Arrival Time Research Articles

Enhanced and precise traveling wave fault location accuracy on transmission lines utilizing dual time of arrival

Enhanced and precise traveling wave fault location accuracy on transmission lines utilizing dual time of arrival

Improving arterial stiffness prediction with machine learning utilizing hemodynamics and biomechanical features derived from phase contrast magnetic resonance imaging

Arterial stiffness has emerged as a prominent marker of risk for cardiovascular diseases. Few studies are interested in predicting symptomatic or asymptomatic arterial stiffness from hemodynamics and biomechanics parameters. Machine learning models can be used as an intelligent tool for arterial stiffness detection based on hemodynamic and biomechanical parameters. Indeed, in the case of arterial stiffness hemodynamics and biomechanics parameters present significant change, such as an increase in age, local wave velocity, arterial elastance, Young’s modulus, reflected wave amplitude, decrease in arterial compliance, reflected wave arrival time, and reflection coefficient. This study aims to assess the impact of artificial intelligence using machine-learning algorithms for the detection of arterial stiffness. The ability of various machine-learning approaches can be investigated to predict wall stiffness in the carotid artery and to evaluate the risk of cardiovascular events. A mathematical model developed in previous work was used to determine hemodynamic and biomechanical parameters. Accuracy, sensitivity, and specificity are calculated to evaluate the performance of the proposed models. All used classifiers demonstrated high performance in predicting arterial stiffness, notably with the Support Vector Machine, Artificial Neural Network, and Decision Tree classifiers achieving exceptional accuracies of 100%. In this study, the potential of machine learning based on hemodynamic parameters for the prediction of symptomatic and asymptomatic arterial stiffness was demonstrated.

Association of the distance travelled, and the call to hospital arrival time with early neonatal mortality in neonates born to mothers using emergency medical services at term gestation: a retrospective observational study

BackgroundMost neonatal deaths in India occur due to a lack of timely and proper care just before and during delivery. Timely availability of an ambulance for institutional delivery has led...

Optimization and Numerical Verification of Microseismic Monitoring Sensor Network in Underground Mining: A Case Study

A scientific and reasonable microseismic monitoring sensor network is crucial for the prevention and control of rockmass instability disasters. In this study, three feasible sensor network layout schemes for the microseismic monitoring of Sanshandao Gold Mine were proposed, comprehensively considering factors such as orebody orientation, tunnel and stope distributions, blasting excavation areas, construction difficulty, and maintenance costs. To evaluate and validate the monitoring effectiveness of the sensor networks, three layers of seismic sources were randomly generated within the network. Four levels of random errors were added to the calculated arrival time data, and the classical Geiger localization algorithm was used for locating validation. The distribution of localization errors within the monitoring area was analyzed. The results indicate that when the arrival time data are accurate or the error is between 0% and 2%, scheme 3 is considered the most suitable layout; when the error of the arrival time data is between 2% and 10%, scheme 2 is considered the optimal layout. These research results can provide important theoretical and technical guidance for the reasonable design of microseismic monitoring systems in similar mines or projects.

Modeling the interaction between wildfires and windthrows: A pilot case study for Italian Alps

Wildland fires and windthrows represent relevant disturbances for forest ecosystems worldwide. In this context, especially for Italian catchments, the interaction between windthrows and changes in wildfire behaviour starting from ALS data processing is scarcely investigated. Therefore, this research aims to compute a multi-temporal analysis of the interaction between windthrows and wildfire behaviour in a forested area (Veneto region, northern Italy), recently affected by the renamed Vaia windstorm. The semi-empirical FlamMap model was applied, starting from ALS data processing implemented in R for mapping the spatial distribution of forest attributes and fuels within the catchment. The role of windthrows in altering wildfire behaviour was investigated considering ALS point clouds acquired before and after the occurrence of the storm Vaia. Digital Terrain Models (DTMs), Canopy Height Models (CHMs), topographic data and metrics describing forest structure were extracted from ALS data for both scenarios at 5 m resolution, to compare changes in wildfire behaviour over time. Differences in Rate of Spread (RoS), flame length (FL), midflame windspeed (WS) and arrival time (AT) were assessed, and their correlation with windstorm damages was investigated at the catchment detail. , An increase of RoS, FL, and WS greater than 30 m/min, 3 m and 1.1 m/s were respectively estimated in windthrown areas, as well as a decrease of AT greater than 30 min, attesting the key role of windthrows in altering wildfire behaviour over time. The correlation between windthrows and changes in wildfire attributes was finally modeled by computing regression analysis, with R2 of 0.86, 0.93, 0.62, and 0.91 resulted for RoS, FL, WS and AT. This research represents a pilot case study for better detecting changes in wildfires behaviour due to windthrows occurrence, therefore proposing and carrying out effective planning and management strategies for disturbed forest stands over time.

