Arrival Time Research Articles

Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm

BackgroundThe Department of Rehabilitation Medicine is key to improving patients’ quality of life. Driven by chronic diseases and an aging population, there is a need to enhance the efficiency and resource allocation of outpatient facilities. This study aims to analyze the treatment preferences of outpatient rehabilitation patients by using data and a grading tool to establish predictive models. The goal is to improve patient visit efficiency and optimize resource allocation through these predictive models.MethodsData were collected from 38 Chinese institutions, including 4,244 patients visiting outpatient rehabilitation clinics. Data processing was conducted using Python software. The pandas library was used for data cleaning and preprocessing, involving 68 categorical and 12 continuous variables. The steps included handling missing values, data normalization, and encoding conversion. The data were divided into 80% training and 20% test sets using the Scikit-learn library to ensure model independence and prevent overfitting. Performance comparisons among XGBoost, random forest, and logistic regression were conducted using metrics, including accuracy and receiver operating characteristic (ROC) curves. The imbalanced learning library’s SMOTE technique was used to address the sample imbalance during model training. The model was optimized using a confusion matrix and feature importance analysis, and partial dependence plots (PDP) were used to analyze the key influencing factors.ResultsXGBoost achieved the highest overall accuracy of 80.21% with high precision and recall in Category 1. random forest showed a similar overall accuracy. Logistic Regression had a significantly lower accuracy, indicating difficulties with nonlinear data. The key influencing factors identified include distance to medical institutions, arrival time, length of hospital stay, and specific diseases, such as cardiovascular, pulmonary, oncological, and orthopedic conditions. The tiered diagnosis and treatment tool effectively helped doctors assess patients’ conditions and recommend suitable medical institutions based on rehabilitation grading.ConclusionThis study confirmed that ensemble learning methods, particularly XGBoost, outperform single models in classification tasks involving complex datasets. Addressing class imbalance and enhancing feature engineering can further improve model performance. Understanding patient preferences and the factors influencing medical institution selection can guide healthcare policies to optimize resource allocation, improve service quality, and enhance patient satisfaction. Tiered diagnosis and treatment tools play a crucial role in helping doctors evaluate patient conditions and make informed recommendations for appropriate medical care.

Three‐Dimensional Characterization of Global Ionospheric Disturbances During the 15 January 2022 Tonga Volcanic Eruption

AbstractThe global 3‐dimensional structure of the concentric traveling ionospheric disturbances (CTIDs) triggered by 2022 Tonga volcano was reconstructed by using the 3‐dimensional computerized ionospheric tomography (3DCIT) technique and extensive global navigation satellite system (GNSS) observations. This study provides the first estimation of the CTIDs vertical wavelengths, ∼736 km, which was much larger than the gravity wave (GW) vertical wavelength, 240–400 km, estimated using ICON neutral wind observations. Notable trend with the variation of azimuth was also found in horizontal speeds at 200 and 500 km altitudes and differences between them. These results imply that (a) the global propagation of Lamb waves determined the arrival time of local ionospheric disturbances, and (b) the arriving Lamb waves caused vertical atmospheric perturbations that are not typical of GWs, resulting in local thermospheric horizontal wave propagation which is faster than the Lamb wave propagation at lower altitudes.

PKS 2332–017 and PMN J1916–1519: Candidate Blazar Counterparts to Two High-energy Neutrino Events

Abstract We report our counterpart identification study for two high-energy neutrino events IC-130127A and IC-131204A listed in the IceCube Event Catalog of Alert Tracks. These two events belong to Gold alerts, which have a significant probability of being of astrophysical origin. Within the events’ 90% positional uncertainty regions, we, respectively, find PKS 2332–017 and PMN J1916–1519. The first source is a flat-spectrum radio quasar at redshift z = 1.18, and the second is a blazar of an uncertain type with photometric z = 0.968. As they correspondingly had a γ-ray flare temporally coincident with the arrival times of IC-130127A and IC-131204A, we identify them as the respective neutrino emitters. Detailed analysis of the γ-ray data for the two blazars, obtained with the Large Area Telescope onboard the Fermi Gamma-ray Space Telescope, is conducted. The two flares, respectively, from PKS 2332–017 and PMN J1916–1519 lasted ~4 yr and ~4 months and showed possible emission hardening by containing high-energy ~2–10 GeV photons in the emissions. Accompanying the flare of PKS 2332–017, optical and mid-infrared brightening variations were also observed. We discuss the properties of the two sources and compare the properties with those of the previously reported (candidate) neutrino-emitting blazars.

