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The swivel construction method is a specially designed process used to build bridges that cross rivers, valleys, railroads and other obstacles. To carry out this construction method safely, real-time monitoring of the bridge rotation process is required to ensure a smooth swivel operation without collisions. However, the traditional means of monitoring using Electronic Total Station tools cannot realize real-time monitoring, and monitoring using motion sensors or GPS is cumbersome to use.

This study proposes a monitoring method based on a series of computer vision (CV) technologies, which can monitor the rotation angle, velocity and inclination angle of the swivel construction in real-time. First, three proposed CV algorithms was developed in a laboratory environment. The experimental tests were carried out on a bridge scale model to select the outperformed algorithms for rotation, velocity and inclination monitor, respectively, as the final monitoring method in proposed method. Then, the selected method was implemented to monitor an actual bridge during its swivel construction to verify the applicability.

In the laboratory study, the monitoring data measured with the selected monitoring algorithms was compared with those measured by an Electronic Total Station and the errors in terms of rotation angle, velocity and inclination angle, were 0.040%, 0.040%, and −0.454%, respectively, thus validating the accuracy of the proposed method. In the pilot actual application, the method was shown to be feasible in a real construction application.

In a well-controlled laboratory the optimal algorithms for bridge swivel construction are identified and in an actual project the proposed method is verified. The proposed CV method is complementary to the use of Electronic Total Station tools, motion sensors, and GPS for safety monitoring of swivel construction of bridges. It also contributes to being a possible approach without data-driven model training. Its principal advantages are that it both provides real-time monitoring and is easy to deploy in real construction applications.

This study investigates the challenges and opportunities that organisations face in implementing Quality 4.0 as an approach to quality management and investigate the current state of Quality 4.0 implementation.

This study uses a qualitative research methodology to interview senior managers from globally based manufacturing and service industries.

The study explicates that most organisations implemented Quality 4.0 to improve their flexibility, efficiency, transparency and productivity while focusing on improving service quality, customer satisfaction and reducing cost. In terms of sustainability of Quality 4.0 the key factors found were a consistent effort from the top management, continuous training to employees, building leadership quality and creating a habit of using Quality 4.0.

The findings of this study offer useful guidance to organisations desirous of implementing Quality 4.0. In addition, the findings have identified key sustainability factors, helping organisations ensure a successful implementation and long-term returns from Quality 4.0.

The findings of this study contribute to the body of knowledge related to Quality 4.0 and help organisations in their digital transformation journey. In addition, it is one of the first studies to investigate the key factors for Quality 4.0 sustainability.

With the continuous growth of online education, the quality issue of online educational videos has become increasingly prominent, causing students in online learning to face the dilemma of knowledge confusion. The existing mechanisms for controlling the quality of online educational videos suffer from subjectivity and low timeliness. Monitoring the quality of online educational videos involves analyzing metadata features and log data, which is an important aspect. With the development of artificial intelligence technology, deep learning techniques with strong predictive capabilities can provide new methods for predicting the quality of online educational videos, effectively overcoming the shortcomings of existing methods. The purpose of this study is to find a deep neural network that can model the dynamic and static features of the video itself, as well as the relationships between videos, to achieve dynamic monitoring of the quality of online educational videos.

The quality of a video cannot be directly measured. According to previous research, the authors use engagement to represent the level of video quality. Engagement is the normalized participation time, which represents the degree to which learners tend to participate in the video. Based on existing public data sets, this study designs an online educational video engagement prediction model based on dynamic graph neural networks (DGNNs). The model is trained based on the video’s static features and dynamic features generated after its release by constructing dynamic graph data. The model includes a spatiotemporal feature extraction layer composed of DGNNs, which can effectively extract the time and space features contained in the video's dynamic graph data. The trained model is used to predict the engagement level of learners with the video on day T after its release, thereby achieving dynamic monitoring of video quality.

