Ανάλυση μοντέλου ενεργειακής διαχείρισης στόλου ηλεκτρικών οχημάτων σύμφωνα με την ελαστικότητα που προσφέρουν οι χρήστες ηλεκτρικών οχημάτων

Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Παραγωγή και Διαχείρηση Ενέργειας

Block CUR: Decomposing Matrices using Groups of Columns

A common problem in large-scale data analysis is to approximate a matrix using a combination of specifically sampled rows and columns, known as CUR decomposition. Unfortunately, in many real-world environments, the ability to sample specific individual rows or columns of the matrix is limited by either system constraints or cost. In this paper, we consider matrix approximation by sampling predefined \emph{blocks} of columns (or rows) from the matrix. We present an algorithm for sampling useful column blocks and provide novel guarantees for the quality of the approximation. This algorithm has application in problems as diverse as biometric data analysis to distributed computing. We demonstrate the effectiveness of the proposed algorithms for computing the Block CUR decomposition of large matrices in a distributed setting with multiple nodes in a compute cluster, where such blocks correspond to columns (or rows) of the matrix stored on the same node, which can be retrieved with much less overhead than retrieving individual columns stored across different nodes. In the biometric setting, the rows correspond to different users and columns correspond to users' biometric reaction to external stimuli, {\em e.g.,}~watching video content, at a particular time instant. There is significant cost in acquiring each user's reaction to lengthy content so we sample a few important scenes to approximate the biometric response. An individual time sample in this use case cannot be queried in isolation due to the lack of context that caused that biometric reaction. Instead, collections of time segments ({\em i.e.,} blocks) must be presented to the user. The practical application of these algorithms is shown via experimental results using real-world user biometric data from a content testing environment.Comment: shorter version to appear in ECML-PKDD 201

Variant Ranker: a web-tool to rank genomic data according to functional significance

BACKGROUND: The increasing volume and complexity of high-throughput genomic data make analysis and prioritization of variants difficult for researchers with limited bioinformatics skills. Variant Ranker allows researchers to rank identified variants and determine the most confident variants for experimental validation. RESULTS: We describe Variant Ranker, a user-friendly simple web-based tool for ranking, filtering and annotation of coding and non-coding variants. Variant Ranker facilitates the identification of causal variants based on novelty, effect and annotation information. The algorithm implements and aggregates multiple prediction algorithm scores, conservation scores, allelic frequencies, clinical information and additional open-source annotations using accessible databases via ANNOVAR. The available information for a variant is transformed into user-specified weights, which are in turn encoded into the ranking algorithm. Through its different modules, users can (i) rank a list of variants (ii) perform genotype filtering for case-control samples (iii) filter large amounts of high-throughput data based on user custom filter requirements and apply different models of inheritance (iv) perform downstream functional enrichment analysis through network visualization. Using networks, users can identify clusters of genes that belong to multiple ontology categories (like pathways, gene ontology, disease categories) and therefore expedite scientific discoveries. We demonstrate the utility of Variant Ranker to identify causal genes using real and synthetic datasets. Our results indicate that Variant Ranker exhibits excellent performance by correctly identifying and ranking the candidate genes CONCLUSIONS: Variant Ranker is a freely available web server on http://paschou-lab.mbg.duth.gr/Software.html . This tool will enable users to prioritise potentially causal variants and is applicable to a wide range of sequencing data

Meta-Analysis of Tourette Syndrome and Attention Deficit Hyperactivity Disorder Provides Support for a Shared Genetic Basis

