Burst Time Research Articles

Impact of LWR assembly structural features on cladding burst behavior under LOCA conditions

This provides an initial scoping study on clad balloon and burst behavior for burnup extension of reactor fuel. The associated issues with burnup extension are fuel fragmentation, relocation, and dispersal in the event of cladding failure. The general finding of this work is that the structural features, spacer grids and mixing vanes, locally suppress cladding deformation but have little impact on the overall clad performance during loss-of-coolant accidents. The work detailed in a previous report by Capps et al. focused on core optimization via neutronics, thermal hydraulics and thermomechanical analysis for burnups beyond 62 GWD/tU and enrichments above 5%. Uncertainty of fuel fragmentation relocation and dispersal in high-burnup rods during accident conditions was also investigated. The dispersal aspect of fuel fragmentation depends on cladding rupture. Thus, assessing uncertainties in the rupture behavior is helpful in estimating the dispersal of high-burnup fuel. This study builds on the previous work by assessing the impact of assembly structural features on cladding balloon and burst behavior in a full-length fuel rod.In this work, the BISON fuel performance code was used to generate 2D radial and height meshes containing structural features commonly used in nuclear fuel assemblies. First, meshes were generated with spacer grids. Results were then compared to the cladding burst temperature and balloon strain results from the previous work. A mesh sensitivity study was performed to ensure that mixing vanes and spacer grid effects were appropriately considered, resulting in a more refined mesh than the previous study. The balloon deformation and burst times of the cladding were compared to the original case. Consideration was also given to the effect of rod initial pressure. 3D quarter rod simulations were also performed and found good agreement with the 2D simulations in clad deformation and reasonable agreement in burst times.

Search for Galactic Core-collapse Supernovae in a Decade of Data Taken with the IceCube Neutrino Observatory

The IceCube Neutrino Observatory has been continuously taking data to search for O(0.5–10) s long neutrino bursts since 2007. Even if a Galactic core-collapse supernova is optically obscured or collapses to a black hole instead of exploding, it will be detectable via the O(10) MeV neutrino burst emitted during the collapse. We discuss a search for such events covering the time between 2008 April 17 and 2019 December 31. Considering the average data taking and analysis uptime of 91.7% after all selection cuts, this is equivalent to 10.735 yr of continuous data taking. In order to test the most conservative neutrino production scenario, the selection cuts were optimized for a model based on an 8.8 solar mass progenitor collapsing to an O–Ne–Mg core. Conservative assumptions on the effects of neutrino oscillations in the exploding star were made. The final selection cut was set to ensure that the probability to detect such a supernova within the Milky Way exceeds 99%. No such neutrino burst was found in the data after performing a blind analysis. Hence, a 90% C.L. upper limit on the rate of core-collapse supernovae out to distances of ≈25 kpc was determined to be 0.23 yr−1. For the more distant Magellanic Clouds, only high neutrino luminosity supernovae will be detectable by IceCube, unless external information on the burst time is available. We determined a model-independent limit by parameterizing the dependence on the neutrino luminosity and the energy spectrum.

Refinement of the Proposed Gamma-Ray Burst Time Delay Model

Refinement of the Proposed Gamma-Ray Burst Time Delay Model

Determination of critical material damage parameter for Indian clad tube burst behavior under severe accident scenario

During loss of coolant accident scenario in PHWRs, heat removal from clad tubes containing the uranium oxides fuel may get severely impaired. This results in increase in temperature of the fuel clad and subsequent increase in the clad tube internal pressure. This causes the clad tube to deform and balloon leading to the increase in diameter of the fuel clad. After a critical value of deformation is reached, the clad tube gets burst. The burst process is due to initiation of crack in the clad due to creep deformation process in the presence of internal pressure and high temperature environment. In this work, experimental data of fuel clad bursts from literature have been used to evaluate a critical damage parameter that can be used to predict the onset of burst phenomenon of clad tubes. Finite element modelling of the fuel clad has been carried out to simulate the ballooning deformation of the fuel clad tubes due to creep. Norton’s creep model has been used in the simulation. The critical damage parameter evaluated, using analysis and experimental data, has been used subsequently to predict burst time of Zircaloy-4 fuel clad when subjected to different types of loading environments. An exponential decay type correlation has been obtained from the finite element analysis results which can be used by safety analysts to predict burst pressure of Zircaloy-4 fuel clad of Indian PHWR as a function of burst temperature or vice-versa.

