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This research aimed to visualize and analyze the co-word network and thematic clusters of the intellectual structure in the field of linked data during 1900–2021.

This applied research employed a descriptive and analytical method, scientometric indicators, co-word techniques, and social network analysis. VOSviewer, SPSS, Python programming, and UCINet software were used for data analysis and network structure visualization.

The top ranks of the Web of Science (WOS) subject categorization belonged to various fields of computer science. Besides, the USA was the most prolific country. The keyword ontology had the highest frequency of co-occurrence. Ontology and semantic were the most frequent co-word pairs. In terms of the network structure, nine major topic clusters were identified based on co-occurrence, and 29 thematic clusters were identified based on hierarchical clustering. Comparisons between the two clustering techniques indicated that three clusters, namely semantic bioinformatics, knowledge representation, and semantic tools were in common. The most mature and mainstream thematic clusters were natural language processing techniques to boost modeling and visualization, context-aware knowledge discovery, probabilistic latent semantic analysis (PLSA), semantic tools, latent semantic indexing, web ontology language (OWL) syntax, and ontology-based deep learning.

This study adopted various techniques such as co-word analysis, social network analysis network structure visualization, and hierarchical clustering to represent a suitable, visual, methodical, and comprehensive perspective into linked data.

An integrated intuitionistic fuzzy (IF) modelling-based framework for examining the performance analysis of a packaging unit (PU) in three different stages has been proposed.

For the series and parallel configuration of PU, a mathematical model based on the intuitionistic fuzzy Lambda–Tau (IFLT) approach was developed in order to calculate various reliability parameters at various spreads. For determining membership and non-membership function-based reliability parameters for the top event, AND/OR gate transitions expression was employed.

For 15%–30% spread, unit’s availability for the membership function falls by 0.006442%, and it falls even more by 0.014907% with an increase in spread from 30% to 45%. In contrast, for 15%–30% spread, the availability of non-membership function-based systems reduces by 0.007491% and further diminishes. Risk analysis has presented applying an emerging approach called intuitionistic fuzzy failure mode and effect analysis (IFFMEA). For each of the stated failure causes, the output values of the intuitionistic fuzzy hybrid weighted Euclidean distance (IFHWED)-based IFFMEA have been tabulated. Failure causes like HP1, MT6, FB9, EL16, DR23, GR27, categorized under subsystems, namely hopper, motor, fluidized bed dryer, distributor, grader and bin, respectively, with corresponding IFFMEA output scores 1.0975, 1.0190, 0.8543, 1.0228, 0.9026, 1.0021, were the most critical one to contribute in the system’s failure.

The limitation of the proposed framework lies in the fact that the results obtained for both reliability and risk aspects mainly depend on the correctness of raw data provided by the experts. Also, an approximate model of PU is obtained from plant experts to carry performance analysis, and hence more attention is required in constructing the model. Under IFLT, reliability parameters of PU have been calculated at various spreads to study and analyse the failure behaviour of the unit for both membership and non-membership function in the IFS of [0.6,0.8]. For both membership- and non-membership-based results, availability of the considered system shows decreasing trend. To improve the performance of the considered system, risk assessment was carried using IFFMEA technique, ranking all the critical failure causes against IFHWED score value, on which more attention should be paid so as to avoid sudden failure of unit.

The livelihood of millions of farmers and workers depends on sugar industries. So perpetual running of these industries is very important from this viewpoint. On the basis of findings of reliability parameters, the maintenance manager could frame a correct maintenance policy for long-run availability of the sugar mills. This long-run availability will generate revenue, which, in turn, will ensure the livelihood of the farmers.

Mathematical modelling of the considered unit has been done applying basic expressions of AND/OR gate. IFTOPSIS approach has been implemented for ranking result comparison obtained under IFFMEA approach. Eventually, sensitivity analysis was also presented to demonstrate the stability of ranking of failure causes of PU.

The calculation of buildings’ carbon footprint (CFP) is an important basis for formulating energy-saving and emission-reduction plans for building. As an important building type, there is currently no model that considers the time factor to accurately calculate the CFP of hospital building throughout their life cycle. This paper aims to establish a CFP calculation model that covers the life cycle of hospital building and considers time factor.

On the basis of field and literature research, the basic framework is built using dynamic life cycle assessment (DLCA), and the gray prediction model is used to predict the future value. Finally, a CFP model covering the whole life cycle has been constructed and applied to a hospital building in China.

