Language delay is not predictable from available risk factors

Aims. To investigate factors associated with language delay in a cohort of 30-month-old children and determine if identification of language delay requires active contact with families. Methods. Data were collected at a pilot universal 30-month health contact. Health visitors used a simple two-item language screen. Data were obtained for 315 children; language delay was found in 33. The predictive capacity of 13 variables which could realistically be known before the 30-month contact was analysed. Results. Seven variables were significantly associated with language delay in univariate analysis, but in logistic regression only five of these variables remained significant. Conclusion. The presence of one or more risk factors had a sensitivity of 89% and specificity of 45%, but a positive predictive value of only 15%. The presence of one or more of these risk factors thus can not reliably be used to identify language delayed children, nor is it possible to define an “at risk” population because male gender was the only significant demographic factor and it had an unacceptably low specificity (52.5%). It is not possible to predict which children will have language delay at 30 months. Identification of this important ESSENCE disorder requires direct clinical contact with all families

Enhancing the Early Literacy Development of Children at Risk for Reading Difficulties

This paper reviews the dynamic and interactive links between the development of children’s language phonological awareness, and reading. Some of the key issues explored are procedures to enhance children’s language development, decoding and word recognition skills, along with some relevant assessment and programming strategies that can facilitate children’s early reading development. In particular, the paper supports the suggestion that deficits in phonological awareness are often a consequence of slow vocabulary development (a classic marker of language delay) and that teachers need to be able to adapt their language and dialogue interactions for children with language delays

Language and social/emotional problems identified at a universal developmental assessment at 30 months

Background: Preschool language and neurodevelopmental problems often persist and impede learning. The aims of the current study are to assess the uptake of a new universal 30 month health visitor contact and to quantify the prevalence of language delay and social/emotional difficulties.<p></p> Methods: All families of 30 month old children in four Glasgow localities were offered a visit from their health visitor. Structured data were collected relating to language, social and emotional development using three instruments; The Strengths and Difficulties Questionnaire (SDQ), the abbreviated Sure Start Language Measure and a two-item language screen.<p></p> Results: From an eligible population of 543 children, there was a 90% return rate of contact forms from the health visitors, and assessments were completed on 78% of eligible children. Visit completion rates did not differ significantly by socio-economic status. 3-8% of children were reported to have language delay depending on the method of assessment. 8.8% of children scored in the “abnormal” range of SDQ total difficulties scores and 31.1% had an abnormality in at least one subscale. There was substantial overlap between language delay and abnormal scores on the SDQ.<p></p> Conclusions: Universal assessment of neurodevelopmental function at 30 months identified a significant proportion of children, including those previously considered at low risk, with both language and social/emotional difficulties. Further work is required to assess the precise nature of these difficulties and to assess the potential impact on services.<p></p&gt

A Parallel Algorithm for Solving the Advection Equation with a Retarded Argument

We describe a parallel implementation of a difference scheme for the advection equation with time delay on a hybrid architecture computation system. The difference scheme has the second order in space and the first order in time and is unconditionally stable. Performance of a sequential algorithm and several parallel implementations with the MPI technology in the C++ language has been studied

Learning to Translate in Real-time with Neural Machine Translation

Translating in real-time, a.k.a. simultaneous translation, outputs translation words before the input sentence ends, which is a challenging problem for conventional machine translation methods. We propose a neural machine translation (NMT) framework for simultaneous translation in which an agent learns to make decisions on when to translate from the interaction with a pre-trained NMT environment. To trade off quality and delay, we extensively explore various targets for delay and design a method for beam-search applicable in the simultaneous MT setting. Experiments against state-of-the-art baselines on two language pairs demonstrate the efficacy of the proposed framework both quantitatively and qualitatively.Comment: 10 pages, camera read

17-11 Evaluation of Transit Priority Treatments in Tennessee

Many big cities are progressively implementing transit friendly corridors especially in urban areas where traffic may be increasing at an alarming rate. Over the years, Transit Signal Priority (TSP) has proven to be very effective in creating transit friendly corridors with its ability to improve transit vehicle travel time, serviceability and reliability. TSP as part of Transit Oriented Development (TOD) is associated with great benefits to community liveability including less environmental impacts, reduced traffic congestions, fewer vehicular accidents and shorter travel times among others.This research have therefore analysed the impact of TSP on bus travel times, late bus recovery at bus stop level, delay (on mainline and side street) and Level of Service (LOS) at intersection level on selected corridors and intersections in Nashville Tennessee; to solve the problem of transit vehicle delay as a result of high traffic congestion in Nashville metropolitan areas. This study also developed a flow-delay model to predict delay per vehicle for a lane group under interrupted flow conditions and compared some measure of effectiveness (MOE) before and after TSP. Unconditional green extension and red truncation active priority strategies were developed via Vehicle Actuated Programming (VAP) language which was tied to VISSIM signal controller to execute priority for transit vehicles approaching the traffic signal at 75m away from the stop line. The findings from this study indicated that TSP will recover bus lateness at bus stops 25.21% to 43.1% on the average, improve bus travel time by 5.1% to 10%, increase side street delay by 15.9%, and favour other vehicles using the priority approach by 5.8% and 11.6% in travel time and delay reduction respectively. Findings also indicated that TSP may not affect LOS under low to medium traffic condition but LOS may increase under high traffic condition