Arrival Time Research Articles

Implementasi Sistem Kehadiran Guru Berbasis Internet of Thing di SDIT Darul Hikmah

The teacher attendance system currently implemented at SDIT Darul Hikmah Jakarta still uses the manual method by filling in the arrival time and signing the attendance book available in the office. The system has several drawbacks such as manipulation of attendance, damaged or missing attendance books, and ineffective reporting because the general coordinator needs to copy back attendance data to be submitted to the principal and chairman of the foundation as material for evaluating teacher performance. Therefore, this study aims to create an Internet of Thing-based presence system using NodeMCU ESP 32 and a fingerprint sensor. This research process uses the Prototype method. This research produced a teacher attendance recorder that teachers can use as proof of attendance when they arrive at class. In addition, the website utilized to manage teacher attendance data to make reports and is used by teachers to fill out learning agendas that can provide feedback to student parents regarding student participation in learning through the Telegram application.

Six Sigma Technique to Reduce Waiting Time: A Practical Application in Passport Affairs Directorate

This study investigates the impact of the Six Sigma methodology on improving waiting times in passport directorates. The Six Sigma methodology is crucial for enhancing efficiency and effectiveness in passport directorates, aiming to reduce waiting times and increase customer satisfaction by organizing services more effectively. The research was conducted in the Passport Affairs Department (Mansour Passports) from November 8, 2022, to April 19, 2023, with a sample size of 53 employees and citizens. The problem of this study is to shed light on the role played by the Six Sigma methodology in improving waiting time at the Passport Affairs Department. The study adopted the DMAIC methodology (Define, Measure, Analyze, Improve, and Control) and used statistical software (SPSS) to calculate Six Sigma levels for both citizens and employees. Access and service data analysis was conducted using Win QSB software, and arrival and service times were calculated for all stages (counter employees, accounts, and manager's computer). The most significant results indicate that the directorate operates at the first level of Sigma levels, while citizens are at the second level. There is a significant and inverse effect of Six Sigma principles on waiting. The total time a citizen waits from entering the passport office to receiving their passport is very long. Paper type: Research Paper

Potential effects of a mobile stroke unit on time to treatment and outcome in patients treated with thrombectomy or thrombolysis: A Danish-German cross-border analysis.

A mobile stroke unit (MSU) reduces delays in stroke treatment by allowing thrombolysis on board and avoiding secondary transports. Due to the beneficial effect in comparison to conventional emergency medical services, current guidelines recommend regional evaluation of MSU implementation. In a descriptive study, current pathways of patients requiring a secondary transport for mechanical thrombectomy were reconstructed from individual patient records within a Danish (n = 122) and an adjacent German region (n = 80). Relevant timestamps included arrival times (on site, primary hospital, thrombectomy centre) as well as the initiation of acute therapy. An optimal MSU location for each region was determined. The resulting time saving was translated into averted disability-adjusted life years (DALYs). For each region, the optimal MSU location required a median driving time of 35 min to a stroke patient. Time savings in the German region (median [Q1; Q3]) were 7 min (-15; 31) for thrombolysis and 35 min (15; 61) for thrombectomy. In the Danish region, the corresponding time savings were 20 min (8; 30) and 43 min (25; 66). Assuming 28 thrombectomy cases and 52 thrombolysis cases this would translate to 9.4 averted DALYs per year justifying an annual net MSU budget of $0.8M purchasing power parity dollars (PPP-$) in the German region. In the Danish region, the MSU would avert 17.7 DALYs, justifying an annual net budget of PPP-$1.7M. The effects of an MSU can be calculated from individual patient pathways and reflect differences in the hospital infrastructure between Denmark and Germany.