QUALITATIVE ANALYSIS OF TEMPERATURE ERROR OF NEGATIVE PRESSURE WAVE METHOD FOR DETERMINING THE LOCATION OF A PIPELINE LEAK

The article is devoted to the qualitative analysis of the temperature error of the negative pressure wave method for determining the location of a pipeline leak. Recording the arrival time of the negative wave at both ends of the pipeline allows you to determine the location of a pipeline leak. In contrast to known works on this topic, this article proposes a method for qualitative analysis of the error in determining a leak location. Based on the variational optimization method, an optimal ratio of the mode indicators at which the minimum error in determining the location of a leak can be achieved is defined. A method for qualitative analysis of the error in determining the leak location using the negative pressure difference wave method is proposed. The optimal correlation of the mode functions is determined at which the minimum error in determining the leak location is achieved. An expression for estimating the relative error in determining the leak location using the negative pressure difference wave method is obtained.

Modeling demand responsive service for the elderly healthcare activities considering temporal and spatial variations

ABSTRACT This paper develops a cost-effective demand-responsive transportation (DRT) model aimed at enhancing the accessibility and convenience of clinical visits for the elderly by optimizing two NP-hard problems. First, a set of pick-up stops is optimized based on potential stop locations, spatial and temporal demand, and acceptable walking distance. Next, vehicle routing and scheduling are optimized, considering vehicle capacity, users’ desired arrival times, and travel companions, to minimize total costs. An elite genetic algorithm (EGA) is developed to effectively search for the optimal solution. In the case study, demands are generated from a survey on elderly clinical visits and population census data in Xi’an. The proposed EGA is applied to different clustered demands classified by time interval to identify optimal solutions. Sensitivity analysis is conducted to explore the relationships between model parameters and decision variables for the studied DRT system.

Sustainable Synchronization of Truck Arrival and Yard Crane Scheduling in Container Terminals: An Agent-Based Simulation of Centralized and Decentralized Approaches Considering Carbon Emissions

Background: Container terminal congestion is often measured by the average turnaround time for external trucks. Reducing the average turnaround time can be resolved by controlling the yard crane operation and the arrival times of external trucks (truck appointment system). Because the truck appointment system and yard crane scheduling problem are closely interconnected, this research investigates synchronization between the approaches used in truck appointment systems and yard crane scheduling strategies. Rubber-tired gantry (RTG) operators for yard crane scheduling operations strive to reduce RTG movement time as part of the container retrieval service. However, there is a conflict between individual agent goals. While seeking to minimize truck turnaround time, RTGs may travel long distances, ultimately slowing down the RTG service. Methods: We address a method that balances individual agent goals while also considering the collective objective, thereby minimizing turnaround time. An agent-based simulation is proposed to simulate scenarios for yard crane scheduling strategies and truck appointment system approaches, which are centralized and decentralized. This study explores the combined effects of different yard scheduling strategies and truck appointment procedures on performance indicators. Various configurations of the truck appointment system and yard scheduling strategies are modeled to investigate how those factors affect the average turnaround time, yard crane utilization, and CO2 emissions. Results: At all levels of truck arrival rates, the nearest-truck-first-served (NTFS) scenario tends to provide lower external truck turnaround times than the first-come-first-served (FCFS) and nearest-truck longest-waiting-time first-served (NLFS) scenario. Conclusions: The decentralized truck appointment system (DTAS) generally shows slightly higher efficiency in emission reduction compared with centralized truck appointment system (CTAS), especially at moderate to high truck arrival rates. The decentralized approach of the truck appointment system should be accompanied by the yard scheduling strategy to obtain better performance indicators.

Integrating arrival time estimation in truck scheduling: an explorative study in grocery retailing

ABSTRACT This paper studies a new managerial approach that integrates predictive trucks’ estimated time of arrival (ETA) into truck scheduling to deal with the truck arrival time uncertainty. The approach exploits ETA to reschedule trucks by the up-to-date information and aims to minimize the trucks’ waiting time. The approach was applied in a real case. Results quantified the impact of adopting ETA for truck scheduling, also showing how the ETA accuracy and peak truck arrival rate affect the total trucks’ waiting time. The paper enriches both truck scheduling literature, investigating a new managerial approach to deal with truck arrival time uncertainty, which greatly threatens the synchronization of logistics flows, and Industry 4.0 literature, showing how real-time data availability can change traditional decision-making processes. Insights for the approach application are discussed, together with the economic, environmental, and social benefits by supply chain actors (i.e. carriers, warehouse managers, and drivers).

