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The ability to economically and effectively service products provides an avenue for extending the product’s useful life. Quantifying approaches are needed to assist in the serviceability evaluation. In this study, the serviceability characteristics of a product are categorized into three groups: disassembly, reassembly, and handling. Often, many of the serviceability characteristics of a product cannot be defined completely or properly using crisp design data. Moreover, the serviceability analysis is an imprecise science characterized by ill‐structured and subjective evaluation criteria. A formal methodology for representing and processing the design information of an artifact using a hierarchical fuzzy neural network (FNN) model is presented. First, three FNNs are used to compute the disassembly, reassembly, and handling indices. Second, the output of the three FNNs is fed into a separate FNN to compute the serviceability index. The designer can use the proposed model to rank alternate designs by computing the serviceability indices of each candidate design. The working of the proposed model is demonstrated by using two designs for a steam iron.

Archivists are charged with the preservation of their collections by reducing deterioration because of temperature, relative humidity, atmospheric pollutants and other factors. The methods archivists use to preserve their collections may have a negative impact on the environment. This paper aims to identify factors for building environmentally sustainable archives to help guide archival environmental sustainability practices.

This paper identifies factors through a literature review, and conducts a content analysis of the websites of seven national/state archives. The analysis focuses on the policy statements of these archives.

The authors found that the literature lists 31 factors under 7 categories: electricity, facilities, water, exhibitions, pollution, collection practices and education and outreach. The content analysis of the policy documents or statements demonstrated that archives applied and addressed mostly “resource-related” efforts to protect the environment, such as factors related to electricity, facilities, water and pollution. However, factors related to “work-related” efforts, such as exhibitions, collection practices and education and outreach, were ignored.

This study can provide insights to archivists on current implementation and help to guide their further environmental sustainability practices.

Little is known regarding archivists’ implementation of environmentally sustainable practices. This study focuses on identifying factors for environmental sustainability of archives addressed by literature and existing archives, trying to find the gap between literature and practice.

The purpose of this paper is to provide an integrated semantic information retrieval (IR) solution based on an ontology-improved vector space model for situations where a digital collection is established or curated. It aims to create a retrieval approach which could return the results by meanings rather than by keywords.

In this paper, the authors propose a semantic term frequency algorithm to create a semantic vector space model (SeVSM) based on ontology. To support the calculation, a multi-branches tree model is created to represent the ontology and a set of algorithms is developed to operate it. Then, a semantic ontology-based IR system based on the SeVSM model is designed and developed to verify the effectiveness of the proposed model.

The experimental study using 30 queries from 15 different domains confirms the effectiveness of the SeVSM and the usability of the proposed system. The results demonstrate that the proposed model and system can be a significant exploration to enhance IR in specific domains, such as a digital library and e-commerce.

This research not only creates a semantic retrieval model, but also provides the application approach via designing and developing a semantic retrieval system based on the model. Comparing with most of the current related research, the proposed research studies the whole process of realizing a semantic retrieval.

This study aims to evaluate a method of building a biomedical knowledge graph (KG).

This research first constructs a COVID-19 KG on the COVID-19 Open Research Data Set, covering information over six categories (i.e. disease, drug, gene, species, therapy and symptom). The construction used open-source tools to extract entities, relations and triples. Then, the COVID-19 KG is evaluated on three data-quality dimensions: correctness, relatedness and comprehensiveness, using a semiautomatic approach. Finally, this study assesses the application of the KG by building a question answering (Q&A) system. Five queries regarding COVID-19 genomes, symptoms, transmissions and therapeutics were submitted to the system and the results were analyzed.

With current extraction tools, the quality of the KG is moderate and difficult to improve, unless more efforts are made to improve the tools for entity extraction, relation extraction and others. This study finds that comprehensiveness and relatedness positively correlate with the data size. Furthermore, the results indicate the performances of the Q&A systems built on the larger-scale KGs are better than the smaller ones for most queries, proving the importance of relatedness and comprehensiveness to ensure the usefulness of the KG.

The KG construction process, data-quality-based and application-based evaluations discussed in this paper provide valuable references for KG researchers and practitioners to build high-quality domain-specific knowledge discovery systems.

During the COVID-19 pandemic, in order to prevent the spread of disease, the National Library of Indonesia Cataloging Department adopted remote working. There is a need to examine the productivity of remote cataloging as this form of cataloging becomes more prevalent.

The study was conducted using a mixed methods approach. The authors analyzed data to assess cataloging librarians' productivity based on system logs. Then, the authors interviewed librarians to understand librarians' perspectives concerning productivity and remote cataloging, and also to seek insights into factors that may affect productivity while working remotely.

The analysis found higher productivity in terms of quantity of cataloging. Librarians' productivity during remote cataloging is not statistically related to individual factors of age, years of experience, or gender. The in-depth interviews found that other factors may hinder the quality and quantity of the remote cataloging, including the working environment, infrastructure, and lack of policies on remote working.

The findings were based on a study conducted in the National Library of Indonesia, which may not apply to libraries with different infrastructures or existing policies in remote cataloging. However, the authors identified numerous factors that could be related to remote cataloging productivity. More work needs to be done to identify these factors that impact productivity by conducting further surveys.

The research provides evidence showing the productivity of cataloging can be higher in remote working mode. The study provides insights for library managers to decide whether to implement remote cataloging and what additional perspectives could be considered for the better implementation of remote cataloging.

The gap in the literature about remote cataloging and productivity has been bridged.

Citation contexts have been found useful in many scenarios. However, existing context-based recommendations ignored the importance of diversity in reducing the redundant issues and thus cannot cover the broad range of user interests. To address this gap, the paper aims to propose a novelty task that can recommend a set of diverse citation contexts extracted from a list of citing articles. This will assist users in understanding how other scholars have cited an article and deciding which articles they should cite in their own writing.