Direct Medical Cost for Acute Ischemic Stroke Patients in Vietnam

Ischemic stroke is one of the leading causes of global disease burden. Vietnam, among other low- and middle-income countries, bears nearly 90% of the ischemic stroke burden. This study was a cost-of-illness study conducted from the healthcare system perspective to estimate the direct medical costs and associated factors for acute ischemic stroke patients at Bach Mai Hospital, a major hospital in Hanoi, Vietnam. A total of 500 eligible patients with acute ischemic stroke at the Neurology Department of Bach Mai Hospital from July 2021 to December 2021 were included. Patient characteristics and treatment information were collected via medical records and patient interviews. The majority of the patients were men (65.8%), with a mean age of 65.3 years. The average direct medical cost per patient was 10.5 ± 7.3 million Vietnam Dong (~454.2 ± 313.8 US dollars). The cost of hospital beds accounted for most of the total cost (34.6%), followed by medications and infusion fluid costs (27%) and diagnostic imaging (12.6%). Factors significantly associated with higher direct medical costs were the duration of stay, stroke severity, complications, number of comorbidities, and intravenous thrombolysis treatment. The study highlighted the need for a reduction in the duration of hospital stay and an improvement in onset-to-hospital arrival time.

Proactive real-time scheduling method for apron service vehicles based on mixed strategies

Due to the uncertainty of flight arrival and departure times, the scheduling of apron vehicles in airports has always faced significant challenges. This paper considers the real-time scheduling problem of apron vehicles under dynamic requests, establishing a new parking rule to allow apron vehicles to dynamically adjust parking areas. In order to maximize service level and minimize operational costs, this paper proposes a proactive real-time scheduling method for apron vehicles based on mixed strategies. Through strategies such as flexible waiting and dynamic relocation, this paper responds proactively to future requests. The proposed strategies are validated through implementation in large and medium-sized scale cases at Beijing Capital International Airport and Nanjing Lukou International Airport in China, respectively. Numerical results indicate that the proposed scheduling method is effective and applicable. The flexible waiting strategy significantly reduces vehicle travel distance, while the dynamic relocation strategy is significant in shortening request response times.

DEVS: Secure and optimal decentralized energy trading scheme for Electric Vehicle and Charging Station using game theory

SummaryWith the advent and popularity of electric vehicles (EVs), the intelligent transportation system has adopted them as an alternative to fossil fuel or gasoline vehicles owing to their benefits of reduced greenhouse gas emissions. However, it becomes critical to schedule the vast EVs at the charging station (CS) efficiently while maintaining security, privacy, and optimality during the energy trading. Therefore, we propose a blockchain and zero‐sum game theory‐based energy trading scheme, that is, DEVS, for optimal and secure EV charging at the CS. We incorporate the Interplanetary File System with the blockchain to ameliorate the cost‐efficiency and reliability of EV energy trading. Furthermore, we implement the zero‐sum game theory between players EV and CS to yield an optimal payoff based on pure and mixed strategies during the energy trading procedure. Moreover, we deploy and execute the smart contract (SC) of the blockchain‐based proposed DEVS scheme in the Remix integrated development environment to secure EV energy trading transactions. Additionally, the security of the proposed DEVS scheme is checked and analyzed using a fuzzing‐based Echidna tool for protected energy trading. Finally, the performance evaluation of the proposed DEVS scheme is analyzed using various standard metrics such as profit comparison based on strategies, energy price comparison, CS profit comparison for pure strategy (based on arrival time), convergence comparison, and profit (EV and CS) at saddle point and mixed strategy to avail preserved and optimal energy trading for EVs.

