Numerical algorithms for time-fractional subdiffusion equation with second-order accuracy

This article aims to fill in the gap of the second-order accurate schemes for the time-fractional subdiffusion equation with unconditional stability. Two fully discrete schemes are first proposed for the time-fractional subdiffusion equation with space discretized by finite element and time discretized by the fractional linear multistep methods. These two methods are unconditionally stable with maximum global convergence order of $O(\tau+h^{r+1})$ in the $L^2$ norm, where $\tau$ and $h$ are the step sizes in time and space, respectively, and $r$ is the degree of the piecewise polynomial space. The average convergence rates for the two methods in time are also investigated, which shows that the average convergence rates of the two methods are $O(\tau^{1.5}+h^{r+1})$. Furthermore, two improved algorithms are constrcted, they are also unconditionally stable and convergent of order $O(\tau^2+h^{r+1})$. Numerical examples are provided to verify the theoretical analysis. The comparisons between the present algorithms and the existing ones are included, which show that our numerical algorithms exhibit better performances than the known ones.

Numerical analysis for the time distributed-order and Riesz space fractional diffusions on bounded domains

Subdiffusion equations with distributed-order fractional derivatives describe some important physical phenomena. In this paper, we consider the time distributed-order and Riesz space fractional diffusions on bounded domains with Dirichlet boundary conditions. Here, the time derivative is defined as the distributed-order fractional derivative in the Caputo sense, and the space derivative is defined as the Riesz fractional derivative. First, we discretize the integral term in the time distributed-order and Riesz space fractional diffusions using numerical approximation. Then the given equation can be written as a multi-term time–space fractional diffusion. Secondly, we propose an implicit difference method for the multi-term time–space fractional diffusion. Thirdly, using mathematical induction, we prove the implicit difference method is unconditionally stable and convergent. Also, the solvability for our method is discussed. Finally, two numerical examples are given to show that the numerical results are in good agreement with our theoretical analysis.

A Novel High Order Space-Time Spectral Method for the Time Fractional Fokker--Planck Equation

The fractional Fokker-Planck equation is an important physical model for simulating anomalous diffusions with external forces. Because of the non-local property of the fractional derivative an interesting problem is to explore high accuracy numerical methods for fractional differential equations. In this paper, a space-time spectral method is presented for the numerical solution of the time fractional Fokker-Planck initial-boundary value problem. The proposed method employs the Jacobi polynomials for the temporal discretization and Fourier-like basis functions for the spatial discretization. Due to the diagonalizable trait of the Fourier-like basis functions, this leads to a reduced representation of the inner product in the Galerkin analysis. We prove that the time fractional Fokker-Planck equation attains the same approximation order as the time fractional diffusion equation developed in [23] by using the present method. That indicates an exponential decay may be achieved if the exact solution is sufficiently smooth. Finally, some numerical results are given to demonstrate the high order accuracy and efficiency of the new numerical scheme. The results show that the errors of the numerical solutions obtained by the space-time spectral method decay exponentially.

Tracking locomotive position to evaluate sugarcane railway schedules and predict arrival time

This research utilised software developed for managing the Australian sugar industry's cane rail transport operations and GPS data used to track locomotives to ensure safe operation of the railway system to improve transport operations. As a result, time usage in the sugarcane railway can now be summarised and locomotive arrival time to sidings and mills can be predicted. This information will help the development of more efficient run schedules and enable mill staff and harvesters to better plan their shifts ahead, enabling cost reductions through better use of available time.

Empirical evaluation of the travel time reliability indicators: A case study for Brisbane corridor under recurrent and non-recurrent conditions

This research quantifies traffic congestion and travel time reliability with case study on a major arterial road in Brisbane. The focus is on the analysis of impact of incidents (e.g., road accidents) on travel time reliability. Real traffic (Bluetooth) and incident records from Coronation Drive, Brisbane are utilized for the study. The findings include significant impact of incidents on traffic congestion and travel time reliability. The knowledge gained is useful in various applications such as traveler information systems, and cost-benefit analysis of various strategies to reduce the traffic incidents and its' impacts.

Impact of real-time traffic characteristics on freeway crash occurrence: Systematic review and meta-analysis

The development of methods for real-time crash prediction as a function of current or recent traffic and roadway conditions is gaining increasing attention in the literature. Numerous studies have modeled the relationships between traffic characteristics and crash occurrence, and significant progress has been made. Given the accumulated evidence on this topic and the lack of an articulate summary of research status, challenges, and opportunities, there is an urgent need to scientifically review these studies and to synthesize the existing state-of-the-art knowledge. This paper addresses this need by undertaking a systematic literature review to identify current knowledge, challenges, and opportunities, and then conducts a meta-analysis of existing studies to provide a summary impact of traffic characteristics on crash occurrence. Sensitivity analyses were conducted to assess quality, publication bias, and outlier bias of the various studies; and the time intervals used to measure traffic characteristics were also considered. As a result of this comprehensive and systematic review, issues in study designs, traffic and crash data, and model development and validation are discussed. Outcomes of this study are intended to provide researchers focused on real-time crash prediction with greater insight into the modeling of this important but extremely challenging safety issue.

