Burst Data Research Articles

Using non-continuous accelerometry to identify cryptic nesting events of Galapagos giant tortoises

BackgroundTriaxial accelerometers have revolutionized wildlife research by providing an unprecedented understanding of the behavior of free-living animals. Machine learning is often applied to acceleration data to classify diverse animal behaviors across taxa. However, the high frequency, continuous data collection typically favored for behavioral classification studies often generates very large data sets, which may inhibit remote data acquisition and make data storage challenging. Coarse-frequency sampling or non-continuous bursts of acceleration data reduce these problems. To analyze such data, a suite of variables that summarize key features of the behavior of interest can be generated. These variables can then be used in numerous classification approaches, accommodating variation in data collection methods or sampling regimes. We demonstrate the potential for non-continuous accelerometer data to identify long-duration behavior and employ machine learning to classify the nesting behaviors of the critically endangered eastern Santa Cruz giant tortoise (Chelonoidis donfaustoi).ResultsWe field validated 112 nesting events from 21 giant tortoises. We then derived summary statistics based on accelerometry (e.g., overall dynamic body acceleration, metrics comparing acceleration before and after the probable event) and used them as inputs for Random Forest and Boosted Regression Tree classification algorithms. Our models produced a harmonic mean of precision and sensitivity (F1-score) of 0.91. We tested the generality of our model and found that the model performs well when applied to both novel individuals and years. The most important variable in accurately classifying data sequences was the proportion of acceleration data bursts above an activity threshold followed by the average overall dynamic body acceleration value of the bursts.ConclusionsThese results demonstrate the feasibility and efficacy of using non-continuous accelerometer data to identify prolonged, biologically relevant behaviors in free-living wildlife. By using summary variables that do not require continuous sampling, this approach facilitates long-term monitoring of animal behavior. Similar methodology has potential to inform priority questions in ecology and conservation, such as predicting wildlife responses to climate change and identifying critical habitats, with applications across diverse species and behaviors.

Fuel performance modelling of Cr-coated Zircaloy cladding under DBA/LOCA conditions

Chromium coatings are being developed for advanced technology fuel (ATF) claddings, offering negligible corrosion during normal operation, improved resistance to high-temperature steam oxidation, and superior high-temperature strength, the latter two being of utmost relevance during design basis accidents (DBAs). Demonstrating the improved response of Cr-coated Zircaloy requires the development or extension of fuel performance codes to coating simulations.In this work, material models and correlations for Cr-coated Zircaloy cladding have been derived or obtained from the literature and implemented into TRANSURANUS and the FRAPTRAN-TUmech suite. These extended tools have been used to simulate two complementary LOCA tests: QUENCH-L1 rod 4 (out-of-pile bundle test on fresh Zircaloy cladding) and IFA-650.10 (in-pile single rod test on high-burnup Zircaloy-UO2 fuel), enabling a gradual cross-verification of results between codes and a comparative performance analysis between coated and uncoated cladding.The results indicate negligible impact of coating properties other than creep on the burst time. While the superior high-temperature creep resistance of coated cladding slightly delays the burst time, additional burst data would be necessary to draw sound conclusions on the balloon size. Regarding the modelling approach, treating the coated cladding as a composite material through the definition of effective properties might result in worse performance relative to uncoated cladding, contradicting experimental observations. Therefore, the separate modelling of the coating and the cladding is recommended.

Testing the standardizability of, and deriving cosmological constraints from, a new Amati-correlated gamma-ray burst data compilation

By using gamma-ray burst (GRB) data to simultaneously constrain Amati correlation parameters and cosmological parameters in six spatially flat and nonflat dark energy cosmological models, we show that an updated 220 GRB version of the Jia et al. [1] GRB data compilation are standardizable through the Amati correlation and so can be used for cosmological analyses. However, the resulting GRB data constraints on the current value of the nonrelativistic matter density parameter, Ω m 0, are in > 2σ tension with those from a joint analysis of better-established Hubble parameter [H(z)] and baryon acoustic oscillation (BAO) data for most of the cosmological models we consider, indicating that these GRB data cannot be jointly used with better-established H(z) + BAO data to constrain cosmological parameters.

