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In response to the challenges posed by the conventional manual flange docking method in the LNG (Liquefied Natural Gas) loading process, such as low positioning accuracy, constraints on production efficiency and safety hazards, this study analyzed the LNG five-axis loading arm’s main functions and structural characteristics.

An automated solution for the joints of the LNG loading arm was designed. The forward kinematic model of the LNG loading arm was established using the Denavit–Hartenberg (D-H) parameter method, and its workspace was analyzed. The Newton–Raphson iteration method was employed to solve the inverse kinematics of the LNG loading arm, facilitating trajectory planning. The relationship between the target position and the joint variables was established to verify the stability of the arm’s motion. Flange center identification was achieved using the Hough transform function. Based on the ROS platform, combined with Gazebo and Rviz, an experimental simulation of automatic docking of the LNG loading arm was conducted.

The docking errors in the XYZ directions were all less than 0.8 mm, meeting the required docking accuracy. Moreover, the motion performance of the loading arm during docking was smooth and free of abrupt changes, validating its capability to accomplish the automatic docking task.

The proposed trajectory planning and automatic docking scheme can be used for the rapid filling of LNG filling arms and LNG tankers to improve the efficiency of LNG transportation. In guiding the docking, the proposed automatic docking scheme is an accurate and efficient way to improve safety.

Experts may adjust their assessments through communication and mutual influence, and this dynamic evolution relies on the spread of internal trust relationships. Due to differences in educational backgrounds and knowledge experiences, trust relationships among experts are often incomplete. To address such issues and reduce decision biases, this paper proposes a probabilistic linguistic multi-attribute group decision consensus model based on an incomplete social trust network (InSTN).

In this paper, we first define the new trust propagation operators based on the operations of Probability Language Term Set (PLTS) with algebraic t-conorm and t-norm, which are combined with trust aggregation operators to estimate InSTN. The adjustment coefficients are then determined through trust relations to quantify their impact on expert evaluation. Finally, the particle swarm algorithm (PSO) is used to optimize the expert evaluation to meet the consensus threshold.

This study demonstrates the feasibility of the method through the selection of treatment plans for complex cases. The proposed consensus model exhibits greater robustness and effectiveness compared to traditional methods, mainly due to the effective regulation of trust relations in the decision-making process, which reduces decision bias and inconsistencies.

This paper introduces a novel probabilistic linguistic multi-attribute swarm decision consensus model based on an InSTN. It proposes a redefined trust propagation and aggregation approach to estimate the InSTN. Moreover, the computational efficiency and decision consensus accuracy of the proposed model are enhanced by using PSO optimization.

This paper aims to propose a lightweight, high-accuracy object detection model designed to enhance seam tracking quality under strong arcs and splashes condition. Simultaneously, the model aims to reduce computational costs.

The lightweight model is constructed based on Single Shot Multibox Detector (SSD). First, a neural architecture search method based on meta-learning and genetic algorithm is introduced to optimize pruning strategy, reducing human intervention and improving efficiency. Additionally, the Alternating Direction Method of Multipliers (ADMM) is used to perform structural pruning on SSD, effectively compressing the model with minimal loss of accuracy.

Compared to state-of-the-art models, this method better balances feature extraction accuracy and inference speed. Furthermore, seam tracking experiments on this welding robot experimental platform demonstrate that the proposed method exhibits excellent accuracy and robustness in practical applications.

This paper presents an innovative approach that combines ADMM structural pruning and meta-learning-based neural architecture search to significantly enhance the efficiency and performance of the SSD network. This method reduces computational cost while ensuring high detection accuracy, providing a reliable solution for welding robot laser vision systems in practical applications.

Unlocking the potential of Big Data Analytics (BDA) has proven to be a transformative factor for the Architecture, Engineering and Construction (AEC) industry. This has prompted researchers to focus attention on BDA in the AEC industry (BDA-in-AECI) in recent years, leading to a proliferation of relevant research. However, an in-depth exploration of the literature on BDA-in-AECI remains scarce. As a result, this study seeks to systematically explore the state-of-the-art review on BDA-in-AECI and identify research trends and gaps in knowledge to guide future research.

This state-of-the-art review was conducted using a mixed-method systematic review. Relevant publications were retrieved from Scopus and then subjected to inclusion and exclusion criteria. A quantitative bibliometric analysis was conducted using VOSviewer software and Gephi to reveal the status quo of research in the domain. A further qualitative analysis was performed on carefully screened articles. Based on this mixed-method systematic review, knowledge gaps were identified and future research agendas of BDA-in-AECI were proposed.