A place carved out by time

A travel story about South Dakota. "Time travel is much easier in life than in the movies. Driving out of Rapid City in South Dakota, you cover 500,000 years in the first hour. That journey brings you to the Badlands, a vast natural excavation site that has been created by water and wind. At the same time, you’re deposited into the deep past..."--publisher website

7-point Subjective Global Assessment is more time sensitive than conventional Subjective Global Assessment in detecting nutrition changes

Background: It is important for nutrition intervention in malnourished patients to be guided by accurate evaluation and detection of small changes in the patient’s nutrition status over time. However, the current Subjective Global Assessment (SGA) is not able to detect changes in a short period of time. The aim of the study was to determine whether 7-point SGA is more time sensitive to nutrition changes than the conventional SGA. Methods: In this prospective study, 67 adult inpatients assessed as malnourished using both the 7-point SGA and conventional SGA were recruited. Each patient received nutrition intervention and was followed up post-discharge. Patients were reassessed using both tools at 1, 3 and 5 months from baseline assessment. Results: It took significantly shorter time to see a one-point change using 7-point SGA compared to conventional SGA (median: 1 month vs. 3 months, p = 0.002). The likelihood of at least a one-point change is 6.74 times greater in 7-point SGA compared to conventional SGA after controlling for age, gender and medical specialties (odds ratio = 6.74, 95% CI 2.88-15.80, p<0.001). Fifty-six percent of patients who had no change in SGA score had changes detected using 7-point SGA. The level of agreement was 100% (k = 1, p < 0.001) between 7-point SGA and 3-point SGA and 83% (k=0.726, p<0.001) between two blinded assessors for 7-point SGA. Conclusion: The 7-point SGA is more time sensitive in its response to nutrition changes than conventional SGA. It can be used to guide nutrition intervention for patients.

A real-time software architecture for robotics and automation

This paper describes a software architecture for real-world robotic applications. We discuss issues of software reliability, testing and realistic off-line simulation that allows the majority of the automation system to be tested off-line in the laboratory before deployment in the field. A recent project, the automation of a very large mining machine is used to illustrate the discussion.

The sleeping elephant in the room: Practices and policies regarding sleep-rest time in ECEC

The National Quality Framework (NQF) for Early Childhood Education and Care (ECEC) in Australia identifies the need for services to make provision for each child’s sleep, rest and relaxation within a national early year’s policy framework that also requires that opportunities for learning and physical health are optimised, and that the agency of each child and their family is respected. Against this background, the scheduling of a standard sleep-time in ECEC centres remains a common practice, even in rooms catering for older children for whom daytime sleep may no longer be necessary. This article draws upon existing scholarship to explore the issues and tensions associated with sleep-rest, in the context of Australian curriculum and quality standards documents. We review accounts from educators, parents and children and contemporary views regarding high quality practice in ECEC, with an aim of supporting critical reflection on practice and continuous quality improvement in ECEC.

Alive SMC^2: Bayesian model selection for low-count time series models with intractable likelihoods

In this paper we present a new method for performing Bayesian parameter inference and model choice for low count time series models with intractable likelihoods. The method involves incorporating an alive particle filter within a sequential Monte Carlo (SMC) algorithm to create a novel pseudo-marginal algorithm, which we refer to as alive SMC^2. The advantages of this approach over competing approaches is that it is naturally adaptive, it does not involve between-model proposals required in reversible jump Markov chain Monte Carlo and does not rely on potentially rough approximations. The algorithm is demonstrated on Markov process and integer autoregressive moving average models applied to real biological datasets of hospital-acquired pathogen incidence, animal health time series and the cumulative number of poison disease cases in mule deer.

Chirp-spread-spectrum-based real time location system for construction safety management: A case study

Observing the working procedure of construction workers is an effective means of maintaining the safety performance of a construction project. It is also difficult to achieve due to a high worker-to-safety-officer ratio. There is an imminent need for the development of a tool to assist in the real-time monitoring of workers, in order to reduce the number of construction accidents. The development and application of a real time locating system (RTLS) based on the Chirp Spread Spectrum (CSS) technique is described in this paper for tracking the real-time position of workers on construction sites. Experiments and tests were carried out both on- and off-site to verify the accuracy of static and dynamic targets by the system, indicating an average error of within one metre. Experiments were also carried out to verify the ability of the system to identify workers’ unsafe behaviours. Wireless data transfer was used to simplify the deployment of the system. The system was deployed in a public residential construction project and proved to be quick and simple to use. The cost of the developed system is also reported to be reasonable (around 1800USD) in this study and is much cheaper than the cost of other RTLS. In addition, the CCS technique is shown to provide an economical solution with reasonable accuracy compared with other positioning systems, such as ultra wideband. The study verifies the potential of the CCS technique to provide an effective and economical aid in the improvement of safety management in the construction industry.

Characteristics of a sample of first time drink driving offenders in Queensland - a qualitative analysis

Drink driving incidents in the Australian community continue to be a major road safety problem resulting in a third of all fatalities. Drink driving prevalence remains high; with the rate of Australians who self report drink driving remaining at 11%-12.1% [1,2]. The focus of research in the area to date has been with recidivist offenders who have a higher probability of reoffending, while there is comparatively limited research regarding first time offenders. An important and understudied area relates to the characteristics of first offenders and predictors of recidivism. This study examined the findings of in-depth focussed interviews with a sample of 20 individual first time drink driving offenders in Queensland recruited at the time of court mention.

Predictive motion planning for AUVs subject to strong time-varying currents and forecasting uncertainties

This paper presents a novel path planning method for minimizing the energy consumption of an autonomous underwater vehicle subjected to time varying ocean disturbances and forecast model uncertainty. The algorithm determines 4-Dimensional path candidates using Nonlinear Robust Model Predictive Control (NRMPC) and solutions optimised using A*-like algorithms. Vehicle performance limits are incorporated into the algorithm with disturbances represented as spatial and temporally varying ocean currents with a bounded uncertainty in their predictions. The proposed algorithm is demonstrated through simulations using a 4-Dimensional, spatially distributed time-series predictive ocean current model. Results show the combined NRMPC and A* approach is capable of generating energy-efficient paths which are resistant to both dynamic disturbances and ocean model uncertainty.