Cost effective second generation AC-modules: Development and testing aspects

In the framework of the European research project PV2GO, a new AC-module inverter was developed, taking into account all relevant aspects from a European market's point of view (standards, market, application, and research and development goals). The project goal was to achieve the overall system costs of 3 Euro per Wp for a modular plug-and-play photovoltaic system. For the photovoltaic-module, a standard 130-Wp Eurosolare module was chosen. The research and development (R&D) goal was to develop an advanced DC-control system consisting of a state-of-the-art programmable digital device and an Application Specific Integrated Circuit (ASIC) for the AC-control of the inverter. According to the topology concept, thermal and magnetic designs were optimized with regard to production technology and packaging for large-scale production. The new AC-modules were tested in a number of field-test sites in various parts of Europe and their reliability was assessed through Highly Accelerated Stress Tests. Efficiency and power quality have been tested in the laboratory. Further in the PV2GO project an optimization study of the manufacturing process of the new generation of AC-modules for high volume output was done. Another task was the pre-certification procedure to assure compliance with the European guidelines and standards.     

Environmental-aware virtual data center network

Cloud computing services have recently become a ubiquitous service delivery model, covering a wide range of applications from personal file sharing to being an enterprise data warehouse. Building green data center networks providing cloud computing services is an emerging trend in the Information and Communication Technology (ICT) industry, because of Global Warming and the potential GHG emissions resulting from cloud services. As one of the first worldwide initiatives provisioning ICT services entirely based on renewable energy such as solar, wind and hydroelectricity across Canada and around the world, the GreenStar Network (GSN) was developed to dynamically transport user services to be processed in data centers built in proximity to green energy sources, reducing Greenhouse Gas (GHG) emissions of ICT equipments. Regarding the current approach, which focuses mainly in reducing energy consumption at the micro-level through energy efficiency improvements, the overall energy consumption will eventually increase due to the growing demand from new services and users, resulting in an increase in GHG emissions. Based on the cooperation between Mantychore FP7 and the GSN, our approach is, therefore, much broader and more appropriate because it focuses on GHG emission reductions at the macro-level. This article presents some outcomes of our implementation of such a network model, which spans multiple green nodes in Canada, Europe and the USA. The network provides cloud computing services based on dynamic provision of network slices through relocation of virtual data centers.     

Statistical relational learning of trust

The learning of trust and distrust is a crucial aspect of social interaction among autonomous, mentally-opaque agents. In this work, we address the learning of trust based on past observations and context information. We argue that from the truster’s point of view trust is best expressed as one of several relations that exist between the agent to be trusted (trustee) and the state of the environment. Besides attributes expressing trustworthiness, additional relations might describe commitments made by the trustee with regard to the current situation, like: a seller offers a certain price for a specific product. We show how to implement and learn context-sensitive trust using statistical relational learning in form of a Dirichlet process mixture model called Infinite Hidden Relational Trust Model (IHRTM). The practicability and effectiveness of our approach is evaluated empirically on user-ratings gathered from eBay. Our results suggest that (i) the inherent clustering achieved in the algorithm allows the truster to characterize the structure of a trust-situation and provides meaningful trust assessments; (ii) utilizing the collaborative filtering effect associated with relational data does improve trust assessment performance; (iii) by learning faster and transferring knowledge more effectively we improve cold start performance and can cope better with dynamic behavior in open multiagent systems. The later is demonstrated with interactions recorded from a strategic two-player negotiation scenario.     

Analyzing the techniques that improve fault tolerance of aggregation trees in sensor networks

Sensor networks are finding significant applications in large scale distributed systems. One of the basic operations in sensor networks is in-network aggregation. Among the various approaches to in-network aggregation, such as gossip and tree, including the hash-based techniques, the tree-based approaches have better performance and energy-saving characteristics. However, sensor networks are highly prone to failures. Numerous techniques suggested in the literature to counteract the effect of failures have not been carefully analyzed. In this paper, we focus on the performance of these tree-based aggregation techniques in the presence of failures. First, we identify a fault model that captures the important failure traits of the system. Then, we analyze the correctness of simple tree aggregation with our fault model. We then use the same fault model to analyze the techniques that utilize redundant trees to improve the variance. The impact of techniques for maintaining the correctness under faults, such as rebuilding or locally fixing the tree, is then studied under the same fault model. We also do the cost-benefit analysis of using the hash-based schemes which are based on FM sketches. We conclude that these fault tolerance techniques for tree aggregation do not necessarily result in substantial improvement in fault tolerance.     

A short and versatile finite element multiscale code for homogenization problems

We describe a multiscale finite element (FE) solver for elliptic or parabolic problems with highly oscillating coefficients. Based on recent developments of the so-called heterogeneous multiscale method (HMM), the algorithm relies on coupled macro- and microsolvers. The framework of the HMM allows to design a code whose structure follows the classical finite elements implementation at the macro level. To account for the fine scales of the problem, elementwise numerical integration is replaced by micro FE methods on sampling domains. We discuss a short and flexible FE implementation of the multiscale algorithm, which can accommodate simplicial or quadrilateral FE and various coupling conditions for the constrained micro simulations. Extensive numerical examples including three dimensional and time dependent problems are presented illustrating the efficiency and the versatility of the computational strategy.     