Improving Round Robin Scheduling with Dynamic Time Quantum (IRRDQ)

In the realm of real-time and multitasking environments, the Round Robin (RR) CPU scheduling algorithm is extensively utilized. Renowned for its fairness and avoidance of process starvation, RR allocates a specific time quantum to each process. However, the frequent context switching between processes during CPU scheduling, owing to the short time quantum, can adversely impact system performance by increasing average waiting time and response waiting time. To address these concerns, this paper introduces "Improving Round Robin Scheduling Using Dynamic Time Quantum (IRRDQ)," a modified version of RR aimed at diminishing turnaround time, average waiting time, and context switching. In our proposed approach, we arrange all incoming processes based on minimum burst time and dynamically assign an optimal time quantum to each process using the Shortest Job First (SJF) algorithm. The computation of time quantum is tailored to the burst times of individual processes. Through experimental demonstrations, we showcase the significant performance improvement of our algorithm compared to RR and other existing algorithms.

DDOS ATTACK DETECTION USING HYBRID (CCN AND LSTM) ML MODEL

LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Networks) are two types of deep learning algorithms; by combining the strengths of LSTM and CNN, researchers have developed deep learning models that can effectively detect SDN (Software-Defined Network) attacks including Distributed Denial of Service. These models effectively analyze network traffic, encompassing temporal and spatial characteristics, resulting in precise identification of malicious traffic.In this research, a hybrid model composed of CNN and LSTM is used to detect the DDoS attack in SDN network. Where the CNN component of the model can identify spatial patterns in network traffic, such as the characteristics of individual packets, while the LSTM component can capture temporal patterns in traffic over time, such as the timing and frequency of traffic bursts. The proposed model has been trained on a labeled network traffic dataset, with one class representing normal traffic and another class representing DDoS attack traffic. During the training process, the model adjusts its weights and biases to minimize the difference between its predicted output and the actual output for each input sample. Once trained, the hybrid model classifies incoming network traffic in the dataset as either normal or malicious with an initial accuracy of (78.18%) and losses of (39.77%) at the 1st epoch till it reaches an accuracy of (99.99%) with losses of (9.29×10-5) at the epoch number 500. It should be mentioned that the hybrid model of CNN and LSTM for DDoS detection is implemented using Python Anaconda platform with an ETA 28ms/step.

Fluorescence correlation spectroscopy measurements on amyloid fibril reveal at least two binding modes for fluorescent sensors

Interaction of fluorophore with amyloid structure is central to the design and development of fluorescent sensors. In literature ensemble spectroscopy results reveal at least two distinct binding modes for most of the torsional probes like Thioflavin-T, SYPRO Orange, etc. though single molecule localization microscopy and spectroscopy results consider only one binding mode. To address this discrepancy we employed fluorescence correlation spectroscopy (FCS) to measure the binding kinetics on immobilized fibril structures with single molecule sensitivity. Our results indicate that there are at least two possible biding configurations with differing relaxation time constants indicating possible weaker and stronger binding configurations depending on the local environment on the fibril structure. Critical evaluation of on-time distributions from the recorded wide-field image stack for single molecule localization microscopy image, further highlight the presence of two burst times. Such bi-modal binding behavior is attributed to the differing binding configurations on fibril architecture.

Average Max Round Robin Algorithm: A Case Study

Round Robin Algorithm is one wellknown algorithm in real time system. Several variants of round robin algorithms are in the fields. Average max round robin algorithm is a breakthrough to optimize context switching or also called quantum. Context switching is one big problem in round robin algorithm. To optimize high context switching is the key solution. This will make this algorithm efficient. There should be a way to optimize this context switching. Then the average max round robin algorithm is one solution to this problem. The average max algorithm is defined by finding the average of burst time then add the maximum burst time to the average burst time. Then calculate again the average of the two. The calculation will be iterated in the next round robin cycle. Here, in this journal, three case studies are discussed. Each with different burst times to understand this average max round robin algorithm more clearly. In the first case study we get turn around time 34 ms, and average waiting time 20.6 ms. In the second case study, we get average turn around time 21.8 ms, and the average waiting time 13 ms. And in the last case study, the third one, we get turn around time 12.2 ms, and the average waiting time 6.6 ms. There is no calculation for the second iteration for all case studies. Since the left burst time is only in one process. Optimizing the context switching, minimizing average turnaround time, and average waiting time is the key solution to round robin algorithm.

