Probabilistic load flow in AC electrified railways

System analysis within the traction power system is vital to the design and operation of an electrified railway. Loads in traction power systems are often characterised by their mobility, wide range of power variations, regeneration and service dependence. In addition, the feeding systems may take different forms in AC electrified railways. Comprehensive system studies are usually carried out by computer simulation. A number of traction power simulators have been available and they allow calculation of electrical interaction among trains and deterministic solutions of the power network. In the paper, a different approach is presented to enable load-flow analysis on various feeding systems and service demands in AC railways by adopting probabilistic techniques. It is intended to provide a different viewpoint to the load condition. Simulation results are given to verify the probabilistic-load-flow models.

Probabilistic load flow calculation for AC traction supplies with AT feeding

Power load flow analysis is essential for system planning, operation, development and maintenance. Its application on railway supply system is no exception. Railway power supplies system distinguishes itself in terms of load pattern and mobility, as well as feeding system structure. An attempt has been made to apply probability load flow (PLF) techniques on electrified railways in order to examine the loading on the feeding substations and the voltage profiles of the trains. This study is to formulate a simple and reliable model to support the necessary calculations for probability load flow analysis in railway systems with autotransformer (AT) feeding system, and describe the development of a software suite to realise the computation.

Situation Review of Barley Yellow Dwarf Virus in West Asia and North Africa

Symptoms of barley yellow dwarf (BYD) have been observed on cereals in nearly all countries of West Asia and North Africa. Its incidence. however, has varied during the last 15 years. Observations from field surveys are summarized. Since symptoms of barley yellow dwarf virus (BYDV) are of low diagnostic value, especially in wheat (Triticum aestivum L.), more precise qualitative and quantitative detection was derived by vector transmission and serology. In 1985 and 1986. preliminary surveys by enzyme-linked immunosorbent assay (ELlS A) indicated that BYDV incidence in the regions surveyed in Syria, Morocco, and Tunisia was around 7. 22. and 24%. respectively. By vector transmission PAV-, RPV-, and RMV-like isolates ofBYDV were identified in Morocco and the PAV-like isolate in Syria. By serology PAV-like isolates were identified in Ethiopia, Lebanon. Morocco. Syria. and Tunisia. and MA V-like isolates were identified from Morocco and Tunisia. The PAV-like type was the most common in all countries surveyed. Screening for BYDV resistance by natural infection has been carried out in a number of countries of the region during the last few years. Screening for resistance by aphid inoculation was initiated in Syria in 1986 at the International Center for Agricultural Research in the Dry Areas (ICARDA). Such screening is expected to follow in other countries of the region soon.

Rich probabilistic representations for bearing only decentralised data fusion

The aim of this paper is to demonstrate the validity of using Gaussian mixture models (GMM) for representing probabilistic distributions in a decentralised data fusion (DDF) framework. GMMs are a powerful and compact stochastic representation allowing efficient communication of feature properties in large scale decentralised sensor networks. It will be shown that GMMs provide a basis for analytical solutions to the update and prediction operations for general Bayesian filtering. Furthermore, a variant on the Covariance Intersect algorithm for Gaussian mixtures will be presented ensuring a conservative update for the fusion of correlated information between two nodes in the network. In addition, purely visual sensory data will be used to show that decentralised data fusion and tracking of non-Gaussian states observed by multiple autonomous vehicles is feasible.

Probabilistic subgroup identification using Bayesian finite mixture modelling : a case study in Parkinson’s disease phenotype identification

This article explores the use of probabilistic classification, namely finite mixture modelling, for identification of complex disease phenotypes, given cross-sectional data. In particular, if focuses on posterior probabilities of subgroup membership, a standard output of finite mixture modelling, and how the quantification of uncertainty in these probabilities can lead to more detailed analyses. Using a Bayesian approach, we describe two practical uses of this uncertainty: (i) as a means of describing a person’s membership to a single or multiple latent subgroups and (ii) as a means of describing identified subgroups by patient-centred covariates not included in model estimation. These proposed uses are demonstrated on a case study in Parkinson’s disease (PD), where latent subgroups are identified using multiple symptoms from the Unified Parkinson’s Disease Rating Scale (UPDRS).