Models with spatiotemporal feature extraction layers consisting of four types of DGNNs can accurately predict the engagement level of online educational videos. Of these, the model using the temporal graph convolutional neural network has the smallest prediction error. In dynamic graph construction, using cosine similarity and Euclidean distance functions with reasonable threshold settings can construct a structurally appropriate dynamic graph. In the training of this model, the amount of historical time series data used will affect the model’s predictive performance. The more historical time series data used, the smaller the prediction error of the trained model.

A limitation of this study is that not all video data in the data set was used to construct the dynamic graph due to memory constraints. In addition, the DGNNs used in the spatiotemporal feature extraction layer are relatively conventional.

In this study, the authors propose an online educational video engagement prediction model based on DGNNs, which can achieve the dynamic monitoring of video quality. The model can be applied as part of a video quality monitoring mechanism for various online educational resource platforms.

The work efficiency and energy consumption of astronauts in the space station are the key issues in the operation of the space station, and how to evaluate the lean value of their activities is also complex and abstract. Combined with the idea of lean management, this paper aims to propose an space station dynamic value stream mapping system that can monitor and continuously improve the value flow and energy flow of astronauts in real time through lean methods.

Through systematic literature review, it is found that there is little research on the issue of lean management for astronauts. In manufacturing and services, value stream mapping is widely used for lean management. However, the static value stream map lacks the characteristics of real-time dynamics. This paper proposes to take the three modules of Muda detection, action recognition and energy monitoring as the basic content of the astronaut lean management framework to make the value stream and energy stream dynamic.

The theoretical framework of astronaut lean management is initially constructed, and the reasons for astronaut Muda and improvement ideas are also analyzed.

In fact, practitioners can use the proposed framework to identify the value of astronauts. Academically, these results collect research on dynamic value stream and lean management, providing a new way to identify value in aerospace using lean methods. Finally, the future research goals of astronaut lean management are put forward.

The activities using drum resources restrict the operation of multi-project systems. However, existing monitoring methods are not suitable for the characteristics of drum activities in the multi-project system. The authors therefore propose an adaptive capacity constraint buffer monitoring model based on the attributes of drum activities, aiming to build a high-efficiency progress control framework for multiple projects.

Considering the attributes and the interrelationship of drum activities, the monitoring reference points are determined on the basis of decentralized buffers. The authors next set action thresholds according to the relationship between the drum activities' interval margin and buffer consumption, and then the corresponding monitoring measures are taken.

The empirical results show that, compared to the classic methods, the proposed approach can effectively monitor the progress of the drum plan and realize the dual optimization of multi-project duration and cost.

The buffer consumption at the follow-up monitoring time point is neglected when determining the action thresholds. Prediction methods can be introduced to present more all-sided monitoring.

This paper fulfils the dual optimization of multi-project duration and cost. It provides a reference guide for project managers.

A capacity constraint buffer monitoring method suitable for a multi-project environment is produced.

One of the recommendations in process-based cognitive therapies suggests that functional analysis created by the clinician should be supported by empirical data collected through daily sampling. It enables the computing of a dynamic network of psychological processes and symptoms supporting clinical decisions but also therapeutic progress monitoring. However, the experience sampling solutions available in the market do not enable the automatic creation of this kind of network; thus, the use of this approach in clinical practice is practically impossible without advanced statistical skills and significant time investment. The purpose of the present paper is to describes a protocol of a research project based on a participatory approach aiming to create a solution enabling therapists not only to set up a personalized daily sampling for their patients and collect the data but also providing a fully automated visualization of the network adapted for therapeutic purposes.

The project will require creating a platform for therapists where they can set up monitoring and receive dynamic networks visualization, creating an experience sampling application for patients connected to the platform and creating an optimal data visualization system that will enable therapists to accurately and quickly interpret the network. A series of participatory workshops, qualitative and quantitative studies are described.

The presented studies will enable us to evaluate the ergonomy of use of both platform and app in laboratory and ecological settings along with the evaluation of network interpretation accuracy.

