale vehicle tracking tasks in high-resolution aerial images captured by high-altitude UAVs. Significantly, the algorithm proposed in this paper has sufficient robustness for small-scale tracking tasks of aerial videos captured at different altitudes.

Equipment for the Detection of People Falling

There are people whose state of health requires permanent supervision. There are a number of medical or aging-related conditions that can have quite an impact on the daily life of these people, therefore, in order to provide them with proper assistance and care, various health monitoring devices have been developed. This article proposes the use of advanced technologies with the help of which we can observe and record the changes in the position of a person with such problems. The equipment has a sensor that can determine the position of the body by measuring the height from the ground. The device can be integrated into a belt or bracelet to record data in real time. This data is analyzed and interpreted by means of a program. The program can detect changes in posture, providing notifications and alerts when the person has suffered a fall. Greatly simplifying surveillance, the device also provides an objective assessment of a person's balance ability. Since each person has a specific posture and movement, the developed device must be tested and adapted to adjust the coefficients in the program, to avoid the triggering of false events.

A hybrid behavior- and Bayesian network-based framework for cyber–physical anomaly detection

In recent years, the increasing Internet connectivity and heterogeneity of industrial protocols have been raising the number and nature of cyber-attacks against Industrial Control Systems (ICS). Such cyber-attacks may lead to cyber anomalies and further to the failure of physical components, thus leading to cyber–physical attacks. In this paper, we present a novel unsupervised cyber–physical anomaly detection framework based on a hybrid “multi-formalism” approach that combines the outcomes of multiple unsupervised behavior-based anomaly detectors through a Bayesian network-based probabilistic modeling of the ICS. More precisely, the framework consists of two behavior-based anomaly detection modules that monitor separately and simultaneously the behavior of cyber and physical data acquired from the ICS. The outputs of such modules are filtered and combined through a Bayesian network-based modeling in order to improve the trustworthiness of the detected anomalies and to provide the detection probability of cyber, physical, and cyber–physical anomalies, taking into account possible cascading effects over the cyber–physical process. The outcomes achieved through the implementation of our framework on the hardware-in-the-loop Water Distribution Testbed (WDT) dataset show very high detection performance with a strong ability to reject false positive events and to isolate and localize the anomalies over the cyber–physical process.

Crosswell frequency-domain reverse time migration imaging with wavefield decomposition

Abstract Crosswell seismic technique can provide high-resolution imaging and monitoring of the subsurface. However, compared to the surface seismic, crosswell data contain more complex wave components, which increases the difficulty of seismic processing and migrations. Conventional acoustic reverse time migration (RTM) mainly uses the cross-correlation of the source forward and the receiver backward wavefields. The redundant information generated by cross-correlation may undermine the imaging reliability in the crosswell models. Thus, we develop a novel wavefield decomposition imaging condition and only used cross-correlation information of incident and reflection waves in the same propagation directions, which eliminates the artifacts generated from the cross-correlation of wave information unrelated to reflections in the crosswell image. We perform RTM in the frequency domain to maintain efficiency in multi-shot problems. A mono-frequency wavefield decomposition method is applied to separate and process the seismic data. The forward and backward wavefields are reclassified into the up- and downpropagating components. The L1 norm is introduced to enhance the robustness of the proposed imaging method. We then use this method to synthesize data from layering models and analyze the imaging results generated from each pair of cross-correlations using source and receiver wavefields. Results show that the cross-correlation information belonging to the same propagation contributes most to the crosswell image, and the other information always generates migration noises. Moreover, we apply the proposed method to a real-field dataset. Processing results validate the effectiveness of the proposed means for eliminating false events in the crosswell models and improving image quality.

A Graphical Calibration Method for a Water Quality Model Considering Process Variability Versus Delay Time: Theory and a Case Study

Process Variability (PV) is a significant water quality time-series measurement. It is a critical element in detecting abnormality. Typically, the quality control system should raise an alert if the PV exceeds its normal value after a proper delay time (DT). The literature does not address the relation between the extended process variability and the time delay for a warning. The current paper shows a graphical method for calibrating a Water Quality Model based on these two parameters. The amount of variability is calculated based on the Euclidean distance between records in a dataset. Typically, each multivariable process has some relation between the variability and the time delay. In the case of a short period (a few minutes), the PV may be high. However, as the relevant DT is longer, it is expected to see the PV converge to some steady state. The current paper examines a method for estimating the relationship between the two measurements (PV and DT) as a detection tool for abnormality. Given the user’s classification of the actual event for true and false events, the method shows how to build a graphical map that helps the user select the best thresholds for the model. The last section of the paper offers an implementation of the method using real-world data.

