ABOUT THE ACCELERATION RATE OF RELATIVISTIC BEAMS BY A SURFACE WAVE IN A DIELECTRIC LASER ACCELERATOR

An analysis of the dependence of the acceleration rate of charged particles by a surface wave arising when a la-ser pulse/(plane wave) is incident on the interface between two dielectric media on the phase velocity of the excited wave is carried out. It is shown that at resonance acceleration this dependence has a maximum, for ultra-relativistic particles the acceleration rate tends to zero. The dependences of the acceleration rate on the phase velocity of the excited wave for various refractive indices (dielectric permittivities) of optically transparent medias are investigated analytically and numerically.

Initial Derivation of a Predictive Model for Left Ventricular Longitudinal Strain (LS) in Early Sepsis

Septic shock is a common deadly disease often associated with cardiovascular dysfunction. Left ventricular longitudinal strain (LV LS) has been proposed as a sensitive marker to measure cardiovascular function; however, it is not available universally in standard clinical echocardiograms. We sought to derive a predictive model for LV LS, using machine learning techniques with the hope that we may uncover surrogates for LV LS. We found that left ventricular ejection fraction, tricuspid annular plane systolic excursion, sepsis source, height, mitral valve Tei index, LV systolic dimension, aortic valve ejection time, and peak acceleration rate were all predictive of LV LS in this initial exploratory model. Future modeling work may uncover combinations of these variables which may be powerful surrogates for LV LS and cardiovascular function.

Dynamic recovery actions in multi-objective liner shipping service with buffer times

This paper investigates the dynamic recovery policies for liner shipping service with the consideration of buffer time allocation and uncertainties. We aim to allocate the buffer time at the tactical level and then determine the optimal policy, including speed optimization strategy, port skipping and acceleration rate choice, for recovering from disruptions due to various uncertainties or random adverse events, which cause vessel delays. To achieve this, we attempt to obtain the optimal balance among economic, environmental and service-reliable objectives. A novel mathematical formulation is introduced to solve the robust vessel scheduling problem with short- and long-term decisions. Furthermore, we propose and test two heuristics to solve the proposed model. Experiments on the container liner shipping service show the validity of the model and some managerial insights are gained from them.

Particle-in-cell Simulation of 3He Enrichment in Solar Energetic Particle Events

3He enrichment is one distinctive feature of impulsive solar energetic particle events. This study is designed to investigate the process of plasma wave–particle resonance, which plays a key role in selectively accelerating heavy ions. We apply a 1.5 dimensional particle-in-cell simulation to model the electron-beam–plasma interaction that generates electron and ion cyclotron waves, namely proton and 4He cyclotron waves, whose dispersions are dependent on the magnetization parameter α = ω pe/Ωce and the temperature ratio τ = T e /T p . The background particles, e.g., 3He and 4He, resonate with the excited cyclotron waves and experience selective heating or acceleration. Specifically, the resonant modes of 3He ions lead to a more effective acceleration rate compared to those of the 4He ions. The simulation results provide a potential solution for understanding the abundance of heavy ions in the solar wind.

A Deep Learning Method for Monitoring Vehicle Energy Consumption with GPS Data

A monitoring method for energy consumption of vehicles is proposed in the study. It is necessary to have parameters estimating fuel economy with GPS data obtained while driving in the proposed method. The parameters are trained by fuel consumption data measured with a data logger for the reference cars. The data logger is equipped with a GPS sensor and OBD connection capability. The GPS sensor measures vehicle speed, acceleration rate and road gradient. The OBD connector gathers the fuel consumption signaled from OBD port built in the car. The parameters are trained by a 5-layer deep-learning construction with input data (speed, acceleration, gradient) and labels (fuel consumption data) in the typical classification approach. The number of labels is about 6–8 and the number of neurons for hidden layers increases in proportionate to the label numbers. There are about 160–200 parameters. The parameters are calibrated to consider the wide range of fuel efficiency and deterioration degree in age for various test cars. The calibration factor is made from the certified fuel economy and model year taken from the car registration form. The error range of the estimated fuel economy from the measured value is about −6% to +7% for the eight test cars. It is accurate enough to capture the vehicle dynamics for using the input and output data in point-to-point classification style for training steps. Further, it is simple enough to hit fuel economy of the other test cars because fuel economy is a kind of averaged value of fuel consumption for the time period or driven distance for monitoring steps. You can predict or monitor energy consumption for any vehicle with the GPS-measured speed/acceleration/gradient data by the pre-trained parameters and calibration factors of the reference vehicles according to fuel types such as gasoline, diesel and electric. The proposed method requires just a GPS sensor that is cheap and common, and the calculating procedure is so simple that you can monitor energy consumption of various vehicles in real-time with ease. However, it does not consider weight, weather and auxiliary changes and these effects will be addressed in the future works with a monitoring service system under preparation.

