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Aim to the limitations of grey relational analysis of interval grey number, based on the generalized greyness of interval grey number, this paper tries to construct a grey angle cosine relational degree model from the perspective of proximity and similarity.

Firstly, the algorithms of the generalized greyness of interval grey number and interval grey number vector are given, and its properties are analyzed. Then, based on the grey relational theory, the grey angle cosine relational model is proposed based on the generalized greyness of interval grey number, and the relationship between the classical cosine similarity model and the grey angle cosine relational model is analyzed. Finally, the validity of the model in this paper is illustrated by the calculation examples and an application example of related factor analysis of maize yield.

The results show that the grey angle cosine relational degree model has strict theoretical basis, convenient calculation and is easy to program, which can not only fully utilize the information of interval grey numbers but also overcome the shortcomings of greyness relational degree model. The grey angle cosine relational degree is an extended form of cosine similarity degree of real numbers. The calculation examples and the related factor analysis of maize yield show that the model proposed in this paper is feasible and valid.

The research results not only further enrich the grey system theory and method but also provide a basis for the grey relational analysis of the sequences in which the interval grey numbers coexist with the real numbers.

The paper succeeds in realizing the algorithms of the generalized greyness of interval grey number and interval grey number vector, and the grey angle cosine relational degree, which provide a new method for grey relational analysis.

This paper aims to propose a set of six-axis robot arm welding seam tracking experiment platform based on Halcon machine vision library to resolve the curve seam tracking issue.

Robot-based and image coordinate systems are converted based on the mathematical model of the three-dimensional measurement of structured light vision and conversion relations between robot-based and camera coordinate systems. An object tracking algorithm via weighted local cosine similarity is adopted to detect the seam feature points to prevent effectively the interference from arc and spatter. This algorithm models the target state variable and corresponding observation vector within the Bayes framework and finds the optimal region with highest similarity to the image-selected modules using cosine similarity.

The paper tests the approach and the experimental results show that using metal inert-gas (MIG) welding with maximum welding current of 200A can achieve real-time accurate curve seam tracking under strong arc light and splash. Minimal distance between laser stripe and welding molten pool can reach 15 mm, and sensor sampling frequency can reach 50 Hz.

Designing a set of six-axis robot arm welding seam tracking experiment platform with a system of structured light sensor based on Halcon machine vision library; and adding an object tracking algorithm to seam tracking system to detect image feature points. By this technology, this system can track the curve seam while welding.

Purpose – As researchers need partners to collaborate with, this study aims to provide author recommendation for academic researchers for potential collaboration, conference planning, and compilation of scientific working groups with the help of social information. Hereby the chapter analyzes and compares different similarity metrics in information and computer science.

Methodology/approach – The study uses data from the multidiscipline information services Web of Science and Scopus as well as the social bookmarking service CiteULike to measure author similarity and recommend researchers to unique target researchers. The similarity approach is based on author co-citation, bibliographic coupling of authors and collaborative filtering methods. The developed clusters and graphs are then evaluated by these target researchers.

Findings – The analysis shows, for example, that different methods for social recommendation complement each other and that the researchers evaluated user- and tag-based data from a social bookmarking system positively.

Research limitations/implications – The present study, providing author recommendation for six target physicists, is supposed to be a starting point for further approaches on social academic author recommendation.

Practical implications – The chapter investigates in recommendation methods and similarity algorithm models as basis for an implementation of a social recommendation system for researchers in academics and knowledge-intensive organizations.

Originality/value of chapter – The comparison of different similarity measurements and the user evaluation provide new insights into the construction of social data mining and the investigation of personalized recommendation.

The authors constructed an automatic essay scoring (AES) model in a discussion forum where the result was compared with scores given by human evaluators. This research proposes essay scoring, which is conducted through two parameters, semantic and keyword similarities, using a SentenceTransformers pre-trained model that can construct the highest vector embedding. Combining these models is used to optimize the model with increasing accuracy.

