Arrival Time Interval Research Articles

Piston effect of subway trains under different operating conditions and its influence on passenger thermal comfort

With the rapid development of rail transportation, environmental issues of subway become more and more significant, while the thermal comfort of passengers also draws wide attention. There are various arrival conditions for actual subway trains, and their effects on subway stations and passengers have rarely been studied. This paper is the first to investigate the piston effect of subway trains under single and double train arrival conditions and its impact on passenger thermal comfort. Firstly, a transient 3D numerical model based on the real metro station is established. Then, the influencing factors of the piston effect are analyzed, including the maximum travel speed of single train and the different arrival time intervals of two trains. Moreover, inhomogeneity coefficient (Kt and Ku) and the predicted mean vote (PMV) model and the predicted percentage dissatisfaction (PPD) index are introduced to analyze the thermal comfort. The results show that the two-train condition has a more uniform temperature and velocity distribution than the single-train condition. However, the best thermal comfort is obtained for the two arrival conditions of single train traveling at 16.2 m/s and two-train arrival time interval of 1s from the PMV and PPD. Besides, the fresh air volume of the two-train model is increased by 100% than that of the single-train model, which indicates that the double-train model is better than the single-train for station ventilation. These studies provide important theoretical guidance and references for optimizing metro train operation strategies and the design of thermal environments in metro stations.

Impact of Community Socioeconomic Characteristics on Emergency Medical Service Delays in Responding to Fatal Vehicle Crashes

This study aimed to determine the impact of community socioeconomic status on emergency medical services' response time for fatal vehicle crashes. Authors used the 2019 National Highway Traffic Safety Administration Fatality Analysis Reporting System and 2019-2020 Area Health Resource Files to obtain emergency medical services' time intervals and county socioeconomic characteristics (e.g., median household income, availability of trauma centers, and rurality), generating a study sample of 18,540 individuals involved in fatal vehicle crashes between January and December 2019. Generalized linear models with log-link and Gamma-family were used to obtain estimates, and other variables were adjusted in the model. Both the mean time of the emergency medical service arrival to the site of the crash and the mean transport time from the crash site to hospital varied by county SES. Counties with a higher mean household income had 12% shorter emergency medical services' arrival times and up to 7% shorter emergency medical services' hospital transport times than counties with lower SES. The emergency medical services' hospital transport times by emergency medical services also varied by proximity to trauma centers and were 15% shorter in counties that had ≥2 trauma centers than in counties without trauma centers. This study shows socioeconomic disparities in emergency medical service rescue time for fatal vehicle crashes. Community characteristics play a major role in emergency medical services' arrival time intervals. Prior research demonstrated a strong link between the timeliness of emergency medical service response and the likelihood of survival in fatal motor vehicle accidents. These findings showing that socioeconomically disadvantaged areas and those lacking trauma facilities had slower emergency medical service rescue times, suggest that socioeconomic status may be a predictor of mortality in fatal motor vehicle accidents. Effective emergency medical services are essential to reduce the morbidity and mortality among motor vehicle crash victims; however, disparities exist in the timeliness of these services by geographic and socioeconomic county characteristics. Further research is urgently needed to inform policy interventions.

Building fire alarm model based on fire source inversion according to smoke arrival time intervals

Accurate and timely triggering of a fire alarm has always been essential for building fire safety. The inversion of fire source parameters by multiple detectors to distinguish between true and false fire alarms has become a popular research topic. However, previously proposed inversion models are based on time series and are computationally intensive. Their inversion accuracy directly depends on the size of the database. Therefore, this paper proposes a fire source inversion method employing multiple smoke alarms based on Bayesian inference. A fire dynamics model along with an actual sensor response time are used to implement a fire alarm. Moreover, fire alarm authenticity is defined to distinguish false and true fire alarms, enabling early fire detection. Verification test results indicate that the proposed model can reduce the number of false alarms and realize early fire alarm detection.

Impact of COVID-19 outbreak on acute gallbladder disease in the emergency department.

