Arrival Time Research Articles

Localization for surface microseismic monitoring based on arrival time correction and VFOM

Abstract Unconventional resources have emerged as the primary source to meet the escalating demand for energy consumption, with hydraulic fracturing standing out as an effective means of boosting production. The utilization of microseismic monitoring is crucial for acquiring real-time or semi-real-time extension information of the fracture network to guide the fracturing process. The precise positioning of microseismic events is a fundamental aspect of microseismic monitoring. Traditional methods relying on (relative) arrival time significantly impact positioning accuracy due to picking errors. While waveform-based methods offer high accuracy, they require precise velocity models and are time-consuming. To overcome challenges associated with arrival time pickup and velocity accuracy, we introduce a virtual field optimization method (VFOM) based on arrival time correction. Initially, an equivalent velocity model is established, and the arrival time difference resulting from the model transformation of the master event is calculated to correct the observed arrival time of the target event. Subsequently, we match detector pairs, establish hyperboloids based on the corrected arrival time difference, and employ the intersection point of all hyperboloids as the positioning result. After that, we use the location results of the master event to enhance the accuracy of the target event. Finally, we apply the proposed method to both synthetic test and field datasets, demonstrating a significant improvement in the positioning accuracy and stability provided by the novel method. The robustness against arrival time error renders it a suitable choice for surface monitoring applications where signal quality is compromised. Furthermore, the simplified velocity model significantly diminishes the computational requirements in the positioning process, enhancing its efficiency, and consequently holds vast potential for application in real-time monitoring.

Development of a predictive algorithm for patient survival after traumatic injury using a five analyte blood panel.

Severe trauma can induce systemic inflammation but also immunosuppression, which makes understanding the immune response of trauma patients critical for therapeutic development and treatment approaches. By evaluating the levels of 59 proteins in the plasma of 50 healthy volunteers and 1000 trauma patients across five trauma centers in the United States, we identified 6 novel changes in immune proteins after traumatic injury and further new variations by sex, age, trauma type, comorbidities, and developed a new equation for prediction of patient survival. Blood was collected at the time of arrival at Level 1 trauma centers and patients were stratified based on trauma level, tissues injured, and injury types. Trauma patients had significantly upregulated proteins associated with immune activation (IL-23, MIP-5), immunosuppression (IL-10) and pleiotropic cytokines (IL-29, IL-6). A high ratio of IL-29 to IL-10 was identified as a new predictor of survival in less severe patients with ROC area of 0.933. Combining machine learning with statistical modeling we developed an equation ("VIPER") that could predict survival with ROC 0.966 in less severe patients and 0.8873 for all patients from a five analyte panel (IL-6, VEGF-A, IL-21, IL-29, and IL-10). Furthermore, we also identified three increased proteins (MIF, TRAIL, IL-29) and three decreased proteins (IL-7, TPO, IL-8) that were the most important in distinguishing a trauma blood profile. Biologic sex altered phenotype with IL-8 and MIF being lower in healthy women, but higher in female trauma patients when compared to male counterparts. This work identifies new responses to injury that may influence systemic immune dysfunction, serving as targets for therapeutics and immediate clinical benefit in identifying at-risk patients.

Examining the Retail Delivery Choice Behavior in a Technology-Aware Market

This study aims to provide valuable insights into consumer preferences for delivery services in online shopping in Chile. The COVID-19 pandemic has accelerated the evolution of delivery and logistics services, leading to increased competition among online stores. Chile, with its highly digitally enabled population and a competitive landscape of online retailers, serves as an ideal reference case for Latin America. By analyzing key delivery attributes such as delivery time, order arrival time range, compensation policies for delivery delays, and delivery prices, we offer valuable insights into consumer behavior. These insights will, in turn, inform the formulation of effective strategies within the online shopping industry. We examine the following aspects: (a) The willingness of consumers to pay for the service attributes; (b) The relative importance assigned to these attributes by consumers; and (c) The relationship between consumer preferences and socioeconomic characteristics. Using Multinomial Logit Models and a database from a Discrete Choice Experiment, we have discovered that the most significant attributes of delivery service are the time until product arrival and the existence of compensation in case of delivery delays. Additionally, we found that consumers are willing to pay more for the same delivery service if the product is large, as large products generally have higher prices. Furthermore, we observed that delivery time preferences vary by gender and for small products, and price sensitivity varies according to educational level, household size, and socioeconomic status. To the best of our knowledge, no previous research of this kind has been conducted for Chile.

