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In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

This study aimed to examine the mental health information entities and associations between the biomedical, psychological and social domains of bipolar disorder (BD) by analyzing social media data and scientific literature.

Reddit posts and full-text papers from PubMed Central (PMC) were collected. The text analysis was used to create a psychological dictionary. The text mining tools were applied to extract BD entities and their relationships in the datasets using a dictionary- and rule-based approach. Lastly, social network analysis and visualization were employed to view the associations.

Mental health information on the drug side effects entity was detected frequently in both datasets. In the affective category, the most frequent entities were “depressed” and “severe” in the social media and PMC data, respectively. The social and personal concerns entities that related to friends, family, self-attitude and economy were found repeatedly in the Reddit data. The relationships between the biomedical and psychological processes, “afraid” and “Lithium” and “schizophrenia” and “suicidal,” were identified often in the social media and PMC data, respectively.

Mental health information has been increasingly sought-after, and BD is a mental illness with complicated factors in the clinical picture. This paper has made an original contribution to comprehending the biological, psychological and social factors of BD. Importantly, these results have highlighted the benefit of mental health informatics that can be analyzed in the laboratory and social media domains.

The purpose of this paper is to construct a structural definition-based terminology ontology system that defines the meanings of academic terms on the basis of properties and links terms with properties that are structured by conceptual categories (classes). This study also aims to test the possibility of semantic searches by generating inference rules and setting very complicated search scenarios.

For the study, 55,236 keywords from the articles of the “Korea Citation Index” were structurally defined and relationships among terms and properties were built. Then, the authors converted the RDB data into RDF and designed ontologies using the ontology developing tool Protégé. The authors also tested the designed ontology with the inference engine of the Protégé editor. The generated reference rules were tested by TBox and SPARQL queries.

The authors generated inference control rules targeting high-input-ratio data in the properties of classes by calculating the input ratio of real input data in the system, and then the authors executed a semantic search by SPARQL query by setting very complicated search scenarios, for which it would be difficult to deduce results via a simple keyword search. As a result, it was confirmed that the search results show the logical combination of semantically related term data.

The proposed terminology ontology system was constructed with the author keywords from research papers, it will be useful in searching the research papers which include the keywords as search results by the complex combination of semantic relation. And the Structural Terminology Net database could be utilized as an index database in retrieval services and the mining of informal big data through the application of well-defined semantic concepts to each term.

This paper presented a methodology for supporting IR using expanded queries based on a novel model of structural terminology-based ontology. The user who wants to access the specific topic can create query that brings the semantically relevant information. The search results show the logical combination of semantically related term data, which would be difficult to deduce results via traditional IR systems.

This paper aims to investigate the problem of finite-time consensus tracking control without unwinding for formation flying spacecraft in the presence of external disturbances.

Two distributed finite-time controllers are developed using the backstepping sliding mode. The first robust controller can compensate for external disturbances with known bounds, and the second one can compensate for external disturbances with unknown bounds.

Because the controllers are designed on the basis of rotation matrix, which represents the set of attitudes both globally and uniquely, the system can overcome the drawback of unwinding, which results in extra fuel consumption. Through introducing a novel virtual angular velocity, exchange of control signals between neighboring spacecraft becomes unnecessary, and it is able to reduce the communication burden.

The two robust controllers can deal with unwinding that may result in fuel consumption by traveling a long distance before returning to a desired attitude when the closed-loop system is close to the desired attitude equilibrium.

Two finite-time controllers without unwinding are proposed for formation flying spacecraft by using backstepping sliding mode. Furthermore, exchange of control signals between neighboring spacecraft is unnecessary.

This study aims to develop metadata of conceptual elements based on the text structure of research articles on Korean studies, to propose a search algorithm that reflects the combination of semantically relevant data in accordance with the search intention of research paper and to examine the algorithm whether there is a difference in the intention-based search results.

This study constructed a metadata database of 5,007 research articles on Korean studies arranged by conceptual elements of text structure and developed F1(w)-score weighted to conceptual elements based on the F1-score and the number of data points from each element. This study evaluated the algorithm by comparing search results of the F1(w)-score algorithm with those of the Term Frequency- Inverse Document Frequency (TF-IDF) algorithm and simple keyword search.

The authors find that the higher the F1(w)-score, the closer the semantic relevance of search intention. Furthermore, F1(w)-score generated search results were more closely related to the search intention than those of TF-IDF and simple keyword search.

Even though the F1(w)-score was developed in this study to evaluate the search results of metadata database structured by conceptual elements of text structure of Korean studies, the algorithm can be used as a tool for searching the database which is a tuning process of weighting required.

A metadata database based on text structure and a search method based on weights of metadata elements – F1(w)-score – can be useful for interdisciplinary studies, especially for semantic search in regional studies.

This paper presents a methodology for supporting IR using F1(w)-score—a novel model for weighting metadata elements based on text structure. The F1(w)-score-based search results show the combination of semantically relevant data, which are otherwise difficult to search for using similarity of search words.