Acute gallbladder disease (AGD) is frequent in the emergency department (ED) and usually requires emergency surgery. However, only a few studies have reported the impact of COVID-19 on AGD. The goal of this study was to evaluate the time between symptom onset and surgery and the perioperative severity of AGD during the COVID-19 pandemic compared to before the era of COVID-19. This retrospective, single-center cohort study included patients who presented to the ED with suspected AGD and who underwent emergency cholecystectomy. We designed a before-after comparative study, and the intervention was the COVID-19 outbreak. The 6-month period after the COVID-19 outbreak was defined as the post-COVID group, whereas the pre-COVID group consisted of the same period in the previous year. The primary outcome was the time from symptoms to surgery. We evaluated the time intervals between symptom onset and ED arrival and between ED arrival and surgery. The secondary outcomes were preoperative and postoperative severity indexes. A total of 316 patients was analyzed. The post-COVID group showed longer duration from symptom onset to ED arrival (34.0 hours vs. 15.0 hours, P<0.001) and longer time interval from ED arrival to surgery (16.2 hours vs. 10.2 hours, P<0.001) than the pre-COVID group. The overall time interval between symptom onset to surgery was longer in the post-COVID group than the pre-COVID group (71.5 hours vs. 33.5 hours, P<0.001). The post-COVID group showed higher preoperative Simplified Acute Physiology Score II scores than the pre-COVID group (20.1 vs. 18.2, P=0.045). The proportion of moderate or severe disease increased in the post-COVID group (78% vs. 65%, P=0.017). The durations of hospital stay (7.0 days vs. 5.0 days, P<0.001) and intensive care unit stay (27.1 hours vs. 10.8 hours, P=0.008) were longer in the post-COVID group than in the pre-COVID group. During the pandemic, the time interval between symptom onset to surgery was significantly increased among patients with AGD. Concomitantly, higher preoperative severity indexes and longer hospital stay were reported with a delay in emergency surgery.

Accurate Redetermination of the Focal Depth by Using the Time Intervals between the Inner Core Phases PKIKP and pPKIKP

The hypocenter parameters of an earthquake may give us an insight into the Earth’s structure and tectonic processes. Among the hypocenter parameters, the focal depth is normally more difficult to estimate than the earthquake location (latitude and longitude). We propose to use the pPKIKP-PKIKP arrival time intervals for estimating the focal depth. We analyze the sensitivity of the pPKIKP-PKIKP arrival time interval to the earthquake depth. We measure the pPKIKP-PKIKP arrival time interval on seismograms (the vertical component), and invert the time interval data set using the simulated annealing inversion algorithm. We illustrate the inversion approach on two teleseismic earthquakes which have shallow and deep focal depths, and demonstrate that the approach is indeed appropriate to the shallow and deep event. We can obtain a reliable estimate on focal depth, even though the seismic station is sparse or in a remote part of the epicenter.

A public blockchain consensus mechanism for fault-tolerant distributed computing in LEO satellite communications

In LEO (Low Earth Orbit) satellite communication systems, the satellite network is made up of a large number of satellites, the dynamically changing network environment affects the results of distributed computing. In order to improve the fault tolerance rate, a novel public blockchain consensus mechanism that applies a distributed computing architecture in a public network is proposed. Redundant calculation of blockchain ensures the credibility of the results; and the transactions with calculation results of a task are stored distributed in sequence in Directed Acyclic Graphs (DAG). The transactions issued by nodes are connected to form a net. The net can quickly provide node reputation evaluation that does not rely on third parties. Simulations show that our proposed blockchain has the following advantages: 1. The task processing speed of the blockchain can be close to that of the fastest node in the entire blockchain; 2. When the tasks' arrival time intervals and demanded working nodes(WNs) meet certain conditions, the network can tolerate more than 50% of malicious devices; 3. No matter the number of nodes in the blockchain is increased or reduced, the network can keep robustness by adjusting the task's arrival time interval and demanded WNs.

