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With the development of data mining technology, diverse and broader domain knowledge can be extracted automatically. However, the research on applying knowledge mapping and data visualization techniques to genealogical data is limited. This paper aims to fill this research gap by providing a systematic framework and process guidance for practitioners seeking to uncover hidden knowledge from genealogy.

Based on a literature review of genealogy's current knowledge reasoning research, the authors constructed an integrated framework for knowledge inference and visualization application using a knowledge graph. Additionally, the authors applied this framework in a case study using “Manchu Clan Genealogy” as the data source.

The case study shows that the proposed framework can effectively decompose and reconstruct genealogy. It demonstrates the reasoning, discovery, and web visualization application process of implicit information in genealogy. It enhances the effective utilization of Manchu genealogy resources by highlighting the intricate relationships among people, places, and time entities.

This study proposed a framework for genealogy knowledge reasoning and visual analysis utilizing a knowledge graph, including five dimensions: the target layer, the resource layer, the data layer, the inference layer, and the application layer. It helps to gather the scattered genealogy information and establish a data network with semantic correlations while establishing reasoning rules to enable inference discovery and visualization of hidden relationships.

This research delves into the dynamic realm of app design by examining the impact of app icon familiarity and authority on image fit, influencing users' app usage intention. Focusing on the distinctive circumstances of Chinese and Korean customers, the study aims to provide insightful information about how application user behavior changes.

Utilizing structural equation modeling, the study employs data from 293 Korean and Chinese consumers. The research design incorporates a thoughtful approach, including parallel translation methods, focus group interviews, and pre-experimental testing to ensure survey accuracy and validity. The study strategically selects stimuli from the Apple App Store rankings, emphasizing icon features and type considerations.

The results provide important new information about the connections between usage intention, image fit, authority, and familiarity with app icons. Notably, app icon familiarity and authority positively influence image fit. Furthermore, app icon image fit emerges as a positive predictor of usage intention, mediating the complex interplay between familiarity, authority, and intention. The study also identifies moderating effects, shedding light on the nuanced role of app icon features and types.

Originating from a comprehensive exploration of icons, this study significantly contributes to the field by exploring icon differences and uncovering the intricate mechanisms guiding users' decisions. The findings offer valuable insights for app designers, marketers, and researchers seeking a deeper understanding of user behavior in diverse cultural contexts, thereby enhancing the theoretical and practical foundations in app usability and consumer behavior.

This paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system – on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.

This simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones’ operations. An optimization model integrated with the simulation system can update the optimality of drones’ schedules and delivery assignments.

An extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.

The goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery’s energy is impacted and requires battery change/recharging while flying.

Gas transmission pipelines are at constant risk of gas leakage or fire due to various atmospheric environments, corrosion on pipe metal surfaces and other external factors. This study aims to reduce the human and financial risks associated with gas transmission by regularly monitoring pipeline performance, controlling situations and preventing disasters.

Facility managers can monitor the status of gas transmission lines in real-time by integrating sensor information into a building information modeling (BIM) 3D model. Using the Monitoring Panel plugin, coded in C# programming language and operated through Navisworks software, the model provides up-to-date information on pipeline safety and performance.

By collecting project information on the BIM and installing critical sensors, this approach allows facility manager to observe the real-time safety status of gas pipelines. If any risks of gas leakage or accidents are identified by the sensors, the BIM model quickly shows the location of the incident, enabling facility managers to make the best decisions to reduce financial and life risks. This intelligent gas transmission pipeline approach changes traditional risk management and inspection methods, minimizing the risk of explosion and gas leakage in the environment.

This research distinguishes itself from related work by integrating sensor data into a BIM model for real-time monitoring and providing facility managers with up-to-date safety information. By leveraging intelligent gas transmission pipelines, the system enables quick identification and location of potential hazards, reducing financial and human risks associated with gas transmission.

This paper aims to empirically examine the impact of economic development and energy consumption in Saudi Arabia (the leading OPEC giant and the Arab energy icon country) between 1971 and 2015, whilst incorporating globalization, financial development and capital accumulation.

This study uses econometric tools and the analytical framework based on the autoregressive distributed lag (ARDL) model.

The study found that, unlike economic development, globalization and financial development increased energy consumption. Also, capital accumulation created a boost in the country’s energy consumption. Results of variance decomposition indicate that the innovative shocks in globalization and financial development affected energy consumption at the rates of 15.28% and 28.98%, respectively, over 15 years’ period, while shocks in capital accumulation affected energy consumption at a rate of only about 1.24%. In addition, the results of impulse response function show that globalization and economic development were highly responsive to shocks in financial development, and capital accumulation greatly spurred financial development.