The NANOGrav 12.5-Year Data Set: Dispersion Measure Misestimations with Varying Bandwidths

Noise characterization for pulsar-timing applications accounts for interstellar dispersion by assuming a known frequency dependence of the delay it introduces in the times of arrival (TOAs). However, calculations of this delay suffer from misestimations due to other chromatic effects in the observations. The precision in modeling dispersion is dependent on the observed bandwidth. In this work, we calculate the offsets in infinite-frequency TOAs due to misestimations in the modeling of dispersion when using varying bandwidths at the Green Bank Telescope. We use a set of broadband observations of PSR J1643−1224, a pulsar with unusual chromatic timing behavior. We artificially restricted these observations to a narrowband frequency range, then used both the broad- and narrowband data sets to calculate residuals with a timing model that does not account for time variations in the dispersion. By fitting the resulting residuals to a dispersion model and comparing the fits, we quantify the error introduced in the timing parameters due to using a reduced frequency range. Moreover, by calculating the autocovariance function of the parameters, we obtained a characteristic timescale over which the dispersion misestimates are correlated. For PSR J1643−1224, which has one of the highest dispersion measures (DM) in the NANOGrav pulsar timing array, we find that the infinite-frequency TOAs suffer from a systematic offset of ∼22 μs due to incomplete frequency sampling, with correlations over about one month. For lower-DM pulsars, the offset is ∼7 μs. This error quantification can be used to provide more robust noise modeling in the NANOGrav data, thereby increasing the sensitivity and improving the parameter estimation in gravitational wave searches.

Modelling of Propagation Characteristics of Acoustic Pulse from Partial Discharge in Polymeric Insulating Materials

The partial discharge (PD) event in high-voltage insulation releases energy, exerts mechanical pressure, and generates elastic waves. Detecting and locating these PD events through short-duration acoustic pulses is well established, particularly in gas-insulated systems and oil-insulated transformers. However, its full potential remains untapped in solid insulation systems, where the propagation capability of the acoustic pulse and the acoustic reflections pose fundamental challenges to the acoustic emission (AE) detection technique. This study investigates the influence of reflections and multiple paths on the propagating acoustic pulse in polymeric insulating materials using a finite element method (FEM) in COMSOL. It was observed that the reflections from the boundary influence the propagating pulse’s shape, peak magnitude, and arrival time. An analytical MATLAB model further quantifies the impact of multiple propagation paths on the shape, magnitude, and arrival time of the pulse travelling in a cylinder. Additionally, a Perfect Matched Layer (PML) was implemented in the COMSOL model to eliminate the reflections from the boundary, and it revealed that the acoustic pulse magnitude decreases with distance following the inverse square law. In essence, the models aid in measuring how reflections contribute to the observed signals, facilitating the precise identification of the source of the PD event in the tested system.

Significant Earth’s responses of the 2022 Tonga eruption across Taiwan from multiple sensor observations

On 15 January 2022, a massive underwater volcano erupted in the Tonga region, releasing a significant amount of volcanic ash and gases into the atmosphere. The United States Geological Survey (USGS) estimated the seismic source to have a surface wave magnitude (Ms) of 5.8. This eruption was observed from space, and the resulting atmospheric shockwave swept across the Pacific Ocean. Reports from various locations worldwide indicated rapid fluctuations in air pressure following this event. Taiwan, situated in the western Pacific, approximately 8,500 km from the eruption source, observed significant changes. During this volcanic eruption, both rapid air pressure changes and several significant changes in the Earth’s physical parameters were observed in Taiwan. The Tonga eruption is a unique event, and comprehensive observations provide an opportunity to explore and explain the mechanisms behind this extreme event. Data from ground surface air pressure gauges, coastal tide gauges, underwater pressure gauges, infrasound sensors, digital microphones, and seismometers were collected. These data were analyzed to identify their origin and explain their interactions. The results of this study first present the detailed propagation characteristics of air pressure waves in the Taiwan region and verify the occurrence of a specific tsunami phenomenon caused by the atmospheric disturbance from the Tonga eruption. It follows a distinct mechanism, explaining its arrival time and wave height distribution around Taiwan, which is different from conventional tsunamis of tectonic origins, which are formed by rapid changes in water caused by earthquakes or underwater landslides.

