INTRODUCTION Many institutions have open access (OA) policies that require faculty members to deposit their articles in an institutional repository (IR). A clear motivation is that a policy will result in increased self-archiving. The purpose of this longitudinal study is to compare the impact of a campus-wide OA policy and mediated solicitation of author manuscripts, using quantitative analysis to determine the rate of article deposits over time. METHODS Metadata for faculty articles published by authors at Oregon State University between 2011 and 2014 was produced by integrating citation metadata from a bibliographic database and the IR. Author names, affiliations, and other metadata were parsed and matched to compare rates of deposit for three separate time periods relating to different OA promotional strategies. RESULTS Direct solicitation of author manuscripts is more successful in facilitating OA than an OA policy—by number of articles deposited as well as the number of unique authors participating. Author affiliation and research areas also have an impact on faculty participation in OA. DISCUSSION Outreach to colleges and departments has had a positive effect on rate of deposit for those communities of scholars. Additionally, disciplinary practice may have more influence on its members’ participation in OA. CONCLUSION Until more federal policies require open access to articles funded by grants, or institutional policies are in place that require article deposit for promotion and tenure, policies will only be as effective as the library mediated processes that are put in place to identify and solicit articles from faculty.

Simulated annealing (SA) algorithm is a popular intelligent optimisation algorithm, but its efficiency is unsatisfactory. To improve its efficiency, this paper presents a swarm SA (SSA) algorithm by exploiting the learned knowledge from searching history. In SSA, a swarm of individuals run SA algorithm collaboratively. Inspired by ant colony optimisation (ACO) algorithm, SSA stores knowledge in construction graph and uses the solution component selection scheme of ACO algorithm to generate candidate solutions. Candidate list with bounded length is used to speed up SSA. The effect of knowledge-based sampling is verified on benchmark travelling salesman problems. Comparison studies show that SSA algorithm has promising performance in terms of convergence speed and solution accuracy.

This article describes the design and development of an interoperable application that supports green open access with long-term sustainability and improved user experience of article deposit. The lack of library resources and the unfriendly repository user interface are two significant barriers that hinder green open access. Tasked to implement the open access mandate, librarians at an American research university developed a comprehensive system called Easy Deposit 2 to automate the support workflow of green open access. Easy Deposit 2 is a web application that is able to harvest new publications, to source manuscripts on behalf of the library, and to facilitate self-archiving to a university’s institutional repository. The article deposit rate increased from 7.40% to 25.60% with the launch of Easy Deposit 2. The results show that a computer system can implement routine tasks to support green open access with success. Recent developments in digital repository provide new opportunities for innovation, such as Easy Deposit 2, in supporting open access. Academic librarians are vital in promoting “openness” in scholarly communication, such as transparency and diversity in the sharing of publication data.

In this paper, we describe a fusion approach to finding opinionated blog postings. Our approach to opinion blog retrieval consisted of first applying traditional IR methods to retrieve on‐topic blogs and then boosting the ranks of opinionated blogs based on combined opinion scores generated by multiple assessment methods. Our opinion module is composed of the Opinion Term Module, which identifies opinions based on the frequency of opinion terms (i.e., terms that occur frequently in opinion blogs), the Rare Term Module, which uses uncommon/rare terms (e.g., “sooo good”) for opinion classification, the IU Module, which uses IU (I and you) collocations, and the Adjective‐Verb Module, which uses computational linguistics' distribution similarity approach to learn the subjective language from training data.