Pattern of admission, outcome and predictors of trauma patients visiting the surgical emergency department in comprehensive specialized hospital: a retrospective follow-up study

Background: Traumatic injuries represent a huge burden in the developing world, and a significant proportion has found in low-income and middle-income countries. However, the pattern, outcome and factors of injury varies from setup to setup and is less studied in public health problems. Objective: To assess pattern of admission, outcome and its predictors among trauma patients visiting the surgical emergency department in a comprehensive specialized hospital. Methods: A retrospective follow-up study was conducted, and data were taken from the medical records of patients from 2019 to 2021. A simple random sampling technique was used to get a sample size of 386 from injured patient charts. Data were entered into Epi-Data version 4.6 software and exported to STATA version 14.1 for analysis. The dependent variable was injured patient’s outcome, which could be died or not died. The independent variables with P value less than 0.25 in the bi-variable regression analysis were considered for the multivariable regression. Adjusted odds ratio (AOR) with the 95% CI were used to declare statistical significance. Result: About 13.99% of injured patients with (95% CI: 10, 17) had died during the study period. The leading cause of injury was assault (62.44%) followed by road traffic accidents (26.17%). Severe Glasgow Coma Scale (AOR 6.6; 95% CI: 2.6–16.4), length of hospital stay more than or equal to 7 days (AOR=2.8; 95% CI: 1.2–6.2), time of arrival in between 1 and 24 h (AOR=0.15; 95% CI: 0.06–0.37), and upper trunk injury (AOR=6.3; 95% CI: 1.3–28.5) were significantly associated with mortality. Conclusion and recommendation: Mortality after traumatic injury was considerably high. Severe Glasgow Coma Scale, Length of hospital stay more than or equal to 7 days, time of arrival in between 1 and 24 h, and upper trunk injury were the associated factors for mortality. Priority should be given for injured patients with decreased levels of consciousness and upper trunk injury. The establishment of organized pre-hospital emergency services and provision of timely arrival is recommended. The authors recommend prospective follow-up study.

Fuzzy-based pelican optimization for battery management and energy scheduling in solar aided electric vehicle

Electric vehicles (EVs) are considered an essential mode of transportation due to their advantages, non-polluting and noise-free characteristics. The batteries are considered the primary power source for electric vehicles, ensuring energy supply and thus improving the user experience. However, designing charging stations and uncontrolled charge scheduling are the barriers evaluated in charging stations. To solve these issues, a fuzzy-based pelican optimization algorithm (POA) is proposed in this work. Set of rules are generated in the fuzzy for optimal energy management inn charging station. POA schedules the EVs in the charging station based on arrival time and SOC to reduce the running cost. Demand in the charging station is based on the state of charge (SOC) of the EVs arriving at the charging station. Solar power with battery and ultracapacitor is used as an input source for charging stations. The energy management in the system is based on fuzzy to satisfy the load demand from PV and energy storage devices. This proposed method is implemented in the Simulink tool to evaluate the performance. This simulation result show the effectiveness of the proposed with optimal scheduling and energy management. By this method running cost are reduced compared to the existing algorithms.

Mapping the Ecological Terrain of Stroke Prehospital Delay: A Nationwide Registry Study.

