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Prefabricated columns connected by grouted sleeves are increasingly used in practical projects. However, seismic fragility analyses of such structures are rarely conducted. Seismic fragility analysis has an important role in seismic hazard evaluation. In this paper, the seismic fragility of sleeve connected prefabricated column is analyzed.

A model for predicting the seismic demand on sleeve connected prefabricated columns has been created by incorporating engineering demand parameters (EDP) and probabilities of seismic failure. The incremental dynamics analysis (IDA) curve clusters of this type of column were obtained using finite element analysis. The seismic fragility curve is obtained by regression of Exponential and Logical Function Model.

The IDA curve cluster gradually increased the dispersion after a peak ground acceleration (PGA) of 0.3 g was reached. For both columns, the relative displacement of the top of the column significantly changed after reaching 50 mm. The seismic fragility of the prefabricated column with the sleeve placed in the cap (SPCA) was inadequate.

The sleeve was placed in the column to overcome the seismic fragility of prefabricated columns effectively. In practical engineering, it is advisable to utilize these columns in regions susceptible to earthquakes and characterized by high seismic intensity levels in order to mitigate the risk of structural damage resulting from ground motion.

This Study aims to present the seismic hazard assessment of the earthquake-prone eastern of Iran that has become more important due to its growing economic importance. Many cities in this region have experienced life and financial losses due to major earthquakes in recent years. Thus, in this study the seismic hazard maps and curves, and site-specific spectrums were obtained by using probabilistic approaches for the region.

The seismotectonic information, seismicity data and earthquake catalogues were gathered, main active seismic sources were identified and seismic zones were considered to cover the potential active seismic regions. The seismic model based on logic tree method used two seismic source models, two declustered catalogues, three choices for earthquake recurrence parameters and maximum considered earthquakes and four ground motion predicting (attenuation) models (GMPE).

The results showed a wide range of seismic hazards levels in the study region. The peak ground acceleration (PGAs) for 475 years returns period ranges between 0.1 g in the north-west part of the region with low seismic activity, to 0.52 g in the south-west part with high levels of seismicity. The PGAs for a 2,475-year period, also ranged from 0.12 to 0.80 g for the same regions. The computed hazard results were compared to the acceptable level of seismic hazard in the region based on Iran seismic code.

A new probabilistic approach has been developed for obtaining seismic hazard maps and curves; these results would help engineers in design of earthquake-resistant structures.

Electric street car (ESC) is a globally popular clean and safe electric transport system for urban agglomeration. India envisions achieving “all-electric transport” by 2030, yet ESC as a modal transport alternative is not distinct in the policy discussion. The emerging market for electric transportation in urban spaces requires a detailed demand study at the service user level to remove behavioural barriers and design integrated energy planning in developing economies. This paper explores the probabilistic uptake intentions of the daily public transport commuters for ESCs over e-buses from the only Indian city with operational ESCs, Kolkata.

Using a random utility model on primary survey data from daily commuters, the authors identify demographic, psychometric and socio-economic factors influencing probabilistic uptake of ESC over e-buses.

It estimates that 38% of the commuters demand ESC over e-buses, given the alternatives' comparative details. Factors like frequent availability and technological upgradation would increase the uptake of ESCs.

The study highlights that even though there are infrastructural challenges in the implementation of ESC, so does any other electric transport system; it is worth considering as a decarbonising transport alternative, given the high up-take intension of the users.

This is the first attempt to study the demand for ESC in developing economies, identifying the factors which may be considered in the sustainable urban transportation policy perspective.

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.

The purpose of this study is to provide an overview of the research conducted on hospitality and tourism articles published in Q1 category journals from 1990 to 2023. This study also aims to measure the topic prevalence in selected journals throughout the years, their change over time and similarities of journals.

Latent dirichlet allocation algorithm is used as a topic modeling method to identify and analyze topics in hospitality and tourism research over the past 30 years.

The results of the study indicate that hospitality and tourism research has recently focused on topics such as employee behavior, customer satisfaction, online reviews, medical tourism and tourist experience. However, the results also indicate a negative trend in topics such as hotel management, sustainability, profession, economic growth and tourist destination.

This study can be used to examine the evolution of research patterns over time, find hot and cold themes and uncover untapped or understudied areas. This can aid academics in their investigations and practitioners in making sound strategic decisions.

This study contributes to the existing literature by providing a new approach and comprehensive analysis of hospitality and tourism research topics. It delineates an overview of the progression of hospitality and tourism research over the past 30 years, identifies the trending topics and explores the potential impacts that these identified topics may have on future studies.

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.

TikTok, a social media application (app), was originally positioned as a short music video community suitable for young users, and the app is user-generated content (UGC) short video of vertical music. Users can make their own creative videos. Following the rhythm of the music, users can shoot various video content, personal talents, life records, performances, dances, plot interpretations, etc. However, what are the profiles and preferences of TikTok users, whereby the social media app is mainly developed by UGC? What is the impact of TikTok on the development of social media? In addition, what is UGC's social media model for user interactions in social networks? The purpose of this paper is to address and study these proposed issues.