Assessing drywell designs for managed aquifer recharge via canals and repurposed wells

This study explores innovative drywell designs for managed aquifer recharge (MAR) in agricultural settings, focusing on smaller diameter and deeper drywells, including the repurposing of dried or abandoned wells. Numerical simulations assessed the impact of drywell diameter (5–120 cm), depth (15–55 m), screen height, and subsurface heterogeneity on infiltration (I) and recharge (R) volumes over a one-year period under constant head conditions. Results indicate that smaller diameter drywells can effectively infiltrate and recharge significant water volumes. A 5 cm diameter drywell exhibited only a 48% decrease in infiltration efficiency compared to a standard 120 cm drywell, while being easier to install and requiring less space. Deeper drywells substantially enhanced both I and R; a 20 cm diameter drywell at 55 m depth infiltrated 1.9 × 105 m3 and recharged 3.56 × 104 m3 within a year, with faster arrival times of recharge water. The study also proposes integrating drywells into existing irrigation canal networks. Simulations suggest that installing drywells every 70 m along canals could infiltrate 7.14 × 105 to 1.43 × 106 m3 of water per kilometer annually, significantly enhancing groundwater recharge in regions where traditional MAR methods are limited. Subsurface heterogeneity was found to increase I and R volumes compared to homogeneous conditions, emphasizing the importance of site-specific assessments. An economic analysis revealed that deeper, small-diameter drywells offer lower levelized costs, down to $0.46 per cubic meter of recharged water, making them economically viable alternatives. However, technical challenges such as clogging, water quality concerns, and regulatory requirements were identified. Pretreatment measures like sedimentation chambers and geotextile membranes are recommended to mitigate clogging, though their costs require further evaluation. Pilot-scale studies are recommended to validate simulation findings, assess technical and regulatory challenges, and refine designs for practical implementation.

High fidelity simulations of contaminant dispersion in an urban environment with comparison to magnetic resonance imaging measurements

The dispersion of a contaminant in an urban environment has the potential to impact a large population of people. In this work, a complex urban canopy flow based on the Oklahoma City downtown business district circa 2003 is studied using Magnetic Resonance Imaging (MRI) and high-fidelity Large Eddy Simulations (LES). MRI is a novel experimental technique that can provide high-resolution measurements in four dimensions (three spatial and temporal) for lab scale models. The experiments and simulations use the same geometry and boundary conditions providing a one-to-one comparison of the two methods. Results are presented on the time-averaged velocity and concentration fields, the temporal dynamics of the concentration plumes for a transient release, and a novel Cloud Identification Algorithm that can separate plumes produced by periodic contaminant releases used for ensemble averaging over many releases. The MRI and LES datasets both include millions of measurement voxels and the comparisons highlight the complex 3D nature of the flow including strong vertical velocities in spanwise street canyons and flow acceleration in streamwise street canyons. The concentration fields are qualitatively similar albeit the LES shows larger dispersion. A quantitative analysis with performance measures compares the datasets pointwise and demonstrates that the two 3D datasets are similar with respect to many measures including a fractional bias of 0.02 (ideal=0.0), correlation coefficient of 0.87 (ideal = 1.0), and the fraction points within a factor of 2 is 0.98 (ideal = 1.0). Plume analysis compares the arrival and residence time of contaminant and is found to vary significantly with location within the urban environment with arrival times between 0 and 1.25 and differences within the contaminant cloud less than 10% at most locations.

