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This study aims to provide a comprehensive analysis of two-sided matching (TSM) research, an interdisciplinary field that integrates both theoretical and practical perspectives. By examining 756 research articles from the Web of Science database, this paper seeks to identify key trends, collaboration patterns and emerging research topics within the TSM domain.

The research utilizes bibliometric analysis combined with a structural topic model to analyze TSM-related articles published between January 1, 2000, and September 30, 2022. The study identifies leading subfields, journals, countries/regions and institutions based on publication volume, total citations and average citations per article. Interaction and collaboration patterns among these entities are examined through co-occurrence and coupling networks. Additionally, five major research topics are identified and explored using topic modeling and co-word networks. This hybrid knowledge mining approach better reveals the inherent structural changes in topic clusters. Topic distribution and network analysis are beneficial in capturing the attention allocation of different entities to knowledge.

The analysis reveals five prominent research topics in TSM: communication resource allocation, stable matching research, computing task assignment, TSM decision-making and market matching mechanism design. These topics represent the main directions of TSM research. The study also uncovers a shift in research focus from theoretical aspects to practical applications. Furthermore, the distribution of knowledge and interaction patterns among key entities align with the identified research trends.

This study offers a novel and detailed overview of TSM research highlighting significant trends and collaboration patterns within the field. By integrating bibliometric methods with structural topic modeling the study provides unique insights into the evolution of TSM research making it a valuable resource for both academic and professional communities.

To ensure the stable operation of ironmaking process and the quality and output of sinter, the multi-objective optimization of sintering machine batching process was carried out.

The purpose of this study is to establish a multi-objective optimization model with iron taste content and batch cost as targets, constrained by field process requirements and sinter quality standards, and to propose an improved balance optimizer algorithm (LILCEO) based on a lens imaging anti-learning mechanism and a population redundancy error correction mechanism. In this method, the lens imaging inverse learning strategy is introduced to initialize the population, improve the population diversity in the early iteration period, avoid falling into local optimal in the late iteration period and improve the population redundancy error correction mechanism to accelerate the convergence rate in the early iteration period.

By selecting nine standard test functions of BT series for simulation experiments, and comparing with NSGA-?, MOEAD, EO, LMOCSO, NMPSO and other mainstream optimization algorithms, the experimental results verify the superior performance of the improved algorithm. The results show that the algorithm can effectively reduce the cost of sintering ingredients while ensuring the iron taste of sinter, which is of great significance for the comprehensive utilization and quality assurance of sinter iron ore resources.

An optimization model with dual objectives of TFe content and raw material cost was developed taking into account the chemical composition and quality indicators required by the blast furnace as well as factors such as raw material inventory and cost constraints. This model was used to adjust and optimize the sintering raw material ratio. Addressing the limitations of existing optimization algorithms for sintering raw materials including low convergence accuracy slow speed limited initial solution production and difficulty in practical application we proposed the LILCEO algorithm. Comparative tests with NSGA-III MOEAD EO LMOCSO and NMPSO algorithms demonstrated the superiority of the proposed algorithm. Practical applications showed that the proposed method effectively overcomes many limitations of the current manual raw material ratio model providing scientific and stable decision-making guidance for sintering production operations.

The tourism industry is undergoing a metamorphosis as an outcome of the integration of new technologies, which has an impact on products, business experiences, ecosystems and tourism complexes. As a result, to adjust to these changes, the sector needs to develop new business alliances, models and skill sets. Emerging technologies such as virtual reality (VR), augmented reality (AR), artificial intelligence (AI), and data analytics are set to bring in a new chapter in the history of travel experiences. Deepening global linkages, optimising operating efficiency, and transforming visitor experiences are all being brought about by these advancements. The report also evaluates the wider effects of this digital revolution, such as the levelling of access to travel information, the introduction of fresh business strategies and the crucial role of cyber security. With practical advice drawn from empirical research, real-world case studies and industry expert interviews, this study prepares businesses and travel destinations for success in the digital age. ‘From Pixels to Paradise’ is an indispensable tool for all those involved in the travel and tourism sector, helping them navigate the digital environment and take advantage of the opportunities brought about by the convergence of travel and technology. This study tries to map out a route towards a more sustainable, engaging and technologically improved tourism experience against the backdrop of significant industry changes.

This study aims to explore the differences in the tourists’ perceived destination image on travel e-commerce platforms (e.g. Ctrip and Fliggy) and social media platforms (e.g. Xiaohongshu and Weibo).