The results show that BDA has been adopted to support AEC decision-making, safety and risk assessment, structural health monitoring, damage detection, waste management, project management and facilities management. BDA also plays a major role in achieving construction 4.0 and Industry 4.0. The study further revealed that data mining, cloud computing, predictive analytics, machine learning and artificial intelligence methods, such as deep learning, natural language processing and computer vision, are the key methods used for BDA-in-AECI. Moreover, several data acquisition platforms and technologies were identified, including building information modeling, Internet of Things (IoT), social networking and blockchain. Further studies are needed to examine the synergies between BDA and AI, BDA and Digital twin and BDA and blockchain in the AEC industry.

The study contributes to the BDA-in-AECI body of knowledge by providing a comprehensive scope of understanding and revealing areas for future research directions beneficial to the stakeholders in the AEC industry.

Considering the influence of deformation error, the target poses must be corrected when compensating for positioning error but the efficiency of existing positioning error compensation algorithms needs to be improved. Therefore, the purpose of this study is to propose a high-efficiency positioning error compensation method to reduce the calculation time.

The corrected target poses are calculated. An improved back propagation (BP) neural network is used to establish the mapping relationship between the original and corrected target poses. After the BP neural network is trained, the corrected target poses can be calculated with short notice on the basis of the pose correction similarity.

Under given conditions, the calculation time when the trained BP neural network is used to predict the corrected target poses is only 1.15 s. Compared with the existing algorithm, this method reduces the calculation time of the target poses from the order of minutes to the order of seconds.

The proposed algorithm is more efficient while maintaining the accuracy of the error compensation.

This method can be used to quickly position the error compensation of a large parallel mechanism.

The anomaly detection task for oil and gas pipelines based on acoustic signals faces issues such as background noise coverage, lack of effective features, and small sample sizes, resulting in low fault identification accuracy and slow efficiency. The purpose of this paper is to study an accurate and efficient method of pipeline anomaly detection.

First, to address the impact of background noise on the accuracy of anomaly signals, the adaptive multi-threshold center frequency variational mode decomposition method(AMTCF-VMD) method is used to eliminate strong noise in pipeline signals. Secondly, to address the strong data dependency and loss of local features in the Swin Transformer network, a Hybrid Pyramid ConvNet network with an Agent Attention mechanism is proposed. This compensates for the limitations of CNN’s receptive field and enhances the Swin Transformer’s global contextual feature representation capabilities. Thirdly, to address the sparsity and imbalance of anomaly samples, the SpecAugment and Scaper methods are integrated to enhance the model’s generalization ability.

In the pipeline anomaly audio and environmental datasets such as ESC-50, the AMTCF-VMD method shows more significant denoising effects compared to wavelet packet decomposition and EMD methods. Additionally, the model achieved 98.7% accuracy on the preprocessed anomaly audio dataset and 99.0% on the ESC-50 dataset.

This paper innovatively proposes and combines the AMTCF-VMD preprocessing method with the Agent-SwinPyramidNet model, addressing noise interference and low accuracy issues in pipeline anomaly detection, and providing strong support for oil and gas pipeline anomaly recognition tasks in high-noise environments.

The study seeks to explore the intricate dynamics among customer relationship management (CRM) practices, guest satisfaction and loyalty in the hospitality context. Additionally, it aims to examine the moderating influence of guest engagement on the relationships between CRM practices and guest satisfaction and loyalty.

An integrated theoretical framework is developed by incorporating CRM practices and guest engagement into the satisfaction-loyalty framework. Two research instruments were adapted from the literature to assess the perspectives of customers and employees in the hotel industry in Kashmir. The customer survey measured guest satisfaction, loyalty, and engagement, while the employee survey focused on CRM practices, including key customer focus and CRM organization. Data was collected using a pen-and-paper survey with convenience sampling across 10 qualifying hotels, each classified as 3-star or above. A total of 270 matched responses from guests and employees were obtained and analyzed using descriptive analysis, structural equation modeling (SEM), and moderation analysis with SPSS and AMOS software. The study utilized a rigorous data matching process to ensure reliability, with guest-employee pairs verified and cross-checked with hotel records.

The results indicate CRM practices play a pivotal role in shaping guest satisfaction and loyalty. Notably, personalization and a targeted customer approach emerged as the most influential factors in enhancing tourist satisfaction. Similarly, prospecting, personalization, and effective knowledge management significantly contributed to visitor loyalty. The establishment of robust relationships is underscored through collaborative active guest engagement. Furthermore, the study highlights the nuanced relationship between satisfaction and loyalty moderated by guest engagement. High levels of guest engagement amplify the positive impact of satisfaction on loyalty, while lower engagement levels attenuate this effect. Moreover, the moderating influence of guest engagement on the relationships between CRM practices and guest satisfaction and CRM practices and guest loyalty was notably strong at elevated guest engagement levels and relatively weaker at lower engagement levels.