The Determinant of Positive eWom Intention: Perspective Social Media Users

The current technology, whose acceleration rate is high-speed, cannot be denied. You can see that all the tools are getting more advanced, and anything is quicker and easier. Especially now, everyone can do anything via smartphones, from reading newspapers to paying bills to shop online. Consumers need to open a smartphone, select the desired item, make payments and wait for the goods to arrive in front of their house (Sazali & Rozi, 2020). The presence of smartphones and online shopping makes communication between consumers and a particular company or brand easier (Parvin et al., 2020). Another more straightforward thing is that some sellers allow cash on a delivery payment system or goods pay when they arrive home. The following reason why online shopping is more popular now is that the price is lower, there is no need to come directly to the store, the area is not a barrier for consumers, it can access 24 hours, and there is even a free shipping fee (Muljono et al., 2018). The many conveniences that can obtain in shopping online make Indonesians more consumptive. But here's a positive that marketers should quickly grasp. Given the substantial population of Indonesia, and also have the characteristics of each individual. The diverse characteristics of the Indonesian population and the needs and desires of consumers for the products that consumers will buy are different. For example, in terms of sports goods (Nike, Adidas, and Reebok), fast fashion (Zara, H&M, Uniqlo), and also smartphones (Apple, Samsung, and Huawei). The shift in online behavior currently happening in Indonesia is a new opportunity (Nurjanah et al., 2019). The number of these factors is a challenge for marketers to increase sales and reach the target market. In the past, social media was created only as a means of entertainment for its users. Still, now social media is a source of consumer information, and the evolution of social media use is pervasive in the business world (Yuan et al., 2021). Keywords: Brand trust, online brand community trust, brand attachment, repurchase intention, positive eWOM intention

Extreme Gradient Boosting (XGBoost) Model for Vehicle Trajectory Prediction in Connected and Autonomous Vehicle Environment

Connected and autonomous vehicles (CAVs) have the ability to receive information on their leading vehicles through multiple sensors and vehicle-to-vehicle (V2V) technology and then predict their future behaviour thus to improve roadway safety and mobility. This study presents an innovative algorithm for connected and autonomous vehicles to determine their trajectory considering surrounding vehicles. For the first time, the XGBoost model is developed to predict the acceleration rate that the object vehicle should take based on the current status of both the object vehicle and its leading vehicle. Next Generation Simulation (NGSIM) datasets are utilised for training the proposed model. The XGBoost model is compared with the Intelligent Driver Model (IDM), which is a prior state-of-the-art model. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are applied to evaluate the two models. The results show that the XGBoost model outperforms the IDM in terms of prediction errors. The analysis of the feature importance reveals that the longitudinal position has the greatest influence on vehicle trajectory prediction results.

AN ANALYSIS OF TYPE I DILEMMA ZONE AT SIGNALISED INTERSECTIONS

Intersections, for vehicles coming from different directions, are conflict points in road networks. When a driver approaching a signalised intersection encounters the yellow light, he/she is in a dilemma either to safely stop or to pass through the intersection during clearance time. The decision to stop or to pass may change depending on some factors such as duration of yellow light, deceleration and acceleration rate, width of intersection, speed and length of vehicle, etc. This study aims to put forth the effects of some related factors affecting the length of the Type I dilemma zone. To perform this study, five factors including vehicle speed, maximum deceleration rate, perception-reaction time, clearance time, the total intersection width-vehicle length were considered and a total of 648 different traffic cases were investigated. The study results showed that the Type I dilemma zone length increased with the increase of speed, total intersection width-vehicle length and perception-reaction time, but decreased with the increase of clearance time and deceleration rate.

Uncertainty propagation and truncation errors in LPT kinematics

Lagrangian Particle Tracking (LPT) has become a near-standard approach for performing accurate 3D flow measurements, thanks notably to the technical breakthroughs brought by the Iterative Particle Reconstruction (IPR: Wieneke, 2013) and Shake-the-Box (STB: Schanz et.al, 2016) procedures. These decisive progresses have triggered a number of studies relative to the eduction of flow kinematics and dynamics based on particle trajectory analyses. Novara & Scarano (2013), and others, focused on polynomial approximations of the trajectories, which analytically provide the material derivatives used to estimate pressure gradients. In particular, approximations based on second order polynomials fits of a small number of particle positions are used in commercially available softwares and among research teams as a straightforward solution to obtain the first and second order derivatives with a limited effect of the measurement noise. Additionally the analyses conducted during the 2020 LPT challenge (Leclaire, 2020 ; Sciacchitano, 2020) have addressed the performance of methodologies used by different groups with respect to second order trajectory fits for both multi-pulse and four-pulse (Novara et. al, 2016) LPT cases. On more advanced theoretical grounds, Geseman et. al (2016) have proposed the trackfit approach using penalized B-splines with considerations on the time-varying acceleration rate (i.e. jolt or jerk) and spectral content of noisy particle tracks