Delays in hospital presentation limit access to acute stroke treatments. While prior research has focused on patient-level factors, broader ecological and social determinants have not been well studied. We aimed to create a geospatial map of prehospital delay and examine the role of community-level social vulnerability. We studied patients with ischemic stroke who arrived by emergency medical services in 2015 to 2017 from the American Heart Association Get With The Guidelines-Stroke registry. The primary outcome was time to hospital arrival after stroke (in minutes), beginning at last known well in most cases. Using Geographic Information System mapping, we displayed the geography of delay. We then used Cox proportional hazard models to study the relationship between community-level factors and arrival time (adjusted hazard ratios [aHR] <1.0 indicate delay). The primary exposure was the social vulnerability index (SVI), a metric of social vulnerability for every ZIP Code Tabulation Area ranging from 0.0 to 1.0. Of 750 336 patients, 149 145 met inclusion criteria. The mean age was 73 years, and 51% were female. The median time to hospital arrival was 140 minutes (Q1: 60 minutes, Q3: 458 minutes). The geospatial map revealed that many zones of delay overlapped with socially vulnerable areas (https://harvard-cga.maps.arcgis.com/apps/webappviewer/index.html?id=08f6e885c71b457f83cefc71013bcaa7). Cox models (aHR, 95% CI) confirmed that higher SVI, including quartiles 3 (aHR, 0.96 [95% CI, 0.93-0.98]) and 4 (aHR, 0.93 [95% CI, 0.91-0.95]), was associated with delay. Patients from SVI quartile 4 neighborhoods arrived 15.6 minutes [15-16.2] slower than patients from SVI quartile 1. Specific SVI themes associated with delay were a community's socioeconomic status (aHR, 0.80 [95% CI, 0.74-0.85]) and housing type and transportation (aHR, 0.89 [95% CI, 0.84-0.94]). This map of acute stroke presentation times shows areas with a high incidence of delay. Increased social vulnerability characterizes these areas. Such places should be systematically targeted to improve population-level stroke presentation times.

Facial Recognition Attendance System Using OpenCV implemented in Python

The Facial Recognition Attendance System is a sophisticated software solution that employs OpenCV and Python to automate the process of attendance tracking in real-time. Designed to replace outdated manual methods, this system utilizes state-of-the-art facial recognition technology to accurately identify individuals as they enter a monitored space. Upon detection and verification, the system records the individual's attendance by logging their arrival time, thereby streamlining the attendance process. This method significantly enhances efficiency, reduces potential for error, and ensures a secure and contactless check-in experience. With its user-friendly interface and quick setup, the system is an ideal application for educational institutions, corporate offices, and public events that require reliable attendance management.

Analysis of converter transformer pressboard insulation degradation under surge using mathematical morphology

Nowadays, with the significant expansion of industrial growth, the bulk power requirement can only be satisfied through high-voltage direct current HVDC transmission. The converter transformer is the utmost vital part of the HVDC transmission. Pressboard insulation is most commonly used as inter-disc insulation in converter transformers. During working conditions due to elevated temperature and different operational stresses, insulation material gets deteriorated. It may cause a risk to the life of the converter transformer. The effects of elevated temperatures as well as frequency on pressboard insulation of the converter transformer are examined in this study. The condition evaluation and morphological changes in pressboard insulation at elevated temperatures can evaluate with the help of frequency domain spectroscopy (FDS) and atomic force microscopy (AFM) techniques. The impact of elevated temperatures on insulation material can be analyzed based on surface roughness and dielectric parameters. In MATLAB Simulink environment, a dual winding single-phase converter transformers valve side star winding 60 discs model is constructed for impulse test. Based upon arrival time and velocity of traveling wave, insulation degradation location can be identified by using mathematical morphology. The simulation results demonstrate that the suggested method can notably located degradation across disc winding.