Inundation and evacuation of shoreline populations during landslide-triggered tsunamis: an integrated numerical and statistical hazard assessment

Abstract. The volcanic island of Stromboli (southern Tyrrhenian Sea, Italy) is renowned for its persistent, periodic, low-intensity explosive activity, whose spectacular manifestations attract tens of thousands of tourists every year. However, sporadic more intense major explosive and effusive eruptions and paroxysms pose serious threats to the island. In addition to direct hazards, granular slides of volcanic debris and pyroclastic avalanches, which can rapidly reach the sea and potentially generate tsunamis, are often associated with such unpredictable eruptive activity. Due to the very fast propagation of the tsunami around the island and the consequent short tsunami warning time (ranging from less than a minute to only a few minutes), mitigation efforts and evacuation from the Strombolian coast must be carefully planned. In this paper, we describe a new GIS-assisted procedure that allows us to combine the outputs of an ensemble of 156 pre-computed landslide-generated tsunami hazard scenarios (with variable landslide volume, position, and density), statistical exposure data (i.e. the number of inhabitants and tourists), and digital geographic information to obtain a quantitative (scenario-based) risk analysis. By means of the analysis of the road network and coastal morphology, we develop a model with routes and times to reach a safe area from every pixel in the inundated area and an appraisal of the time needed to escape versus the wave arrival time. This allows us to evaluate and quantify the effectiveness of potential risk mitigation by means of evacuation. The creation of an impact score linking the predicted inundation extent and the tsunami warning signals is intended, in the long term, to be used to predict the intensity of future tsunamis and to adapt evacuation plans accordingly. The model, here applied to Stromboli, is general and can be applied to other volcanic islands. Evacuating an island hosting several thousand tourists every summer with very little warning time underlines the absolute necessity for such mitigation efforts, aimed at informing hazard planners and managers and all other stakeholders.

Image Features and Diagnostic Value of Contrast-Enhanced Ultrasound for Ductal Carcinoma In Situ of the Breast: Preliminary Findings.

To explore the image features and the diagnostic value of contrast-enhanced ultrasound (CEUS) for ductal carcinoma in situ (DCIS) of the breast. A total of 96 female patients with a solitary and histologically proven DCIS were analyzed retrospectively, and 100 female cases of invasive ductal carcinoma (IDC) lesions were used as the control group. The Breast Imaging Reporting and Data System (BI-RADS) category of breast lesions was assessed according to conventional ultrasound features. The DCIS lesions were classified into mass type and non-mass type. The CEUS characteristics of these breast lesions were retrospectively analyzed qualitatively and quantitatively. The final gold standard was biopsy or surgery with histo-pathological examination. Comparing the ultrasound images of DCIS with that of IDC, there were significant differences in echo pattern, calcification morphology, and calcification distribution (p < .05 for all). There was a significant difference between DCIS and IDC in enhancement intensity, perfusion defects, peripheral high enhancement, intratumoral vessels, and arrival time (AT) (p < .05 for all). In the logistic multivariate regression analysis, two indicators linked with DCIS were recognized: perfusion defects (p = .002) and peripheral high enhancement (p < .001). In forecasting DCIS, the logistic regression equation resulted in an AUC of 0.689, a specificity of 0.720, and a sensitivity of 0.563. CEUS showed differences in enhancement characteristics between DCIS and IDC, with perfusion defects and peripheral high enhancement being associated with DCIS.

Predicting Electric Vehicle Charging Demand in Residential Areas Using POI Data and Decision‐Making Model

Transport electrification is a crucial element of the ongoing energy transition, essential for achieving carbon peaking and carbon neutrality goals. The proliferation of electric vehicles (EVs) introduces significant challenges to power distribution network stability due to their aggregated charging load in residential areas, particularly during peak electricity consumption periods. This paper proposes a method to predict the spatiotemporal distribution of EV charging demand in residential areas using geographic information points of interest (POI) data features and a decision‐making model. Utilizing real historical data, probability distribution models for EV users' arrival times and charging characteristics were constructed using Gaussian Mixture Models (GMM). The spatiotemporal characteristics of EV travel and charging behaviors were analyzed, and a comprehensive charging decision model incorporating both emergency and stochastic scenarios was developed. The model's efficacy in capturing the probability distributions of characteristic variables was validated through a case study. The results demonstrate the model's potential for accurately predicting EV charging demand, providing valuable insights for infrastructure planning and resource allocation. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Reduction of arrival time jitter or energy spread with arclike variable bunch compressors

