Sampling cluster endurance for peer-to-peer based content distribution networks

Several types of Content Distribution Networks are being deployed over the Internet today, based on different architectures to meet their requirements (e.g., scalability, efficiency and resiliency). Peer-to-peer (P2P) based Content Distribution Networks are promising approaches that have several advantages. Structured P2P networks, for instance, take a proactive approach and provide efficient routing mechanisms. Nevertheless, their maintenance can increase considerably in highly dynamic P2P environments. In order to address this issue, a two-tier architecture called Omicron that combines a structured overlay network with a clustering mechanism is suggested in a hybrid scheme. In this paper, we examine several sampling algorithms utilized in the aforementioned hybrid network that collect local information in order to apply a selective join procedure. Additionally, we apply the sampling algorithms on Chord in order to evaluate sampling as a general information gathering mechanism. The algorithms are based mostly on random walks inside the overlay networks. The aim of the selective join procedure is to provide a well balanced and stable overlay infrastructure that can easily overcome the unreliable behavior of the autonomous peers that constitute the network. The sampling algorithms are evaluated using simulation experiments as well as probabilistic analysis where several properties related to the graph structure are reveale

Sampling cluster endurance for peer-to-peer based content distribution networks

Several types of Content Distribution Networks are being deployed over the Internet today, based on different architectures to meet their requirements (e.g., scalability, efficiency and resiliency). Peer-to-peer (P2P) based Content Distribution Networks are promising approaches that have several advantages. Structured P2P networks, for instance, take a proactive approach and provide efficient routing mechanisms. Nevertheless, their maintenance can increase considerably in highly dynamic P2P environments. In order to address this issue, a two-tier architecture called Omicron that combines a structured overlay network with a clustering mechanism is suggested in a hybrid scheme. In this paper, we examine several sampling algorithms utilized in the aforementioned hybrid network that collect local information in order to apply a selective join procedure. Additionally, we apply the sampling algorithms on Chord in order to evaluate sampling as a general information gathering mechanism. The algorithms are based mostly on random walks inside the overlay networks. The aim of the selective join procedure is to provide a well balanced and stable overlay infrastructure that can easily overcome the unreliable behavior of the autonomous peers that constitute the network. The sampling algorithms are evaluated using simulation experiments as well as probabilistic analysis where several properties related to the graph structure are reveale

Multistate resistive switching in silver nanoparticle films.

Resistive switching devices have garnered significant consideration for their potential use in nanoelectronics and non-volatile memory applications. Here we investigate the nonlinear current-voltage behavior and resistive switching properties of composite nanoparticle films comprising a large collective of metal-insulator-metal junctions. Silver nanoparticles prepared via the polyol process and coated with an insulating polymer layer of tetraethylene glycol were deposited onto silicon oxide substrates. Activation required a forming step achieved through application of a bias voltage. Once activated, the nanoparticle films exhibited controllable resistive switching between multiple discrete low resistance states that depended on operational parameters including the applied bias voltage, temperature and sweep frequency. The films' resistance switching behavior is shown here to be the result of nanofilament formation due to formative electromigration effects. Because of their tunable and distinct resistance states, scalability and ease of fabrication, nanoparticle films have a potential place in memory technology as resistive random access memory cells

Functional Classification of Skeletal Muscle Networks. I. Normal Physiology

Extensive measurements of the parts list of human skeletal muscle through transcriptomics and other phenotypic assays offer the opportunity to reconstruct detailed functional models. Through integration of vast amounts of data present in databases and extant knowledge of muscle function combined with robust analyses that include a clustering approach, we present both a protein parts list and network models for skeletal muscle function. The model comprises the four key functional family networks that coexist within a functional space; namely, excitation-activation family (forward pathways that transmit a motoneuronal command signal into the spatial volume of the cell and then use Ca2+ fluxes to bind Ca2+ to troponin C sites on F-actin filaments, plus transmembrane pumps that maintain transmission capacity); mechanical transmission family (a sophisticated three-dimensional mechanical apparatus that bidirectionally couples the millions of actin-myosin nanomotors with external axial tensile forces at insertion sites); metabolic and bioenergetics family (pathways that supply energy for the skeletal muscle function under widely varying demands and provide for other cellular processes); and signaling-production family (which represents various sensing, signal transduction, and nuclear infrastructure that controls the turn over and structural integrity and regulates the maintenance, regeneration, and remodeling of the muscle). Within each family, we identify subfamilies that function as a unit through analysis of large-scale transcription profiles of muscle and other tissues. This comprehensive network model provides a framework for exploring functional mechanisms of the skeletal muscle in normal and pathophysiology, as well as for quantitative modeling

Pushing the Limits: Cognitive, Affective, and Neural Plasticity Revealed by an Intensive Multifaceted Intervention.