Gilles de la Tourette Sydrome (TS) is a childhood onset neurodevelopmental disorder, characterized phenotypically by the presence of multiple motor and vocal tics. It is often accompanied by multiple psychiatric comorbidities, with Attention Deficit/Hyperactivity Disorder (ADHD) among the most common. The extensive co-occurrence of the two disorders suggests a shared genetic background. A major step toward the elucidation of the genetic architecture of TS was undertaken by the first TS Genome-wide Association Study (GWAS) reporting 552 SNPs that were moderately associated with TS (p < 1E-3). Similarly, initial ADHD GWAS attempts and meta-analysis were not able to produce genome-wide significant findings, but have provided insight to the genetic basis of the disorder. Here, we examine the common genetic background of the two neuropsychiatric phenotypes, by meta-analyzing the 552 top hits in the TS GWAS with the results of ADHD first GWASs. We identify 19 significant SNPs, with the top four implicated genes being TBC1D7, GUCY1A3, RAP1GDS1, and CHST11. TBCD17 harbors the top scoring SNP, rs1866863 (p:3.23E-07), located in a regulatory region downstream of the gene, and the third best-scoring SNP, rs2458304 (p:2.54E-06), located within an intron of the gene. Both variants were in linkage disequilibrium with eQTL rs499818, indicating a role in the expression levels of the gene. TBC1D7 is the third subunit of the TSC1/TSC2 complex, an inhibitor of the mTOR signaling pathway, with a central role in cell growth and autophagy. The top genes implicated by our study indicate a complex and intricate interplay between them, warranting further investigation into a possibly shared etiological mechanism for TS and ADHD

Functionalised nanodiamond as a platform for skeletal tissue engineering

Diamond has come of age, as a material for technology and life science applications. Despite its reputation as a gemstone, high grade single crystal diamond can be grown in the laboratory at modest cost. Other forms of diamond, such particulate nanodiamonds (typically 5nm in size) can be formed. Its superior mechanical and functionalisation properties make diamond a great candidate for skeletal tissue engineering material. In this thesis, the potential of functionalised diamond as a nanomaterial for the chondrogenic and osteogenic differentiation of Mesenchymal Stem Cells (MSCs) has been investigated. Results in chapter 4 describe the biocompatibility of human Adipose Derived Stem Cells (hADSCs) with oxygen–functionalised BBD-PPy scaffolds and oxygen–terminated nanodiamonds (NDs) and the ability of the cells to form uniform monolayers on ND substrates. Chapter 5 verifies the ability of hydrogen and oxygen terminated NDs to sustain hADSC proliferation and chondrogenic differentiation. In the same chapter, the ability of another type of mesenchymal stem cells (MSCs), chondrogenic precursor/ stem cells (CSPCs) to differentiate on H–NDs and O–NDs into three key skeletal precursors (chondrocytes, osteocytes and adipocytes) is demonstrated through staining and colorimetric quantification assays. In Chapter 6, a novel 3D scaffold made out of fibrin that incorporated H–NDs is characterised. CSPCs underwent chondrogenic and osteogenic differentiation in this novel structure. Differentiation outcomes were qualitatively demonstrated through section staining and were subsequently quantified using quantitative real time Polymerase Chain Reaction (qRT-PCR). Results indicate chondrogenic differentiation and potential endochondral ossification was promoted by increasing concentrations of H–NDs in the scaffolds, without enhancing the Young’s modulus of the constructs

Alternative Forms of Resilience. A typology of approaches for the study of Citizen Collective Responses in Hard Economic Times

A variety of theoretical and conceptual perspectives have been applied to studying collective citizen initiatives arising in response to hard economic times, such as solidarity-based exchanges and networks, cooperative structures, barter clubs, credit unions, ethical banks, time banks, alternative social currency, citizens' self-help groups, neighbourhood assemblies and social enterprises. Since the global financial crisis of 2008, scholarly attention on novel, alternative, resilient structures has increased, especially in regions that have been most affected. A comprehensive literature review is therefore needed on these initiatives which usually aim to meet basic needs such as food, shelter, health and education at the community level, or build and envision autonomous communities. This paper has four aims. First, it proposes a new, all-encompassing conceptual framework, alternative forms of resilience, to embrace all issues and groups related to such initiatives, during the new millennium and its economic and political challenges, while taking into account the impact of the 2008 crisis. Secondly, it offers a comprehensive literature review on collective citizen initiatives studied through different theoretical, methodological and conceptual understandings. Thirdly, it provides a new typology of several approaches on novel, collective and solidarity-oriented critical resilience initiatives which take into account political issues, be they policy or social-movement related. Finally, it points to future research areas which would aim to systematically address the political and non-political features of citizen-collective responses