Prevalence and Propagation of Lightning‐Generated Whistlers in Van Allen Probes EFW Burst Data

AbstractWe assess the prevalence of ducted and non‐ducted whistler propagation using burst mode data from the Van Allen Probes Electric Field and Waves instrument (EFW). We have identified burst periods containing lightning‐generated whistlers (LGWs), resulting in a data set available for future use. The entire burst data set is filtered through an analysis of the search coil magnetometer (SCM) noise, identifying signals in frequency space that exceed an adaptive noise threshold. We implement DBSCAN (Density‐Based Spatial Clustering of Applications with Noise) to identify individual whistlers and clusters of whistlers. With magnetic spectral analysis, we calculate the mean wave normal angle (WNA) for each LGW group. We use ray tracing to estimate the expected WNA distributions for non‐ducted LGWs to compare to the data and determine methods for identifying potentially ducted LGWs. The ray‐tracing results provide a clear threshold in WNA for ducted whistlers. Using this threshold, we estimate that at least of LGWs in this data set are ducted. We find that the majority of potentially ducted whistlers are below and nearly half of LGWs below and within 9–18 MLT may be ducted.

Multi-Stage Burst Localization Based on Spatio-Temporal Information Analysis for District Metered Areas in Water Distribution Networks

Burst events in Water Distribution Networks (WDNs) pose a significant threat to the safety of water supply, leading people to focus on efficient methods for burst localization and prompt repair. This paper proposes a multi-stage burst localization method, which includes preliminary region determination and precise localization analysis. Based on the hydraulic model and spatio-temporal information, the effective sensor sequences and monitoring areas of the nodes are determined. In the first stage, the preliminary burst region is determined based on the monitoring region of sensors and the alarm sensors. In the second stage, localization metrics are used to analyze the dissimilarity degree between burst data from the hydraulic model and the monitoring data from the effective sensors at each node. This analysis helps identify candidate burst nodes and determine their localization priorities. The localization model is tested on the C-Town network to obtain comparative results. The method effectively reduces the burst region, minimizes the search region, and significantly improves the efficiency of burst localization. For precise localization, it accurately localizes the burst event by prioritizing the possibilities of the burst location.

Positive and unlabelled machine learning reveals new fast radio burst repeater candidates

ABSTRACT Fast radio bursts (FRBs) are astronomical radio transients of unknown origin. A minority of FRBs have been observed to originate from repeating sources, and it is unknown which apparent one-off bursts are hidden repeaters. Recent studies increasingly suggest that there are intrinsic physical differences between repeating and non-repeating FRBs. Previous research has used machine learning classification techniques to identify apparent non-repeaters with repeater characteristics, whose sky positions would be ideal targets for future observation campaigns. However, these methods have not sufficiently accounted for the positive and unlabelled (PU) nature of the data, wherein true labels are only available for repeaters. Modified techniques that do not inadvertently learn properties of hidden repeaters as characteristic of non-repeaters are likely to identify additional repeater candidates with greater accuracy. We present in this paper the first known attempt at applying PU-specific machine learning techniques to study FRBs. We train an ensemble of five PU-specific classifiers on the available data and use them to identify 66 repeater candidates in burst data from the CHIME/FRB collaboration, 18 of which were not identified with the use of machine learning classifiers in past research. Our results additionally support repeaters and non-repeaters having intrinsically different physical properties, particularly spectral index, frequency width, and burst width. This work additionally opens new possibilities to study repeating and non-repeating FRBs using the framework of PU learning.

Constraints on the parameters of modified Chaplygin–Jacobi and modified Chaplygin–Abel gases in f(T) gravity

