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Comparison of artificial neural networks and autoregressive model for inflows forecasting of Roseires Reservoir for better prediction of irrigation water supply in Sudan
No full textThe Blue Nile River is utilized in Sudan as the main source of irrigation water. However, the river has a long, dry, low-flow season (October–May), which necessitates the use of regulations and rules to manage its water use during this period. This depends on the use of accurate lead time forecasts of inflows to the reservoirs built along the river. Thus a reliable and tested forecasting tool is needed to provide inflow forecast, with sufficient lead time. In the present study, artificial neural network (ANN) is used to model the recession curve of the flow hydrograph at El-Deim gauging station, which subsequently is used as inflows to the Roseires Reservoir on the Blue Nile River. Different scenarios of ANN have been tested to forecast 23 10-day mean discharges during the recession period and their performances were assessed. Results from the optimal ANN model were compared to those simulated with an autoregressive (AR1) model to check their accuracy. Modelling results showed that the ANN model developed is capable of accurately forecasting the inflows to the Roseires Reservoir and outperforms the AR1 model. It has then proposed for use in operation of the reservoir for purposes of predicting irrigation water supply
What serious game studios want from ICT research: identifying developers’ needs
No full textAlthough many scholars recognise the great potential of games for teaching and learning, the EU-based industry for such “serious” games” is highly fragmented and its growth figures remain well behind those of the leisure game market. Serious gaming has been designated as a priority area by the European Commission in its Horizon 2020 Framework Programme for Research and Innovation. The RAGE project, which is funded as part of the Horizon 2020 Programme, is a technology-driven research and innovation project that will make available a series of self-contained gaming software modules that support game studios in the development of serious games. As game studios are a critical factor in the uptake of serious games, the RAGE projects will base its work on their views and needs as to achieve maximum impact. This paper presents the results of a survey among European game studios about their development related needs and expectations. The survey is aimed at identifying a baseline reference for successfully supporting game studios with advanced ICTs for serious games
Flood risk assessment for urban water system in a changing climate using artificial neural network
No full textChanges in rainfall patterns due to climate change are expected to have negative impact on urban drainage systems, causing increase in flow volumes entering the system. In this paper, two emission scenarios for greenhouse concentration have been used, the high (A1FI) and the low (B1). Each scenario was selected for purpose of assessing the impacts on the drainage system. An artificial neural network downscaling technique was used to obtain local-scale future rainfall from three coarse-scale GCMs. An impact assessment was then carried out using the projected local rainfall and a risk assessment methodology to understand and quantify the potential hazard from surface flooding. The case study is a selected urban drainage catchment in northwestern England. The results show that there will be potential increase in the spilling volume from manholes and surcharge in sewers, which would cause a significant number of properties to be affected by flooding
Adjustment to University : factors and strategies to support success and retention.
No full textPoor adjustment to University in the first academic year predicts student attrition and low academic results (Gerdes & Mallinckrodt, 1994. Typical markers of maladjustment are loneliness, depression, and poor emotional management (Beyers & Goossens, 2002; Nightingale et al., 2103). Research indicates that this is a significant problem, with a third of UK undergraduates showing stable poor adjustment across the first year (Nightingale et al., 2013).This presentation summarises the findings of two papers that are part of the outcomes of a research project funded by the University of Bolton (UoB) Learning Enhancement Fund 2014-15 which examines factors and strategies to support the transition to the university in first year undergraduates. The presentation will firstly report the findings of an exhaustive literature review conducted to examine factors that contribute to poor adjustment to university and strategies that have been successful in supporting adjustment. Secondly, it will show the initial findings from a series of focus groups which were conducted by the student researchers with UoB volunteer students as well as key UoB student support officers. The preliminary findings of the thematic analysis of the data will be highlighted and implications of the results for devising strategies to support undergraduates with the transition to university which target success and retention will be outlined
e-Intervention to boost trainee teachers' peer assessment.
No full textThis poster presents a summary of an on-going action research that was inspired by our desire to determine the impact of formative assessment strategies embedded in the teacher education programme. We are revising the way our trainees see the strategies used in their course that aim to enhance their peer assessment skills, self-assessment and reflective practice; how they believe these strategies improve their learning and whether the trainee teachers are able to apply these strategies in their teaching practice. The initial outcome has indicated the needs of using multimedia and developing an eTool that will support initial teacher educators in the analysis and reflection upon essential teaching skills required to deliver a 15 minute micro-teach session.In addition, we have taken the opportunity to develop a collaborative project with the University of Presov (Slovakia) to explore good/best practices in self-assessment and peer-assessment using technology, while we have an ERASMUS fellow researcher supporting the project.The poster meets the Conference theme as it not only involves research students in the project (ERASMUS and a Teacher Educator student final project), but also explores areas for further learning enhancements involving research
An intelligent fault diagnosis method using variable weight artificial immune recognizers (V-AIR)
No full textThe Artificial Immune Recognition System (AIRS), which has been proved to be a successful classification method in the field of Artificial Immune Systems, has been used in many classification problems and gained good classification effect. However, the network inhibition mechanisms used in these methods are based on the threshold inhibition and the cells with low affinity will be deleted directly from the network, which will misrepresent the key features of the data set for not considering the density information within the data. In this paper, we utilize the concept of data potential field and propose a new weight optimizing network inhibition algorithm called variable weight artificial immune recognizer (V-AIR) where we replace the network inhibiting mechanism based on affinity with the inhibiting mechanism based on weight optimizing. The concept of data potential field was also used to describe the data distribution around training samples and the pattern of a training data belongs to the class with the largest potential field. At last, we used this algorithm to rolling bearing analog fault diagnosis and reciprocating compressor valves fault diagnosis, which get a good classification effect
Policy recommendations for learning analytics from three stakeholder workshops: Learning Analytics Review 6
No full textThis document presents policy recommendations related to the use of learning analytics and educational data mining from three LACE workshops
Clustering Power System Approach with Smart Distribution Network Controller
No full textThe ongoing trend to sustainable energy supply based on renewable energies is coming along with new challenges in network control. Especially in the low voltage distribution networks, new control structures are required, since they have been considered as a passive system in regards of network control. As a feasible solution for structuring power networks the Clustering Power System Approach (CPSA) has been introduced, in order to allow adequate control architecture. This paper focuses on a new network control application for clustered power systems, being suitable for directed manipulation of power flows in low voltage networks. The core component is a smart inverter system in combination with a battery storage, which will be placed into the connection lines between interacting clusters. The network control application is introduced in this paper and initial results of the capabilities of the smart inverter system, especially at asymmetrical grid conditions, are presented