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An increasing amount of attention is being paid to three-dimensional (3D) printing technology. The technology itself is based on diverse technologies such as laser beams and materials. Hence, 3D printing technology is a converging technology that produces 3D objects using a 3D printer. To become technologically competitive, many companies and nations are developing technologies for 3D printing. So to know its technological evolution is meaningful for developing 3D printing in the future. The paper aims to discuss these issues.

To get technological competitiveness of 3D printing, the authors should know the most important and essential technology for 3D printing. An understanding of the technological evolution of 3D printing is needed to forecast its future technologies and build the R & D planning needed for 3D printing. In this paper, the authors propose a methodology to analyze the technological evolution of 3D printing. The authors analyze entire patent documents related to 3D printing to construct a technological evolution model. The authors use the statistical methods such as time series regression, association analysis based on graph theory, and principal component analysis for patent analysis of 3D printing technology.

Using the proposed methodology, the authors show the technological analysis results of 3D printing and predict its future aspects. Though many and diverse technologies are developed and involved in 3D printing, the authors know only a few technologies take lead the technological evolution of 3D printing. In this paper, the authors find this evolution of technology management for 3D printing.

If not all, most people would agree that 3D printing technology is one of the leading technologies to improve the quality of life. So, many companies have developed a number of technologies if they were related to 3D printing. But, most of them have not been considered practical. These were not effective research and development for 3D printing technology. In the study, the authors serve a methodology to select the specific technologies for practical used of 3D printing.

Diverse predictions for 3D printing technology have been introduced in many academic and industrial fields. Most of them were made by subjective approaches depended on the knowledge and experience of the experts concerning 3D printing technology. So, they could be fluctuated according to the congregated expert groups, and be unstable for efficient R & D planning. To solve this problem, the authors study on more objective approach to predict the future state of 3D printing by analyzing the patent data of the developed results so far achieved. The contribution of this research is to take a new departure for understanding 3D printing technology using objective and quantitative methods.

Mobile peer‐to‐peer communications is an essential phase in the evolution of mobile communications technologies, motivating this research which aims to focus on how established industry stakeholders and new entrants can adapt themselves to the new situation.

Based on existing literature, the authors identified three distinctive evolution paths for mobile peer‐to‐peer communications and developed an analysis framework for their comparison. The authors validated the analysis by conducting a questionnaire study among domain experts, and analyzed its results using statistical analysis.

Internet‐driven evolution has high value proposition, is profitable and has subscription fees as an important revenue model. Telecom‐driven evolution creates value, leverages markets, leverages competence, is likely to encounter regulatory intervention and benefits all customer segments. Proprietary evolution has a successful revenue model, results in alliances of competitors and is competence‐enhancing to mobile device vendors.

Future work consists mainly of analyzing quantitatively the implications of the new technologies when they become readily available and evaluating the value analysis framework in other applicable cases.

Internet‐driven evolution enables new business opportunities to independent service operators and equipment vendors by enabling opportunities in profiting from sales of advanced devices and networks. Telecom‐driven evolution benefits mostly incumbent mobile network operators. Proprietary evolution enables limited competition against incumbent actors by independent service operators.

This study is one of the first journal publications on mobile peer‐to‐peer communications from a holistic techno‐economic point of view, beneficial to both academics and practitioners.

“It should also be noted that the objective of convergence and equal distribution, including across under-performing areas, can hinder efforts to generate growth. Contrariwise, the objective of competitiveness can exacerbate regional and social inequalities, by targeting efforts on zones of excellence where projects achieve greater returns (dynamic major cities, higher levels of general education, the most advanced projects, infrastructures with the heaviest traffic, and so on). If cohesion policy and the Lisbon Strategy come into conflict, it must be borne in mind that the former, for the moment, is founded on a rather more solid legal foundation than the latter” European Commission (2005, p. 9)Adaptation of Cohesion Policy to the Enlarged Europe and the Lisbon and Gothenburg Objectives.

The paper aims to provide a methodology by which organisational knowledge can be extracted and visualised dynamically over time, providing a glimpse into the knowledge evolution processes that occur within organisations.

Recursive analysis of email interactions is offered as a case to account for the knowledge structure evolution related to the different programs of international non-governmental organization (INGO). Several methods are used: analysis of the network expansion to see whether the process is random or uniform is performed, visualisation of the network configuration changes throughout studied time period; and the statistical examination of network formation.