A Knowledge Map of the Relationship between Diabetes and Stroke: A Bibliometric Analysis Study

Introduction: The correlation between diabetes and stroke has been studied extensively in epidemiological research. Here, we used bibliometric software to visualize and analyze the literature related to diabetic stroke to provide an overview of the current state of research, hotspots, and future trends in the field. Methods: Based on the Web of Science Core Collection (WoSCC) database, we collected studies related to diabetic stroke from 2007 to December 2022. We used CiteSpace (version 6.1.R5), VOSviewer, and Scimago Graphica to create knowledge maps and conduct visual analyses on authors, countries, institutions, cited references, and keywords, and Origin for statistical analysis. Results: We included a total of 5,171 papers on diabetic stroke from the WoSCC database. Overall, there was a steady increase in the number of publications, with a high number of emerging scientists. The USA was the most productive and influential country, which dominated national collaborations. The most common subject category was “neurology.” In total, 12 major clusters were generated from the cited references. Keyword analysis showed that keywords related to poststroke injury and treatment are those with the highest burst intensity and latest burst time. Conclusions: Individual disease treatment remains a hot topic, and how to balance acute stroke treatment and glycemic control is currently a difficult clinical problem. At the same time, the mechanism of their interaction and the prevention and treatment of related causative factors remain a hot topic of current and future research.

Analysis of FeCrAl cladding performance under loss-of-coolant accident conditions

Modeling developments and simulations of iron-chromium-aluminum (FeCrAl) cladding behavior under loss-of-coolant accident (LOCA) conditions have been performed using the BISON fuel performance code. First, we describe the modeling capabilities in BISON relevant to LOCA conditions, with an emphasis on new models added to the code in this work. The latter include updated models for FeCrAl cladding plasticity, burst failure and high-temperature oxidation. Subsequently, we describe BISON simulations of both FeCrAl and Zircaloy cladded fuel rods under the conditions of a postulated large-break LOCA (LB-LOCA) scenario in a BWR. The results indicate that the FeCrAl cladding will experience significantly lower ballooning, oxidation, and hydrogen production rates than Zircaloy cladding under the same conditions. However, the calculated burst time and temperature were similar for the Zircaloy and FeCrAl claddings. Finally, we perform extended simulations of fuel rod behavior for the conditions of a prolonged unmitigated LB-LOCA, including consideration of energy deposition due to the exothermic cladding oxidation reaction. The results point to a significantly reduced oxide thickness and hydrogen gas production for FeCrAl cladding compared to Zircaloy cladding.

Dynamic interlinkages between cryptocurrencies, NFTs, and DeFis and optimal portfolio investment strategies