The role of anesthesia in endovascular therapy management of ischemic stroke: A systematic review and meta-analysis

BackgroundEndovascular treatment and thrombolytic therapy are widely used as the standard treatment for acute ischemic strokes. General Anesthesia (GA) is preferred over Conscious Sedation (CS) because it is associated with lower pain, anxiety, agitation and aspiration risk. However time is very fundemental in acute ischemic stroke treatment. MethodsThe search was done on the 30th of December 2022. The inclusion criteria of the studies were if they were randomized controlled clinical trials in design and compared between using conscious sedation and general anesthesia in endovascular treatment along with thrombolytic therapy. ResultsThe total number of patients included in our analysis was 743 from six clinical trials. Our models showed a significant association between conscious sedation and improved disability (OR=1.32; 95%CI: 1.10–1.59). The model demonstrated that conscious sedation was significantly associated with lower mean stroke to procedure time (WMD=-11.93; 95%CI: -23.72- -0.14) and lower mean hospital arrival to procedure time (WMD=-8.30; 95%CI: -13.88- -2.72). Moreover, our analysis revealed that conscious sedation was significantly associated with lower hypotensive episodes (WMD=0.67; 95%CI: 0.48–0.95) and lower use of vasoactive drugs (WMD=0.75; 95%CI: 0.57–0.99). There were no significant differences between the two study groups in technical failures, successful recanalization or mortality. ConclusionCS demonstrated improved disability, shorter times to procedure, fewer hypotensive events, and lower use of vasoactive drugs. All of which reflect a better quality for CS over GA. Given together, these findings can help guide the clinical decision which should be tailored to the patients’ needs.

On the Generalizability of Time-of-Flight Convolutional Neural Networks for Noninvasive Acoustic Measurements.

Bulk wave acoustic time-of-flight (ToF) measurements in pipes and closed containers can be hindered by guided waves with similar arrival times propagating in the container wall, especially when a low excitation frequency is used to mitigate sound attenuation from the material. Convolutional neural networks (CNNs) have emerged as a new paradigm for obtaining accurate ToF in non-destructive evaluation (NDE) and have been demonstrated for such complicated conditions. However, the generalizability of ToF-CNNs has not been investigated. In this work, we analyze the generalizability of the ToF-CNN for broader applications, given limited training data. We first investigate the CNN performance with respect to training dataset size and different training data and test data parameters (container dimensions and material properties). Furthermore, we perform a series of tests to understand the distribution of data parameters that need to be incorporated in training for enhanced model generalizability. This is investigated by training the model on a set of small- and large-container datasets regardless of the test data. We observe that the quantity of data partitioned for training must be of a good representation of the entire sets and sufficient to span through the input space. The result of the network also shows that the learning model with the training data on small containers delivers a sufficiently stable result on different feature interactions compared to the learning model with the training data on large containers. To check the robustness of the model, we tested the trained model to predict the ToF of different sound speed mediums, which shows excellent accuracy. Furthermore, to mimic real experimental scenarios, data are augmented by adding noise. We envision that the proposed approach will extend the applications of CNNs for ToF prediction in a broader range.

Determining the possibility of passage through narrow alleys using ultrasonic sensors

The complex road topography of South Korea presents significant challenges to the timely arrival of emergency vehicles. Compounding the issue, obstacles such as legally or illicitly constructed structures, and improperly parked or stationary vehicles, frequently obstruct the path of emergency vehicles. To address these challenges, this study introduces a novel system aimed at enhancing emergency response times. The system employs ultrasonic sensors that can be integrated into personal devices to measure the width of the numerous narrow alleys prevalent in Korea's densely populated regions. Experiments demonstrate that within a 1-meter range in front of a narrow alley with widths varying between 270 cm and 450 cm where vehicle maneuvering is possible, it's possible to accurately gauge the width using two ultrasonic sensors, achieving a precision within a 5 cm margin of error. This level of accuracy enables the practical assessment of whether emergency vehicles can access the area in real-time by identifying the alley's narrowest point. The proposed system is a cost-effective method using easy-to-buy devices for augmenting emergency preparedness and enhancing emergency response times by ensuring that emergency vehicles can navigate through alleys, thereby fostering a safer living environment.