Training clinical psychologists : The current situation and a way forward [Guest Editorial]

In the past, training in clinical psychology in Australia and overseas has been dominated by definitions of input— hours of classes or supervision and of specific components. While prospective practitioners have been required to demonstrate the acquisition of generic competencies, satisfaction of these input driven criteria has been required for both accreditation and registration. Ironically, for a discipline that prides itself on requiring empirical bases for practice and communicating those to students (Calhoun, Moras, Pilkonis, & Rehm, 1998), training criteria have been primarily derived from accepted wisdom, rather than from a sound body of data. The situation has been remarkably like that of a treatment establishing standards of fidelity before its effective components are known—an action our profession has correctly criticised in the past (Herbert & Mueser, 1992).

Text mining and probabilistic language modeling for online review spam detecting

In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are specifically composed to mislead readers, so they may appear the same as legitimate reviews (i.e., ham). As a result, discriminatory features that would enable individual reviews to be classified as spam or ham may not be available. Guided by the design science research methodology, the main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. In particular, a novel text mining model is developed and integrated into a semantic language model for the detection of untruthful reviews. The models are then evaluated based on a real-world dataset collected from amazon.com. The results of our experiments confirm that the proposed models outperform other well-known baseline models in detecting fake reviews. To the best of our knowledge, the work discussed in this article represents the first successful attempt to apply text mining methods and semantic language models to the detection of fake consumer reviews. A managerial implication of our research is that firms can apply our design artifacts to monitor online consumer reviews to develop effective marketing or product design strategies based on genuine consumer feedback posted to the Internet.

Construction of Francophone families’ health literacy in a linguistic minority situation

With the increase in international mobility, healthcare systems should no longer be ignoring language barriers. In addition to the benefit of reducing long‐term costs, immigrant‐friendly organizations should be concerned with mitigating the way language barriers increase individuals’ social vulnerabilities and inequities in health care and health status. This paper reports the findings of a qualitative, exploratory study of the health literacy of 28 Francophone families living in a linguistic‐minority situation in Canada. Analysis of interviews revealed that participants’ social vulnerability, mainly due to their limited social and informational networks, influenced the construction of family health literacy. Disparities in access to healthcare services could be decreased by having health professionals’ work in alliance with Francophone community groups and by hiring bilingual health professionals. Linguistic isolation and lack of knowledge about local cultural organizations among Francophone immigrants were two important findings of this study

Probabilistic matching of image sets for video-based face recognition

We address the problem of face recognition on video by employing the recently proposed probabilistic linear discrimi-nant analysis (PLDA). The PLDA has been shown to be robust against pose and expression in image-based face recognition. In this research, the method is extended and applied to video where image set to image set matching is performed. We investigate two approaches of computing similarities between image sets using the PLDA: the closest pair approach and the holistic sets approach. To better model face appearances in video, we also propose the heteroscedastic version of the PLDA which learns the within-class covariance of each individual separately. Our experi-ments on the VidTIMIT and Honda datasets show that the combination of the heteroscedastic PLDA and the closest pair approach achieves the best performance.

On the application of the probabilistic linear discriminant analysis to face recognition across expression

Facial expression is one of the main issues of face recognition in uncontrolled environments. In this paper, we apply the probabilistic linear discriminant analysis (PLDA) method to recognize faces across expressions. Several PLDA approaches are tested and cross-evaluated on the Cohn-Kanade and JAFFE databases. With less samples per gallery subject, high recognition rates comparable to previous works have been achieved indicating the robustness of the approaches. Among the approaches, the mixture of PLDAs has demonstrated better performances. The experimental results also indicate that facial regions around the cheeks, eyes, and eyebrows are more discriminative than regions around the mouth, jaw, chin, and nose.