In this work, we explore the parameter constraints of two dark energy models, namely the Modified Chaplygin–Jacobi gas (MCJG) and Modified Chaplygin–Abel gas (MCAG), within the context of [Formula: see text] gravity model in a non-flat Friedmann–Lemaître–Robertson–Walker (FLRW) universe. Our investigation involves comparing the equation of state for the MCJG and MCAG dark energy models with the equation of state derived from the [Formula: see text] gravity model. To derive constraints for the dark energy and [Formula: see text] gravity models, we use recent astronomical datasets, including [Formula: see text] data, type Ia supernovae observations, Gamma Ray Bursts data, quasar data, and Baryon Acoustic Oscillation (BAO) measurements. We present the reduced Hubble parameter in terms of observable parameters such as [Formula: see text] (density parameter of radiation), [Formula: see text] (density parameter of dark matter), [Formula: see text] (density parameter associated with spatial curvature), [Formula: see text] (density parameter of Modified Chaplygin–Jacobi gas), [Formula: see text] (density parameter of Modified Chaplygin–Abel gas), and [Formula: see text] (present value of the Hubble parameter). We explore the cosmological evolution through various cosmic diagnostic parameters, including the deceleration parameter, [Formula: see text] diagnostic, and statefinder diagnostic pair [Formula: see text]. These diagnostic parameters offer valuable insights into the expansion dynamics and the nature of dark energy in the universe. We have also assessed the viability of the models using the information criteria. Our aim is to shed light on the nature of dark energy and its connection to the [Formula: see text] gravity model, and ultimately gain a deeper understanding of the underlying mechanisms driving the accelerated expansion of our universe.

Monitoring resilience in bursts

The possibility to anticipate critical transitions through detecting loss of resilience has attracted attention in many fields. Resilience indicators rely on the mathematical concept of critical slowing down, which means that a system recovers more slowly from external perturbations when it gets closer to tipping point. This decrease in recovery rate can be reflected in rising autocorrelation and variance in data. To test whether resilience is changing, resilience indicators are often calculated using a moving window in long, continuous time series of the system. However, for some systems, it may be more feasible to collect several high-resolution time series in short periods of time, i.e., in bursts. Resilience indicators can then be calculated to detect a change of resilience between such bursts. Here, we compare the performance of both methods using simulated data and showcase the possible use of bursts in a case study using mood data to anticipate depression in a patient. With the same number of data points, the burst approach outperformed the moving window method, suggesting that it is possible to downsample the continuous time series and still signal an upcoming transition. We suggest guidelines to design an optimal sampling strategy. Our results imply that using bursts of data instead of continuous time series may improve the capacity to detect changes in resilience. This method is promising for a variety of fields, such as human health, epidemiology, or ecology, where continuous monitoring can be costly or unfeasible.

Searching for QPOs in BATSE short gamma-ray bursts based on narrowband and broadband features

Gamma-ray bursts (GRBs), especially short GRBs, are often considered potential candidates for exhibiting kilohertz quasi-periodic oscillations (QPOs) due to their origin from binary mergers. It has already been discovered that two bursts exhibit QPOs. While systematic searches for QPOs in GRBs typically concentrate on the kilohertz range, there has been no comprehensive exploration in the hundred-hertz range. In this study, we systematically conducted QPO searches on all BATSE short burst data within the 0-1000 Hz range. Using nested significance tests, we observed that the reference distributions for different GRBs are quite similar. This observation prompted us to analyze the data by selectively focusing on those with larger statistical values, obviating the need to iterate through all the data and significantly reducing computational workload. Ultimately, our findings did not reveal any compelling evidence for QPOs, which may suggest that the GRB jet has lost the early merging memory.

Methods for implementing integrated step-selection functions with incomplete data

Integrated step-selection analyses (iSSAs) are versatile and powerful frameworks for studying habitat and movement preferences of tracked animals. iSSAs utilize integrated step-selection functions (iSSFs) to model movements in discrete time, and thus, require animal location data that are regularly spaced in time. However, many real-world datasets are incomplete due to tracking devices failing to locate an individual at one or more scheduled times, leading to slight irregularities in the duration between consecutive animal locations. To address this issue, researchers typically only consider bursts of regular data (i.e., sequences of locations that are equally spaced in time), thereby reducing the number of observations used to model movement and habitat selection. We reassess this practice and explore four alternative approaches that account for temporal irregularity resulting from missing data. Using a simulation study, we compare these alternatives to a baseline approach where temporal irregularity is ignored and demonstrate the potential improvements in model performance that can be gained by leveraging these additional data. We also showcase these benefits using a case study on a spotted hyena (Crocuta crocuta).