High-frame-rate contrast-enhanced ultrasound to differentiate between clear cell renal cell carcinoma and angiomyolipoma

BackgroundTo investigate the diagnostic efficacy of high-frame-rate contrast-enhanced ultrasound (H-CEUS) in differentiating between clear cell renal cell carcinoma (CCRCC) and angiomyolipoma (AML).MethodsA retrospective study was performed on the clinical data of 79 patients diagnosed with CCRCC and 31 patients diagnosed with AML at the First Affiliated Hospital of Nanchang University between October 2022 and December 2023. Conventional ultrasound (US) and H-CEUS examinations were conducted on all patients prior to surgery, dynamic images were recorded from the US, and the qualitative and quantitative parameters of H-CEUS were collected. The t-test, χ² test and non-parametric Mann-Whitney test were employed to assess differences in clinical data, US characteristics, and qualitative and quantitative parameters of H-CEUS between the CCRCC and AML groups. The independent risk factors of CCRCC were identified using binary logistic regression. The receiver operator characteristic (ROC) curve was constructed to evaluate the diagnostic effectiveness of clinical + US and H-CEUS in differentiating between CCRCC and AML.ResultsThe CCRCC group and the AML group exhibited significant differences in patient gender, operation mode, nodular echo, and nodule blood flow (χ²=11.698, -, -,=10.582; P<0.001, <0.001, <0.001, and = 0.014, respectively). In addition, the H-CEUS qualitative analysis demonstrated significant differences between the AML group and the CCRCC group with respect to enhancement mode, regression mode, peak intensity, enhancement uniformity, no enhancement, and presence or absence of pseudocapsule (χ²=41.614, -, -, = 2.758, = 42.099, -; P<0.001, <0.001, <0.001, 0.097, <0.001, and <0.001, respectively). The Arrival time (AT) in the CCRCC group was significantly shorter than that in the AML group, as determined by quantitative analysis of H-CEUS (Z=-3.266, P = 0.001). Furthermore, the Peak intensity (PI), Ascent slope (AS), and The area under the curve (AUC) exhibited significantly higher values in the CCRCC group compared to the AML group (Z=-2.043,=-2.545,=-3.565; P = 0.041, = 0.011, and <0.001, respectively). Logistic regression analysis indicated that only gender, nodule echo, the pseudocapsule, AS, and AUC of H-CEUS were independent risk factors of CCRCC. The ROC curve revealed that combining gender and nodule echo yielded a sensitivity of 92.4%, specificity of 64.5%, and an AUC of 0.847 in distinguishing between CCRCC and AML. When combining the H-CEUS parameters of pseudocapsule, AS, and AUC, the sensitivity, specificity, and AUC for distinguishing between CCRCC and AML were 84.8%, 96.8%, and 0.918, respectively. No statistically significant difference was observed in the diagnostic effectiveness of the two methods (Z=-1.286, P = 0.198). However, H-CEUS demonstrated better AUC and specificity.ConclusionsH-CEUS enhances the sensitivity and specificity of differentiating between CCRCC and AML by improving the temporal resolution, offering a more precise diagnostic foundation for identifying the most appropriate therapy for patients.