The findings of this study have implication for promoting an efficient and sustainable energy systems that enhance sustainable development based on the accrued benefits of globalization, financial development and capital accumulation.

Given the increasing level of globalization, financial development and energy consumption, our study uses econometric tools and the analytical framework based on the ARDL model to revisit how energy consumption is influenced by economic development in Saudi Arabia by incorporating other determinants of energy consumption such as globalization, financial development and capital accumulation. The results were validated based on the innovative accounting.

This study aims to fill various gaps detected in the literature on mass prestige (hereafter referred to as masstige) theory. The originality of the work stems from the multidimensional application of Paul’s (2015) model, the introduction of brand addiction as a construct from the consumer-brand relationship (CBR) theory within the context of wines and the exploration of a new and less studied sector in masstige strategies.

A structured questionnaire was distributed to collect data from masstige wine brand buyers in Portugal, of whom 166 completed the questionnaire correctly. A conceptual model was developed and tested using partial least squares structural equation modelling.

The findings include that only two dimensions of Paul’s (2015) masstige scale affect brand addiction: brand knowledge and excitement and status. Brand addiction has a positive effect on brand loyalty and electronic word of mouth (eWOM), and brand loyalty has a positive impact on eWOM. Theoretical and managerial implications were explored.

This research added a CBR perspective to masstige theory and applied masstige theory to wine brands for the first time. These three distinctive aspects collectively contribute to the novelty and significance of the research, opening up exciting possibilities for future investigations and providing a valuable contribution to the academic community and the wine industry alike.

The purpose of this study is twofold: first, to provide a nuanced understanding of the information concept that encapsulates both its universal foundations and context-dependent intricacies, and second, to propose a theoretical framework that integrates these diverse viewpoints, thereby contributing to the theoretical discourse and practical applications in information science/studies (IS).

The aim of this paper is to explore the relationship between universalism and pluralism in connection with semiotics. The paper adopts a conceptual approach based on semiotics to address the long-standing debate over the definition of information in IS. By engaging with two foundational semiotic theories—C.S. Peirce's pragmatic semiotics and Ferdinand de Saussure's structuralist semiotics – the research seeks to bridge the conflicting perspectives of universalism and pluralism within the field.

The findings reveal that Peirce's semiotics, with its universalist core, views information as dynamic and shaped by context, while Saussure's approach, which is rooted in linguistics, emphasizes the relational aspects of sign systems and connects them to pluralism. This comparative analysis facilitates a broad understanding of information that transcends the limitations of singular perspectives. In conclusion, the paper seeks to demonstrate that the integration of universalist and pluralist perspectives through semiotics offers a more holistic approach to the study of information in IS. It contributes to the field by suggesting that the tension between these perspectives is not only reconcilable but also essential for a richer and more complete understanding of information. This theoretical foundation paves the way for future research and practical advancements in IS and advocates for approaches that recognize the complexity and multifaceted nature of information.

The study engages with theoretical frameworks, predominantly semiotic theories by C.S. Peirce and Ferdinand de Saussure. While this provides a deep conceptual understanding, the lack of empirical data may limit the practical applicability of the findings. The research focuses on two semiotic perspectives, which, although foundational, do not represent the entirety of semiotics or information theory. Other significant semiotic theories and perspectives could provide alternative or complementary insights. The concepts of universalism and pluralism in information science are highly intricate and multifaceted. This study's approach to synthesizing these concepts, while comprehensive, might oversimplify some aspects of these complex paradigms.

The paper's originality lies in its unique integration of semiotic theories into the discourse of IS. This integration offers a novel perspective on understanding the concept of information, bridging the gap between universalist and pluralist approaches. The study presents a new conceptual framework for understanding information that combines elements of universalism (consistent, objective understanding of information) and pluralism (context-dependent, subjective interpretations). This dual approach is relatively unexplored in IS literature, providing fresh insights into the complex nature of information.

The purpose of this article is to contribute to the digital development and utilization of China’s intangible cultural heritage resources, research on the theft of intangible cultural heritage resources and knowledge integration based on linked data is proposed to promote the standardized description of intangible cultural heritage knowledge and realize the digital dissemination and development of intangible cultural heritage.