Synchronizing free-electron laser pulses for time-resolved experiments is necessary as an unsynchronized system reduces the effective repetition rate of the experiment. Additionally, free-electron laser longitudinal coherence and spectral brightness are limited by electron beam energy spread. In this paper, a variable bunch compressor design and compression schemes were developed to: reduce klystron-induced arrival time jitter by 85% while preserving peak current and transverse emittance and to minimize electron beam correlated energy spread for potential self-seeding or HB-SASE modes. Published by the American Physical Society 2024

Attendance patterns of provisioned Australian humpback dolphins (Sousa sahulensis) in Tin Can Bay, Australia – Further indication of male bonding and alliance

Tin Can Bay, Queensland, is one of four free-ranging dolphin feed provisioning sites in Australia and the only one featuring Australian humpback dolphins (Sousa sahulensis). The site attracts the same 5–10 dolphins a year, most of which return regularly, and some which have been in attendance since the 1990s. Historical records were obtained for the period March 2010 to March 2021 covering dolphin attendance to the provisioning station including dolphin name, arrival time, and departure time. Tourist numbers were also obtained which included the total number and how many of that total directly took part in provisioning. Attendance of dolphins to the provisioning site has increased over the years both in frequency and total numbers. There was evidence of group cohesion between four particular dolphins (one mother-offspring pair and one male pair). The strongest pairing was seen between two males, indicating bond formation in adult male humpback dolphins, which may be indicative of an alliance. Specific events were also looked at in detail including cyclones, flooding COVID-19 restrictions, which reduced dolphin and/or human attendance.

Individual return patterns of spawning flannelmouth sucker (Catostomus latipinnis) to a desert river tributary

Tributaries provide temporal and spatial habitat heterogeneity in river networks that can be critical for parts of the life history of a species. Tributary fidelity can benefit individual fish undergoing spawning migrations by reducing time and energy spent exploring new areas and leveraging previous experience, but anthropogenic activities that fragment or degrade these systems can eliminate those benefits. We used multistate models based on passive integrated transponder (PIT) detection data from 2013 to 2023 to estimate the proportion of flannelmouth suckers (Catostomus latipinnis) migrating to a tributary, McElmo Creek, from the mainstem San Juan River for spawning. Survival varied among years and among states. The top model for migration probability included sex, with males slightly more likely to migrate (0.93 vs 0.90), and the next model identified interannual variation in migration probability ranging from 0.875 to 0.999 across years, indicating high site fidelity. Individuals showed consistency in relative arrival timing across years, with the highest correlation generally during years with greater spring discharge and extended tributary residence time. Successful tributary spawning may be important for the maintenance of the mainstem San Juan River flannelmouth sucker population, but site fidelity may be maladaptive where tributaries are vulnerable to human alterations.

Single Station Seismic Event Location With a Neural‐Guided Mixture Model: Model‐Based Weighting for Improved Accuracy

AbstractIn many regions of the world with complex seismic activity, the availability of seismic stations is limited. This motivated us to develop a probabilistic method for single‐station earthquake location. Single station location methods may provide an efficient and cost‐effective way to monitor small‐magnitude seismic events over large areas of the world. This method relies on the outputs of two neural networks, which predict the initial spatial location of specific areas (convolutional) used in the prior probability and seismic phase recognition (combination of convolutional and Long Short Term Memory) used in the likelihood function. These estimates and a 3D travel time likelihood function are used in a Bayesian framework. In this model, the initial spatial location is expressed as a Multivariate Gaussian Mixture Model, and the likelihood function refines the localization using the arrival time difference between S and P waves in a 3D space. The likelihood function uses a Fast Marching Eikonal method with a 3D velocity structure. We tested this method on the Ridgecrest 2019 seismic sequence (Mw = 7.1) and on West Texas and found promising results for locating with a single station. Our approach demonstrates that using a Bayesian model with strong priors obtained through deep learning algorithms is effective. Our model can predict epicenter and depth with mean errors of 7.55 km in longitude, 13.54 km in latitude and 0.630 km in depth at the two different locations studied. This method demonstrates the effectiveness of combining deep learning for initial estimations with probabilistic 3D location refinement.