A DEM‐based parallel computing hydrodynamic and transport model

The sudden and accidental water pollution response system (SAWPRS) for Yangtze River in central China required to develop a hydrodynamic and transport model, which is readily available and capable of simulating a large river system within GIS environment. This study facilitates such effort by developing a parallel computing method based on digital elevation model (DEM) using overlapping domain decomposition approach (ODDA) and message passing interface (MPI) protocol. The hydrodynamic and transport model was redesigned using finite volume method for hydrodynamic and transport model dispersion, the SIMPLEC method for solving the flow field, and the pressure weighted interpolating method for the flow field modification. This modelling approach was verified in two experiments using different sets of computer clusters. The model output was evaluated against the measured data collected for the year 1998 for Wanzhou, an upstream river segment of Yangtze River. The relative error was found to be less than 10%. The performance of parallel computation was found excellent as evident from the cost efficiency values greater than 0.81 in both experiments and increased computation speed while increasing the number of computer clusters. Overall, the parallel computing modelling system developed here was found to meet all requirements of SAWPRS. Copyright © 2010 John Wiley & Sons, Ltd.     

Polyol method for the preparation of nanosized Gd2O3, boehmite and other oxides

A modified microwave-assisted polyol method was applied to prepare nanoparticulate ceramic powders of different oxides, i.e. Gd₂O₃, AlO(OH) (boehmite) and TiO₂. Due to the good dielectric properties of the utilised solvents, such as ethylene glycol, diethylene glycol and 1,4 butanediol, a significant decrease in reaction time was achieved under microwave heating. In the case of AlO(OH) and Gd₂O₃, <5 nm primary particle size were obtained. Boehmite was found to be intercalated with the solvent. The general applicability of the process is shown and the advantages in terms of properties and processibility are described. The powders thus prepared were investigated using X-ray diffractometry, electron microscopy and physisorption.     

NKG2C+ NK Cells for Immunotherapy of Glioblastoma Multiforme

In glioblastoma, non-classical human leucocyte antigen E (HLA-E) and HLA-G are frequently overexpressed. HLA-E loaded with peptides derived from HLA class I and from HLA-G contributes to inhibition of natural killer (NK) cells with expression of the inhibitory receptor CD94/NKG2A. We investigated whether NK cells expressing the activating CD94/NKG2C receptor counterpart were able to exert anti-glioma effects. NKG2C+ subsets were preferentially expanded by a feeder cell line engineered to express an artificial disulfide-stabilized trimeric HLA-E ligand (HLA-E*spG). NK cells expanded by a feeder cell line, which facilitates outgrowth of conventional NKG2A+, and fresh NK cells, were included for comparison. Expansion via the HLA-E*spG feeder cells selectively increased the fraction of NKG2C+ NK cells, which displayed a higher frequency of KIR2DL2/L3/S2 and CD16 when compared to expanded NKG2A+ NK cells. NKG2C+ NK cells exhibited increased cytotoxicity against K562 and KIR:HLA-matched and -mismatched primary glioblastoma multiforme (GBM) cells when compared to NKG2A+ NK cells and corresponding fresh NK cells. Cytotoxic responses of NKG2C+ NK cells were even more pronounced when utilizing target cells engineered with HLA-E*spG. These findings support the notion that NKG2C+ NK cells have potential therapeutic value for treating gliomas.     

Facile Microwave Assisted Synthesis of Silver Nanostars for Ultrasensitive Detection of Biological Analytes by SERS

We report a very simple, rapid and reproducible method for the fabrication of anisotropic silver nanostars (AgNS) that can be successfully used as highly efficient SERS substrates for different bioanalytes, even in the case of a near-infra-red (NIR) excitation laser. The nanostars have been synthesized using the chemical reduction of Ag⁺ ions by trisodium citrate. This is the first research reporting the synthesis of AgNS using only trisodium citrate as a reducing and stabilizing agent. The key elements of this original synthesis procedure are rapid hydrothermal synthesis of silver nanostars followed by a cooling down procedure by immersion in a water bath. The synthesis was performed in a sealed bottom flask homogenously heated and brought to a boil in a microwave oven. After 60 s, the colloidal solution was cooled down to room temperature by immersion in a water bath at 35 °C. The as-synthesized AgNS were washed by centrifugation and used for SERS analysis of test molecules (methylene blue) as well as biological analytes: pharmaceutical compounds with various Raman cross sections (doxorubicin, atenolol & metoprolol), cell lysates and amino acids (methionine & cysteine). UV-Vis absorption spectroscopy, (Scanning) Transmission Electron Microscopy ((S)TEM) and Atomic Force Microscopy (AFM) have been employed for investigating nanostars’ physical properties.