Cosmological transition epoch from gamma-ray burst correlations

The redshift zt and the jerk parameter jt of the transition epoch are constrained by using two model-independent approaches involving the direct expansion of the Hubble rate and the expansion of the deceleration parameter around z=zt. To extend our analysis to high-redshifts, we employ the Amati, Combo, Yonetoku and Dainotti gamma-ray burst correlations. The circularity problem is prevented by calibrating these correlations through the Bézier interpolation of the updated observational Hubble data. Each gamma-ray burst data set is jointly fit with type Ia supernovae and baryonic acoustic oscillations through a Monte Carlo analysis, based on the Metropolis-Hastings algorithm, to obtain zt, jt and the correlation parameters. The overall results are compatible with the concordance model with some exceptions. We also focus on the behaviors of the dark energy, verifying its compatibility with a cosmological constant, and the matter density Ωm and compare them with the expectations of the concordance paradigm.

Dynamic balancing method of power distribution and consumption tasks based on state iterative prediction and resource peaking shifting

In the context of the new power system, the widespread access to massive distributed new energy sources has led to the power distribution and consumption tasks characterized by multiple time scales, wide random distribution, and large demand differences, resulting in unpredictable resource peaks in the tasks computing resource demand curve. In view of this situation, a method of forecasting and dynamic balancing of computing resource demand for power distribution and consumption tasks based on state iteration was proposed: firstly, the tasks computing resource demand model was established under the analysis of the attributes and parameter demand of the power distribution and consumption tasks scenario. Secondly, on the basis of the short-term effectiveness prediction of the traditional Markov model, the first-order difference of the state is used for data training to track the state fluctuation, and the historical state and the predicted state are used for state iteration, so as to avoid the convergence of long-term prediction. Finally, a dynamic balancing model is established according to the time-scale characteristics of cyclical and non-cyclical tasks, and the optimal configuration of load imbalance is achieved through the identification and adjustment of historical data and burst data. The simulation results show that the improved Markov model based on first-order difference and state iteration has the short-term accuracy of the traditional model and the long-term traceability of data fluctuations. The dynamic balancing model can combine the characteristics of historical data and burst data to effectively reduce the imbalance of resource demand, and show good ability to cope with resource imbalance deviation.

Search for Gravitational-wave Transients Associated with Magnetar Bursts in Advanced LIGO and Advanced Virgo Data from the Third Observing Run

Gravitational waves are expected to be produced from neutron star oscillations associated with magnetar giant flares and short bursts. We present the results of a search for short-duration (milliseconds to seconds) and long-duration (∼100 s) transient gravitational waves from 13 magnetar short bursts observed during Advanced LIGO, Advanced Virgo, and KAGRA’s third observation run. These 13 bursts come from two magnetars, SGR 1935+2154 and Swift J1818.0−1607. We also include three other electromagnetic burst events detected by Fermi-GBM which were identified as likely coming from one or more magnetars, but they have no association with a known magnetar. No magnetar giant flares were detected during the analysis period. We find no evidence of gravitational waves associated with any of these 16 bursts. We place upper limits on the rss of the integrated incident gravitational-wave strain that reach 3.6 × 10−23 /Hz at 100 Hz for the short-duration search and 1.1 × 10−22 /Hz at 450 Hz for the long-duration search. For a ringdown signal at 1590 Hz targeted by the short-duration search the limit is set to 2.3 × 10−22 /Hz . Using the estimated distance to each magnetar, we derive upper limits on the emitted gravitational-wave energy of 1.5 × 1044 erg (1.0 × 1044 erg) for SGR 1935+2154 and 9.4 × 1043 erg (1.3 × 1044 erg) for Swift J1818.0−1607, for the short-duration (long-duration) search. Assuming isotropic emission of electromagnetic radiation of the burst fluences, we constrain the ratio of gravitational-wave energy to electromagnetic energy for bursts from SGR 1935+2154 with the available fluence information. The lowest of these ratios is 4.5 × 103.