This paper aims to give the centralized and distributed fusion estimator for nonlinear multi-sensor networked systems with packet loss compensation and correlated noises and give the corresponding square-root cubature Kalman filter.

Based on the Gaussian approximation recursive filter framework, the authors derive the centralized fusion filter and using the projection theorem, the authors derive the centralized fusion smoother. Then, based on the fast batch covariance intersection fusion algorithm, the authors give the corresponding results for distributed fusion estimators.

Designing the fusion estimators for nonlinear multi-sensor networked systems with packet loss compensation and correlated noises is necessary. It is useful for general nonlinear systems.

Throughout the whole study, the main highlights of this paper are as follows: packet loss compensation and correlated noises are considered in nonlinear multi-sensor networked systems. There are no relevant conclusions in the existing literature; centralized and distributed fusion estimators are derived based on the above system; for the posterior covariance with compensation factor and correlated noises, a new square-root factor of the error covariance is derived; and the new square-root factor of the error covariance is used to replace the numerical implementation of the covariance in cubature Kalman filter (CKF), which simplified the problem in calculating the posterior covariance in CKF.

Product trials are an effective way to influence consumer attitudes. While research has established several factors that influence whether consumers will try a product or not, it is less understood how marketers can optimize the trial experience itself. The purpose of this paper is to explore flow as an optimal state and the factors that give rise to it during a product trail.

This research consists of three experimental studies in which people trial new music. This paper explores the ability of curiosity to optimize consumers’ flow experience during the trial and their attitudes toward the trialed product. This paper manipulates curiosity before the trial using information about the music (Study 1) and music previews (Study 3) and also demonstrates that curiosity is naturally elevated among those high in openness to experience (Study 2).

The results demonstrate that curiosity before a product trial fosters an optimal experience during the trial in the form of flow states, defined as an enjoyable state of full engagement, which in turn mediates more positive attitudes toward the trialed product. This paper demonstrates that curiosity can be evoked using product information or a preview of the content and can vary based on individual differences in openness to experience. The relationship between curiosity and flow is moderated by the valence of the information that is used to elicit curiosity, such that negative-valence information thwarts the relationship.

While the studies conducted by the authors focus on the positive influence of curiosity in the trial of music, the effects may be different for other products. These studies are also limited to two different manipulations of curiosity.

This research has implications for marketers, as it demonstrates the relevance of flow and how to enable it in product trials to optimize effectiveness. The manipulations also demonstrate how to manage the amount of information that is given to consumers before they trial a product.

This research reveals that flow states optimize the product trial experience. This research also advances the understanding of the relationship between curiosity and flow by moderating their relationship with the valence of information that elicits curiosity. The findings also broaden the relevance of curiosity and flow in marketing by demonstrating their benefits within product trials.

The purpose of this paper is to use a theoretical framework (i.e. resource-based view) to investigate causal relationships between the supply chain integration, market orientation, information technology (IT) application and firm performance of container shipping firms in Taiwan.

The data are collected from 124 container shipping firms in Taiwan. The methodologies used in the paper are exploratory factor analysis, confirmatory factor analysis and structural equation modeling.

The findings indicated that IT application and market orientation positively influence supply chain integration. It revealed that IT-based and market-oriented firms more easily integrate market information related to partners, departments and customers. Further, supply chain integration and market orientation have a positive effect on firm performance.

Such a result indicated a market-oriented firm quickly responds to market information and competitors’ strategies; and thus, it would make effort to offer satisfactory customer services in dynamically fluctuating markets, and obtain better firm performance. Also, the integration provides higher operation efficiency and obtains an enhanced performance improvement.

The results also indicated that firms are able to develop their market-oriented strategies through electronic information transmission and exchange platform. Based on the study’s findings, managerial strategies are proposed to improve firm performance.

This paper clarifies the concept of supply chain integration, market orientation and IT application in the context of container shipping, and investigates the inter-relationship between supply chain integration, market orientation, IT application and firm performance and develop a conceptual model in the context of container shipping.

This paper proposes a scheme to estimate the technical efficiency at child care centers for the less‐than‐three‐year‐old infants by Data Envelopment Analysis (DEA) and to manage the quality of care service through implementing flexible and efficient government subsidy system. The result of technical efficiency estimation shows that there exists the heterogeneity in technical efficiency a substantial opportunity for improvement in technical efficiency across child care centers. This result implies that government may bring up the competition by giving subsidy differentially based on efficiency and use the money which has been used inefficiently other purposes. Both can improve the quality of child care service.

This paper is to develop a quality measure to evaluate the quality level of child care service in the regional level. By utilizing the biannual intensive child care statistical reports, ten variables are integrated and summarized as a quality measure for child care service in regional level by employing Principal Component Analysis (PCA). Conclusively, it is possible to get a comprehensive measure and the measure obtained from data between 2003 and 2008 illustrates the difference in child care service quality among regions over years. With the measure developed by this research, each region can also get very good insight into what kinds of factors of child care service should be paid more attention to in order to improve the quality of its child care service. Moreover, the measure obtained in this paper is proven reliable and robust in that it reflects the quality of child care service in each region and gives us statistically uniform quality scores with a different data set.