Asymmetry Between Queues with Correlated Service and Correlated Arrivals

It is known that a correlation in either the service or interarrival times causes a deterioration in the performance of a queuing system. This study aimed to determine which of the two correlations—in the service times or in the interarrival times—has a stronger influence on the expected queue length, assuming an identical autocorrelation function in both cases. To achieve this goal, a formula for the expected queue length in a system with correlated arrivals was derived first. This new formula, along with a known formula for the expected queue length in a system with correlated service, was used to compare the influence of the two correlations. Various scenarios were studied, such as cases where the common correlation was positive or negative, where the variance of the service or interarrival time was low or high, and where the system load was low or high. Furthermore, both the time-dependent and the steady-state behaviors of the systems were compared. The following two key observations were made. If the impact of other factors on the queue length is minor, then a positive correlation has a worse effect on the queue length when present in service times than in arrival times. On the contrary, a negative correlation has a worse effect on the queue length when present in arrival times than in service times.

Fourier-ResPINN: a new solution for solving the travel time of first arrival

Fourier-ResPINN: a new solution for solving the travel time of first arrival

Efficient Coupling of Structures for Lossless Ion Manipulations with Ion Trap Mass Analyzers Using Phase Modulation.

Phased structures for lossless ion manipulation offer significant improvements over the scanning second gate method for coupling with ion trap mass analyzers. With an experimental run time of under 1 min for select conditions and an average run time of less than 4 min, this approach significantly reduces experimental time while enhancing the temporal duty cycle. The outlined SLIM system connects to an ion trap mass analyzer via a PCB stacked ring ion guide, which replaces the commercial ion optics and capillary inlet. By applying a discrete and repeating injection pulse and solving a series of algebraic equations, the system reconstructs an arrival time distribution with a minimal degree of error with enhanced ion throughput. To demonstrate the feasibility of this approach, the 3.4-m SLIM system resolves gas-phase conformers for various small peptides and proteins. This system and methodology also enable direct implementation between SLIM and ion trap mass analyzers traditionally interfaced with front separation systems such as liquid chromatography.

Picosecond timing performances of the FERS time unit

Next-generation high-energy physics experiments will employ high-granularity detectors with thousands of readout channels, necessitating the use of low-power, compact ASICs. The CAEN FrontEnd Readout System (FERS) 5200 integrates these ASICs within small, synchronizable, and distributable systems featuring both Front and Back Ends. The A5203 FERS module incorporates the recently released CERN picoTDC ASIC, enabling high-resolution timing measurements for Time of Arrival (ToA) and Time over Threshold (ToT).In this work, we analyze the performance of the A5203 unit, which has a 3.125 ps least significant bit (LSB), delivering ToA measurements with an RMS resolution of approximately 7 ps for signals of fixed amplitude, and approximately 20 ps RMS for input signals with variable amplitude over a 50 dB dynamic range over a single board. The amplitude-dependent walk effect is corrected using ToT, which, in addition to walk correction, is also utilised for signal amplitude reconstruction and background reduction.The A5203 has been successfully applied across various experimental and industrial contexts. In the concluding section of this paper, we will discuss its application within the ProVision project: a PET scanner specialised in imaging aggressive prostate cancer at an early-stage.

A DAQ system with low-dead-time, high-precision TDC for APD detection efficiency calibration

Correlated photon calibration based on spontaneous parametric down-conversion (SPDC) provides a highly precise means to calibrate the detection efficiency of avalanche photodiode (APD). During the calibration process of detection efficiency via SPDC, precise measurement of the arrival time of correlated photons and accurate photon counting are essential. To achieve this goal, a data acquisition (DAQ) system with low-dead-time, high-resolution Time-to-Digital Converter (TDC) was designed in this paper. This TDC is designed based on tapped delay line (TDL), which is implemented on Xilinx Kintex-7 series field programmable gate array (FPGA). This TDC can accurately measure the time of arrival of input signals. Upon arrival of a photon signal, the TDC rapidly generates a timestamp to record the arrival time of the photon signal. Utilizing these timestamps, time delay and accurate measurements of time intervals can be achieved. The specially designed TDC input stage structure and encoding algorithm enable the alternating propagation and sampling of `01' and `10' transitions on TDL. Coupled with a pipelined architecture, the TDC's dead time is close to one clock cycle, which is 2.33 ns in the current implementation version. The structure of mode recognition and triggering ensures that the TDC can correctly calibrate time measurements and count photon counts. Experimental results show that the TDC achieves an RMS precision of better than 11.08 ps, and this measurement precision is maintained over long time intervals (0–10 us). The reliability of this DAQ for photon counting has been verified through standard signal sources.