Milliarcsecond localization of the hyperactive repeating FRB 20220912A

ABSTRACT We present very long-baseline interferometry (VLBI) observations of the hyperactive repeating FRB 20220912A using the European VLBI Network (EVN) outside of regular observing sessions (EVN-Lite). We detected 150 bursts from FRB 20220912A over two observing epochs in 2022 October. Combining the burst data allows us to localize FRB 20220912A to a precision of a few milliarcseconds, corresponding to a transverse scale of less than 10 pc at the distance of the source. This precise localization shows that FRB 20220912A lies closer to the centre of its host galaxy than previously found, although still significantly offset from the host galaxy’s nucleus. On arcsecond scales, FRB 20220912A is coincident with a persistent continuum radio source known from archival observations; however, we find no compact persistent emission on milliarcsecond scales. The 5σ upper limit on the presence of such a compact persistent radio source is 120 μJy, corresponding to a luminosity limit of (D/362.4 Mpc)$^2\, 1.8\times 10^{28}$erg s−1 Hz−1. The persistent radio emission is thus likely to be from star formation in the host galaxy. This is in contrast to some other active FRBs, such as FRB 20121102A and FRB 20190520B.

Flexible multi-step hypothesis testing of human ECoG data using cluster-based permutation tests with GLMEs

BackgroundTime series analysis is critical for understanding brain signals and their relationship to behavior and cognition. Cluster-based permutation tests (CBPT) are commonly used to analyze a variety of electrophysiological signals including EEG, MEG, ECoG, and sEEG data without a priori assumptions about specific temporal effects. However, two major limitations of CBPT include the inability to directly analyze experiments with multiple fixed effects and the inability to account for random effects (e.g. variability across subjects). Here, we propose a flexible multi-step hypothesis testing strategy using CBPT with Linear Mixed Effects Models (LMEs) and Generalized Linear Mixed Effects Models (GLMEs) that can be applied to a wide range of experimental designs and data types. MethodsWe first evaluate the statistical robustness of LMEs and GLMEs using simulated data distributions. Second, we apply a multi-step hypothesis testing strategy to analyze ERPs and broadband power signals extracted from human ECoG recordings collected during a simple image viewing experiment with image category and novelty as fixed effects. Third, we assess the statistical power differences between analyzing signals with CBPT using LMEs compared to CBPT using separate t-tests run on each fixed effect through simulations that emulate broadband power signals. Finally, we apply CBPT using GLMEs to high-gamma burst data to demonstrate the extension of the proposed method to the analysis of nonlinear data. ResultsFirst, we found that LMEs and GLMEs are robust statistical models. In simple simulations LMEs produced highly congruent results with other appropriately applied linear statistical models, but LMEs outperformed many linear statistical models in the analysis of “suboptimal” data and maintained power better than analyzing individual fixed effects with separate t-tests. GLMEs also performed similarly to other nonlinear statistical models. Second, in real world human ECoG data, LMEs performed at least as well as separate t-tests when applied to predefined time windows or when used in conjunction with CBPT. Additionally, fixed effects time courses extracted with CBPT using LMEs from group-level models of pseudo-populations replicated latency effects found in individual category-selective channels. Third, analysis of simulated broadband power signals demonstrated that CBPT using LMEs was superior to CBPT using separate t-tests in identifying time windows with significant fixed effects especially for small effect sizes. Lastly, the analysis of high-gamma burst data using CBPT with GLMEs produced results consistent with CBPT using LMEs applied to broadband power data. ConclusionsWe propose a general approach for statistical analysis of electrophysiological data using CBPT in conjunction with LMEs and GLMEs. We demonstrate that this method is robust for experiments with multiple fixed effects and applicable to the analysis of linear and nonlinear data. Our methodology maximizes the statistical power available in a dataset across multiple experimental variables while accounting for hierarchical random effects and controlling FWER across fixed effects. This approach substantially improves power leading to better reproducibility. Additionally, CBPT using LMEs and GLMEs can be used to analyze individual channels or pseudo-population data for the comparison of functional or anatomical groups of data.

Deep neural networks for estimation of gamma-ray burst redshifts

Abstract While the available set of Gamma-ray Burst (GRB) data with known redshift is currently limited, a much larger set of GRB data without redshift is available from different instruments. This data includes well-measured prompt gamma-ray flux and spectral information. We estimate the redshift of a selection of these GRBs detected by Fermi-GBM and Konus-Wind using Machine Learning techniques that are based on spectral parameters. We find that Deep Neural Networks with Random Forest models employing non-linear relations among input parameters can reasonably reproduce the pseudo-redshift distribution of GRBs, mimicking the distribution of GRBs with spectroscopic redshift. Furthermore, we find that the pseudo-redshift samples of GRBs satisfy (i) Amati relation between the peak photon energy of the time-averaged energy spectrum in the cosmological rest frame of the GRB Ei, p and the isotropic-equivalent radiated energy Eiso during the prompt phase; and (ii) Yonetoku relation between Ei, p and isotropic-equivalent luminosity Liso, both measured during the peak flux interval.