While the study findings encourage organizations to prioritize customer relationship development, hospitality entities must emphasize the adoption of CRM philosophy and robust guest engagement measures. Actively involving guests in co-creating services can yield incremental benefits in terms of attracting, retaining, and effectively serving guests.

This study introduces novel dimensions to the existing CRM framework within the hospitality context, specifically exploring the impact of hotel-specific elements (personalization and prospecting) on customer satisfaction and loyalty. Furthermore, it innovatively investigates the moderating role of guest engagement in the satisfaction-loyalty relationship, expanding its scope to include the relationships between CRM practices and guest satisfaction and guest loyalty.

This study investigates near field communication (NFC) payment method adoption in the Republic of Kosova, aiming to understand factors influencing consumer behavior toward NFC technology adoption. Using the Unified Theory of Acceptance and Use of Technology (UTAUT-3) model and perceived risk theory, the research seeks to establish relationships between various factors and user intentions regarding NFC payment technology.

Using a quantitative approach, the research used a comprehensive questionnaire of 40 questions rated on a seven-point Likert scale across 16 constructs aligned with the research objectives. A convenience sampling method was used, distributing electronic questionnaires to 200 individuals representing diverse demographics in the Republic of Kosova.

The study identified significant support for numerous hypotheses, demonstrating substantial correlations between factors like performance expectancy, effort expectancy, social influence, habit, facilitating conditions and personal innovativeness with behavioral intention to use and behavioral intention to adopt NFC payments.

Because convenience sampling was used, there are restrictions on the study’s sample size. Moreover, although the study delves into noteworthy elements impacting the adoption of NFC payment systems, it might not cover all possible factors that could influence consumer behavior in this regard.

Policymakers, NFC product developers, companies in the technology and payment sectors and Republic of Kosova customers all gain strategically from the research’s findings. Policymakers may make informed judgments about legislation, improve product development and marketing tactics and empower consumers to accept NFC payments by having a better understanding of consumer preferences and behaviors related NFC technology.

Understanding consumer preferences and behaviors regarding NFC technology can refine product development and marketing strategies, inform policymaking and empower consumers’ decisions about adopting NFC payments.

This study’s innovative approach in combining the UTAUT-3 model and perceived risk theory contributes significantly to the understanding of factors influencing users’ intentions in adopting emerging payment technologies, filling a gap in NFC payment literature.

This study aims to revisit the properties of 24-item version of mindfulness scale proposed by Bohlmeijer et al. (2011) in an Indian context to add to the existing global knowledge base on mindfulness.

A questionnaire was administered to 531 adult employees working in the IT/ITES sector in India. Their responses were analysed using exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and validity statistics to validate the Indian adaptation of the five-facet mindfulness questionnaire (FFMQ).

The findings confirmed that the Indian version of the 24-item short form of the FFMQ (denoted as FFMQ-SF) matches the findings of Bohlmeijer et al. (2011). It can, therefore, provide valuable insights to both employees and management on the benefits of mindfulness in the workplace.

This paper also presents the limitations of this work along with scholarly and practical implications. It enhances the global understanding of mindfulness, with applications in education, health and well-being, workplaces, social justice, spirituality and personal growth.

This study justifies and presents a unique instrument for assessing employee mindfulness and is beneficial for both management and employees in navigating the evolving hybrid work environment. It promotes present-moment awareness in a non-judgemental manner, facilitating perspective shifts, improved self-regulation and experiential acceptance. Additionally, the study affirms the five-dimensional structure underlying the mindfulness construct.

Although visual prototypicality in fashion is an observed phenomenon, empirical examinations of the link between fashion products' design prototypicality and consumer evaluations still need to be included. The present study analyzes the influence of the visual prototypicality of fashion products on consumer-perceived product values and brand preference.

An online survey adopting the fashion product images with significantly differing levels of visual prototypicality was used to collect data from 456 US consumers. The hypothesized relationships among visual prototypicality, product values and brand preference were analyzed through multi-group analysis.

Perceived visual typicality of fashion product designs significantly increased the hedonic and utilitarian value of the product and only indirectly increase brand preference. The hypothesized positive relationship between visual prototypicality and the product’s social value was found to be significant only in the low-price levels but became insignificant in the high-price levels.

The findings of this study contribute to the extant literature by first providing an initial analysis of the mechanism of visual prototypicality in the fashion product design field. The results confirm that visual prototypicality indirectly influences consumers' brand evaluations by the product’s perceived value. This relationship was previously assumed but not empirically proven only in non-fashion product categories. The study also presents additional new points, further enriching the understanding of visual typicality. Additionally, the results show the complex relationship between the visual prototypicality of fashion product designs and the perceived social value of the product, which varies depending on the price range.