PurposeThis study aims to scrutinize time-varying return and volatility interlinkages among major cryptocurrencies, NFT tokens and DeFi assets between 1 July 2018 and 19 February 2023 and determine optimal portfolio allocations and hedging effectiveness under different portfolio construction techniques.Design/methodology/approachThis work examines time-varying return and volatility interlinkages among major cryptocurrencies, NFT tokens, and DeFi assets between 1 July 2018 and 19 February 2023. To this end, the time-varying parameter-vector autoregression (TVP-VAR)-based connectedness methodology of Antonakakis et al. (2020) This approach is an extended version of the Diebold–Yilmaz (DY) method (Diebold and Yılmaz, 2014) and has advantages over the original DY. First, unlike the DY, it is free of the selection of a particular window size. Second, it has robustness for the outliers. Furthermore, following Broadstock et al. (2022), the author estimates time-varying optimal portfolio weights and hedging effectiveness under different portfolio construction scenarios.FindingsThis study's results indicate the following results: (1) The overall connectedness indices prominently capture well-known financial/geopolitical distress incidents; (2) the leading cryptocurrencies (ETH, BTC and BNB) are the largest transmitter of return shocks, while LINK and BTC are the largest transmitters/recipients of volatility shocks; (3) cryptocurrencies, NFTs and DeFi form distinct cluster groups in terms of return and volatility connectedness; (4) the connectedness networks estimated around the 2022 cryptocurrency crash and the FTX's filing for the bankruptcy are characterized by the strongest return and volatility interlinkages; (5) optimal portfolio strategies computed by different portfolio construction techniques display similar motifs and have sustained growth paths except for some short-lived drop backs.Research limitations/implicationsThis study's findings imply several policy suggestions for investors, stakeholders and policymakers. First, the study's time-based dynamic interlinkages can help market participants in their optimal portfolio decisions. In particular, the persistent net receiving roles of the DeFi assets and the NFTs throughout the episode, especially around the financial/geopolitical turmoil, underpin their safe haven potentials (Umar et al., 2022a, b). Finally, since the total connectedness indices (TCIs) are prone to significantly increase around financial/geopolitical burst times, these tools can be valuable for policy makers to monitor risk.Originality/valueThe contribution of knowledge is at least threefold. First, the author focuses on the dynamic time interlinkages among major cryptocurrencies, NFTs and DeFi assets in July 2018 and February 2023 considering the prominent recent financial/geopolitical incidents. Second, the author estimates network topologies of dynamic connectedness around financial/geopolitical bursts and compared them in terms of interlinkages. Finally, the author calculates the time-varying optimal portfolio allocations and hedging effectiveness under different portfolio construction techniques.

Obtaining advantages from knowledge base: mapping the potential to develop new technologies

ABSTRACT It is necessary for the company to find suitable new growth points when growth hits the ceiling. Technology opportunity discovery is one kind of knowledge based risk mitigation strategy (TOD). Computer-based TOD has the advantage of fast and repeatable analysis, which can be used as a complement and reference to expert decision methods. This research shows an approach to help firms to discover technology opportunities based on their knowledge foundation to obtain a competitive advantage. The proposed method involves (1) identifying the critical technologies by text analysis, measuring the importance of technology in the network and the time of bursts, (2) evaluating the relative technological advantage (RTA) of the firms as knowledge base, (3) Calculating the relatedness between technologies, assessing the development potential of non-RTA technologies from the firm’s current knowledge base and the relatedness between technologies, (4) proposing a technology opportunity analysis framework that integrates the potential and importance for technological development. This article chooses Self-Driving Vehicles (SDV) as a case study to further explain and validate the approach. This approach helps firms understand their current state and growth potential. Such recommendations are based on the knowledge foundation of different firms and therefore are individualised.

Reanalysis of the X-Ray-burst-associated FRB 200428 with Insight-HXMT Observations

A double-peak X-ray burst from the Galactic magnetar SGR J1935+2154 was discovered to be associated with the two radio pulses of FRB 200428 separated by 28.97 ± 0.02 ms. Precise measurements of the timing and spectral properties of the X-ray bursts are helpful for understanding the physical origin of fast radio bursts. In this paper, we have reconstructed some information about the hard X-ray events, which were lost because the High Energy X-ray Telescope (HE) on board the Insight-HXMT mission was saturated by this extremely bright burst, and used the information to improve the temporal and spectral analyses of the X-ray burst. The arrival times of the two X-ray peaks by fitting the new Insight-HXMT/HE lightcurve with multi-Gaussian profiles are 2.77 ± 0.45 ms and 34.30 ± 0.56 ms after the first peak of FRB 200428, respectively, while these two parameters are 2.57 ± 0.52 ms and 32.5 ± 1.4 ms if the fitting profile is a fast-rise and exponential decay function. The spectrum of the two X-ray peaks could be described by a cutoff power law with cutoff energy ∼60 keV and photon index ∼1.4; the latter is softer than the result obtained in Li et al. when the two X-ray peaks appeared.

