A PROTOTYPE GRID FRAMEWORK FOR THE CHEMICAL PROCESS INDUSTRIES

This article presents a GRID framework for distributed computations in the chemical process industries. We advocate a generic agent-based GRID environment in which chemical processes can be represented, simulated, and optimized as a set of autonomous, collaborative software agents. The framework features numerous advantages in terms of scalability, software reuse, security, and distributed resource discovery and utilization. It is a novel example of how advanced distributed techniques and paradigms can be elegantly applied in the area of chemical engineering to support distributed computations and discovery functions in chemical process engineering. A prototype implementation of the proposed framework for chemical process design is presented to illustrate the concepts.     

Generalized Hirsch h-index for disclosing latent facts in citation networks

What is the value of a scientist and its impact upon the scientific thinking? How can we measure the prestige of a journal or a conference? The evaluation of the scientific work of a scientist and the estimation of the quality of a journal or conference has long attracted significant interest, due to the benefits by obtaining an unbiased and fair criterion. Although it appears to be simple, defining a quality metric is not an easy task. To overcome the disadvantages of the present metrics used for ranking scientists and journals, J. E. Hirsch proposed a pioneering metric, the now famous h-index. In this article we demonstrate several inefficiencies of this index and develop a pair of generalizations and effective variants of it to deal with scientist ranking and publication forum ranking. The new citation indices are able to disclose trendsetters in scientific research, as well as researchers that constantly shape their field with their influential work, no matter how old they are. We exhibit the effectiveness and the benefits of the new indices to unfold the full potential of the h-index, with extensive experimental results obtained from the DBLP, a widely known on-line digital library.     

Time Dependence and Moments of a Family of Time-Varying Parameter Garch in Mean Models

Abstract.  In this paper we consider the time series dependence, stationarity, and higher moments issues of a family of first-order conditionally heteroskedastic in mean models with a possibly time-varying mean parameter. The interest in these models lies in the fact that economic theory and physics often require the connection between the first and second conditional moments of time series. Our results reveal important properties of these models, which are consistent with stylized facts in financial and turbulence data sets. They can also be employed for model identification, estimation, and testing.     

Relationship between structure and dynamic mechanical properties of a carbon nanofiber reinforced elastomeric nanocomposite

The tensile and dynamic mechanical properties of a nanocomposite, containing modified carbon nanofibers (MCNFs) homogenously dispersed in an elastomeric ethylene/propylene (EP) copolymer semicrystalline matrix (84.3 wt% P), have been correlated with the structure development. These properties were characterized by in situ synchrotron X-ray diffraction during stretching, dynamic mechanical analysis and X-ray analysis techniques over a wide temperature range. Upon sequential drawing, the tensile strength of the nanocomposite film was notably higher than that of the unfilled polymer even though both samples exhibited a similar amount of crystal fraction and the same degree of crystal orientation, revealing the effect of nanofiller reinforcement in the semicrystalline matrix. The mechanical spectra of the 10 wt% MCNF filled samples in both stretched and non-stretched states showed broadening of the elastic modulus at high temperatures, where the corresponding crystallinity index also decreased. It is conceivable that a significant fraction of chain orientation is induced in the vicinity of the nanofillers during stretching, and these stretched chains with reduced mobility significantly enhance the thermal mechanical properties.     

C-Testable modified-Booth multipliers

In this paper the testability of modified-Booth array multipliers for standard cells based design environments is examined for first time. In such cases the structure of the cells may be unknown, thus Cell Fault Model (CFM) is adopted. Two C-testable designs are proposed. A design for an N _x × N_y bits modified-Booth multiplier, which uses ripple carry addition at the last stage of the multiplication, is first proposed. The design requires the addition of only one extra primary input and 38 test vectors with respect to CFM. A second C-testable design is given using carry lookahead addition at the last stage which is the case of practical implementations of modified-Booth multipliers. Such a C-testable design using carry lookahead addition is for first time proposed in the open literature. This second design requires the addition of 4 extra primary inputs. One-level and two-levels carry lookahead adders, are considered. The C-testable design requires 61 test vectors for the former and 73 test vectors for the latter, respectively. The hardware and delay overheads imposed by both C-testable designs are very small and decrease when the size of the multiplier increases.     

PaaSword: A Holistic Data Privacy and Security by Design Framework for Cloud Services

Enterprises increasingly recognize the compelling economic and operational benefits from virtualizing and pooling IT resources in the cloud. Nevertheless, the significant and valuable transformation of organizations that adopt cloud computing is accompanied by a number of security threats that should be considered. In this paper, we outline significant security challenges presented when migrating to a cloud environment and propose PaaSword – a novel holistic framework that aspires to alleviate these challenges. Specifically, the proposed framework involves a context-aware security model, the necessary policies enforcement mechanism along with a physical distribution, encryption and query middleware.     

Prefetching in Content Distribution Networks via Web Communities Identification and Outsourcing

Content distribution networks (CDNs) improve scalability and reliability, by replicating content to the “edge” of the Internet. Apart from the pure networking issues of the CDNs relevant to the establishment of the infrastructure, some very crucial data management issues must be resolved to exploit the full potential of CDNs to reduce the “last mile” latencies. A very important issue is the selection of the content to be prefetched to the CDN servers. All the approaches developed so far, assume the existence of adequate content popularity statistics to drive the prefetch decisions. Such information though, is not always available, or it is extremely volatile, turning such methods problematic. To address this issue, we develop self-adaptive techniques to select the outsourced content in a CDN infrastructure, which requires no apriori knowledge of request statistics. We identify clusters of “correlated” Web pages in a site, called Web site communities, and make these communities the basic outsourcing unit. Through a detailed simulation environment, using both real and synthetic data, we show that the proposed techniques are very robust and effective in reducing the user-perceived latency, performing very close to an unfeasible, off-line policy, which has full knowledge of the content popularity.     

Improving the performance of passive network monitoring applications with memory locality enhancements

Passive network monitoring is the basis for a multitude of systems that support the robust, efficient, and secure operation of modern computer networks. Emerging network monitoring applications are more demanding in terms of memory and CPU resources due to the increasingly complex analysis operations that are performed on the inspected traffic. At the same time, as the traffic throughput in modern network links increases, the CPU time that can be devoted for processing each network packet decreases. This leads to a growing demand for more efficient passive network monitoring systems in which runtime performance becomes a critical issue.In this paper we present locality buffering, a novel approach for improving the runtime performance of a large class of CPU and memory intensive passive monitoring applications, such as intrusion detection systems, traffic characterization applications, and NetFlow export probes. Using locality buffering, captured packets are being reordered by clustering packets with the same port number before they are delivered to the monitoring application. This results in improved code and data locality, and consequently, in an overall increase in the packet processing throughput and decrease in the packet loss rate. We have implemented locality buffering within the widely used libpcap packet capturing library, which allows existing monitoring applications to transparently benefit from the reordered packet stream without modifications. Our experimental evaluation shows that locality buffering improves significantly the performance of popular applications, such as the Snort IDS, which exhibits a 21% increase in the packet processing throughput and is able to handle 67% higher traffic rates without dropping any packets.     

Strengthening ties towards a highly-connected world

Data Mining and Knowledge Discovery - Online social networks provide a forum where people make new connections, learn more about the world, get exposed to different points of view, and access...