This paper aims to give meaning to the smart tourism destination (STD) certification, highlighting its main advantages and limitations. The case of Medellín (Colombia) presents characteristics worth studying. The city has suffered from stigmatization and has recently started the steps to become an STD with the Spanish company Segittur (December 2020). Thus, this study aims to focus on the implications of the STD process, especially in an area that has been impacted by tourist activity.

This paper is a qualitative and exploratory case study about Medellín in Colombia and its tourism development in an STD framework. After a theoretical exploration of the STD from an urban perspective, the paper will present a multiple data corpus to analyze the city’s certification process to turn into an STD. This methodology explores stakeholders on the destination, including an observation, and allows us to obtain an overview of the implications of the STD certification for Medellín.

During the past decade, Medellín passed from a dangerous destination to a trendy destination. Findings reveal interesting results, considering the need to consider all aspects of territory as a central issue for the STD settlement and look beyond a technological approach.

This paper lets to understand better the STD process established by the institution. Moreover, it highlights the gap between the coveted certification to become the first STD in Colombia and the realities of a Latin American territory.

Este articulo pretende dar un significado a la certificación de destino turístico inteligente (DTI), destacando sus principales ventajas y limitaciones. El caso de Medellín (Colombia) presenta características relevantes de estudio. La ciudad ha sido ampliamente estigmatizada y recientemente ha iniciado el proceso de certificación para convertirse en destino turístico inteligente con la entidad española Segittur (dic. 2020). Dicho esto, queremos enfocarnos en las implicaciones de este proceso, especialmente en un área que ha sido impactada por la actividad turística.

Este artículo comprende un estudio cualitativo y exploratorio sobre Medellín en Colombia y su desarrollo turístico en un marco de DTI. Luego de una exploración teórica del destino turístico inteligente desde una perspectiva urbana, el artículo presentará un corpus de múltiples datos para analizar el proceso de certificación de la ciudad en su conversión en una DTI. Nuestra metodología explora a los stakeholders sobre el destino, incluyendo una observación, y nos permite obtener una visión general de las implicaciones de la certificación DTI para Medellín.

Durante la última década, Medellín pasó de ser un destino denominado de alto riesgo a un destino altamente recomendado. Los hallazgos revelan resultados interesantes, mostrando la necesidad de considerar todos los aspectos del territorio como un tema central para el asentamiento del DTI y mirar más allá de un enfoque tecnológico.

Este trabajo permite tener una mejor comprensión del proceso de DTI establecido por la entidad Segittur. Además, resalta la brecha existente entre la deseada certificación y las realidades de un territorio latinoamericano.

This study aims to investigate the effects of smart tourism technology experience (STTE) on tourist satisfaction and happiness in Bordeaux, with a focus on understanding the mediating role of self-gratification. By examining these relationships, the study seeks to provide insights into how smart tourism technologies can enhance tourist experiences.

The study uses partial least squares-structural equation modeling (PLS-SEM) to analyze data collected from 380 tourists who visited Bordeaux. The measurement model assesses reliability and validity, while the structural model evaluates the proposed hypotheses and the mediation effects of self-gratification.

The results confirm that STTE positively impacts tourist satisfaction, with accessibility, informativeness and personalization significantly enhancing tourist satisfaction, while interactivity does not. Tourist satisfaction, in turn, positively affects tourist happiness. Furthermore, self-gratification partially mediates the relationship between tourist satisfaction and happiness, highlighting its importance in the smart tourism context.

This research extends the understanding of STTE by demonstrating its effects on tourist satisfaction and happiness. It introduces the mediating role of self-gratification, providing a novel perspective on how personalized smart tourism experiences contribute to overall tourist happiness.

The safety management of construction machines is of primary importance. Considering that traditional construction machine safety monitoring and evaluation methods cannot adapt to the complex construction environment, and the monitoring methods based on sensor equipment cost too much. This paper aims to introduce computer vision and deep learning technologies to propose the YOLOv5-FastPose (YFP) model to realize the pose estimation of construction machines by improving the AlphaPose human pose model.

