The Relationship between Working Memory and Cognitive Functioning in Children

One-hundred and forty-four Year 1 children (51% boys and 49% girls, mean age 6) from Queensland State primary schools participated in a study to investigate the relationship between working memory and cognitive functioning. Children were given two tests of cognitive functioning (the School-Years Screening Test for the Evaluation of Mental Status (SYSTEMS) and the Kaufman Brief Intelligence Test (K-BIT)) and six subtests of working memory from the Working Memory Test Battery for Children (WMTB-C) (Backward Digit Recall, Listening Recall, Digit Recall, Word List Matching, Word List Recall and Non-word List Recall). The two cognitive tests correlated at r = .50. Results showed a high correlation between SYSTEMS and the Phonological Loop (PL) component of working memory. The K-BIT also correlated highly with PL component. The SYSTEMS and K-BIT showed various levels of correlation with the working memory sub-tests. A measurement model utilising Confirmatory Factor Analysis method showed a strong relationship between working memory and cognitive functioning, the degree of fit for the model was very high at GFI = .996

Investigation into the use of satellite remote sensing data products as part of a multi-modal marine environmental monitoring network

In this paper it is investigated how conventional in-situ sensor networks can be complemented by the satellite data streams available through numerous platforms orbiting the earth and the combined analyses products available through services such as MyOcean. Despite the numerous benefits associated with the use of satellite remote sensing data products, there are a number of limitations with their use in coastal zones. Here the ability of these data sources to provide contextual awareness, redundancy and increased efficiency to an in-situ sensor network is investigated. The potential use of a variety of chlorophyll and SST data products as additional data sources in the SmartBay monitoring network in Galway Bay, Ireland is analysed. The ultimate goal is to investigate the ability of these products to create a smarter marine monitoring network with increased efficiency. Overall it was found that while care needs to be taken in choosing these products, there was extremely promising performance from a number of these products that would be suitable in the context of a number of applications especially in relation to SST. It was more difficult to come to conclusive results for the chlorophyll analysis

Improving data driven decision making through integration of environmental sensing technologies

Coastal and estuarine zones contain vital and increasingly exploited resources. Traditional uses in these areas (transport, fishing, tourism) now sit alongside more recent activities (mineral extraction, wind farms). However, protecting the resource base upon which these marine-related economic and social activities depend requires access to reliable and timely data. This requires both acquisition of background (baseline) data and monitoring impacts of resource exploitation on aquatic processes and the environment. Management decisions must be based on analysis of collected data to reduce negative impacts while supporting resource-efficient, environmentally sustainable uses. Multi-modal sensing and data fusion offer attractive possibilities for providing such data in a resource efficient and robust manner. In this paper, we report the results of integrating multiple sensing technologies, including autonomous multi-parameter aquatic sensors with visual sensing systems. By focussing on salinity measurements, water level and freshwater influx into an estuarine system; we demonstrate the potential of modelling and data mining techniques in allowing deployment of fewer sensors, with greater network robustness. Using the estuary of the River Liffey in Dublin, Ireland, as an example, we present the outputs and benefits resulting from fusion of multi-modal sensing technologies to predict and understand freshwater input into estuarine systems and discuss the potential of multi-modal datasets for informed management decisions

Coastal fog detection using visual sensing

Use of visual sensing techniques to detect low visibility conditions may have a number of advantages when combined with other methods, such as satellite based remote sensing, as data can be collected and processed in real or near real time. Camera-enabled visual sensing can provide direct confirmation of modelling and forecasting results. Fog detection, modelling and prediction are a priority for maritime communities and coastal cities due to economic impacts of fog on aviation, marine, and land transportation. Canadian and Irish coasts are particularly vulnerable to dense fog under certain environmental conditions. Offshore oil and gas production on Grand Bank (off the Canadian East Coast) can be adversely affected by weather and sea state conditions. In particular, fog can disrupt the transfer of equipment and people to/from the production platforms by helicopter. Such disruptions create delays and the delays cost money. According to offshore oil and gas industry representatives at a recent workshop on metocean monitoring and forecasting for the NL offshore, there is a real need for improved forecasting of visibility (fog) out to 3 days. The ability to accurately forecast future fog conditions would improve the industry’s ability to adjust its schedule of operations accordingly. In addition, it was recognized by workshop participants that the physics of Grand Banks fog formation is not well understood, and that more and better data are needed

An affordable smart sensor network for water level management in a catchment

Accurately monitoring water levels at high special and temporal scale is the key element in every catchment or flood risk management system. Building, evaluating and testing hydrological models typically require years of high frequency datasets. Current water level monitoring systems are expensive and usually deployed sparsely throughout a catchment. This may not provide sufficient information to simulate the hydrological variations of a catchment. In this paper, we evaluate the Kingspan Sonic SignalMan ultra-sonic sensor that is designed for monitoring liquid, such as diesel, AdBlue, lubricants additives etc., level autonomously over years. The cost of this sensor is relatively low, which enables the deploying of a water level monitoring system at a much larger spatial scale at an affordable cost. A smart sensor network for catchment management is proposed based on the use of the Kingspan sensor

Familiarity of speaker accent on Irish children’s performance on a sentence comprehension task

Objectives: This study sought to determine whether children’s performance on a sentence comprehension task is affected when sentences are spoken in an unfamiliar native accent. Method: Fifty typically developing school-aged children living in Southern Ireland (Cork) participated; 25 in a younger group (mean 7;08 years) and 25 in an older group (mean 9;09 years). The children completed a computer-based comprehension task during which 20 sentences were spoken in a Cork accent (familiar) and 20 were in a Tyrone accent (unfamiliar). The sentences were matched for syllable length and syntactic complexity. Main results: The younger children made significantly more errors when sentences were spoken in an unfamiliar accent. The older children made a similar number of incorrect responses to both familiar and unfamiliar accents. Conclusion: Younger children’s performance on comprehension tasks may be reduced when sentences are spoken in an unfamiliar accent. Possible explanations and the clinical implications are discussed

A smart city-smart bay project - establishing an integrated water monitoring system for decision support in Dublin Bay

Environmental and water quality monitoring is key to measuring and understanding the chemical and biological quality of water and for taking reactive remedial action. Over the coming years, monitoring of water bodies will increase within Europe, in order to comply with the requirements of the Water Framework Directive (WFD, Council Directive 2000/60/EC), and globally owing to pressure from climate change. The establishment of high quality long-term monitoring programmes is regarded as essential if the implementation of the WFD is to be effective. However, the traditional spot/grab sampling using conventional sampling and laboratory based techniques can introduce a significant financial burden, and is unlikely to provide a reasonable estimate of the true maximum and/or mean concentration for a particular physico-chemical variable in a water body with marked temporal variability. When persistent fluctuations occur, it is likely only to be detected through continuous measurements, which have the capability of detecting sporadic peaks of concentration. The aim of this work is to demonstrate the potential for continuous monitoring data in decision support as part of a smart city project. The multi-modal data system shows potential for low-cost sensing in complex aquatic environments around the city. Continuous monitoring data from both visual and water quality sensors is collected and data from grab samples collected support the observations of trends in water quality