Search for gravitational-wave bursts in LIGO data at the Schenberg antenna sensitivity range

Search for gravitational-wave bursts in LIGO data at the Schenberg antenna sensitivity range

Assessing Skin Sympathetic Nerve Activity and Cardiac Effects in Patients with Gastric Electrical Stimulation

Background:Gastric Electrical Stimulation (GES) is a therapy for gastroparesis patients, alleviating symptoms like nausea, vomiting, and poor gastric emptying. The therapeutic impact of GES is believed to stem from changes in autonomic nerve activity, particularly parasympathetic activity via the vagal nerves. This study examines skin sympathetic nerve activity (SKNA) to assess autonomic nerve bursts, investigating the effects of GES on nerve activity and cardiac function.
 Methods:SKNA signals were recorded from 48 patients at three locations: left side of the neck (SKNA1), right side of the neck (SKNA2), and via ECG Lead 1 across the chest (SKNA3). Signals were band pass filtered (500 Hz to 1000 Hz) to eliminate ECG and muscle artifacts and highlight nerve activity. Using Labchart software, the absolute value integral (iSKNA) was calculated for each 100-millisecond sample, with resulting values exported to Excel. Data was collected during different GES conditions: GES On, GES at ½ voltage, GES Off, and GES back on. The threshold for nerve activity bursts was determined as the mean of iSKNA values + two times the standard deviation in each section.
 Results:Histograms of average aSKNA values showed reduced nerve activity with GES off. Left and vagal nerve activity on SKNA1 was significantly lower with GES fully on or off compared to GES at ½ voltage. No other significant differences were observed in variabilities or average aSKNAs in SKNA1 or SKNA2.
 Future Directions & Potential Impact:Burst analysis of iSKNA data will address key questions. Variations in burst activity with different GES conditions may elucidate GES's therapeutic effects on gastroparesis. Subsequent segmentation of data into patients with resolved and unresolved symptoms could reveal the relationship between burst activity and therapeutic efficacy. Additionally, correlation of burst data with heart rate variability analysis will provide insights into the impact of GES on cardiac function. This study will advance our understanding of GES effects and safety, potentially improving patient outcomes.

MODELING UNKNOWN STOCHASTIC DYNAMICAL SYSTEM VIA AUTOENCODER

We present a numerical method to learn an accurate predictive model for an unknown stochastic dynamical system from its trajectory data. The method seeks to approximate the unknown flow map of the underlying system. It employs the idea of autoencoder to identify the unobserved latent random variables. In our approach, we design an encoding function to discover the latent variables, which are modeled as unit Gaussian, and a decoding function to reconstruct the system's future states. Both the encoder and decoder are expressed as deep neural networks (DNNs). Once the DNNs are trained by the trajectory data, the decoder serves as a predictive model for the unknown stochastic system. Through an extensive set of numerical examples, we demonstrate that the method is able to produce long-term system predictions by using short bursts of trajectory data. It is also applicable to systems driven by non-Gaussian noises.

Plant Transcriptome Analysis with HISAT-StringTie-Ballgown and TopHat-Cufflinks Pipelines.

The development of next-generation sequencing technology has led to a burst of data in a single assay. Management of a large dataset requires high demands on bioinformatic skills and computing resources. Here we present two popular pipelines for RNA-seq data analysis, using open-source software tools HISAT-StringTie-Ballgown and TopHat-Cufflinks. To meet the need of plant scientist, we describe in detail how to perform such comprehensive analysis beginning with raw RNA-seq reads and available reference genome. It allows biologists to align short reads to a reference genome, measure the transcript abundance, and analyze gene differential expression under two or more conditions. We also discuss other RNA-seq tools that are comparable or alternative to this protocol.