Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity.

A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework--a dynamic knowledge repository--wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline

Agent Based Test and Repair of Distributed Systems

This article demonstrates how to use intelligent agents for testing and repairing a distributed system, whose elements may or may not have embedded BIST (Built-In Self-Test) and BISR (Built-In Self-Repair) facilities. Agents are software modules that perform monitoring, diagnosis and repair of the faults. They form together a society whose members communicate, set goals and solve tasks. An experimental solution is presented, and future developments of the proposed approach are explore

Semantic Support for Computational Land-Use Modelling

Postprin

Citation Analysis: A Comparison of Google Scholar, Scopus, and Web of Science

When faculty members are evaluated, they are judged in part by the impact and quality of their scholarly publications. While all academic institutions look to publication counts and venues as well as the subjective opinions of peers, many hiring, tenure, and promotion committees also rely on citation analysis to obtain a more objective assessment of an author’s work. Consequently, faculty members try to identify as many citations to their published works as possible to provide a comprehensive assessment of their publication impact on the scholarly and professional communities. The Institute for Scientific Information’s (ISI) citation databases, which are widely used as a starting point if not the only source for locating citations, have several limitations that may leave gaps in the coverage of citations to an author’s work. This paper presents a case study comparing citations found in Scopus and Google Scholar with those found in Web of Science (the portal used to search the three ISI citation databases) for items published by two Library and Information Science full-time faculty members. In addition, the paper presents a brief overview of a prototype system called CiteSearch, which analyzes combined data from multiple citation databases to produce citation-based quality evaluation measures

A Semantic-Based Information Management System to Support Innovative Product Design

International competition and the rapidly global economy, unified by improved communication and transportation, offer to the consumers an enormous choice of goods and services. The result is that companies now require quality, value, time to market and innovation to be successful in order to win the increasing competition. In the engineering sector this is traduced in need of optimization of the design process and in maximization of re-use of data and knowledge already existing in the company. The “SIMI-Pro” (Semantic Information Management system for Innovative Product design) system addresses specific deficiencies in the conceptual phase of product design when knowledge management, if applied, is often sectorial. Its main contribution is in allowing easy, fast and centralized collection of data from multiple sources and in supporting the retrieval and re-use of a wide range of data that will help stylists and engineers shortening the production cycle. SIMI-Pro will be one of the first prototypes to base its information management and its knowledge sharing system on process ontology and it will demonstrate how the use of centralized network systems, coupled with Semantic Web technologies, can improve inter-working activities and interdisciplinary knowledge sharing

Ontology-Based Recommendation of Editorial Products

Major academic publishers need to be able to analyse their vast catalogue of products and select the best items to be marketed in scientific venues. This is a complex exercise that requires characterising with a high precision the topics of thousands of books and matching them with the interests of the relevant communities. In Springer Nature, this task has been traditionally handled manually by publishing editors. However, the rapid growth in the number of scientific publications and the dynamic nature of the Computer Science landscape has made this solution increasingly inefficient. We have addressed this issue by creating Smart Book Recommender (SBR), an ontology-based recommender system developed by The Open University (OU) in collaboration with Springer Nature, which supports their Computer Science editorial team in selecting the products to market at specific venues. SBR recommends books, journals, and conference proceedings relevant to a conference by taking advantage of a semantically enhanced representation of about 27K editorial products. This is based on the Computer Science Ontology, a very large-scale, automatically generated taxonomy of research areas. SBR also allows users to investigate why a certain publication was suggested by the system. It does so by means of an interactive graph view that displays the topic taxonomy of the recommended editorial product and compares it with the topic-centric characterization of the input conference. An evaluation carried out with seven Springer Nature editors and seven OU researchers has confirmed the effectiveness of the solution