Energy challenges for ICT

The energy consumption from the expanding use of information and communications technology (ICT) is unsustainable with present drivers, and it will impact heavily on the future climate change. However, ICT devices have the potential to contribute signi - cantly to the reduction of CO2 emission and enhance resource e ciency in other sectors, e.g., transportation (through intelligent transportation and advanced driver assistance systems and self-driving vehicles), heating (through smart building control), and manu- facturing (through digital automation based on smart autonomous sensors). To address the energy sustainability of ICT and capture the full potential of ICT in resource e - ciency, a multidisciplinary ICT-energy community needs to be brought together cover- ing devices, microarchitectures, ultra large-scale integration (ULSI), high-performance computing (HPC), energy harvesting, energy storage, system design, embedded sys- tems, e cient electronics, static analysis, and computation. In this chapter, we introduce challenges and opportunities in this emerging eld and a common framework to strive towards energy-sustainable ICT

Multi-Attribute Seismic Analysis Using Unsupervised Machine Learning Method: Self-Organizing Maps

Seismic attributes are a fundamental part of seismic interpretation and are routinely used by geoscientists to extract key information and visualize geological features. By combining different findings from each attribute, they can provide a good insight of the area and help overcome many geological challenges. However, individually analyzing multiple attributes to find relevant information can be time-consuming and inefficient, especially when working with large datasets. It can lead to miscalculations, errors in judgement and human bias. This is where Machine Learning (ML) methods can be implemented to improve existing interpretations or find additional information. ML can help by handling large volumes of multi-dimensional data and interrelating them. Methods such as Self Organizing Maps (SOM) allow multi-attribute analysis and help extract more information as compared to quantitative interpretation. SOM is an unsupervised neural network that can find meaningful and reliable patterns corresponding to a specific geological feature (Roden and Chen, 2017). The purpose of this thesis was to understand how SOM can help make interpretations of direct hydrocarbon indicators (DHI) in the Statfjord Field area easier. Several AVO attributes were generated to detect DHIs and were then used as input for multi-attribute SOM analysis. SOMPY package in Python was used to train the model and generate SOM classification results. Data samples were classified based on BMU hits and clusters in the data. The classification was then applied to the whole dataset and converted to seismic sections for comparison and interpretation. SOM classified seismic lines were compared with the results of the AVO attributes. Since DHIs are anomalous data, they were expected to be represented by small data clusters and BMUs with low hits. While SOM reproduced the seismic reflectors well, it did not define the DHI features clearly for them to be easily interpreted. Use of fewer seismic attributes and computational limitations of the machine could be some of the reasons behind not achieving desired results. However, the study has room for improvement and the potential to produce meaningful results. Improvements in model design and training, and also the selection of input attributes are some of the areas that need to be addressed. Furthermore, testing other Python libraries and better handling of large datasets can allow better performance and more accurate results

Hydrodynamics-Biology Coupling for Algae Culture and Biofuel Production

International audienceBiofuel production from microalgae represents an acute optimization problem for industry. There is a wide range of parameters that must be taken into account in the development of this technology. Here, mathematical modelling has a vital role to play. The potential of microalgae as a source of biofuel and as a technological solution for CO2 fixation is the subject of intense academic and industrial research. Large-scale production of microalgae has potential for biofuel applications owing to the high productivity that can be attained in high-rate raceway ponds. We show, through 3D numerical simulations, that our approach is capable of discriminating between situations where the paddle wheel is rapidly moving water or slowly agitating the process. Moreover, the simulated velocity fields can provide lagrangian trajectories of the algae. The resulting light pattern to which each cell is submitted when travelling from light (surface) to dark (bottom) can then be derived. It will then be reproduced in lab experiments to study photosynthesis under realistic light patterns

Synthesis, structure and mechanism of polyoxometalate self-assembly: towards designed nanoscale architectures