Scientific understanding of how much the adult brain can be shaped by experience requires examination of how multiple influences combine to elicit cognitive, affective, and neural plasticity. Using an intensive multifaceted intervention, we discovered that substantial and enduring improvements can occur in parallel across multiple cognitive and neuroimaging measures in healthy young adults. The intervention elicited substantial improvements in physical health, working memory, standardized test performance, mood, self-esteem, self-efficacy, mindfulness, and life satisfaction. Improvements in mindfulness were associated with increased degree centrality of the insula, greater functional connectivity between insula and somatosensory cortex, and reduced functional connectivity between posterior cingulate cortex (PCC) and somatosensory cortex. Improvements in working memory and reading comprehension were associated with increased degree centrality of a region within the middle temporal gyrus (MTG) that was extensively and predominately integrated with the executive control network. The scope and magnitude of the observed improvements represent the most extensive demonstration to date of the considerable human capacity for change. These findings point to higher limits for rapid and concurrent cognitive, affective, and neural plasticity than is widely assumed

The Journal of ERW and Mine Action Issue 17.3 (2013)

Survivor Assistance | Middle East | Syria | Notes from Field | Research and Developmen

Caring About the Plumbing: On the Importance of Architectures in Social Studies of (Peer-to-Peer) Technology

International audienceThis article discusses the relevance, for scholars working on social studies of network media, of "caring about the plumbing" (to paraphrase Bricklin, 2001), i.e., addressing elements of application architecture and design as an integral part of their subject of study. In particular, by discussing peer-to-peer (P2P) systems as a technical networking model and a dynamic of social interaction that are inextricably intertwined, the article introduces how the perspective outlined above is particularly useful to adopt when studying a promising area of innovation: that of "alternative" or "legitimate" (Verma, 2004) applications of P2P networks to search engines, social networks, video streaming and other Internet-based services. The article seeks to show how the Internet's current trajectories of innovation increasingly suggest that particular forms of architectural distribution and decentralization (or their lack), impact specific procedures, practices and uses. Architectures should be understood an "alternative way of influencing economic systems" (van Schewick, 2010), indeed, the very fabric of user behavior and interaction. Most notably, the P2P "alternative" to Internet-based services shows how the status of every Internet user as a consumer, a sharer, a producer and possibly a manager of digital content is informed by, and shapes in return, the technical structure and organization of the services (s)he has access to: their mandatory passage points, places of storage and trade, required intersections. In conclusion, this article is a call to study the technical architecture of networking applications as a "relational property" (Star & Ruhleder, 1996), and integral part of human organization. It suggests that such an approach provides an added value to the study of those communities, groups and practices that, by leveraging socio-technical dynamics of distribution, decentralization, collaboration and peer production, are currently questioning more traditional or institutionalized models of content creation, search and sharing

The muscle proteome reflects changes in mitochondrial function, cellular stress and proteolysis after 14 days of unilateral lower limb immobilization in active young men

Skeletal muscle unloading due to joint immobilization induces muscle atrophy, which has primarily been attributed to reductions in protein synthesis in humans. However, no study has evaluated the skeletal muscle proteome response to limb immobilization using SWATH proteomic methods. This study characterized the shifts in individual muscle protein abundance and corresponding gene sets after 3 and 14 d of unilateral lower limb immobilization in otherwise healthy young men. Eighteen male participants (25.4 ±5.5 y, 81.2 ±11.6 kg) underwent 14 d of unilateral knee-brace immobilization with dietary provision and following four-weeks of training to standardise acute training history. Participant phenotype was characterized before and after 14 days of immobilization, and muscle biopsies were obtained from the vastus lateralis at baseline (pre-immobilization) and at 3 and 14 d of immobilization for analysis by SWATH-MS and subsequent gene-set enrichment analysis (GSEA). Immobilization reduced vastus group cross sectional area (-9.6 ±4.6%, P <0.0001), immobilized leg lean mass (-3.3 ±3.9%, P = 0.002), unilateral 3-repetition maximum leg press (-15.6 ±9.2%, P <0.0001), and maximal oxygen uptake (-2.9 ±5.2%, P = 0.044). SWATH analyses consistently identified 2281 proteins. Compared to baseline, two and 99 proteins were differentially expressed (FDR <0.05) after 3 and 14 d of immobilization, respectively. After 14 d of immobilization, 322 biological processes were different to baseline (FDR <0.05, P <0.001). Most (77%) biological processes were positively enriched and characterized by cellular stress, targeted proteolysis, and protein-DNA complex modifications. In contrast, mitochondrial organization and energy metabolism were negatively enriched processes. This study is the first to use data independent proteomics and GSEA to show that unilateral lower limb immobilization evokes mitochondrial dysfunction, cellular stress, and proteolysis. Through GSEA and network mapping, we identify 27 hub proteins as potential protein/gene candidates for further exploration

Achieving High Reliability and Efficiency in Maintaining Large-Scale Storage Systems through Optimal Resource Provisioning and Data Placement

With the explosive increase in the amount of data being generated by various applications, large-scale distributed and parallel storage systems have become common data storage solutions and been widely deployed and utilized in both industry and academia. While these high performance storage systems significantly accelerate the data storage and retrieval, they also bring some critical issues in system maintenance and management. In this dissertation, I propose three methodologies to address three of these critical issues. First, I develop an optimal resource management and spare provisioning model to minimize the impact brought by component failures and ensure a highly operational experience in maintaining large-scale storage systems. Second, in order to cost-effectively integrate solid-state drives (SSD) into large-scale storage systems, I design a holistic algorithm which can adaptively predict the popularity of data objects by leveraging temporal locality in their access pattern and adjust their placement among solid-state drives and regular hard disk drives so that the data access throughput as well as the storage space efficiency of the large-scale heterogeneous storage systems can be improved. Finally, I propose a new checkpoint placement optimization model which can maximize the computation efficiency of large-scale scientific applications while guarantee the endurance requirements of the SSD-based burst buffer in high performance hierarchical storage systems. All these models and algorithms are validated through extensive evaluation using data collected from deployed large-scale storage systems and the evaluation results demonstrate our models and algorithms can significantly improve the reliability and efficiency of large-scale distributed and parallel storage systems