In this study, firstly, the knowledge organization theory and semantic Web technology are used to describe the intangible cultural heritage digital resource objects in metadata specifications. Secondly, the ontology theory and technical methods are used to build a conceptual model of the intangible cultural resources field and determine the concept sets and hierarchical relationships in this field. Finally, the semantic Web technology is used to establish semantic associations between intangible cultural heritage resource knowledge.

The study findings indicate that the knowledge organization of intangible cultural heritage resources constructed in this study provides a solution for the digital development of intangible cultural heritage in China. It also provides semantic retrieval with better knowledge granularity and helps to visualize the knowledge content of intangible cultural heritage.

This study summarizes and provides significant theoretical and practical value for the digital development of intangible cultural heritage and the resource description and knowledge fusion of intangible cultural heritage can help to discover the semantic relationship of intangible cultural heritage in multiple dimensions and levels.

This study sought to understand which food allergen labeling systems (non-directive, semi-directive, and directive) were attended to and preferred by 34 participants with food hypersensitivity and their perceived corporate social responsibility (CSR) and behavioral intention towards a restaurant that identifies food allergens on menus.

This study used an online survey with open-ended and ranking questions, combined with eye-tracking technology, to explore participants' visual attention and design preferences regarding four menus. This study utilized one-way repeated measures analysis of variance (RM-ANOVA) and heat maps to analyze participants' menu-reading behaviors. A content analysis of survey responses and a ranking analysis of menus were conducted to understand the reasons behind consumers' preferred menu designs.

The advisory statement was not much attended to. Participants identified food allergen information significantly quicker with the directive labeling system (icons) than the other two systems, implying they were eye-catching. Semi-directive labeling system (red text) has lower visit count and was more preferred than two other systems; each labeling system has its strengths and limitations. Participants viewed restaurants that disclosed food allergen information on menus as socially responsible, and they would revisit those restaurants in the future.

This study was one of the first to explore, through use of eye-tracking technology, which food allergen labeling systems were attended to by consumers with food hypersensitivity. The use of triangulation methods strengthened the credibility of the results. The study provided empirical data to restauranteurs in the US on the values of food allergen identification on restaurant menus, although it is voluntary.

The purpose of this paper is to expand and analyze deeply customer emotions, concretize the levels of positive or negative emotions with the aim of using machine learning methods, and build a model to identify customer emotions.

The study proposed a customer emotion detection model and data mining method based on the collected dataset, including 80,593 online reviews on agoda.com and booking.com from 2009 to 2022.

By discerning specific emotions expressed in customers' comments, emotion detection, which refers to the process of identifying users' emotional states, assumes a crucial role in evaluating the brand value of a product. The research capitalizes on the vast and diverse data sources available on hotel booking websites, which, despite their richness, remain largely unexplored and unanalyzed. The outcomes of the model, pertaining to the detection and classification of customer emotions based on ratings and reviews into four distinct emotional states, offer a means to address the challenge of determining customer satisfaction regarding their actual service experiences. These findings hold substantial value for businesses operating in this domain, as the findings facilitate the evaluation and formulation of improvement strategies within their business models. The experimental study reveals that the proposed model attains an exact match ratio, precision, and recall rates of up to 81%, 90% and 90%, respectively.

The study has yet to mine real-time data. Prediction results may be influenced because the amount of data collected from the web is insufficient and preprocessing is not completely suppressed. Furthermore, the model in the study was not tested using all algorithms and multi-label classifiers. Future research should build databases to mine data in real-time and collect more data and enhance the current model.

The study's results suggest that the emotion detection models can be applied to the real world to quickly analyze customer feedback. The proposed models enable the identification of customers' emotions, the discovery of customer demand, the enhancement of service, and the general customer experience. The established models can be used by many service sectors to learn more about customer satisfaction with the offered goods and services from customer reviews.

The research paper helps businesses in the hospitality area analyze customer emotions in each specific aspect to ensure customer satisfaction. In addition, managers can come up with appropriate strategies to bring better products and services to society and people. Subsequently, fostering the growth of the hotel tourism sector within the nation, thereby facilitating sustainable economic development on a national scale.

This study developed a customer emotions detection model for detecting and classifying customer ratings and reviews as 4 specific emotions: happy, angry, depressed and hopeful based on online booking hotel websites agoda.com and booking.com that contains 80,593 reviews in Vietnamese. The research results help businesses check and evaluate the quality of their services, thereby offering appropriate improvement strategies to increase customers' satisfaction and demand more effectively.