All questionnaire items are designed as nominal and ordinal scales (not Likert scale). The obtained data from questionnaires are put into the relational database (N = 2,011). This empirical study takes Taiwan TikTok users as the research object, implements data mining analytics to generate user profiles through clustering analysis and further uses association rules’ analysis to analyze social media apps in social network interaction and social apps’ development by proposing two patterns and several meaningful rules.

This study finds that social media apps is a valuable practical research topic on online social media development. In addition, besides the TikTok, the authors eagerly await subsequent research to provide more valuable findings of social media apps in both theory and practice.

This study presents the research evidences that social media apps such as TikTok will be able to transcend the current development pattern of social media and make good use of the media and technology innovation of apps in social development and social informatics.

Despite agreement on the importance of adopting governance structures for developing competitive advantage, we still know little about why or how governance mechanisms are adopted in the first place. We also acknowledge that family businesses with formal governance mechanisms in place still resort to informal means to make decisions, and we lack knowledge about why certain governance mechanisms are sometimes, but not always, effective and functional. Given these research gaps, and drawing on institutional theory, we aim to explore: How are governance structures adopted and developed in family firms? Once adopted, how do family businesses perceive these governance structures?

Using Mokken Scale Analysis, a method suitable to uncover patterns/sequences of adoption/acquisition over time, we analyze a dataset of 1,488 Spanish family firms to explore if there is a specific pattern in the implementation of governance structures. We complement the analysis with descriptive data about perceived usefulness of such structures.

Our findings highlight two important issues. Family businesses follow a specific process implementing first business governance (board of directors, then executive committee), followed by family governance (family council then family constitution). We suggest they do so in response to institutional pressures, given the exposure they have to business practices, and their need to appear legitimate. Despite formal adoption of governance structures, family businesses do not necessarily consider them useful. We suggest that their perception about the usefulness of the implemented governance structures may lead to their ceremonial adoption, resulting in a gap between the implementation and functionality of such structures.

Our article contributes to the family business literature by bringing novel insights about implementation of governance structures. We take a step back to explain why these governance mechanisms were adopted in the first place. Using institutional theory we enrich governance and family business literatures, by offering a lens that explains why family businesses follow a specific process in adopting governance structures. We also offer a plausible explanation as to why governance structures are ineffective in achieving their theorized role in the context of family businesses, based on the family's perception of the unusefulness of such structures, and the concept of ceremonial adoption.

There is no single recipe that can serve the multiple needs of different family businesses. This indicates that family businesses may need diverse levels of development and order when setting up their governance structures. Accordingly, this study constitutes an important point of demarcation for practitioners interested in examining the effectiveness of governance structures in family firms. We show that an important pre-requisite for examining the effectiveness of governance structures is to start by investigating whether these structures are actually being used or are only adopted ceremonially.

Our paper expands current knowledge on governance in family firms by taking a step back hinting at why are governance structures adopted in the first place. Focusing on how governance is implemented in terms of sequence is novel and relevant for researcher and practitioners to understand how this process unfolds. Our study uses institutional theory, which is a strong theory to support the results. Our paper also uses a novel method to study governance structures in family firms.

The purpose is to provide decision support for tourists recommending scenic spots and corresponding suggestions for the management of scenic spots.

Based on the Baidu index data generated, this paper analyzes the temporal and spatial characteristics of network attention of 5A scenic spots in Sichuan Province. The online comment data are used to build the assessment model of scenic spots based on network attention, and the comment information of tourists is mined and analyzed through statistical analysis. At the same time, the key attributes of scenic spots from the perspective of network attention are evaluated and analyzed by using the probabilistic linguistic term set. Finally, this paper further constructs a recommendation model based on the key attribute set of scenic spots.

This paper uses different types of tourism network information, integrates multi-types of data and methods, fully excavates the value information of tourism network information, constructs the research framework of “scenic spot assessment + scenic spot recommendation” from the perspective of network attention, analyzes the network attention characteristics of scenic spots, evaluates the performance of scenic spots, and implements scenic spot recommendation.

This paper integrates multi-source data and multidisciplinary theoretical methods to form a scenic spot research framework of “assessment + recommendation” from the perspective of network attention.

This study examines the effect of temperature-dependent material models for normal-strength (NSC) and high-strength concrete (HSC) on the thermal analysis of reinforced concrete (RC) walls.

The study performs an one-at-a-time (OAT) sensitivity analysis to assess the impact of variables defining the constitutive and parametric fire models on the wall's thermal response. Moreover, it extends the sensitivity analysis to a variance-based analysis to assess the effect of constitutive model type, fire model type and constitutive model uncertainty on the RC wall's thermal response variance. The study determines the wall’s thermal behaviour reliability considering the different constitutive models and their uncertainty.

It is found that the impact of the variability in concrete’s conductivity is determined by its temperature-dependent model, which differs for NSC and HSC. Therefore, more testing and improving material modelling are needed. Furthermore, the heating rate of the fire scenario is the dominant factor in deciding fire-resistance performance because it is a causal factor for spalling in HSC walls. And finally the reliability of wall's performance decreased sharply for HSC walls due to the expected spalling of the concrete and loss of cross-section integrity.

Limited studies in the current open literature quantified the impact of constitutive models on the behaviour of RC walls. No studies have examined the effect of material models' uncertainty on wall’s response reliability under fire. Furthermore, the study's results contribute to the ongoing attempts to shape performance-based structural fire engineering.