A smart sewer asset information model to enable an ‘Internet of Things’ for operational wastewater management

Real-time prediction of flooding is vital for the successful future operational management of the UK sewerage network. Recent advances in smart infrastructure and the emergence of the Internet of Things (IoT), presents an opportunity within the wastewater sector to harness and report in real-time sewer condition data for operation management. This study presents the design and development of a prototype Smart Sewer Asset Information Model (SSAIM) for an existing sewerage network. The SSAIM, developed using Industry Foundation Class version 4 (IFC4) an open neutral data format for BIM, incorporates distributed smart sensors to enable real-time monitoring and reporting of sewer asset performance. Results describe an approach for sensor data analysis to facilitate the real-time prediction of flooding

Combining data mining and text mining for detection of early stage dementia:the SAMS framework

In this paper, we describe the open-source SAMS framework whose novelty lies in bringing together both data collection (keystrokes, mouse movements, application pathways) and text collection (email, documents, diaries) and analysis methodologies. The aim of SAMS is to provide a non-invasive method for large scale collection, secure storage, retrieval and analysis of an individual’s computer usage for the detection of cognitive decline, and to infer whether this decline is consistent with the early stages of dementia. The framework will allow evaluation and study by medical professionals in which data and textual features can be linked to deficits in cognitive domains that are characteristic of dementia. Having described requirements gathering and ethical concerns in previous papers, here we focus on the implementation of the data and text collection components

Combining mouse and keyboard events with higher level desktop actions to detect mild cognitive impairment

We present a desktop monitoring application that combines keyboard, mouse, desktop and application-level activities. It has been developed to discover differences in cognitive functioning amongst older computer users indicative of mild cognitive impairment (MCI). Following requirements capture from clinical domain experts, the tool collects all Microsoft Windows events deemed potentially useful for detecting early clinical indicators of dementia, with a view to further analysis to determine the most pertinent. Further requirements capture from potential end-users has resulted in a system that has little impact on users? daily activities and ensures data security from initial recording of events through to data analysis. We describe two experiments: firstly, volunteers were asked to perform a short set of known tasks; the second (ongoing) experiment is a longitudinal study, with the software currently successfully running on participants? computers

Quantification of structural changes in the corpus callosumin children with profound hypoxic-ischaemic brain injury

Background Birth-related acute profound hypoxic–ischaemic brain injury has specific patterns of damage including the paracentral lobules. Objective To test the hypothesis that there is anatomically coherent regional volume loss of the corpus callosum as a result of this hemispheric abnormality. Materials and methods Study subjects included 13 children with proven acute profound hypoxic–ischaemic brain injury and 13 children with developmental delay but no brain abnormalities. A computerised system divided the corpus callosum into 100 segments, measuring each width. Principal component analysis grouped the widths into contiguous anatomical regions. We conducted analysis of variance of corpus callosum widths as well as support vector machine stratification into patient groups. Results There was statistically significant narrowing of the mid–posterior body and genu of the corpus callosum in children with hypoxic–ischaemic brain injury. Support vector machine analysis yielded over 95% accuracy in patient group stratification using the corpus callosum centile widths. Conclusion Focal volume loss is seen in the corpus callosum of children with hypoxic–ischaemic brain injury secondary to loss of commissural fibres arising in the paracentral lobules. Support vector machine stratification into the hypoxic–ischaemic brain injury group or the control group on the basis of corpus callosum width is highly accurate and points towards rapid clinical translation of this technique as a potential biomarker of hypoxic–ischaemic brain injur

Known and unknown requirements in healthcare

We report experience in requirements elicitation of domain knowledge from experts in clinical and cognitive neurosciences. The elicitation target was a causal model for early signs of dementia indicated by changes in user behaviour and errors apparent in logs of computer activity. A Delphi-style process consisting of workshops with experts followed by a questionnaire was adopted. The paper describes how the elicitation process had to be adapted to deal with problems encountered in terminology and limited consensus among the experts. In spite of the difficulties encountered, a partial causal model of user behavioural pathologies and errors was elicited. This informed requirements for configuring data- and text-mining tools to search for the specific data patterns. Lessons learned for elicitation from experts are presented, and the implications for requirements are discussed as “unknown unknowns”, as well as configuration requirements for directing data-/text-mining tools towards refining awareness requirements in healthcare applications

Brito Broca e Machado de Assis - Algumas notas

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