The results applied to the case show that the CO₂ emission in the operation stage of the hospital building is much higher than that in other stages, and the total CO₂ emission in the dynamic and static analysis operation stage accounts for 83.66% and 79.03%, respectively; the difference of annual average emission of CO₂ reached 28.33%. The research results show that DLCA is more accurate than traditional static life cycle assessment (LCA) when measuring long-term objects such as carbon emissions in the whole life cycle of hospital building.

This research established a carbon emission calculation model that covers the life cycle of hospital building and considered time factor, which enriches the research on carbon emission of hospital building, a special and extensive public building, and dynamically quantifies the resource consumption of hospital building in the life cycle. This paper provided a certain reference for the green design, energy saving, emission reduction and efficient use of hospital building, obviously, the limitation is that this model is only applicable to hospital building.

The paper undertakes a bibliometric analysis and assessment of journal publications in the field of container terminal operations research (CTOR), in an attempt to identify high-impact papers (HIPs) published in Science Citation Index/Social Science Citation Index (SCI/SSCI) journals of CTOR subject category from 1973 to 2020.

A structured approach for identifying the HIPs is developed based on the utilization of bibliometric and network analyses.

The CTOR papers are assessed in terms of publication outputs, distribution of outputs in SCI/SSCI journals, authorship, institutions and countries, as well as citation life cycles of papers with the highest total citations since their publication until the year 2020. The results show that between 1989 and 2015, there were 82 HIPs in the field of CTOR, which have been cited at least 200 times, with more than 50% of these citations allocated in the second part of paper citation life cycle according to the database of Google Scholar.

The practical implication of the aforementioned reviewing and assessing journal publications of CTOR is that it offers the ability to reveal the tone of its development through addressing main characteristics of the relevant HIPs as determined by the highly cited papers in this field of research.

This paper offers a unique analysis and assessment in the field of CTOR by identifying the relevant HIPs and their associated scientific actors (authors, institutions and countries), thus facilitating the future research effort in the field of CTOR.

This paper aims to systematically understand the development of rapid setup, quantitatively analyze the landscape and reveal new trends and challenges.

Based on 192 literature studies (1987–2021) collected from Scopus and Google Scholar, the papers are classified by: publication time and source; research type and data analysis of papers; pattern of authorship and country; sector-wise focus of the paper; improvement method used in the setup. And CiteSpace is used to analyze the cooccurrence and timeline of keywords.

There has been substantial progress in the past 35 years, including the rapid growth in the number of papers, the expansion in different disciplines, the participation of developing countries, the application in the service industry and the significant impact of setup on cost. And there are still some deficiencies.

There is concern that Google Scholar lacks the quality control needed for its use as a bibliometric tool. Future work is encouraged to conduct an in-depth discussion on high-quality papers.

In small batch production, rapid setup is increasingly essential. Clarifying the research focus and main improvement methods is of great significance for enterprises to meet the changing market needs.

To the best of the authors’ knowledge, this study is the first literature review on rapid setup. It is decided to consider a detailed set of data for better introspection and trace the history reflections and the research future in setup time.

This study aims to understand how practitioners use their insights in software development models to share experiences within and between organizations.

This is a qualitative study of practitioners in software development projects, in large-, medium- or small-size businesses. It analyzes interview material in three-step iterations to understand reflexive practice when using software development models.

The study shows how work processes are based on team members’ experiences and common views. This study highlights the challenges of organizational learning in system development projects. Current practice is unreflective, habitual and lacks systematic ways to address recurring problems and share information within and between organizations. Learning is episodic and sporadic. Knowledge from previous experience is individual not organizational.

Software development teams and organizations tend to learn about, and adopt, software development models episodically. This research expands understanding of how organizational learning takes place within and between organizations with practitioners who participate in teams. Learnings show the potential for further research to determine how new curriculums might be formed for teaching software development model improvements.

This paper proposes a maturity model to indicate the operational excellence (OE) implementation status of businesses in emerging countries, considering its importance in Industry 4.0.

The study involves four steps. First, undertake a study of the literature to identify the variables to consider in the maturity model; second, identify how to organize the variables found in the first step; third, structure a questionnaire to define the maturity model; and lastly, validate the variables found in the literature and the maturity levels.

A total of 23 variables were validated through interviews with general managers, operation managers and experts within organizations. Interviews were carried out in 49 companies from different sectors. Five maturity levels were defined in this study: basic level, beginner level, training level, innovative level and champion level.