The development of the model in the study is divided into seven stages: (1) data collection, (2) pre-processing data, (3) selected pre-trained SentenceTransformers model, (4) semantic similarity (sentence pair), (5) keyword similarity, (6) calculate final score and (7) evaluating model.

The multilingual paraphrase-multilingual-MiniLM-L12-v2 and distilbert-base-multilingual-cased-v1 models got the highest scores from comparisons of 11 pre-trained multilingual models of SentenceTransformers with Indonesian data (Dhini and Girsang, 2023). Both multilingual models were adopted in this study. A combination of two parameters is obtained by comparing the response of the keyword extraction responses with the rubric keywords. Based on the experimental results, proposing a combination can increase the evaluation results by 0.2.

This study uses discussion forum data from the general biology course in online learning at the open university for the 2020.2 and 2021.2 semesters. Forum discussion ratings are still manual. In this survey, the authors created a model that automatically calculates the value of discussion forums, which are essays based on the lecturer's answers moreover rubrics.

This paper seeks to propose a method of discovering uncommercialized research fronts by comparing scientific papers and patents. A comparative study was performed to measure the semantic similarity between academic papers and patents in order to discover research fronts that do not correspond to any patents.

The authors compared structures of citation networks of scientific publications with those of patents by citation analysis and measured the similarity between sets of academic papers and sets of patents by natural language processing. After the documents (papers/patents) in each layer were categorized by a citation‐based method, the authors compared three semantic similarity measurements between a set of academic papers and a set of patents: Jaccard coefficient, cosine similarity of term frequency‐inverse document frequency (tfidf) vector, and cosine similarity of log‐tfidf vector. A case study was performed in solar cells.

As a result, the cosine similarity of tfidf was found to be the best way of discovering corresponding relationships.

This proposed approach makes it possible to obtain candidates of unexplored research fronts, where academic researches exist but patents do not. This methodology can be immediately applied to support the decision making of R&D investment by both R&D managers in companies and policy makers in government.

This paper enables comparison of scientific outcomes and patents in more detail by citation analysis and natural language processing than previous studies which just count the direct linkage from patents to papers.

The World Wide Web has become an essential platform for a news publication, and it has become one of the primary sources of information dissemination in the past few years. Electronic media, i.e., television channels, magazines and newspapers, have started publishing news online. This online information is prompt to be disappeared because of short life-span and imperative to be archived for the long-term and future generations. This paper presents a content-based similarity measure based on the headings of the news articles for linking digital news stories published in various newspapers during the preservation process that helps to ensure future accessibility.

To evaluate the accuracy and assess the effectiveness and worth of the proposed measure for linking news articles in Digital News Story Archive (DNSA), we adopted both, system-centric and user-centric (human judgment) evaluation over different datasets of news articles.

The proposed similarity measure is evaluated using different sizes of datasets, and the results are compared by both user-centric technique, i.e., expert judgment and system-centric techniques, i.e., cosine similarity measure, extended Jaccard measure and common ratio measure for stories (CRMS). The comparison helps to get a broader impact and can be helpful for generalization of the measure for different categories of news articles. Multiple experiments have conducted the findings of which showed that the measure presented viable results for national and international news, while best results for linking sports news articles during preservation based on headings.

The DNSA preserves a huge number of news articles from multiple news sources and to link with a vast collection, which encourages to introduce an efficient linking mechanism with few terms to manipulate. The CRMS is modified to deal with the headings of news articles as a part of the digital news stories preservation framework and comprehensively analysed.

This paper explores the possibility of adding user‐oriented class associations to hierarchical library classification schemes. Some highly associated classes not grouped in the same subject hierarchies, yet relevant to users’ knowledge, are automatically obtained by analyzing a two‐year log of book circulation records from a university library in Taiwan. The library uses the Chinese Decimal Classification scheme, which has similar structure and notation to the Dewey Decimal Classification. Methods, from both collaborative filtering and information retrieval research, were employed and their performance compared based on similarity estimation of classes. The results show that classification schemes can, therefore, be made more adaptable to changes of users and the uses of different library collections by analyzing the circulation patterns of similar users. Limitations of the methods and implications for applications are also discussed.

