Care: an integrated framework to support continuous, adaptable, reflective evaluation of eGovernment systems: A research note

CARE: an Integrated Framework to Support Continuous, Adaptable, Reflective Evaluation of Egovernment SystemsThis is an eGISE network paper. It is motivated by a concern to develop a better approach to learning from the experience of an eGovernment project and applying that knowledge in future projects. The proposed project is based on previous work in the construction industry that developed COLA, a Cross Organisational Learning Approach. Developing a similar strategy for Knowledge Management is likely to be effective because the ‘silo’ culture of local government organisations has parallels with the segmented organisational structures within the construction industry.Engineering and Physical Sciences Research Council (EPSRC) UK (grant GR/T27020/01

A Belief Rule Based Expert System for Datacenter PUE Prediction under Uncertainty

A rapidly emerging trend in the IT landscape is the uptake of large-scale datacenters moving storage and data processing to providers located far away from the end-users or locally deployed servers. For these large-scale datacenters, power efficiency is a key metric, with the PUE (Power Usage Effectiveness) and DCiE (Data Centre infrastructure Efficiency) being important examples. This article proposes a belief rule based expert system to predict datacenter PUE under uncertainty. The system has been evaluated using real-world data from a data center in the UK. The results would help planning construction of new datacenters and the redesign of existing datacenters making them more power efficient leading to a more sustainable computing environment. In addition, an optimal learning model for the BRBES demonstrated which has been compared with ANN and Genetic Algorithm; and the results are promising

A machine learning solution for data center thermal characteristics analysis

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). The energy efficiency of Data Center (DC) operations heavily relies on a DC ambient temperature as well as its IT and cooling systems performance. A reliable and efficient cooling system is necessary to produce a persistent flow of cold air to cool servers that are subjected to constantly increasing computational load due to the advent of smart cloud-based applications. Consequently, the increased demand for computing power will inadvertently increase server waste heat creation in data centers. To improve a DC thermal profile which could undeniably influence energy efficiency and reliability of IT equipment, it is imperative to explore the thermal characteristics analysis of an IT room. This work encompasses the employment of an unsupervised machine learning technique for uncovering weaknesses of a DC cooling system based on real DC monitoring thermal data. The findings of the analysis result in the identification of areas for thermal management and cooling improvement that further feeds into DC recommendations. With the aim to identify overheated zones in a DC IT room and corresponding servers, we applied analyzed thermal characteristics of the IT room. Experimental dataset includes measurements of ambient air temperature in the hot aisle of the IT room in ENEA Portici research center hosting the CRESCO6 computing cluster. We use machine learning clustering techniques to identify overheated locations and categorize computing nodes based on surrounding air temperature ranges abstracted from the data. This work employs the principles and approaches replicable for the analysis of thermal characteristics of any DC, thereby fostering transferability. This paper demonstrates how best practices and guidelines could be applied for thermal analysis and profiling of a commercial DC based on real thermal monitoring data

PV with multiple storage as function of geolocation

© 2018 Elsevier Ltd A real PV array combined with two storage solutions (B, battery, and H, hydrogen reservoir with electrolyzer-fuel cells) is modeled in two geolocations: Oxford, UK, and San Diego, California. All systems meet the same 1-year, real domestic demand. Systems are first configured as standalone (SA) and then as Grid-connected (GC), receiving 50% of the yearly-integrated demand. H and PV are dynamically sized as function of geolocation, battery size B M and H's round-trip efficiency η H . For a reference system with battery capacity B M =10 kW h and η H =0.4, the required H capacity in the SA case is ∼1230 kW h in Oxford and ∼750 kW h in San Diego (respectively, ∼830 kW h and ∼600 kW h in the GC case). Related array sizes are 93% and 51% of the reference 8 kW p system (51% and 28% for GC systems). A trade-off between PV size and battery capacity exists: the former grows significantly as the latter shrinks below 10 kW h, while is insensitive for B M rising above it. Such a capacity achieves timescales’ separation: B, costly and efficient, is mainly used for frequent transactions (daily periodicity or less); cheap, inefficient H for seasonal storage instead. With current PV and B costs, the SA reference system in San Diego can stay within 2·10 4 $CapEx if H's cost does not exceed ∼7$/kW h; this figure increases to 15 $/kWh with Grid constantly/randomly supplying a half of yearly energy (6.5$/kWh in Oxford, where no SA system is found below 2·10 4 $ CapEx). Rescaling San Diego's array (further from its optimal configuration than Oxford's) to the ratio between local, global horizontal irradiance (GHI) and Oxford GHI, yields in all cases a 11% reduction of size and corresponding cost, with the other model outputs unaffected. The location dependent results vary to different extents when extending the modeled timeframe to 18 years. In any case, the variability stays within ±10% of the reference year

Technology transfer model for Austrian higher education institutions

The aim of this paper is to present the findings of a PhD research (Heinzl 2007, Unpublished PhD Thesis) conducted on the Universities of Applied Sciences in Austria. Four of the models that emerge from this research are: Generic Technology Transfer Model (Sect. 5.1); Idiosyncrasies Model for the Austrian Universities of Applied Sciences (Sect. 5.2); Idiosyncrasies-Technology Transfer Effects Model (Sect. 5.3); Idiosyncrasies-Technology Transfer Cumulated Effects Model (Sect. 5.3). The primary and secondary research methods employed for this study are: literature survey, focus groups, participant observation, and interviews. The findings of the research contribute to a conceptual design of a technology transfer system which aims to enhance the higher education institutions' technology transfer performance. © 2012 Springer Science+Business Media, LLC