Message from the editor in Chief

The year 2024 promises many exciting developments for the Global Journal of Computer Sciences: Theory and Research (GJCS). In addition, every year, the number of citations continues to rise strongly in proportion to the increase in the number of articles published. In this respect, first of all, we would like to thank all our authors who have contributed to the success of our journal. While reviewing our new issue, we saw articles from different authors from different countries: Turkey, India The multiculturalism policy followed in our globally-focused journal makes us happy with every new issue. We examine the following research areas in the current issue; “Determination of reinforcement diameters of reinforced concrete deep beams with genetic algorithms”, “Single server queueing model with Poisson arrival and gamma service time distribution”, “Sustainable innovation policy in non-walled technoparks” . We will continue to contribute to literature within the scope of Computer Science with our strong team. Thanks to all our authors the contributors to this issue. Hope to see you in our new issue. Assoc. Prof. Dr. Ezgi Pelin YILDIZ Global Journal of Computer Sciences: Theory and Research (GJCS) CHEF EDITOR

Single server queueing model with Poisson arrival and gamma service time distribution

Queueing theory is pivotal in understanding waiting lines and their pervasive role in everyday life. Its profound application spans across diverse sectors, including computer programming networks, healthcare, transportation, and so on. Researchers have applied many statistical distributions in analyzing queueing data. This article focuses on the analysis of a single server queueing system with Poisson input and service times distributed according to a two-parameter gamma distribution. The study elaborates on the construction and estimation of key queueing properties such as steady state equations, average number of customers in the system, average queue length, expected size of the non-empty queue, distribution of waiting time, average waiting time in the system, and average waiting time in the queue of the M/Gamma/1 queueing model using the two-parameter gamma distribution. Keywords: Gamma distribution; Poisson; service; single server; queuing model.

Wasatch Fault Structure from Machine Learning Arrival Times and High-Precision Earthquake Locations

Abstract On 18 March 2020, a magnitude 5.7 earthquake hit the Salt Lake valley in the state of Utah, United States. Using a dense geophone deployment and machine learning (ML), an additional several thousand events were detected and located. Currently, both the mainshock and the majority of the aftershocks are suspected to have occurred on or near a deeper portion of the Salt Lake segment of the Wasatch fault—part of a large range-bounding fault system thought to be capable of generating an Mw 7.2 earthquake. However, a small subset of aftershocks may have occurred on a portion of the more steeply, eastward dipping, and poorly understood West Valley fault. Unfortunately, the catalog locations and lack of focal mechanisms for this subset of aftershocks provide only a crude constraint on the true fault structure. To better illuminate fault structure, we relocate the ML-generated catalog with a range of magnitudes from −2 to 4.6, using: (1) NonLinLoc, a nonlinear location algorithm, (2) source-specific station terms, and (3) waveform coherence. We further compute first-motion focal mechanisms for 68 events. Results of the relocation suggest a simpler, minimally listric Wasatch fault geometry, contrary to what has been previously proposed. We also find that analysis of the focal mechanisms and waveform similarity indicates minimal event similarity throughout the Magna sequence, suggesting a highly complex and heterogeneous rupture zone, as opposed to rupture on a single plane. These findings suggest an increased seismic hazard due to the overall shallowness of the earthquake sequence and highly varied rupture mechanisms.

A New Data Processing Approach for the SHPB Test Based on PSO-TWER

This study addresses the challenge of accurately determining the arrival time of stress wave signals in SHPB test data processing. To eliminate human error, we introduce the time-window energy ratio method and evaluate six filters for noise reduction using box fractal dimensions. A mathematical model is established to optimize the stress equilibrium and impact process, which is solved using particle swarm optimization, resulting in the PSO-TWER method. We explore the impact of inertia weight and calculation methods on optimization outcomes, defining a stress equilibrium evaluation index. The results indicate that time-window length significantly affects arrival-time outputs, and the dynamic inertia weight factor enhances optimization convergence. The method accurately determines arrival times and effectively screens test data, providing a robust approach for SHPB test data processing.