Impact of satellite trails on H.E.S.S. astronomical observations

Context. The number of satellites launched into Earth’s orbit has almost tripled in the last three years (to over 4000) due to the increasing commercialisation of space. Multiple satellite constellations, consisting of over 400 000 individual satellites, have either been partially launched or are proposed for launch in the near future. Many of these satellites are highly reflective, resulting in a high optical brightness that affects ground-based astronomical observations. Despite this caveat, the potential effect of these satellites on gamma-ray-observing Imaging Atmospheric Cherenkov Telescopes (IACTs) has largely been assumed to be negligible due to their nanosecond-scale integration times. However, this assumption has not been verified to date. Aims. As IACTs are sensitive to optical wavelength light, we aim to identify satellite trails in data taken by the High Energy Stereoscopic System (H.E.S.S.) IACT array. In particular, this study is aimed at quantifying the potential effects on data quality and extensive air shower event classification and reconstruction. Methods. Using night sky background measurements from H.E.S.S., we determined which observation times and pointing directions are affected most by these satellite trails. We then evaluated their impact on the standard Hillas parameter variables used for event analysis. Results. Due to the brightest trails, false trigger events can occur, however, for most modern analyses, the effect on astronomical results will be minimal. We observe a mild increase in the rate of trail detections over time (approximately doubling in three years), which is partially correlated with the number of satellite launches. Overall, the fraction of H.E.S.S. data affected (~0.2% of dark time observations) is currently minimal. We note that these trails could still have a non-negligible effect on future Cherenkov Telescope Array observations if advanced analysis techniques designed to lower the energy threshold of the instrument are applied.

Clinical characteristics and therapeutic strategy for patients with spontaneous isolated abdominal aortic dissection.

Spontaneous isolated abdominal aortic dissection (SIAAD) is a rare aortic emergency and not yet fully understood. This study aims to report the characteristics and treatments of 31 patients with SIAAD in the past 12 years. A total of 31 consecutive patients with SIAAD between 2010 and 2022 were included. The clinical manifestations, treatment strategies, and outcomes were reviewed. Following the SVS/STS reporting standard, we compared the clinical characteristics with different locations of primary entry, or different numbers of dissected zones. Furthermore, we compared the effects of surgical and conservative therapies on the outcome during the follow-up. Among the 31 patients with SIAAD, 16 (51.6%) were in the acute phase on admission. The primary entry of SIAAD was mainly located in Zone 9 (67.7%). Most patient presented with dissection involving 1 or 2 aortic zones (61.3%). In addition, 35.5% and 64.5% of SIAADs involved the visceral and iliac arteries, respectively. Compared with asymptomatic SIAADs, the symptomatic ones had longer dissection lengths (P = 0.008) and tended to involve iliac artery more frequently (P = 0.098). There were differences in the number of dissected aortic zones (P = 0.005) among patients with primary entry located in Zone 5 (Supraceliac aorta), Zone 6-8 (Paravisceral aorta) and Zone 9 (Infrarenal aorta). The involvement of visceral artery (P = 0.039) and iliac artery (P = 0.006) was significantly different between the subgroups of SIAAD involving one, two, and three or more aortic zones. The cumulative incidence of adverse false lumen progression events was significantly lower (P = 0.000) and the rate of false lumen thrombogenesis or disappearance was higher in patients receiving surgery (P = 0.001). The cumulative all-cause mortality was 9.7% at 1-year, and 19.7% at 5-year, with no significant difference between surgical and conservative therapies. Clinical features of SIAAD vary depending on the location of the primary entry and the number of dissected aortic zones. Although surgery was not associated with a lower all-cause mortality compared with conservative therapy, it was associated with a lower incidence of adverse false lumen progression and a higher rate of aortic remodeling.