CAT-SLAM : probabilistic localisation and mapping using a continuous appearance-based trajectory

This paper describes a new system, dubbed Continuous Appearance-based Trajectory Simultaneous Localisation and Mapping (CAT-SLAM), which augments sequential appearance-based place recognition with local metric pose filtering to improve the frequency and reliability of appearance-based loop closure. As in other approaches to appearance-based mapping, loop closure is performed without calculating global feature geometry or performing 3D map construction. Loop-closure filtering uses a probabilistic distribution of possible loop closures along the robot’s previous trajectory, which is represented by a linked list of previously visited locations linked by odometric information. Sequential appearance-based place recognition and local metric pose filtering are evaluated simultaneously using a Rao–Blackwellised particle filter, which weights particles based on appearance matching over sequential frames and the similarity of robot motion along the trajectory. The particle filter explicitly models both the likelihood of revisiting previous locations and exploring new locations. A modified resampling scheme counters particle deprivation and allows loop-closure updates to be performed in constant time for a given environment. We compare the performance of CAT-SLAM with FAB-MAP (a state-of-the-art appearance-only SLAM algorithm) using multiple real-world datasets, demonstrating an increase in the number of correct loop closures detected by CAT-SLAM.

Strategy use of children with Down syndrome in a delay of gratification situation

Background: The capacity to delay gratification has been shown to be a very important developmental task for children who are developing typically. There is evidence that children with Down syndrome have more difficulty with a delay of gratification task than typically developing children of the same mental age. This study focused on the strategies children with Down syndrome use while in a delay of gratification situation to ascertain if these contribute to the differences in delay times from those of typically developing children. Method: Thirty-two children with Down syndrome (15 females) and 50 typically developing children participated in the study. Children with Down syndrome had a mental age, as measured by the Stanford-Binet IV, between 36 and 66 months (M = 45.66). The typically developing children had a mean chronological age of 45.76 months. Children participated in a delay of gratification task where they were offered two or one small treats and asked which they preferred. They were then told that they could have the two treats if they waited for the researcher to return (an undisclosed time of 15 min). If they did not want to wait any longer they could call the researcher back but then they could have only one treat. Twenty-two of the children with Down syndrome and 43 of the typically developing children demonstrated understanding of the task and their data are included here. Sessions were videotaped for later analysis. Results: There were significant differences in the mean waiting times of the two groups. The mean of the waiting times for children with Down syndrome was 181.32 s (SD = 347.62) and was 440.21 s (SD = 377.59) for the typically developing children. Eighteen percent of the group with Down syndrome waited for the researcher to return in comparison to 35% of the typically developing group. Sixty-four percent of children with Down syndrome called the researcher back and the remainder (18%) violated. In the typically developing group 37% called the researcher back and 28% violated. The mean waiting time for the group of children with Down syndrome who called the researcher back was 24 s. Examination of strategy use in this group was therefore very limited. There appeared to be quite similar strategy use across the groups who waited the full 15 min. Conclusions: These results confirm the difficulty children with Down syndrome have in delaying gratification. Teaching strategies for waiting, using information drawn from the behaviours of children who are developing typically may be a useful undertaking. Examination of other contributors to delay ability (e.g., language skills) is also likely to be helpful in understanding the difficulties demonstrated in delaying gratification.

The effect of screen size on mission commander's situation awareness

Situation awareness lost is a common factor leading to human error in the aviation industry. However, few studies have investigated the effect on situation awareness where the control interface is a touch-screen device that supports simultaneous multi-touch input and information output. This research aims to conduct an experiment to evaluate the difference in situation awareness on a large screen device, DiamondTouch (DT107), and a small screen device, iPad, both with multi-touch interactive functions. The Interface Operation and Situation Awareness Testing Simulator (IOSATS), is a simulator to test the three basis interface operations (Search Target, Information Reading, and Change Detection) by implementing a simplified search and rescue scenario. The result of this experiment will provide reliable data for future research for improving operator's situation awareness in the avionic domain.