A novel method for analyzing the leakage and diffusion of hydrogen: First arrival time distribution

Hydrogen energy plays an important role in achieving the carbon peak and carbon neutrality goals. However, owing to its flammability and explosiveness, hydrogen leakage during transportation and use can easily cause severe consequences. Therefore, research on the safety of hydrogen leakage is particularly important. This study proposes a novel method for analyzing the diffusion behavior of hydrogen leakage called first arrival time distribution (FATD). The spatial distribution of the first arrival times of specific concentration values (such as explosion limit and alarm concentration) at each coordinate point in the calculation domain was obtained by processing the concentration field of hydrogen leakage and diffusion via computational fluid dynamics simulations. The hydrogen leakage and diffusion processes were simulated for multiple scenarios using Fluent software, and the method of obtaining the FATD based on the simulations was detailed. The study also analyzed the hydrogen leakage and diffusion processes as well as the effects of mesh quantity and sampling time interval on the FATD; the FATD was additionally applied in actual scenarios to obtain an alarm time distribution for guiding sensor deployment. The results show that the FATD intuitively displays the hydrogen diffusion and evolution processes via specific concentration values in the calculation domain while describing the detailed influences of obstacles on the hydrogen diffusion process. Moreover, the FATD can serve as an indicator of grid independence analysis. This provides a new method for understanding diffusion behavior, reference for accident risk and consequence assessments, and safety guidance for the utilization of hydrogen energy.

A deep learning-based approach for predicting in-flight estimated time of arrival

AbstractPredictability is key for efficient and safe air traffic management. In particular, accurately estimating time of arrival for current passenger flights may help terminal controllers to plan ahead and optimize airport operations in terms of safety and resource allocation. While traditional physics-based simulations are still widely used, they are complex to model and often fail to include many factors affecting the progress of a flight. In this paper, we propose a deep learning approach based on LSTM that leverages the 4D trajectory of the flight and weather data at the destination airport, to accurately predict estimated time of arrival. We evaluate our model on flights arriving at Adolfo Suárez-Madrid Barajas airport (Spain), in the first three quarters of 2022, achieving a mean absolute error of 2.65 min over the entire flight and reporting competitive short- and long-term predictions at different spatial and temporal horizons.

Implementation of Real-Time Bus Tracking and Passenger Information System for MSRTC Mr. Vikrant Talole, Mr. Prasad Sonavane, Miss. Snehal Saykar

The "Real-Time Bus Tracking and Passenger Information System for Maharashtra State Road Transport Corporation (MSRTC)" is a cutting-edge technological solution designed to revolutionize the public transportation experience. In a world where efficiency, accuracy, and real-time information are paramount, this system stands as a beacon of innovation. The system offers real-time tracking capabilities, allowing passengers to monitor the exact location of MSRTC buses in real-time through intuitive mobile applications and web interfaces. Leveraging GPS technology, this feature not only ensures accurate tracking but also enables precise predictions of bus arrival times at designated stops. Passengers gain access to comprehensive route information, including stops, schedules, and landmarks, enhancing their ability to plan journeys effectively. The Real Time Bus Tracking and Passenger Information System for MSRTC stands as a testament to the power of innovation, ushering in a new era of smart public transportation.

The Relationships between Emission Geometry and Jitter Noise in Millisecond Pulsars

The relationships between several parameters of an emission geometry and jitter noise in 17 millisecond pulsars (MSPs) are investigated. By assuming the jitter noise is due only to a pulse variation in phase, the former can be modeled as changes in the plasma flow rate leading to variation in the measured pulse arrival time relative to the predicted time. In the model for pulsar magnetospheres with multiple emission states, the plasma flow is associated with the emission states, and a change in the emission state corresponds to a change in the plasma flow causing variation in the pulse arrival time. These can be specified in an emission geometry defined by the obliquity and viewing angles, measured from the rotation axis to the magnetic axis and to the line of sight, respectively. We calculate the maximum change in the emission state based on the reported jitter noise for each of the MSPs. Using the results, we show that the MSPs possess relatively large obliquity angles, which is consistent with observation, and the jitter noise exhibits dependency on frequency. We find that the jitter noise in our sample displays an exponential decay as a function that combines the obliquity angle and the rotation period, revealing the correlation among the three parameters. This suggests that the magnitude of the jitter noise is likely specific to an MSP. We discuss how jitter noise may be related to the evolution of an MSP.