Insights into Ionic Liquids for Flame Retardant: A Study Based on Bibliometric Mapping

Fire is a typical disaster in the processing industry. Ionic liquids, as a type of green flame retardant, play an important role in process safety. In order to grasp the current research status, hotspots, and frontiers in the field of ionic liquids in flame retardancy, the bibliometric mapping method is applied to study the relevant literature in Web of Science datasets from 2000–2022 in this paper. The results show that the research on ionic liquids in flame retardancy is multidisciplinary and involves some disciplines such as energy science, material science, and environmental protection. Journal of Power Sources, Polymer Degradation and Stability, ACS Applied Materials and Interfaces, and Chemical Engineering Journal are the core journals in the field. The results of keyword co-occurrence indicate that the hotspots of research can be divided into five components: the improvement and application of pure ionic liquids electrolytes, the research of gel polymer electrolytes, applying ionic liquids to enhance the polymer materials’ flame retardancy properties, utilizing ionic liquids and inorganic materials to synergize flame retardant polymers, and using ionic liquids flame retardant to improve material’s multiple properties. The burst terms and time zone diagram’s results point out the combination of computational quantum chemistry to study the flame retardancy mechanism of ionic liquids, the study of fluorinated electrolytes, ionic liquids for smoke suppression, phosphorus-containing ionic liquids for flame retardant, and machine learning-assisted design of ILs flame retardants are the research frontiers and future research trends.

Analysis of photon trajectories from diffusing single molecules.

In single-molecule free diffusion experiments, molecules spend most of the time outside a laser spot and generate bursts of photons when they diffuse through the focal spot. Only these bursts contain meaningful information and, therefore, are selected using physically reasonable criteria. The analysis of the bursts must take into account the precise way they were chosen. We present new methods that allow one to accurately determine the brightness and diffusivity of individual molecule species from the photon arrival times of selected bursts. We derive analytical expressions for the distribution of inter-photon times (with and without burst selection), the distribution of the number of photons in a burst, and the distribution of photons in a burst with recorded arrival times. The theory accurately treats the bias introduced due to the burst selection criteria. We use a Maximum Likelihood (ML) method to find the molecule's photon count rate and diffusion coefficient from three kinds of data, i.e., the bursts of photons with recorded arrival times (burstML), inter-photon times in bursts (iptML), and the numbers of photon counts in a burst (pcML). The performance of these new methods is tested on simulated photon trajectories and on an experimental system, the fluorophore Atto 488.

Average Max Round Robin Algorithm: A Case Study

Round Robin Algorithm is one wellknown algorithm in real time system. Several variants of round robin algorithms are in the fields. Average max round robin algorithm is a breakthrough to optimize context switching or also called quantum. Context switching is one big problem in round robin algorithm. To optimize high context switching is the key solution. This will make this algorithm efficient. There should be a way to optimize this context switching. Then the average max round robin algorithm is one solution to this problem. The average max algorithm is defined by finding the average of burst time then add the maximum burst time to the average burst time. Then calculate again the average of the two. The calculation will be iterated in the next round robin cycle. Here, in this journal, three case studies are discussed. Each with different burst times to understand this average max round robin algorithm more clearly. In the first case study we get turn around time 34 ms, and average waiting time 20.6 ms. In the second case study, we get average turn around time 21.8 ms, and the average waiting time 13 ms. And in the last case study, the third one, we get turn around time 12.2 ms, and the average waiting time 6.6 ms. There is no calculation for the second iteration for all case studies. Since the left burst time is only in one process. Optimizing the context switching, minimizing average turnaround time, and average waiting time is the key solution to round robin algorithm.

Statistical properties and lensing effect on the repeating fast radio burst FRB 180916.J0158+65

ABSTRACT FRB 180916.J0158+65 is a well-known repeating fast radio burst with a period ($16.35~\rm d$) and an active window ($5.0~\rm d$). We give out the statistical results of the dispersion measures and waiting times of bursts of FRB 180916.J0158+65. We find the dispersion measures at the different frequencies show a bimodal distribution. The peaking dispersion measures of the left mode of the bimodal distributions increase with frequency, but the right one is inverse. The waiting times also present the bimodal distribution, peaking at 0.05622 and 1612.91266 s. The peaking time is irrelevant to the properties of bursts, either for the preceding or subsequent burst. By comparing the statistical results with possible theoretical models, we suggest that FRB 180916.J0158+65 suffered from the plasma lensing effects in the propagation path. Moreover, this source may be originated from a highly magnetized neutron star in a high-mass X-ray binary.