The impact of the Hunga Tonga–Hunga Ha’apai volcanic eruption on the Peruvian atmosphere: from the sea surface to the ionosphere

The eruption of the Hunga Tonga Hunga Ha’apai volcano on 15 January 2022 significantly impacted the lower and upper atmosphere globally. Using multi-instrument observations, we described disturbances from the sea surface to the ionosphere associated with atmospheric waves generated by the volcanic eruption. Perturbations were detected in atmospheric pressure, horizontal magnetic field, equatorial electrojet (EEJ), ionospheric plasma drifts, total electron content (TEC), mesospheric and lower thermospheric (MLT) neutral winds, and ionospheric virtual height measured at low magnetic latitudes in the western South American sector (mainly in Peru). The eastward Lamb wave propagation was observed at the Jicamarca Radio Observatory on the day of the eruption at 13:50 UT and on its way back from the antipodal point (westward) on the next day at 07:05 UT. Perturbations in the horizontal component of the magnetic field (indicative of EEJ variations) were detected between 12:00 and 22:00 UT. During the same period, GNSS-TEC measurements of traveling ionospheric disturbances (TIDs) coincided approximately with the arrival time of Lamb and tsunami waves. On the other hand, a large westward variation of MLT winds occurred near 18:00 UT over Peru. However, MLT perturbations due to possible westward waves from the antipode have not been identified. In addition, daytime vertical plasma drifts showed an unusual downward behavior between 12:00 and 16:00 UT, followed by an upward enhancement between 16:00 and 19:00 UT. Untypical daytime eastward zonal plasma drifts were observed when westward drifts were expected. Variations in the EEJ are highly correlated with perturbations in the vertical plasma drift exhibiting a counter-equatorial electrojet (CEEJ) between 12:00 and 16:00 UT. These observations of plasma drifts and EEJ are, so far, the only ground-based radar measurements of these parameters in the western South American region after the eruption. We attributed the ion drift and EEJ perturbations to large-scale thermospheric wind variations produced by the eruption, which altered the dynamo electric field in the Hall and Pedersen regions. These types of multiple and simultaneous observations can contribute to advancing our understanding of the ionospheric processes associated with natural hazard events and the interaction with lower atmospheric layers.Graphical

A Deep Learning-Based Ultrasonic Diffraction Data Analysis Method for Accurate Automatic Crack Sizing

The purpose of this paper is to automate the interpretation of data during ultrasonic diffraction using a non-destructive testing (NDT) technique to accurately size defects for assisting in decision-making. A convolutional neural network (CNN) architecture was developed to automatically measure the length of the defect. Using the architecture, the population of A-scan signals in the scanning path was classified. The defect region was extracted and its size in the scanning direction was obtained by the connected region solution algorithm based on the classification results. The arrival time of diffraction waves was accurately identified by the intelligent denoising framework proposed, combined with Hilbert transform, and then the height of defects was calculated by corresponding geometric relations. The estimation results demonstrate that the measurement method can be considered as a useful technique for crack sizing in industrial structures, even in the case of complex noise.

Seismic source analysis of the destructive earthquake November 21, 2022, Mw 5.6 Cianjur (Indonesia) from relocated aftershock

A destructive shallow earthquake with a magnitude of 5.6 struck Cianjur, West Java, Indonesia on November 21, 2022. This earthquake resulted in 602 casualties and the collapse of over 67,504 residences. The day after the mainshock, we deployed 19 temporary seismic stations to monitor aftershocks for a period of 30 days. We manually picked arrival times for 4499 P-waves and 3419 S-waves and determined locations for 514 events. Following the velocity model update, phase refinement through waveform cross correlation, and relocation using double-difference methods, we were able to determine 442 well-defined hypocenters of the aftershocks. We identified two clusters of aftershocks: one in the NNW-SSE direction, with a length of about 8 km, and another in the WSW-ENE direction, with a length of around 6 km. The seismogenic zone of these clusters ranges from a depth of 3 to 13 km. Our interpretation suggests that these clusters may indicate a conjugate fault. It is possible that the mainshock (Mw5.6) Cianjur earthquake on November 21, 2022 occurred on the WSW-ENE direction with sinistral movement.