Cryospray (CSI-) and electrospray mass spectrometry (ESI-MS) techniques have been utilised to investigate the key features of the ‘in-solution’, self-assembly processes by which complex polyoxometalate systems, such as ((n-C4H9)4N)2n(Ag2Mo8O26)n and ((n- C4H9)4N)3[MnMo6O18((OCH2)3CNH2)2], are formed. CSI-MS monitoring of the rearrangement of molybdenum Lindqvist anions, [Mo6O19]2-, in the presence of silver(I) ions, into a silver-linked β-octamolybdate structure, has allowed elucidation of the role of small isopolyoxomolybdate fragments and AgI ions in the assembly process. The observation of higher mass fragments, each with increasing organic cation contribution concomitant with their increasing metal nuclearity, has supported the previously proposed hypothesis that the organic cations have a structure-directing role in promoting the mode of POM structure growth in solution. The combined use of UV/vis spectroscopy and real-time CSI-MS monitoring of the reaction solution allowed correlation between the decreasing Lindqvist anion concentration and increasing β-octamolybdate anion concentration. Furthermore, UV/vis spectroscopy was used to show that the rate of decrease in Lindqvist anion concentration, and therefore, the inter-conversion of Lindqvist into β-octamolybdate anions, decreases as the carbon chain length of the alkylammonium cations in the system increases. This approach was extended to use ESI-MS monitoring in examining the formation of the more complex, organic-inorganic, Mn-Anderson polyoxomolybdate structure ((n- C4H9)4N)3[MnMo6O18((OCH2)3CNH2)2]. In this investigation, ESI-MS was used to monitor the real-time, ‘in-solution’ rearrangements of α-octamolybdate anions, [α- Mo8O26]4-, and coordination of manganese(III) cations and tris(hydroxymethyl)aminomethane (TRIS) groups in the formation of the Mn-Anderson- TRIS structure. These investigations have led to the proposal that the rearrangement of [α- Mo8O26]4- anions occurs first through decomposition to [Mo4O13]2- cluster species, i.e. halffragments of the octamolybdate anion; followed by decomposition to smaller, stable isopolyoxomolybdate fragment ions such as dimolybdate and trimolybdate fragment ions. It has then been proposed these fragments subsequently coordinate with the tripodal TRIS ligands, manganese ions, and further molybdate anionic units to form the final, derivatized Mn-Anderson-TRIS cluster. Investigations into the encapsulation of the high oxidation state heteroanion templates {IVIIO6} and {TeVIO6} within polyoxomolybdate clusters, have led to the isolation and characterization of two new, molybdenum Anderson-based POM architectures, i.e. Cs4.67Na0.33[IMo6O24]·ca7H2O and Na4((HOCH2CH2)3NH)2[TeMo6O24]·ca10H2O. The use of coordinating caesium and sodium cations allowed the formation of a closely-packed structure composed of the periodate-centred Anderson clusters arranged into two layers, which then form a repeating ABAB pattern through the lattice. In contrast, the main building-blocks of the tellurium-based cluster system features the [TeMo6O24]6- anions and two coordinated cation arrangements, each composed of a {Na2} dimer and coordinated TEAH+ cation. The presence of this structural motif, and its inter-connection with adjacent clusters, has led to chain-like packing arrangements within the greater lattice structure. The introduction of three aromatic, phenanthridinium-based cations into polyoxometalate systems has led to the isolation and characterization of three new POM architectures with emergent photoactivity. The polyoxometalate framework in each is composed of tungsten Keggin clusters, i.e. [PW12O40]3-, which are introduced into the systems as pre-formed building-blocks. Two of the compounds use derivatives of Dihydro-Imidazo- Phenanthridinium (DIP) molecules as cations, i.e. (DIP-1)[PW12O40]·5DMSO·ca1H2O and (DIP-2)[PW12O40]·5DMSO·ca4H2O, whereas the final compound uses an Imidazo- Phenanthridinium (IP) molecule as the cationic unit, i.e. (IPblue)3[PW12O40]·4DMSO. The use of these cations, which have different steric bulk, geometry and charge states, has led to the formation of interesting packing arrangements within the lattice structures of all three compounds. Additionally, further characterization of these compounds has revealed they all possess emergent photoactivity, in the form of intermolecular charge transfer bands in the solid state. Some degree of intermolecular charge transfer in the solution state has also been detected for the DIP-2-based structure

A Green Programming Model for Cloud Software Efficiency

Cloud computing is emerging as a methodology for delivering more energy efficient computing provision. The potential advantages are well-known, and are primarily based on the opportunities to achieve economies of scale through resource sharing: in particular, by concentrating data storage and processing within data centers, where energy efficiency and measurement are well established activities. However, this addresses only a part of the overall energy cost of the totality of the cloud: energy is also required to power the networking connections and the end user systems through which access to the data center is provided, and researchers are beginning to recognize this. One further aspect of cloud provision is less well understood: the impact of application software behavior on the overall system’s energy use. This is of particular concern when one considers the current trend towards “off the shelf” applications accessed from application stores. This mass market for complete applications, or code segments which are included within other applications, creates a very real need for that code to be as efficient as possible, since even small inefficiencies when massively duplicated will result in significant energy loss. This position paper identifies this problem in detail, and proposes a support tool which will indicate to software developers the energy efficiency of their software as it is developed. Fundamental to the delivery of any workable solution is the measurement and selection of suitable metrics, we propose appropriate metrics and indicate how they may be derived and applied within our proposed system. Addressing the potential cost of application development is fundamental to achieving energy saving within the cloud – particularly as the application store model gains acceptance