The purpose of this paper is to put forward a path planning method in complex environments containing dynamic obstacles, which improves the performance of the traditional A* algorithm, this method can plan the optimal path in a short running time.

To plan an optimal path in a complex environment with dynamic and static obstacles, a novel improved A* algorithm is proposed. First, obstacles are identified by GoogLeNet and classified into static obstacles and dynamic obstacles. Second, the ray tracing algorithm is used for static obstacle avoidance, and a dynamic obstacle avoidance waiting rule based on dilate principle is proposed. Third, the proposed improved A* algorithm includes adaptive step size adjustment, evaluation function improvement and path planning with quadratic B-spline smoothing. Finally, the proposed improved A* algorithm is simulated and validated in real-world environments, and it was compared with traditional A* and improved A* algorithms.

The experimental results show that the proposed improved A* algorithm is optimal and takes less execution time compared with traditional A* and improved A* algorithms in a complex dynamic environment.

This paper presents a waiting rule for dynamic obstacle avoidance based on dilate principle. In addition, the proposed improved A* algorithm includes adaptive step adjustment, evaluation function improvement and path smoothing operation with quadratic B-spline. The experimental results show that the proposed improved A* algorithm can get a shorter path length and less running time.

The study aims at inquiring into the relationship between acquirer–target business similarity and mergers and acquisitions (M&A) transaction outcomes.

Relying on textual analysis of acquirers' and targets' business descriptions from M&A transaction synopses, the authors establish that posttransaction operating outcomes are negatively associated with acquirer–target business similarity.

While similar business profiles allow for optimization of overheads, sales growth and margins demonstrate better dynamics when acquirers and targets are more dissimilar, which allows for greater competitive gains. On average, targets are more dissimilar from acquirers than acquirers are from their competitors. The degree of competition within acquirers' industries and acquirer–competitors' business similarity are found to be positively associated with the likelihood of engaging in serial horizontal acquisitions involving more similar targets, mostly from the domestic market. Competitive pressure is evidenced to push acquirers for a faster completion of acquisition process. Cross-border acquisitions are found to be associated with lower acquirer–target and acquirer–competitors' similarity, which suggests that Chinese companies expand overseas primarily for strategic reasons of gaining a competitive edge rather than to simply improve sales.

The paper contributes to the limited pool of empirical literature relying on text mining techniques to establish the determinants of M&A transaction outcomes. The methodology used in the study outperforms the conventional techniques of operationalization of business similarities through General Industry Classification Standard (GICS) industry matching. The study investigates the intermediating role of intraindustry competition in fostering firms' acquisitiveness.

A first objective is to add insight into how constructs of ethnocentrism, xenocentrism and cosmopolitanism relate to each other. Knowledge of how these constructs overlap or work together in affecting consumer preferences will offer global marketers insights for designing appropriate marketing strategies. The second objective is to extend this knowledge by examining the correspondence of these three constructs to a nomological network of dispositional concepts pertinent for product positioning and market segmentation. The third objective is to empirically examine the extent to which the measures, construct structure and associative relationships are robust in different national research settings. The paper aims to discuss these issues.

Surveying British and American consumers, this study examines and analyzes the correspondence of these identity-relevant constructs within a nomological net of pertinent concepts: consciousness-of-kind, global consumption orientation, materialism and natural environment concern.

The hypothesized negative links between CET-XEN and CET-COS, and the predicted positive connection between XEN-COS were all confirmed on the latent factor results for the combined data set. The negative correlation between CET-XEN was of a considerably lower magnitude than that for CET-COS.

To date, no research has used an identity theory framework and simultaneously examined in a cross-cultural context the interrelationships of consumer ethnocentrism consumer xenocentrism and cosmopolitanism – and their differentiating linkages to a multiplicity of consumer dispositions.