Many businesses are implementing OE as a practice to remain competitive in their industries, targeting economic, environmental and social sustainability. However, OE implementation in companies from developing or emerging countries is far below the average level of that in developed companies due to cultural, political or economic constraints. To the best of the authors’ knowledge, there is no model that indicates the status of OE implementation in companies from emerging countries or the different levels to implement OE. This study fills the mentioned gap.

This study analyses the impact of economic and trade policy uncertainty on US and Chinese stock markets. Also, this study examines the hedge and safe-haven properties of US and China stocks against both US and Chinese economic and trade policy uncertainty.

To achieve the desired goals, the authors employ Dynamic Conditional Correlation through Glosten et al. (1993) model based on the Generalized Autoregressive Conditional Heteroscedasticity (DCC-GJR-GARCH (1, 1)) and Quantile cross-spectral (QS) models. The study uses monthly observations spanning from March 2010 to June 2022.

This study evidence that the economic and trade policy uncertainty between USA and China is extremely sensitive and has high volatility clustering effects on DJChina88 and DJUS, respectively. Conversely, against the Chinese economic and trade policy uncertainty, the US stock market indexes show both hedging properties across the period and safe-haven during COVID-19 and Russia–Ukraine crises. In contrast, among the Chinese stock markets, only DJShenzhen and DJShanghai stock indices might provide strong hedging and safe-haven properties against the US economic and trade policy uncertainties; however, DJShenzhen (DJChina88) stock shows weak hedge and safe-haven properties (hedging benefits) against Chinese trade policy uncertainty (CTPU) (Chinese economic policy uncertainty [CEPU]).

The findings have significant implications for investors, portfolio managers and regulators in hedging and making proper decisions under uncertain circumstances.

The study extends the literature on stock market performance to cover the economic and trade policy uncertainty by providing novel evidence during the recent COVID-19 and Russia–Ukraine invasion.

This article presents a case study on the Heritage Building Information Modeling (H-BIM) application in a historic village in Bursa, Turkey. The study addresses how tailor-made and highly structured H-BIM approaches can effectively be implemented in preservation applications for historic vernacular buildings in the rural architecture context.

Using inexpensive digital photogrammetry techniques tightly combined with an object-oriented BIM ontology, parametric meta-modeling and object/system propagation methods, the study employed a holistic H-BIM approach for capturing the materiality, building object behaviors and indigenous construction principles of a characteristic vernacular house that were synthesized in a parametric H-BIM model. The followed stages, steps and connected methods were systematized and articulated in a prototypical H-BIM implementation framework.

The study findings suggested that the developed parametric H-BIM approach can return effective results with the combined use of low-cost and practical digital photogrammetry with BIM methods. The flexibility and adaptability of the parametric H-BIM implementation framework facilitated the synthesis of a comprehensive H-BIM model and allowed an in-depth evaluation of local architectural heritage with its physical, spatial and environmental characteristics. The proposed H-BIM approach also provided significant documentation and system-specific assessment benefits for preserving the vernacular examples which are prone to extinction especially due to structural and systemic deterioration.

The study proposes a feasible, practical and replicable H-BIM implementation methodology for vernacular preservation applications. The knowledge-embedded H-BIM approach, flows and techniques presented in this study provide a holistic and systematic H-BIM framework – with the integrated use of digital photogrammetry and parametric meta-modeling methods – that has the potential for the democratization of H-BIM applications in education and practice.

Numerous challenges in education emerge as our technology-driven society rapidly evolves and manifests more exigent requirements from engineering professionals. Higher education, nonetheless, seems to adapt to such requirements at an unequal speed, generating some tensions between industry and higher education institutions. The purpose of this paper is to share the experiences obtained through a process of assessment and redesign of a large enrollment course of programming from which the authors developed a systematic approach for course design/redesign.

A mixed method approach was deployed for data gathering and evaluation, consisting of close-ended surveys, open-ended questionnaires, information matrices and state of the art compilation. Triangulation of the information offered clear data about the necessity of curriculum redesign; therefore, a new programming course curriculum encompassed with relevant necessities in engineering and science was developed.

The authors produced a coherent and dynamic systematic path for assessment and design/redesign of course curriculum, which the authors find extremely helpful to improve negotiation processes inside higher education institutions, as it can be implemented to improve any large enrollment course curricula in engineering and science.

By following the systematic path for assessment and design/redesign of curricula the authors developed, higher education systems could embark more efficiently in the ever-challenging process of adapt their courses and programs to tackle the upcoming demands of our society.

So far, a systematic path for assessment and design/redesign of course curriculum was not published, and it supports the improvement of pedagogical approaches in academic institutions.