A Seismic Precursor 15 min Before the Giant Eruption of Hunga Tonga‐Hunga Ha'apai Volcano on 15 January 2022

AbstractThe 15 January 2022, eruption at Hunga Tonga‐Hunga Ha'apai (HTHH) volcano started shortly after 4:00UTC. There had been noted unconfirmed precursory events. We analyzed seismometer data recorded in Fiji and Futuna, the closest stations operated during the eruption and located over 750 km away. We extracted Rayleigh waves and estimated their powers and source directions, assuming retrograde particle motions. We found a Rayleigh wave from the HTHH's direction about 15 min before the eruption onset. The arrival time difference of the Rayleigh wave between the two stations was consistent with that of the M5.8 earthquake during the eruption located beneath the HTHH. Referring to other seismic signals and satellite images, we concluded that the Rayleigh wave was the most significant eruption precursor with no apparent surface activity. Including our findings and results of previous studies, we propose a scenario of the beginning of the caldera‐forming eruption.

Assessing running safety of high-speed trains on long-span cable-stayed bridges based on in-situ monitoring data

This paper presents a new method for assessing the running safety of high-speed trains passing through long-span cable-stayed bridges based on in-situ monitoring data. The monitoring data include the girder vertical deflection and girder-end rotation of bridges. Vertical deformation is utilized to derive the vertical acceleration formula of high-speed trains, and girder-end rotation is utilized to judge the arrival time of the train. According to the specified vertical acceleration limit of the train, the vertical deflection threshold of the bridge is derived from the vertical acceleration formula. The first derivative of the sinusoidal half-wave function of the side-span is utilized to obtain the girder-end rotation threshold, and the safety threshold system is established. The bridge deformation safety threshold system is then used to assess the running safety of the bridge. To verify the presented method, the bridge deformation thresholds calculated using the proposed method are compared with finite element analysis results. The proposed method avoids complex train-bridge coupling numerical simulation in acquiring the dynamic response of high-speed trains, largely improving computation efficiency and enabling real-time assessment of train safety.

Data-driven fire modeling: Learning first arrival times and model parameters with neural networks

Data-driven techniques are increasingly being applied to complement physics-based models in fire science. However, the lack of sufficiently large datasets continues to hinder the application of certain machine learning techniques. In this paper, we use simulated data to investigate the ability of neural networks to parameterize dynamics in fire science. In particular, we investigate neural networks that map five key parameters in fire spread to the first arrival time, and the corresponding inverse problem. By using simulated data, we are able to characterize the error, the required dataset size, and the convergence properties of these neural networks. For the inverse problem, we quantify the network’s sensitivity in estimating each of the key parameters. The findings demonstrate the potential of machine learning in fire science, highlight the challenges associated with limited dataset sizes, and quantify the sensitivity of neural networks to estimate key parameters governing fire spread dynamics.

A novel method for rapidly simulating X-ray pulsar signals at a spacecraft

This paper addresses the challenges posed by the low photon flux and limited atmospheric penetration capabilities of X-ray pulsar signals, which are further exacerbated by technical, economic, and temporal constraints during in-orbit observations. These challenges result in a lack of continuous and high-fidelity data support for ground-based X-ray pulsar navigation (XPNAV) research and validation. To overcome these obstacles, we propose an innovative method for the efficient and rapid simulation of X-ray pulsar signals as received by spacecraft. Building on the physical process that, although the arrival times of the same pulsar signal differ between the spacecraft and the Solar System Barycenter (SSB), these times can be calibrated to the same phase at the SSB using a timing model, we leverage the relationship between the pulsar signal’s arrival rate at the SSB and its arrival phase. Using the Order Statistical Method (OSM), we directly and effectively generate the arrival phase series of pulsar signals received by the spacecraft, which follow a non-homogeneous Poisson process (NHPP). Subsequently, from these phase series, we derive the arrival time series of spacecraft-received pulsar signals, incorporating multiple physical effects based on a relationship model between arrival time series and phases. This method significantly reduces the required simulation time while maintaining high accuracy, thereby greatly enhancing simulation efficiency. Validation against data from the Rossi X-ray Timing Explorer (RXTE) and the Neutron Star Interior Composition Explorer (NICER) shows that the correlation coefficients between the accumulated profiles of simulated and measured data exceed 0.99. Furthermore, our method reduces the simulation time of high-flux pulsar signals by 25.7%, high spin frequency signals by 40.8%, and long observation duration signals by 25.38% compared to the latest fast simulation techniques, highlighting its advantages in accuracy, speed, and versatility for simulating pulsar signals across varying flux rates, spin frequencies, and observation durations. This approach provides continuous and effective data support for XPNAV research, significantly advancing theoretical and practical developments in the field.