Train-induced unsteady airflow (TIUA) characteristics of track area in subway station with platform screen doors (PSDs)

Train-induced unsteady airflow (TIUA) has a great effect on the ventilation and thermal environment of a subway line. Track area, connecting tunnel with station, is the source of the TIUA acting on subway station. Exploring the TIUA characteristics of track area contributes to utilizing the TIUA for improving the tunnel and station environment. In this study, a 1D numerical model according to an actual subway line was established by IDA Tunnel software, to analyse the TIUA rates through the inbound and outbound ends of track area in the PSDs subway station. Four key factors, namely, train arrival time interval between both tunnels, train interval, operation modes of piston vent shafts and bypass ducts, were analysed. The results demonstrate that the TIUA rates of track area in PSDs subway station (bypass ducts closed) are mainly affected by the train driving in this tunnel. The TIUA rates of track area decreases about 36%∼41%, as the train interval increases from 180 s to 540 s. By adjusting the operation modes of piston vent shafts, the change of TIUA rates of track area can reach up to about 13%∼27%. By opening bypass ducts, the TIUA rates of track area decreases by 42%∼50%.

High-Speed Rail Station Interconnectivity and Ridership

The objective of this study was to quantify multimodal connectivity of HSR stations and its impact on ridership in four countries: France, Spain, Japan and China. In this study, multimodal connectivity is measured by the number of different modes of transportation connected to HSR stations, the number of installed arrival and departure facilities for each mode, the transfer time from connecting modes to boarding platforms at HSR stations, and the arrival time intervals of public transportation modes. Data were collected from HSR systems of these four countries. The relationship between ridership and the characteristics of multimodal connectivity was identified using regression models developed in this study. All the connectivity variables considered in this study influence ridership in these four countries in different ways. On the whole, bus, subway, and regional railroad services influence ridership significantly. For instance, the more bus services connected to the station, the higher the ridership. This trend is apparent in three of the four countries, France being the exception. Also, subway, light rail, and traditional rail are modes of high-capacity transportation. Their connection to HSR stations always implies high ridership for high-speed rail. The number of facilities also shows significant impacts on HSR ridership. For instance, the more bus and subway stops, and the more bicycle parking and taxi stands, the higher the ridership. Transfer time also has a significant influence.

Passenger management strategy and evacuation in subway station under Covid-19

Under the background of Covid-19 sweeping the world, safe and reasonable passenger flow management strategy in subway stations is an effective means to prevent the spread of virus. Based on the social force model and the minimum cost model, the movement and path selection behavior of passengers in the subway station are modeled, and a strategy for passenger flow management to maintain a safe social distance is put forward. Take Qingdao Jinggangshan Road subway station of China as the simulation scene, the validity of the simulation model is verified by comparing the measured value and simulation value of the time required for passengers from getting off the train to the ticket gate. Simulation results indicate that controlling the time interval between incoming passengers at the entrance can effectively control the social distance between passengers and reduce the risk of epidemic infection. By comparing the evacuation process of passengers under different initial densities, it is found that the greater the initial density of passengers is, the longer the passengers are at risk social distance. In the process of passenger emergency evacuation, the stairs/escalators and ticket gates are bottleneck areas with high concentration of passenger density, which should be strictly disinfected many times on the basis of strictly checking the health code of incoming passengers and controlling the arrival time interval. The simulation results of this paper verify the harmfulness of passenger emergency evacuation without protective measures, and provide theoretical support for the operation and management of subway station under the epidemic situation.

Obfuscated Tor Traffic Identification Based on Sliding Window

Tor is an anonymous communication network used to hide the identities of both parties in communication. Apart from those who want to browse the web anonymously using Tor for a benign purpose, criminals can use Tor for criminal activities. It is recognized that Tor is easily intercepted by the censorship mechanism, so it uses a series of obfuscation mechanisms to avoid censorship, such as Meek, Format-Transforming Encryption (FTE), and Obfs4. In order to detect Tor traffic, we collect three kinds of obfuscated Tor traffic and then use a sliding window to extract 12 features from the stream according to the five-tuple, including the packet length, packet arrival time interval, and the proportion of the number of bytes sent and received. And finally, we use XGBoost, Random Forest, and other machine learning algorithms to identify obfuscated Tor traffic and its types. Our work provides a feasible method for countering obfuscated Tor network, which can identify the three kinds of obfuscated Tor traffic and achieve about 99% precision rate and recall rate.