Smart ETA Predictions: Leveraging AI and Neutrosophic Fuzzy Soft Sets for Real-Time Accuracy

In this paper we aims to provide a clear definition of Neutrosophic Fuzzy Soft Sets and explain its fundamental operations through relevant examples. This work examines the computation of static Expected Time of Arrival (ETA) utilizing neutrosophic fuzzy soft set values and the fundamental Expected Time of Arrival. Our research also investigates the incorporation of sophisticated artificial intelligence (AI) methods to create reliable and adaptable dynamic Expected Time of Arrival(ETA) prediction models. Through the utilization of many types of data, such as current traffic statistics, weather conditions, road conditions, vehicle status, and driver behavior, we suggest a comprehensive system that adapts to changing circumstances and consistently enhances its ability to make accurate predictions. Our methodology utilizes cutting-edge machine learning algorithms to analyze and interpret vast amounts of diverse data. In addition, we tackle the difficulties of managing uncertainty and indeterminacy in data by utilizing Neutrosophic Fuzzy Soft Sets, which improve the model’s resilience and dependability.

Teleseismic evidence for structural heterogeneity in East Japan forearc from seafloor S-net data

We measure and analyze 4381 P-wave and 4307 S-wave arrival times of 48 teleseismic events recorded at 150 stations of a permanent seafloor seismic network (S-net) installed in the outer-rise and forearc region off East Japan. The obtained relative travel-time residuals amounting to ~3 s at the S-net stations are generally negative on the incoming Pacific and Philippine Sea plates and positive on the continental Okhotsk plate, which reflect high and low seismic velocities, respectively. This pattern is generally consistent with previous results on the seismic velocity structure of the crust and upper mantle beneath the East Japan forearc and the outer-rise area. Large early arrivals (~2.0 s) appear in the southern part of the S-net for the teleseismic events in the southwestern direction, which are mainly due to southwestward steepening of the subducting Pacific slab beneath Kanto. In the study region, the Pacific slab is the most significant anomaly with a thickness of ~90 km and a seismic velocity of 5–6 % higher than that of the surrounding mantle. Early arrivals (~1.5 s) also appear at the S-net stations off South Hokkaido, which are caused by northwestward steepening of the Pacific slab beneath the Tohoku-Hokkaido junction area. These results shed new light on the structural heterogeneity and subduction dynamics of the East Japan arc.

Multi-region infectious disease prediction modeling based on spatio-temporal graph neural network and the dynamic model.

Human mobility between different regions is a major factor in large-scale outbreaks of infectious diseases. Deep learning models incorporating infectious disease transmission dynamics for predicting the spread of multi-regional outbreaks due to human mobility have become a hot research topic. In this study, we incorporate the Graph Transformer Neural Network and graph learning mechanisms into a metapopulation SIR model to build a hybrid framework, Metapopulation Graph Transformer Neural Network (M-Graphormer), for high-dimensional parameter estimation and multi-regional epidemic prediction. The framework effectively solves the problem that existing models may lose some hidden spatial dependencies in the data when dealing with the dynamic graph structure of the network due to human mobility. We performed multi-wave infectious disease prediction in multiple regions based on real epidemic data. The results show that the framework is capable of performing high-dimensional parameter estimation and accurately predicting epidemic transmission dynamics in multiple regions even with low data quality. In addition, we retrospectively extrapolate the temporal evolution patterns of contact rate under different interventions implemented in different regions, reflecting the dynamics of intervention intensity and the need for flexibility in adjusting interventions in different regions. To provide early warning of infectious disease transmission, we retrospectively predicted the arrival time of infectious diseases using data from the early stages of outbreaks.

Protocol for extracting flow hydrograph shape metrics for use in time-series flood hydrology analysis.