Inferring interference: Identifying a perturbing tertiary with eccentric gravitational wave burst timing

[Abridged] Binary black holes may form and merge dynamically. These binaries are likely to become bound with high eccentricities, resulting in a burst of gravitational radiation at their point of closest approach. When such a binary is perturbed by a third body, the evolution of the orbit is affected, and gravitational-wave burst times are altered. The bursts times therefore encode information about the tertiary. In order to extract this information, we require a prescription for the relationship between the tertiary properties and the gravitational-wave burst times. In this paper, we demonstrate a toy model for the burst times of a secular three-body system. We show how Bayesian inference can be employed to deduce the tertiary properties when the bursts are detected by next-generation ground-based gravitational-wave detectors. We study the bursts from an eccentric binary with a total mass of $60$~M$_\odot$ orbiting an $6 \times 10^{8}$~M$_\odot$ supermassive black hole. When we assume no knowledge of the eccentric binary, we are unable to tightly constrain the existence or properties of the tertiary, and we recover biased posterior probability distributions for the parameters of the eccentric binary. However, when the properties of the binary are already well-known -- as is likely if the late inspiral and merger are also detected -- we are able to more accurately infer the mass of the perturber, $m_3$, and its distance from the binary, $R$. When we assume measurement precision on the binary parameters consistent with expectations for next-generation gravitational-wave detectors, we can be greater than $90\%$ confident that the binary is perturbed. [...]

Time delay induces a back to back Hopf bifurcation on oncolytic virotherapy.

This study analyzes a basic mathematical model for the dynamic interactions among tumor cells, infected tumor cells and viruses population, focusing on the viral lytic cycle for oncolytic virotherapy. I study the time delay effect of viral infection on tumor cell populations by identifying bifurcation thresholds in both the burst rate and time delay of viral infection in oncolytic virus therapy. Time delay plays an important role in changing the structure of tumor cell populations in a dynamical system. The multi-bifurcation thresholds of the time delay are observed and also dependent on the bursting rate. This study demonstrates a strong relationship between viral burst rates and time delays in population dynamics. The results of this study show that time delay affects oscillation generation and results in back-to-back Hopf bifurcation. This study provides insight into understanding the relationship between the two control parameters, in which tumor cell populations pattern from equilibrium steady-state solutions to periodic solutions and from periodic solutions to equilibrium-state solutions.

Rapid Input-Output Transformation between Local Field Potential and Spiking Activity during Sensation but not Action in the Superior Colliculus.

Sensorimotor transformation is the sequential process of registering a sensory signal in the environment and then responding with the relevant movement at an appropriate time. For visually guided eye movements, neural signatures in the form of spiking activity of neurons have been extensively studied along the dorsoventral axis of the superior colliculus (SC). In contrast, the local field potential (LFP), which represents the putative input to a region, remains largely unexplored in the SC. We therefore compared amplitude levels and onset times of both spike bursts and LFP modulations recorded simultaneously with a laminar probe along the dorsoventral axis of SC in 3 male monkeys performing the visually guided delayed saccade task. Both signals displayed a gradual transition from sensory activity in the superficial layers to a predominantly motor response in the deeper layers, although the transition from principally sensory to mostly motor response occurred ∼500 μm deeper for the LFP. For the sensory response, LFP modulation preceded spike burst onset by <5 ms in the superficial and intermediate layers and only when data were analyzed on a trial-by-trial basis. The motor burst in the spiking activity led LFP modulation by >25 ms in the deeper layers. The results reveal a fast and efficient input-output transformation between LFP modulation and spike burst in the visually responsive layers activity during sensation but not during action. The spiking pattern observed during the movement phase is likely dominated by intracollicular processing that is not captured in the LFP.SIGNIFICANCE STATEMENT What is the sequence of events between local field potential (LFP) modulation and spiking activity during sensorimotor transformation? A trial-by-trial analysis reveals that the LFP activity leads the spike burst in the superficial and intermediate layers of the superior colliculus during visual processing, while both trial-by-trial and the average analyses show that the spike burst leads the LFP modulation during movement generation. These results suggest an almost instantaneous LFP input, spike burst output transformation in the visually responsive layers of the superior colliculus when registering the stimulus. In contrast, substantial intracollicular processing likely results in a saccade-related spike burst that leads LFP modulation.