This research combines three semantic distance algorithms and three diversification re-ranking algorithms for the diversifying recommendation based on the CiteSeer^X data set and then evaluates the generated citation context lists by applying a user case study on 30 articles.

Results show that a diversification strategy that combined “word2vec” and “Integer Linear Programming” leads to better reading experience for participants than other diversification strategies, such as CiteSeer^X using a list sorted by citation counts.

This diversifying recommendation task is valuable for developing better systems in information retrieval, automatic academic recommendations and summarization.

The originality of the research lies in the proposal of a novelty task that can recommend a diversification context list describing how other scholars cited an article, thereby making citing decisions easier. A novel mixed approach is explored to generate the most efficient diversifying strategy. Besides, rather than traditional information retrieval evaluation, a user evaluation framework is introduced to reflect user information needs more objectively.

This paper aims to introduce the construction methods, image organization, collection use and access of benchmark image collections to the digital library (DL) community. It aims to connect two distinct communities: the DL community and image processing researchers so that future image collections could be better constructed, organized and managed for both human and computer use.

Image collections are first identified through an extensive literature review of published journal articles and a web search. Then, a coding scheme focusing on image collections’ creation, organization, access and use is developed. Next, three major benchmark image collections are analysed based on the proposed coding scheme. Finally, the characteristics of benchmark image collections are summarized and compared to DLs.

Although most of the image collections in DLs are carefully curated and organized using various metadata schema based on an image’s external features to facilitate human use, the benchmark image collections created for promoting image processing algorithms are annotated on an image’s content to the pixel level, which makes each image collection a more fine-grained, organized database appropriate for developing automatic techniques on classification summarization, visualization and content-based retrieval.

This paper overviews image collections by their application fields. The three most representative natural image collections in general areas are analysed in detail based on a homemade coding scheme, which could be further extended. Also, domain-specific image collections, such as medical image collections or collections for scientific purposes, are not covered.

This paper helps DLs with image collections to understand how benchmark image collections used by current image processing research are created, organized and managed. It informs multiple parties pertinent to image collections to collaborate on building, sustaining, enriching and providing access to image collections.

This paper is the first attempt to review and summarize benchmark image collections for DL managers and developers. The collection creation process and image organization used in these benchmark image collections open a new perspective to digital librarians for their future DL collection development.

The purpose of this paper is provide a broad view for the standardization efforts of color quality evaluation of color 3D printing techniques. Further, this review paper demonstrates the processes and color properties of most color 3D printing techniques with specific devices and applications to extend the range of possible memberships of standardization group.

Six color 3D printing techniques including plastic-based, paper-based, powder-based, organism-based, food-based and metal-based color 3D printing have been introduced and illustrated with colorization principles and forming features in detail. Moreover, for printed 3D color objects, literature about color measurement, color specification and color reproduction are described and analyzed, respectively.

Four color 3D printing techniques including plastic-based, paper-based, powder-based and food-based color 3D printing show great affinity toward standardization of color quality evaluation, while their colorization principles indicate that it is difficult with a single standard frame. It is possible to develop a completed color quality evaluation standard for color 3D printing based on approaches in color 2D printing when color measurement method and devices are standardized together.

The paper provides an important guide focusing on the efforts to standardize the colorization processes and color quality evaluation of the color 3D printing techniques.

Online knowledge communities make great contributions to global knowledge sharing and innovation. Resource tagging approaches have been widely adopted in such communities to describe, annotate and organize knowledge resources mainly through users’ participation. However, it is unclear what causes the adoption of a particular resource tagging approach. The purpose of this paper is to identify factors that drive users to use a hybrid social tagging approach.

Technology acceptance model and social cognitive theory are adopted to support an integrated model proposed in this paper. Zhihu, one of the most popular online knowledge communities in China, is taken as the survey context. A survey was conducted with a questionnaire and collected data were analyzed through structural equation model.

A new hybrid social resource tagging approach was refined and described. The empirical results revealed that self-efficacy, perceived usefulness (PU) and perceived ease of use exert positive effect on users’ attitude. Moreover, social influence, PU and attitude impact significantly on users’ intention to use a hybrid social resource tagging approach.

Theoretically, this study enriches the type of resource tagging approaches and recognizes factors influencing user adoption to use it. Regarding the practical parts, the results provide online information system providers and designers with referential strategies to improve the performance of the current tagging approaches and promote them.

Engineering design optimization involving computational simulations is usually a time-consuming, even computationally prohibitive process. To relieve the computational burden, the adaptive metamodel-based design optimization (AMBDO) approaches have been widely used. This paper aims to develop an AMBDO approach, a lower confidence bounding approach based on the coefficient of variation (CV-LCB) approach, to balance the exploration and exploitation objectively for obtaining a global optimum under limited computational budget.

In the proposed CV-LCB approach, the coefficient of variation (CV) of predicted values is introduced to indicate the degree of dispersion of objective function values, while the CV of predicting errors is introduced to represent the accuracy of the established metamodel. Then, a weighted formula, which takes the degree of dispersion and the prediction accuracy into consideration, is defined based on the already-acquired CV information to adaptively update the metamodel during the optimization process.

Ten numerical examples with different degrees of complexity and an AIAA aerodynamic design optimization problem are used to demonstrate the effectiveness of the proposed CV-LCB approach. The comparisons between the proposed approach and four existing approaches regarding the computational efficiency and robustness are made. Results illustrate the merits of the proposed CV-LCB approach in computational efficiency and robustness.

The proposed approach exhibits high efficiency and robustness in engineering design optimization involving computational simulations.

CV-LCB approach can balance the exploration and exploitation objectively.