Generalizing the over operator for parallelization and order-independency

The over operator is commonly used for α-blending in various visualization techniques. In the current form, it is a binary operator and must strictly follow a specific composition order of all participating operands, hence posing a significant performance limit. In this paper, we derive a set of generic formulas for the over operator that work with any number of operands and completely remove the restriction on the composition order. We prove the correctness of these formulas and provide a step-by-step illustration in a blending context. We implement both a sequential and a parallel version of the improved over operator and apply them to the image composition process where operands are received out of order with different arrival time intervals. The performance superiority of the improved over operator over the original one is established by rigorous theoretical analyses and further validated by extensive experimental results.

A performance model of automated material handling systems with double closed-loops and shortcuts in 300 mm semiconductor wafer fabrication systems

Automated material handling systems (AMHSs) with double closed-loops and shortcuts have been widely adopted by general 300mm semiconductor fabrication enterprises due to their higher transportation efficiency, more flexible vehicle routing, and lower vehicle congestion compared with those traditional ones with single-loop. An accurate and efficient performance analysis model is crucial to quickly and effectively evaluate the schemes in the early stage of AMHS design. This paper proposes an extended Markov chain model (EMCM) to rapidly and effectively analyze performances of the AMHS with double closed-loops and shortcuts. In the modeling process, the inner closed-loop and shortcuts are abstracted as virtual stations to discretely express the running process of overhead hoist transporters (OHTs) on them. OHT’s running process in AMHS is discretized and equivalent to the state change of Markov chain and a triple-structure model is presented to describe the Markov states, so that the system characteristics, including OHT blockage, system’s double closed-loops, and shortcuts configuration, can be fully considered. The complex Markov process with multiple OHTs is transformed into the process with multiple independent OHTs so as to effectively eliminate the model’s state space explosion. System state probabilities models and blocking probability models are built to effectively represent its state and blocking properties. The stability state equations of EMCM are built so that its steady-state visit ratio can be identified. With production data from a semiconductor fabrication enterprise, the proposed performance analysis model is compared with a simulation model. The result shows that two models have a small relative error in the aspect of empty OHT’s mean arrival time interval, interbay throughput capacity, and OHT’s mean utilization ratio. However, the computing efficiency of the EMCM is significantly higher than that of the simulation model. It demonstrates that the proposed EMCM is effective and efficient for performance analysis in the early stage of AMHS design.

Quantum random number generation based on spontaneous Raman scattering in standard single-mode fiber.

We investigate quantum random number generation based on backward spontaneous Raman scattering in standard single-mode fiber, where the randomness of photon wavelength superposition and arrival time is simultaneously utilized. The experiment uses four avalanche photodiodes working in gated Geiger mode to detect backward Raman scattering photons from four different wavelength channels and a time-to-digital converter placed behind the detectors to record their arrival time. Both information of the wavelength and arrival time interval of photons from different channels are applied to generate random bits. Due to the independence of these two entropy sources, the random number resource of the present system is fully utilized. Five-bit raw data can be obtained for every effective click, which contains 2.87-bit min-entropy. To obtain the optimal generation rate of random bits, appropriate pump power and fiber length are adopted. The post-processing method by the SHA-256 hashing algorithm is used to remove the bias of the raw data, after which the final random bit sequences pass the NIST statistical test.