The shape characteristics of flow hydrographs hold essential information for understanding, monitoring and assessing changes in flow and flood hydrology at reach and catchment scales. However, the analysis of individual hydrographs is time consuming, making the analysis of hundreds or thousands of them unachievable. A method or protocol is needed to ensure that the datasets being generated, and the metrics produced, have been consistently derived and validated. In this lab protocol, we present workflows in Python for extracting flow hydrographs with any available temporal resolution from any Open Access or publicly available gauging station records. The workflow identifies morphologically-defined flow and flood types (i.e. in-channel fresh, high flow and overbank flood) and uses them to classify hydrographs. It then calculates several at-a-station and upstream-to-downstream hydrograph shape metrics including kurtosis, skewness, peak hydrograph stage, peak arrival time, rate-of-rise, peak-to-peak travel time, flood wave celerity, flood peak attenuation, and flood wave attenuation index. Some metrics require GIS-derived data, such as catchment area and upstream-to-downstream channel distance between gauges. The output dataset provides quantified hydrograph shape metrics which can be used to track changes in flow and flood hydrographs over time, or to characterise the flow and flood hydrology of catchments and regions. The workflows are flexible enough to allow for additional hydrograph shape indicators to be added or swapped out, or to use a different hydrograph classification method that suits local conditions. The protocol could be considered a change detection tool to identify where changes in hydrology are occurring and where to target more sophisticated modelling exercises to explain the changes detected. We demonstrate the workflow using 117 Open Access gauging station records that are available for coastal rivers of New South Wales (NSW), Australia.

Predicting container intermodal transport arrival times: An approach based on IoT data

Predicting container intermodal transport arrival times: An approach based on IoT data

The NANOGrav 15 Yr Data Set: Removing Pulsars One by One from the Pulsar Timing Array

Evidence has emerged for a stochastic signal correlated among 67 pulsars within the 15 yr pulsar-timing data set compiled by the NANOGrav collaboration. Similar signals have been found in data from the European, Indian, Parkes, and Chinese pulsar timing arrays. This signal has been interpreted as indicative of the presence of a nanohertz stochastic gravitational-wave background (GWB). To explore the internal consistency of this result, we investigate how the recovered signal strength changes as we remove the pulsars one by one from the data set. We calculate the signal strength using the (noise-marginalized) optimal statistic, a frequentist metric designed to measure the correlated excess power in the residuals of the arrival times of the radio pulses. We identify several features emerging from this analysis that were initially unexpected. The significance of these features, however, can only be assessed by comparing the real data to synthetic data sets. After conducting identical analyses on simulated data sets, we do not find anything inconsistent with the presence of a stochastic GWB in the NANOGrav 15 yr data. The methodologies developed here can offer additional tools for application to future, more sensitive data sets. While this analysis provides an internal consistency check of the NANOGrav results, it does not eliminate the necessity for additional investigations that could identify potential systematics or uncover unmodeled physical phenomena in the data.

Exploring spatiotemporal heterogeneity of urban green freight delivery parking based on new energy vehicle GPS data

To enhance the efficiency and sustainability of urban freight operations, China has initiated the Urban Green Freight Delivery (UGFD) project, which involves optimizing access control policies and introducing new energy vehicles. Identifying the parking trips of new energy vehicles and exploring the spatiotemporal patterns is crucial to actively promoting the optimal layout of temporary stops and the formulation of parking policies in the UGFD project. In this study, we aim to comprehend the spatiotemporal heterogeneity of parking for new energy vehicles both on roads (on-street) and within urban communities (off-street) for promoting the UGFD project. Its specific content includes: (1) proposing a method for identifying valid parking trips for the loading and unloading of goods based on trajectory data of UGFD new energy vehicles; and (2) mapping the identification results of valid parking trips onto communities and roads to analyze the spatiotemporal heterogeneity. Taking Suzhou, Jiangsu Province, China as an example, the identification results show that the established valid parking trips identification method can outperform state-of-the-art methods. The accuracy, precision, recall, and F1 value were found to be 0.957, 0.908, 0.937, and 0.922, respectively. Further examination of parking patterns indicates a bimodal temporal distribution of delivery demand, with peak activity occurring between 08:00–09:00 and between 14:00–17:00, with a higher delivery demand in the morning. Spatially, delivery demand was aggregated, while the parking time distribution of most delivery activities was normal. Additionally, the parking characteristics of communities and roads conformed to the ‘Rank–size rule’, suggesting that most delivery parking activities were concentrated in a few communities and roads. These findings can also be used in UGFD stop station utilization, travel time, arrival time prediction, and other related fields, all of which can further support relevant management departments in discovering abnormal delivery behaviors and reduce their negative impacts.