To the best of the authors’ knowledge, this is the first participatory design protocol for creating a solution that might enable clinicians to use a dynamic network approach in their everyday clinical practice. The challenges and opportunities of creating this kind of mHealth solution are discussed.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

The purpose of the study is to propose a normative approach for market segmentation, profile and monitoring using computing and information technology to analyze User-Generated Content (UGC).

The specific steps include performing a structural analysis of the UGC and extracting the base variables and values from it, generating a consumer characteristics matrix for segmenting process, and finally describing the segments' preferences, regional and dynamic characteristics. The authors verify the feasibility of the method with publicly available data. The external validity of the method is also tested through questionnaires and product regional sales data.

The authors apply the proposed methodology to analyze 53,526 UGCs in the New Energy Vehicle (NEV) market and classify consumers into four segments: Brand-Value Suitors (32%), Rational Consumers (21%), High-Quality Fanciers (26%) and Utility-driven Consumers (21%). The authors describe four segments' preferences, dynamic changes over the past six years and regional characteristics among China's top five sales cities. Then, the authors verify the external validity of the methodology through a questionnaire survey and actual NEV sales in China.

The proposed method enables companies to utilize computing and information technology to understand the market structure and grasp the dynamic trends of market segments, which assists them in developing R&D and marketing plans.

This study contributes to the research on UGC-based universal market segmentation methods. In addition, the proposed UGC structural analysis algorithm implements a more fine-grained data analysis.

The safety and reliability of high-speed trains rely on the structural integrity of their components and the dynamic performance of the entire vehicle system. This paper aims to define and substantiate the assessment of the structural integrity and dynamical integrity of high-speed trains in both theory and practice. The key principles and approaches will be proposed, and their applications to high-speed trains in China will be presented.

First, the structural integrity and dynamical integrity of high-speed trains are defined, and their relationship is introduced. Then, the principles for assessing the structural integrity of structural and dynamical components are presented and practical examples of gearboxes and dampers are provided. Finally, the principles and approaches for assessing the dynamical integrity of high-speed trains are presented and a novel operational assessment method is further presented.

Vehicle system dynamics is the core of the proposed framework that provides the loads and vibrations on train components and the dynamic performance of the entire vehicle system. For assessing the structural integrity of structural components, an open-loop analysis considering both normal and abnormal vehicle conditions is needed. For assessing the structural integrity of dynamical components, a closed-loop analysis involving the influence of wear and degradation on vehicle system dynamics is needed. The analysis of vehicle system dynamics should follow the principles of complete objects, conditions and indices. Numerical, experimental and operational approaches should be combined to achieve effective assessments.

The practical applications demonstrate that assessing the structural integrity and dynamical integrity of high-speed trains can support better control of critical defects, better lifespan management of train components and better maintenance decision-making for high-speed trains.

With increasing complexity of construction projects and new construction processes and methods are adopted, more safety hazards are emerging at construction sites, requiring the application of the modern risk management methods. As an emerging technology, digital twin has already made valuable contributions to safety risk management in many fields. Therefore, exploring the application of digital twin technology in construction safety risk management is of great significance. The purpose of this study is to explore the current research status and application potential of digital twin technology in construction safety risk management.

This study followed a four-stage literature processing approach as outlined in the systematic literature review procedure guidelines. It then combined the quantitative analysis tools and qualitative analysis methods to organize and summarize the current research status of digital twin technology in the field of construction safety risk management, analyze the application of digital twin technology in construction safety risk management and identify future research trends.

The research findings indicate that the application of digital twin technology in the field of construction safety risk management is still in its early stages. Based on the results of the literature analysis, this paper summarizes five aspects of digital twin technology's application in construction safety risk management: real-time monitoring and early warning, safety risk prediction and assessment, accident simulation and emergency response, safety risk management decision support and safety training and education. It also proposes future research trends based on the current research challenges.

This study provides valuable references for the extended application of digital twin technology and offers a new perspective and approach for modern construction safety risk management. It contributes to the enhancement of the theoretical framework for construction safety risk management and the improvement of on-site construction safety.