Adoption of fuzzy fractional order theory under Reco-Mo algorithm in data analysis of optical circuit switches

In order to analyze the optimization value of Reco-Mo based on fuzzy fractional order theory for data analysis of optical circuit switches, first, the co-flow scheduling strategy is expounded. According to the Burkhofvon-Neumann (BvN) matrix decomposition algorithm, the co-flow scheduling algorithm is described. Then, the fuzzy fractional order theory is combined with the co-flow scheduling algorithm, and the Reco-Mo algorithm is proposed. Finally, E-FWO algorithm and E-SCFE algorithm are introduced to carry out comparative analysis in real production environment. The results show that when the number of co-flows is 30, 50, 70, 90 and 110, the co-flow completion time of Reco-Mo algorithm is less than that of E-FWO algorithm and E-SCFE algorithm. At 70, 90, and 110, the accuracy of Reco-Mo algorithm is significantly higher than that of E-FWO and E-SCFE algorithms. When the density of Reco-Mo algorithm is 0.1, the completion time of co-flow is 38.46% and 37.25% lower than that of E-FWO algorithm and E-SCFE algorithm. At 1.0 and 1.3, the accuracy of Reco-Mo algorithm is significantly higher than that of E-FWO and E-SCFE. The completion time of Reco-Mo algorithm under different data transmission volumes (0.5, 1.0, 1.5, 2.0, 2.5 GB) is less than that of E-FWO and E-SCFE. At 1.5, 2.0, and 2.5 GB, the accuracy of Reco-Mo algorithm is significantly higher than that of E-FWO and E-SCFE. When the co-flow arrival time interval is 100 ms, the Reco-Mo algorithm reduces the completion time by 50.34% and 51.97% respectively compared with E-FWO and E-SCFE algorithms. At 100, 200, and 300 ms, the accuracy of Reco-Mo algorithm is significantly higher than that of E-FWO and E-SCFE. In conclusion, compared with E-FWO and E-SCFE algorithms, Reco-Mo algorithm has always maintained its dominant position in terms of co-flow completion time and accuracy under different parameters of co-flow quantity, co-flow density, co-flow transmission data volume, and co-flow arrival interval, which provides a theoretical basis for the application of mathematical algorithm in data processing of optical circuit switches in data centers in the future.

Influence of train arrival characteristics on unorganized ventilation in underground subway station with platform screen doors

Unorganized ventilation significantly increases the energy consumption of subway stations. Many factors affect unorganized ventilation, and its variation is still not clear. This study established a validated network model using Subway Environment Simulation and developed two general scripts to investigate changes in the unorganized ventilation with varying train arrival characteristics, including the train arrival density and arrival time interval for two-side trains. Furthermore, the cooling load caused by the airflow was calculated. The results reveal that the train arrival density changes the superposition of the unorganized ventilation caused by the previous and next pairs of trains, and it also determines the location and state of the existing trains in the tunnel. That affects the unorganized ventilation in the station. The arrival time interval changes the “phase difference” of airflow generated by each side trains. The cooling load caused by unorganized ventilation is much larger than the current perception. Controlling the mechanical ventilation based on the unorganized ventilation rate and optimizing the arrival time interval can reduce the load markedly. Besides, low indoor temperature results in a significant increase in cooling load.

A laboratory experimental technique for simulating combined blast and impact loading

A novel composite projectile is put forward to simulate combined blast and single fragment impact loading, which is comprised of a cylindrical aluminum foam projectile embedded with a fragment simulation projectile (FSP). Comparison between cased explosive and composite projectile in terms of the generated combined loading shows that the composite projectile technique is theoretical feasible. Experimental tests for normal impact of the composite projectile on clamped plates are carried out. Results show that the proposed composite projectile can induce both blast-induced deflection and fragment-induced perforation on the clamped plate. The arrival time interval between blast and fragment has significant influence on the extent of induced damage on the plate and the residual FSP velocity. Numerical simulations with the method of finite elements are carried out to provide further insight into the interaction between the composite projectile and clamped plate, and to explain observations and measurements in experiments. The present study reveals a new synergetic effect of combined blast and single fragment impact loading on clamped plate: arrival time interval between blast and impact loading affects significantly the residual velocity of the FSP.

