Numerical investigation of a high-pressure gas medium preionization by runaway electrons

A comparative simulation of the generation and acceleration of runaway electrons in the discharge gap during the initiation of the discharge by nanosecond and subnanosecond pulses is carried out. We used a numerical model based on the PIC-MCC method. Calculations were carried out for N2 6 atm pressure. Numerical simulation of a formation process of the electron avalanche initiated by an electron field-emitted from the top of the cathode microspike was carried out taking into account the motion of each electron in the avalanche. Characteristic runaway electron trajectories, runaway electron energy gained during the motion through the discharge gap, times required for runaway electrons to reach the anode were calculated. We compared our results with calculations using well-known differential equation of electron acceleration using braking force in Bethe approximation. We solved this equation also for braking force based on real (experimental) ionization cross section. The reasons for the discrepancy in the calculation results are discussed.

Empirical Study of the Impacts of Bicycles on Passenger Car Speeds on Urban Roads without Bicycle Lanes

Higher bicycle mode share has been suggested as part of a solution to reduce the burden of congestion in urban areas. As strategies to promote cycling are implemented, concerns have been raised by some road users and stakeholders citing simulation-based traffic studies that indicate that an increase in the bicycle mode share generates major travel time delays via reduced vehicle speeds unless bicycle lanes are provided. The current research investigates the effects bicycles may have on motorized vehicle speeds on a variety of lower speed and volume urban roads without bicycle lanes. A detailed comparative analysis of passenger car speeds was performed using two vehicle scenarios: (i) a passenger car that was preceded by a bicycle; and (ii) a passenger car that was preceded by another passenger car. The mean and 85th percentile speeds of scenarios (i) and (ii) were analyzed using t-tests. Relationships between speed and gap times with oncoming (opposite direction) traffic were also investigated. The results indicate that, at most sites (92%), bicycles do not reduce passenger car mean speeds by more than 1 mph. Speed reductions are not generally observed in local streets or facilities with adequate gaps in oncoming traffic for overtaking.

Assessing the Delays in Patients Undergoing Orthopedic Surgery and the Associated Factors at Taleghani Hospital

Background: : Transferring the patient to the operating room (OR) and back to the ward should be performed in the shortest time possible. Objectives: : We aimed to identify and classify different delays at our center and the possible factors associated with them. Methods: : We investigated 46 patients scheduled for elective orthopedic surgery at Taleghani Hospital, Tehran, from July 2017 to March 2018. Results: : Studying the time points showed that the main gap times included: T1 (when the surgical team informed OR staff until the orthopedic ward staff was informed (median of 5 minutes), T2 (when the orthopedic ward staff was informed until the patient was transferred to OR), T3 (when the patient reached OR until the patient was laid on OR bed), T6 (when the patient was prepared until the surgery started), T8 (from the end of the procedure until the patient exited the OR and entered the recovery room), T9 (duration spent in the recovery room), each with a median of 10 minutes. Although T5 and T6 were shorter in women (P = 0.005 and 0.020, respectively), the type of surgery or anesthesia did not affect the gaps. Conclusions: : This study showed a total of 75 minutes gap (delays in informing the ward and the time to transfer the patient to the ward), regardless of the duration of anesthesia, surgery, and preparations, which calls for the attention of the hospital’s policymakers to design strategies for reducing these gaps.

A two-stage diffusion modeling approach to the compelled-response task

The issue of how perception and motor planning interact to generate a given choice between actions is a fundamental question in both psychology and neuroscience. Salinas and colleagues have developed a behavioral paradigm, the compelled-response task, where the signal that instructs the subject to make an eye movement is given before the cue that indicates which of two possible target choices is the correct one. When the cue is given rather late, the participant must guess and make an uninformed random choice. Perceptual performance can be tracked as a function of the amount of time during which sensory information is available. In Salina’s accelerated race-to-threshold model, two variables race against each other to a threshold, at which a saccade is initiated. The source of random variability is in the initial state of information buildup across trials. This implies that incorrect decisions are due to the inertia of the racing variables that have, at the start, sampled a constant buildup in the “wrong” direction. Here we suggest an alternative, non-time-homogeneous two-stage-diffusion model that is able to predict both response time distributions and choice probabilities with a few easy-to-interpret parameters and without assuming cross-trial parameter variability. It is falsifiable at the level of qualitative features already, e.g. predicting bimodal RT distributions for particular gap times. It connects the compelled-response paradigm with an approach to decision making that has been uniquely successful in describing both behavioral and neural data in a variety of experimental settings for the last 40 years.

Bayesian Autoregressive Frailty Models for Inference in Recurrent Events

We propose autoregressive Bayesian semi-parametric models for gap times between recurrent events. The aim is two-fold: inference on the effect of possibly time-varying covariates on the gap times and clustering of individuals based on the time trajectory of the recurrent event. Time-dependency between gap times is taken into account through the specification of an autoregressive component for the frailty parameters influencing the response at different times. The order of the autoregression may be assumed unknown and is an object of inference. We consider two alternative approaches to perform model selection under this scenario. Covariates may be easily included in the regression framework and censoring and missing data are easily accounted for. As the proposed methodologies lie within the class of Dirichlet process mixtures, posterior inference can be performed through efficient MCMC algorithms. We illustrate the approach through simulations and medical applications involving recurrent hospitalizations of cancer patients and successive urinary tract infections.

Mixture cure models with time-varying and multilevel frailties for recurrent event data

Many medical studies yield data on recurrent clinical events from populations which consist of a proportion of cured patients in the presence of those who experience the event at several times (uncured). A frailty mixture cure model has recently been postulated for such data, with an assumption that the random subject effect (frailty) of each uncured patient is constant across successive gap times between recurrent events. We propose two new models in a more general setting, assuming a multivariate time-varying frailty with an AR(1) correlation structure for each uncured patient and addressing multilevel recurrent event data originated from multi-institutional (multi-centre) clinical trials, using extra random effect terms to adjust for institution effect and treatment-by-institution interaction. To solve the difficulties in parameter estimation due to these highly complex correlation structures, we develop an efficient estimation procedure via an EM-type algorithm based on residual maximum likelihood (REML) through the generalised linear mixed model (GLMM) methodology. Simulation studies are presented to assess the performances of the models. Data sets from a colorectal cancer study and rhDNase multi-institutional clinical trial were analyzed to exemplify the proposed models. The results demonstrate a large positive AR(1) correlation among frailties across successive gap times, indicating a constant frailty may not be realistic in some situations. Comparisons of findings with existing frailty models are discussed.