Learning from the past: uncovering design process models using an enriched process mining

Design documents and design project footprints accumulated by corporate IT systems have increasingly become valuable sources of evidence for design information and knowledge management. Identification and extraction of such embedded information and knowledge into a clear and usable format will greatly accelerate continuous learning from past design efforts for competitive product innovation and efficient design process management in future design projects. Different from existing systems, this paper proposes a methodology of learning and extracting useful knowledge using past design project documents from design process perspective based on process mining techniques. A new process mining approach that is able to directly handle textual data is proposed at the first stage of the proposed methodology. The outcome is a hierarchical process model that reveals the actual design process hidden behind a large amount of design documents and enables the connection of various design information from different perspectives. At the second stage, the discovered process model is further refined to learn multi-faceted knowledge patterns by applying a number of statistical analysis methods. The outcomes range from task dependency study from workflow analysis, identification of irregular task execution from performance analysis, cooperation pattern discovery from social net analysis, to evaluation of personal contribution based on role analysis. Relying on the knowledge patterns extracted, lessons and best practices can be uncovered which offer great support to decision makers in managing any future design initiatives. The proposed methodology was tested using an email dataset from a university-hosted multi-year multidisciplinary design project

Credit derivatives pricing with default density term structure modelled by Lévy random fields

We model the term structure of the forward default intensity and the default density by using Lévy random fields, which allow us to consider the credit derivatives with an after-default recovery payment. As applications, we study the pricing of a defaultable bond and represent the pricing kernel as the unique solution of a parabolic integro-differential equation. Finally, we illustrate by numerical examples the impact of the contagious jump risks on the defaultable bond price in our model.

Sialyltransferase Inhibition and Recent Advances

Sialic acids, existing as terminal sugars of glycoconjugates, play important roles in various physiological and pathological processes, such as cell–cell adhesion, immune defense, tumor cell metastasis, and inflammation. Sialyltransferases (STs) catalyze the transfer of sialic acid residues to non-reducing oligosaccharide chains of proteins and lipids, using cytidine monophosphate N-acetylneuraminic acid (CMP-Neu5Ac) as the donor. Elevated sialyltransferase activity leads to overexpression of cell surface sialic acids and contributes to many disease developments, such as cancer and inflammation. Therefore, sialyltransferases are considered as potential drug targets for disease treatment. Inhibitors of sialyltransferases thus are of medicinal interest, especially for the cancer therapy. In addition, sialyltransferase inhibitors are useful tool to study sialyltransferase function and related mechanisms. This review highlights recent development of inhibitors of sialyltransferases reported since 2004. The inhibitors are summarized as eight groups: 1) sialic acid analogs, 2) CMP-sialic acid analogs, 3) cytidine analogs, 4) oligosaccharide derivatives, 5) aromatic compounds, 6) flavonoids, 7) lithocholic acid analogs, and 8) others. This article is part of a Special Issue entitled: Physiological Enzymology and Protein Functions

Sialyltransferase Inhibition and Recent Advances

Sialic acids, existing as terminal sugars of glycoconjugates, play important roles in various physiological and pathological processes, such as cell–cell adhesion, immune defense, tumor cell metastasis, and inflammation. Sialyltransferases (STs) catalyze the transfer of sialic acid residues to non-reducing oligosaccharide chains of proteins and lipids, using cytidine monophosphate N-acetylneuraminic acid (CMP-Neu5Ac) as the donor. Elevated sialyltransferase activity leads to overexpression of cell surface sialic acids and contributes to many disease developments, such as cancer and inflammation. Therefore, sialyltransferases are considered as potential drug targets for disease treatment. Inhibitors of sialyltransferases thus are of medicinal interest, especially for the cancer therapy. In addition, sialyltransferase inhibitors are useful tool to study sialyltransferase function and related mechanisms. This review highlights recent development of inhibitors of sialyltransferases reported since 2004. The inhibitors are summarized as eight groups: 1) sialic acid analogs, 2) CMP-sialic acid analogs, 3) cytidine analogs, 4) oligosaccharide derivatives, 5) aromatic compounds, 6) flavonoids, 7) lithocholic acid analogs, and 8) others. This article is part of a Special Issue entitled: Physiological Enzymology and Protein Functions

Evaluation on Mengnong Clover No.1--China\u27s First Variety of Caucasian Clover (\u3cem\u3eTrifolium ambiguum Bieb.\u3c/em\u3e)

Many research reports about Caucasian clover (Trifolium ambiguum Bieb.) could be retrieved. A breeding research for Caucasian clover was started since 1996 in Inner Mongolia Agricultural University, China. The goal was to breed new varieties with strong cold resistance and drought, salt tolerance, as well as quick regenerating capacity after use. By December 2012, China\u27s first new variety of Caucasian clover - Mengnong clover No.1 (Mc No.1) was successfully registered by Forage Variety Approval Committee of Inner Mongolia Autonomous Region. Through a comparison test with red clover (T. pratense ) and white clover (T. repens), Mc No.1 showed outstanding prospects for animal forage and garden use

Scenario Approach for Parametric Markov Models

In this paper, we propose an approximating framework for analyzing parametric Markov models. Instead of computing complex rational functions encoding the reachability probability and the reward values of the parametric model, we exploit the scenario approach to synthesize a relatively simple polynomial approximation. The approximation is probably approximately correct (PAC), meaning that with high confidence, the approximating function is close to the actual function with an allowable error. With the PAC approximations, one can check properties of the parametric Markov models. We show that the scenario approach can also be used to check PRCTL properties directly, without synthesizing the polynomial at first hand. We have implemented our algorithm in a prototype tool and conducted thorough experiments. The experimental results demonstrate that our tool is able to compute polynomials for more benchmarks than state of the art tools such as PRISM and Storm, confirming the efficacy of our PAC-based synthesis.Comment: 24 pages, 8 figure

Comparative analysis between a low pathogenic and a high pathogenic influenza H5 hemagglutinin in cell entry

Avian influenza viruses continue to threaten globally with pandemic potential. The first step in a potential pandemic is the ability of the virus to enter human cells which is mediated by the viral surface glycoprotein hemagglutinin (HA). Viral entry of influenza is dependent upon the processing of the HA0 polypeptide precursor protein into HA1 and HA2 which is mediated by host cellular proteases. The sequence of the cleavage site which is recognized by host proteases has been linked with pathogenesis of various influenza viruses. Here we examined the effects of cleavage site sequences between a highly pathogenic H5N1 strain and a low pathogenic H5N2 strain to determine their effects on viral entry. From this analysis we determined that at the level of viral entry, the only observed difference between the low and high pathogenic strains is their ability to be cleaved by host cellular proteases