This model introduced the object detection module YOLOv5m to improve the recognition accuracy for detecting construction machines. Meanwhile, to better capture the pose characteristics, the FastPose network optimized feature extraction was introduced into the Single-Machine Pose Estimation Module (SMPE) of AlphaPose. This study used Alberta Construction Image Dataset (ACID) and Construction Equipment Poses Dataset (CEPD) to establish the dataset of object detection and pose estimation of construction machines through data augmentation technology and Labelme image annotation software for training and testing the YFP model.

The experimental results show that the improved model YFP achieves an average normalization error (NE) of 12.94 × 10^–3, an average Percentage of Correct Keypoints (PCK) of 98.48% and an average Area Under the PCK Curve (AUC) of 37.50 × 10^–3. Compared with existing methods, this model has higher accuracy in the pose estimation of the construction machine.

This study extends and optimizes the human pose estimation model AlphaPose to make it suitable for construction machines, improving the performance of pose estimation for construction machines.

This study aims to explore a methodology based on the Kano model. And use this method to determine user requirement attributes in the field of school uniform customization. To construct a set of processes that can be used as a reference for constructing a clothing customization platform.

An optimized quantitative Kano model was applied. Initially, a survey was conducted to assess the current market for customized school uniforms in China. Subsequently, a Kano attribute questionnaire was developed, and experts from both supply and procurement sectors were invited to evaluate it. This was followed by categorizing user demands based on the Kano model’s evaluation criteria and conducting a validity analysis using Cronbach’s alpha coefficient. The priority ranking of user demands was determined through a sensitivity analysis of better-worse coefficients. Ultimately, a platform was established, and a fuzzy comprehensive evaluation was conducted.

Regarding user demands, procurement-side demand elements prioritize modular design, fabric libraries and online reviews. In contrast, supply-side demand elements focus on style product libraries, layout adaptation to user habits and the preview effects of 3D models. Elements such as qualification verification, personal information uploading, design draft archiving and main tag categorization have a lesser impact on user satisfaction.

The results of the study provide a complete methodological reference for the construction of a garment customization platform. By applying the Kano model, this study categorizes and filters the demands of users for school uniform customization design platforms in China and establishes a 3D virtual display platform aimed at improving user satisfaction.

Humans now enjoy a better life because of Artificial Intelligence (AI). AI has a significant impact on the creation of smart cities. Modern applications based on big data, Internet of Things (IoT) systems, and deep learning require extensive use of complex computational solutions. Thus, the following problems arise: (1) what are smart cities? (2) what is AI? (3) How is AI used in smart cities? To respond to this problem, the following objective was set: to map how AI is used in smart cities. For this purpose, a qualitative methodology based on a narrative analysis of the literature was used. It is concluded that AI and smart cities are complementary technologies that can assist cities in tackling difficult issues including public safety, transportation, energy management, environmental monitoring, and predictive maintenance. This chapter’s findings, while broadly applicable, offer valuable insights into the Gulf region’s unique context, where rapid urbanization and technological adoption intersect with cultural and environmental considerations. The integration of AI in smart cities presents a promising avenue for the Gulf region to address its specific challenges and leverage its economic and infrastructural strengths, thereby contributing to the broader goals of innovation, development, prosperity, and well-being as envisioned in the region’s Vision 2040 initiatives.

This chapter introduces the Smart Tourism Cities Competitiveness Index (STCCI), an innovative benchmarking tool developed in collaboration with the Seoul Tourism Organization and the Smart Tourism Research Center at Kyung Hee University. The STCCI evaluates cities on smart tourism using a comprehensive framework that covers five key dimensions: attractiveness, accessibility, digitalization readiness, sustainability, and co-partnership, further detailed into 17 categories and 51 specific indicators. The application of STCCI to the Gulf cities of Dubai, Abu Dhabi, and Doha reveals their strengths and areas for enhancement in the realm of smart tourism. Dubai shines in attracting visitors and embracing digital technology, while Abu Dhabi leads in online visibility and the accessibility of tourism information. Doha is recognized for its superior accessibility. Despite these strengths, each city faces unique challenges that need addressing. The STCCI not only provides a deep dive into the current state and potential growth of smart tourism infrastructures in these cities post-pandemic but also positions them on a global scale in terms of competitiveness in smart tourism. The index suggests strategic improvements, supporting the evolution of these cities into advanced smart cities in the Gulf region, characterized by innovation, development, transformation, and prosperity. This chapter contributes valuable perspectives for ongoing smart cities research and development across the Gulf, emphasizing the significance of this approach in guiding the future of urban tourism in a smarter, more connected world.