The results of the presented study indicate that content structure of electronic knowledge networks exhibits hierarchical and centralised tendencies. The social network analysis results suggest that INGO exhibits non-hierarchical and decentralized structure of the individuals contributing to the discussion lists.

By providing the means to carry out network evolution analysis of content structure dynamics and social interactions, the presented work provides a means for probabilistically modelling patterns of organisational knowledge evolution.

The approach allows the exploration of the dynamics of tacit to explicit knowledge, from individual to the group and from informal groups to the whole organisation.

By displaying the large collection of the key phrases that reflected the evolution of the organisational knowledge structure over the time, organisational emails are placed in meaningful context explaining the language of the organisation and context of knowledge structure evolution.

This study aims to explore the dissemination and evolutionary path of online public opinion from a crisis management perspective. By clarifying the influencing factors and dynamic mechanisms of online public opinion dissemination, this study provides insights into attenuating the negative impact of online public opinion and creating a favorable ecological space for online public opinion.

This research employs bibliometric analysis and CiteSpace software to analyze 302 Chinese articles published from 2006 to 2023 in the China National Knowledge Infrastructure (CNKI) database and 276 English articles published from 1994 to 2023 in the Web of Science core set database. Through literature keyword clustering, co-citation analysis and burst terms analysis, this paper summarizes the core scientific research institutions, scholars, hot topics and evolutionary paths of online public opinion crisis management research from both Chinese and international academic communities.

The results show that the study of online public opinion crisis management in China and internationally is centered on the life cycle theory, which integrates knowledge from information, computer and system sciences. Although there are differences in political interaction and stage evolution, the overall evolutionary path is similar, and it develops dynamically in the “benign conflict” between the expansion of the research perspective and the gradual refinement of research granularity.

This study summarizes the research results of online public opinion crisis management from China and the international academic community and identifies current research hotspots and theoretical evolution paths. Future research can focus on deepening the basic theories of public opinion crisis management under the influence of frontier technologies, exploring the subjectivity and emotionality of web users using fine algorithms and promoting the international development of network public opinion crisis management theory through transnational comparison and international cooperation.

Nowadays, the breakout of the COVID-19 pandemic has caused an important change in teaching models. The emotional experience of this change has an important impact on online teaching. This paper aims to explore its time evolution characteristics and provide reference for the development of online teaching in the post epidemic era.

The article firstly crawls the online teaching-related comment text data on Zhihu platform and performs emotional calculation to obtain a one-dimensional time series of daily average emotional values. Then, by using non-linear time-series analysis, this paper reconstructs the daily average emotion value time series in high-dimensional phase space, calculates the maximum Lyapunov exponent and correlation dimension and finally, explores the feature patterns through recurrence plot and recurrence quantification analysis.

It was found that the sequence has typical non-linear chaotic characteristics; its correlation dimension indicates that it contains obvious fractal characteristics; the public emotional evolution shows a cyclical rise and fall. By text mining and temporal evolution analysis, this paper explores the evolution law over chronically of the daily average emotion value time series, provides feasible strategies to improve students' online learning experience and quality and continuously optimizes this new teaching model in the era of pandemic.

Based on social knowledge sharing platform of Q&A, this paper models and analyzes users interaction data under online teaching-related topics. This paper explores the evolution law over a long time period of the daily average emotion value time series using text mining and temporal evolution analysis. It then offers workable solutions to enhance the quality and experience of students' online learning, and it continuously improves this new teaching model in the age of pandemics.

The object of this research is the reconstruction of the existing legal response by European Union states to the phenomenon of immigration. It seeks to analyse the process of conferral of protection.

One main dimension is selected and discussed: the case law of the national courts. The study focuses on the legal status of immigrants resulting from the intervention of these national courts.

The research shows that although the courts have conferred an increasing protection on immigrants, this has not challenged the fundamental principle of the sovereignty of the states to decide, according to their discretionary prerogatives, which immigrants are allowed to enter and stay in their territories. Notwithstanding the differences in the general constitutional and legal structures, the research also shows that the courts of the three countries considered – France, Germany and Spain – have progressively moved towards converging solutions in protecting immigrants.