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 huge burden in the developing world, and significant proportion has found in low- and middle-income countries. However, the pattern, outcome and factors of injury varies from setup to setup and is less studied public health problems. Objective: To assess pattern of admission, outcome and its predictors among trauma patients visiting the surgical emergency department in comprehensive specialized hospital. Methods: A retrospective follow up study was conducted and data was taken from the medical records of patients from 2019 to 2021. 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 that 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 with the 95% Confidence Interval 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 accident (26.17%). Severe Glasgow coma scale (AOR 6.6; 95% CI: 2.6-16.4), Length of hospital stay≥7days (AOR=2.8; 95% CI: 1.2-6.2), time of arrival in between 1-24 hours (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≥7days, time of arrival in between 1-24 hours, and upper trunk injury were the associated factors for mortality. Priority should be given for injured patients with decreased level of consciousness and upper trunk injury. Establishment of organized pre-hospital emergency services and provision of timely arrival is recommended. We recommend prospective follow up study.

Fixed energy solutions to the Euler-Lagrange equations of an indefinite Lagrangian with affine Noether charge

AbstractWe consider an autonomous, indefinite Lagrangian L admitting an infinitesimal symmetry K whose associated Noether charge is linear in each tangent space. Our focus lies in investigating solutions to the Euler-Lagrange equations having fixed energy and that connect a given point p to a flow line $$\gamma =\gamma (t)$$ γ = γ ( t ) of K that does not cross p. By utilizing the invariance of L under the flow of K, we simplify the problem into a two-point boundary problem. Consequently, we derive an equation that involves the differential of the “arrival time” t, seen as a functional on the infinite dimensional manifold of connecting paths satisfying the semi-holonomic constraint defined by the Noether charge. When L is positively homogeneous of degree 2 in the velocities, the resulting equation establishes a variational principle that extends the Fermat’s principle in a stationary spacetime. Furthermore, we also analyze the scenario where the Noether charge is affine.

Generative Algorithms for Fusion of Physics-Based Wildfire Spread Models with Satellite Data for Initializing Wildfire Forecasts

Abstract Increases in wildfire activity and the resulting impacts have prompted the development of high-resolution wildfire behavior models for forecasting fire spread. Recent progress in using satellites to detect fire locations further provides the opportunity to use measurements towards improving fire spread forecasts from numerical models through data assimilation. This work develops a physics-informed approach for inferring the history of a wildfire from satellite measurements, providing the necessary information to initialize coupled atmosphere-wildfire models from a measured wildfire state. The fire arrival time, which is the time the fire reaches a given spatial location, acts as a succinct representation of the history of a wildfire. In this work, a conditional Wasserstein Generative Adversarial Network (cWGAN), trained with WRF-SFIRE simulations, is used to infer the fire arrival time from satellite active fire data. The cWGAN is used to produce samples of likely fire arrival times from the conditional distribution of arrival times given satellite active fire detections. Samples produced by the cWGAN are further used to assess the uncertainty of predictions. The cWGAN is tested on four California wildfires occurring between 2020 and 2022, and predictions for fire extent are compared against high resolution airborne infrared measurements. Further, the predicted ignition times are compared with reported ignition times. An average Sorensen’s coefficient of 0.81 for the fire perimeters and an average ignition time difference of 32 minutes suggest that the method is highly accurate.