Probabilistic linguistic carpooling matching decision-making considering time satisfaction based on prospect theory

To improve the travel efficiency of carpooling demanders and reduce travel costs, a carpooling matching decision-making method is proposed that considers preference satisfaction and time satisfaction in a probabilistic linguistic environment. First, the multiattribute many-to-one carpooling matching problem in the probabilistic linguistic environment is described. To solve this problem, based on probabilistic linguistic term set (PLTS) evaluation matrices, attribute prospect values are calculated via prospect theory, and based on probabilistic linguistic preference relations (PLPRs) for attributes, group consistency attribute weights are calculated. Second, cumulative prospect values are calculated by aggregating attribute prospect values and attribute weights and then standardized to obtain the preference satisfactions of passengers and drivers. Third, based on the interval expected waiting time provided by passengers and the estimated arrival time provided by drivers, the time satisfaction of passengers is calculated considering time tendency coefficients. Then, the comprehensive satisfaction of passengers is calculated by aggregating preference satisfaction and time satisfaction via the geometric average method. Furthermore, based on the comprehensive satisfaction of passengers and the preference satisfaction of drivers, a many-to-one carpooling matching model is constructed and then solved to obtain an optimal many-to-one carpooling matching scheme. An example analysis shows the effectiveness, reliability and accuracy of the proposed method. The main innovations of this study are as follows: (1) A calculation method for the improved score of PLTSs considering hesitation is proposed. (2) Two algorithms for calculating unknown attribute weights are designed based on PLPRs. (3) A calculation method for time satisfaction considering time tendency coefficients is presented. (4) A model for probabilistic linguistic multiattribute many-to-one carpooling matching considering time satisfaction is built.

Combining Fourier Transform Ion Mobility with Charge Detection Mass Spectrometry for the Analysis of Multimeric Protein Complexes.

Charge detection mass spectrometry (CDMS) allows direct mass measurement of heterogeneous samples by simultaneously determining the charge state and the mass-to-charge ratio (m/z) of individual ions, unlike conventional MS methods that use large ensembles of ions. CDMS typically requires long acquisition times and the collection of thousands of spectra, each containing tens to hundreds of ions, to generate sufficient ion statistics, making it difficult to interface with the time scales of online separation techniques such as ion mobility. Here, we demonstrate the application of Fourier transform multiplexing and drift tube ion mobility joined with Orbitrap-based CDMS for the analysis of multimeric protein complexes. Stepped frequency modulation was utilized to enable unambiguous frequency assignment during mobility sweeps and allow spectral averaging, which improves the accuracy and signal-to-noise of ion mobility spectra and CDMS measurements. Fourier transformation of the signal reveals the arrival times and collision cross sections of ions while simultaneously collecting charge information for thousands of individual ions. Combining Fourier transform multiplexing ion mobility and CDMS provides insight into each ion's size and mass while showcasing a potential solution to the duty cycle mismatch of online separation techniques in the single ion regime.

Near Real-time Flood Risk Modelling in Response to Increasing Uncertainties in Flood Predictions

In the face of increasing uncertainties and the expected rise in flood events worldwide, this technical note presents an innovative approach for enhancing rapid response capabilities, exemplified during the Kakhovka Dam breach in Ukraine. Utilizing the Tygron Model, our methodology combines open-source data with high performance computing for swift hydrodynamic simulations—a departure from traditional flood risk management techniques. Central to this approach is the strategic use of social media to gather crowd-sourced feedback during the emergency, enhancing the precision and relevance of flood risk information. During the Kakhovka Dam event, our model processed extensive datasets, enabling effective predictions of flood impacts, including extents, velocities, depths, and arrival times. The near real-time modeling capability allowed for dynamic updates using social media inputs, which were of value for emergency responders to optimize response strategies for relief coordination. While the underlying technology is used for flood simulations, its application in emergency response is novel and promising for more adequate disaster response coordination. However, further research and applications are necessary to refine the approach that can ensure real-time flood risk information during a emergency situations.