The research contributes to a better understanding of the different legal orders analysed.

This paper aims to propose a method for better discovering topic evolution path and semantic relationship from the perspective of patent entity extraction and semantic representation. On the one hand, this paper identifies entities that have the same semantics but different expressions for accurate topic evolution path discovery. On the other hand, this paper reveals semantic relationships of topic evolution for better understanding what leads to topic evolution.

Firstly, a Bi-LSTM-CRF (bidirectional long short-term memory with conditional random field) model is designed for patent entity extraction and a representation learning method is constructed for patent entity representation. Secondly, a method based on knowledge outflow and inflow is proposed for discovering topic evolution path, by identifying and computing semantic common entities among topics. Finally, multiple semantic relationships among patent entities are pre-designed according to a specific domain, and then the semantic relationship among topics is identified through the proportion of different types of semantic relationships belonging to each topic.

In the field of UAV (unmanned aerial vehicle), this method identifies semantic common entities which have the same semantics but different expressions. In addition, this method better discovers topic evolution paths by comparison with a traditional method. Finally, this method identifies different semantic relationships among topics, which gives a detailed description for understanding and interpretation of topic evolution. These results prove that the proposed method is effective and useful. Simultaneously, this method is a preliminary study and still needs to be further investigated on other datasets using multiple emerging deep learning methods.

This work provides a new perspective for topic evolution analysis by considering semantic representation of patent entities. The authors design a method for discovering topic evolution paths by considering knowledge flow computed by semantic common entities, which can be easily extended to other patent mining-related tasks. This work is the first attempt to reveal semantic relationships among topics for a precise and detailed description of topic evolution.

Drawing from knowledge-based view and balanced scorecard approach, this study seeks to develop an integrative model to examine the influence of two knowledge management (KM) strategies, codification KM strategy and personalization KM strategy, on the multi-stage KM evolution (KM adoption, implementation and institutionalization stages), which in turn affects balanced scorecard outcomes (financial performance, internal process performance, customer performance, and growth and learning performance).

Survey data from 244 managers (currently and directly in charge of KM activities) in large Taiwanese firms were collected and used to test the research model using the structural equation modeling (SEM) approach.

The results have revealed that both the codification KM strategy and personalization KM strategy are positive factors for stage-based KM evolution, but their relative importance differs across the three subsamples. Additionally, the results showed that the internal process and customer perspectives play a critical role in measuring performance during the earlier stages of KM evolution, while the financial and learning and growth perspectives emphasize the performance achievements from the latter stages.

Since KM implementation is an evolutionary process, using both financial and non-financial measures to assess organizational performance through KM efforts, such as the four balanced scorecard perspectives, can take full advantage of stage-based KM evolution. The results indicate that the time-lag effect is critical to distinguishing different forms of organizational performance.

Theoretically, this study aims to provide a research model that is capable of understanding the antecedents and consequences of staged-based KM evolution. From a managerial perspective, the findings of this study provide valuable guidelines to policy-makers and practitioners in accelerating KM evolution and achieving organizational performance.

The product family assembly line (PFAL) is a mixed model-assembly line, which is widely used in mass customization and intelligent manufacturing. The purpose of this paper is to study the problem of PFAL, a flexible (evolution) planning method to respond to product evolution for PFAL, to focus on product data analysis and evolution planning method.

The evolution balancing model for PFAL is established and an improved NSGA_II (INSGA_II) is proposed. From the perspective of data analysis, dynamic characteristics of PFAL are researched and analyzed. Especially the tasks, which stability is considered, can be divided into a platform and individual task. In INSGA_II algorithm, a new density selection and a decoding method based on sorting algorithms are proposed to compensate for the lack of traditional algorithms.

The effectiveness and feasibility of the method are validated by an example of PFAL evolution planning for a family of similar mechanical products. The optimized efficiency is significantly improved using INSGA_II proposed in this paper and the evolution planning model proposed has a stronger ability to respond to product evolution, which maximizes business performance over an effective period of time.

The assembly line designers and managers in discrete manufacturing companies can obtain an optimal solution for PFAL planning through the evolution planning model and INSGA-II proposed in this paper. Then, this planning model and optimization method have been successfully applied in the production of small wheel loaders.