Nottingham Trent University

About: Nottingham Trent University is a education organization based out in Nottingham, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 4702 authors who have published 12862 publications receiving 307430 citations. The organization is also known as: NTU & Trent Polytechnic.

Simulating the performance of rainwater collection and reuse systems using behavioural models

Abstract: The collection of rainwater from roofs and its storage and subsequent use make a significant saving in the use of potable water. The capacity of the rainwater store is important because it affects both system and initial costs. The investigation concentrates upon the use of behavioural models to simulate the performance of rainwater collectors. The input data, in time series form, is used to simulate the mass flows through the model, and will be based upon time intervals of a minute, an hour, a day or a month. A preliminary mapping exercise is described that evaluates the accuracy of behavioural models for the sizing of rainwater collection systems using different time intervals and different reservoir operating rules applied to a range of reservoir capacities and collection areas. The preliminary analysis indicated that the yield after spillage (YAS) reservoir operating algorithm based on a hourly time interval could be used as a standard against which other models could be compared and calibrated. The d...

Deep Learning in Mining Biological Data

Abstract: Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorized in three broad types (i.e. images, signals, and sequences), these data are huge in amount and complex in nature. Mining such enormous amount of data for pattern recognition is a big challenge and requires sophisticated data-intensive machine learning techniques. Artificial neural network-based learning systems are well known for their pattern recognition capabilities, and lately their deep architectures—known as deep learning (DL)—have been successfully applied to solve many complex pattern recognition problems. To investigate how DL—especially its different architectures—has contributed and been utilized in the mining of biological data pertaining to those three types, a meta-analysis has been performed and the resulting resources have been critically analysed. Focusing on the use of DL to analyse patterns in data from diverse biological domains, this work investigates different DL architectures’ applications to these data. This is followed by an exploration of available open access data sources pertaining to the three data types along with popular open-source DL tools applicable to these data. Also, comparative investigations of these tools from qualitative, quantitative, and benchmarking perspectives are provided. Finally, some open research challenges in using DL to mine biological data are outlined and a number of possible future perspectives are put forward.

En-face optical coherence tomography - a novel application of non-invasive imaging to art conservation

Abstract: Optical Coherence Tomography (OCT) is an optical interferometric technique developed mainly for in vivo imaging of the eye and biological tissues. In this paper, we demonstrate the potential of OCT for non-invasive examination of museum paintings. Two en-face scanning OCT systems operating at 850 nm and 1300 nm were used to produce B-scan and C-scan images at typical working distances of 2 cm. The 3D images produced by the OCT systems show not only the structure of the varnish layer but also the paint layers and underdrawings (preparatory drawings under the paint layers). The highest ever resolution and dynamic range images of underdrawings are presented and for the first time it is possible to find out non-invasively on which layer the underdrawings were drawn.