Combination between DASH and SVM machine learning in the field of video delivery

With the development of network and streaming media technology, network video traffic is growing rapidly. In order to better control and manage network traffic and guarantee the quality of service of network video, it is necessary to classify network video services effectively. In traffic identification and classification, feature analysis and acquisition of better features are the key points to achieve efficient classification. Starting with the characteristics of packet size distribution, rate, IP alternation, byte number ratio between downstream and upstream, number of sub-stream fragments and average packet arrival time interval, this paper uses Support Vector Machine (SVM) to verify the classification effect of this feature, and achieves a high classification accuracy

Pulse Arrival Time and Pulse Interval as Accurate Markers to Detect Mechanical Alternans

Mechanical alternans (MA) is a powerful predictor of adverse prognosis in patients with heart failure and cardiomyopathy, but its use remains limited due to the need of invasive continuous arterial pressure recordings. This study aims to assess novel cardiovascular correlates of MA in the intact human heart to facilitate affordable and non-invasive detection of MA and advance our understanding of the underlying pathophysiology. Arterial pressure, respiration, and ECG were recorded in 12 subjects with healthy ventricles during voluntarily controlled breathing at different respiratory rate, before and after administration of beta-blockers. MA was induced by ventricular pacing. A total of 67 recordings lasting approximately 90 s each were analyzed. Mechanical alternans (MA) was measured in the systolic blood pressure. We studied cardiovascular correlates of MA, including maximum pressure rise during systole (dPdtmax), pulse arrival time (PAT), pulse wave interval (PI), RR interval (RRI), ECG QRS complexes and T-waves. MA was detected in 30% of the analyzed recordings. Beta-blockade significantly reduced MA prevalence (from 50 to 11%, p < 0.05). Binary classification showed that MA was detected by alternans in dPdtmax (100% sens, 96% spec), PAT (100% sens, 81% spec) and PI (80% sens, 81% spec). Alternans in PAT and in PI also showed high degree of temporal synchronization with MA (80 ± 33 and 73 ± 40%, respectively). These data suggest that cardiac contractility is a primary factor in the establishment of MA. Our findings show that MA was highly correlated with invasive measurements of PAT and PI. Since PAT and PI can be estimated using non-invasive technologies, these markers could potentially enable affordable MA detection for risk-prediction.

A Self-Organizing Grouping Approach for Ship Traffic Scheduling in Restricted One-Way Waterway

AbstractShip scheduling optimization is one of the most effective ways to eliminate the bottlenecks of waterway transportation, especially in restricted one-way waterways. In this study, a novel scheduling model calledself-organizing groupingis proposed to minimize two types of delay time, which are the waiting time and the extra navigation time caused by speed reduction. The proposed model schedules ships in an iterative way based on the distributed scheduling mode. To alleviate the impact of local scheduling on the overall traffic efficiency, a grouping method is proposed, in which the ships are divided into different groups based on their arrival time interval. Moreover, the ships in the same group are scheduled to minimize the interferences among them by incorporating a grouping improvement strategy. The strategy is used to deal with the influence of ships with very small speed. Experiments are carried out by comparing the proposed model with the first-come-first-serve model and the ship self-organizing cooperation model. Simulation results show that the delay time is reduced by 25%‐30% and approximately by 5% compared with that from the two models, respectively. Such advantage also exists for different combinations of ship traffic parameters. In addition, long-distance sailing with limited speed can be avoided using the proposed method, which is beneficial to relieve waterway traffic congestion.

The model of the kernel of the Lindley integral equation based on selective functions

Here considered a model of the kernel of the Lindley integral equation for a queuing system G/G/1. An exact solution of Lindley equation can be obtained using the Fourier transform under analytic continuation to the half-plane of the complex function originally defined on the real axis. However, it is quite difficult to obtain such solution, therefore, one resorts to finding approximate solutions based on the procedure of “degeneration” of the kernel when the kernel is factorized according to its variables. The method based on the use of selective functions is used. As an example, a special case is considered when the arrival time intervals have a gamma distribution, and the service time is constant. The result is shown for the kernel with the exponential form.