Disaggregated spatiotemporal traffic assignment for road reservation service and supply-demand statistical analysis

This paper develops a disaggregated spatiotemporal traffic assignment method with a system-optimal (SO) orientation and analyzes the supply-demand matching degree with four statistical indexes under the background of a road reservation system. Three key issues are addressed. Firstly, the paper illustrates the service process of a road reservation system. It is essential to know how the road reservation system works and the difference between it and other reservation systems. Secondly, a spatiotemporal discretization expression method based on the system-optimal traffic assignment (SOTA) model with predictive origin-destination demand for link travel time is put forward to make the supply space could be reserved, and the demand would not be mutually interfering. Thirdly, the study proposes a reverse feasible spatiotemporal route searching algorithm based on the expected arrival time to individually assign the applicants on the road network. This route searching algorithm does not use the network topology but the spatiotemporal discretized links. The departure time preference was considered in the feasible spatiotemporal route searching algorithm. Moreover, as the real demand distribution of the whole reservation area is not difficult to obtain after a period of updates, it is possible to analyze the supply-demand matching degree of the road network. Thus, four statistical indexes are proposed to assess the state variation of the road network. Simulation results verify the effectiveness and efficiency of the proposed method. The novel reverse feasible routes searching algorithm has a system-optimal trend with inputting the demand individually and an acceptable calculating efficiency. The method proposed by this paper could ensure a reliable road reservation service with accurate demand prediction. Considering the departure time preference would not bring extra burden to the road reservation system but provide more user-friendly service. Through the analysis, the supply-demand matching degree of the road network is significantly influenced by the cohesive capacity connection of the upstream and downstream links. This indicates the network structure and road attributes optimization should be considered in enhancing the road reservation service.

A novel Bayesian optimal detector-based approach for determining the first arrival time of wire breakage-induced near-wall acoustic wave in PCCPs

A novel Bayesian optimal detector-based approach for determining the first arrival time of wire breakage-induced near-wall acoustic wave in PCCPs

Semisupervised Machine Learning Approach for Ddos Detection

Analyzing cyber incident data sets is an important method for deepening our understanding of the evolution of the threat situation. In present generation we come to know about many cyber breaches and hacking taking place. In this project work, we research about the various cyber- attacks and breaches and study the way these attacks are done and find an alternative for the same. We show that rather than by distributing these attacks as because they exhibit autocorrelations, we should model by stochastic process both the hacking breach incident inter- arrival times and breach sizes. We draw a set of cyber securities insights, including that the threat of cyber hacks is indeed getting worse in terms of their frequency. In our project we will be using the algorithms such as Convolution Neural Network (CNN) as existing and Recurrent Neural Network (RNN) as proposed for analyzing our results. From the results obtained its proved that proposed RNN works better than existing CNN.

Spectra and equitemporal surfaces of the photosphere of the ultrarelativistic shell as applied to Gamma-ray bursts

We study the radiation of the ultrarelativistic shell in the diffusion approximation, which takes place at the initial stage of a gamma-ray burst. We get the effective temperature, instantaneous and time-integrated spectra for the parabolic distribution of the initial internal energy of the shell. Also we considered the types of the equitemporal surfaces with a different type of the movement of the shell.

Single-sample image-fusion upsampling of fluorescence lifetime images.

Fluorescence lifetime imaging microscopy (FLIM) provides detailed information about molecular interactions and biological processes. A major bottleneck for FLIM is image resolution at high acquisition speeds due to the engineering and signal-processing limitations of time-resolved imaging technology. Here, we present single-sample image-fusion upsampling, a data-fusion approach to computational FLIM super-resolution that combines measurements from a low-resolution time-resolved detector (that measures photon arrival time) and a high-resolution camera (that measures intensity only). To solve this otherwise ill-posed inverse retrieval problem, we introduce statistically informed priors that encode local and global correlations between the two "single-sample" measurements. This bypasses the risk of out-of-distribution hallucination as in traditional data-driven approaches and delivers enhanced images compared, for example, to standard bilinear interpolation. The general approach laid out by single-sample image-fusion upsampling can be applied to other image super-resolution problems where two different datasets are available.