Search results

This paper explores what factors influence household textile washing behaviour and how these factors relate to greenhouse gas emissions during the textile use stage.

A questionnaire survey related to textile summer washing and care behavior was conducted among households in 16 administrative districts of Shanghai. This study used the modified Consumer Lifestyle Approach framework of the washing and care ecosystem. The research hypotheses were established by selecting related factors from four aspects: household demographic characteristics, economy and consumption characteristics, washing machines and detergents characteristics.

First, we have demonstrated how some course factors do not significantly affect greenhouse emissions. None of the demographics, detergent-related activities, economy and consumption constructs significantly affect greenhouse emissions. Second, we have identified that washing machine and related activities has a direct positive effect on GHG emissions. The washing machine is not only the de facto carrier of all washing activities but also the core of washing activities. Washing machine is crucial in reducing greenhouse emissions and adjusting consumer behaviors.

This paper conducts a study related to the washing and care behavior of households in Shanghai. The paper examines the factors influencing household washing behavior and the relationship between these factors and greenhouse gas emissions during the textile use phase.

In the 21st century, libraries are experiencing a significant decline in users due to shifting reading habits and the impact of technology, necessitating library transformation and a heightened emphasis on library marketing. Special libraries, in particular, rely heavily on patrons for survival, often due to their private ownership and limited resources. This paper aims to discuss the aforementioned objectives.

This paper examines the Taste Library, a special library in Hong Kong, and analyzes its current practices based on an interview with its founder, website content, and social media presence. The 7Ps Marketing Mix model is employed to assess the strengths and weaknesses of the library's current market position.

The Taste Library's existing practices exhibit limitations in attracting young patrons. To address this issue, we propose marketing strategies focused on enhancing social network presence, offering digitized content, and engaging in school outreach.

By concentrating on youth marketing, this study offers valuable insights for special libraries in developing strategic plans for transitioning and maintaining sustainability.

Few studies concentrate on marketing small special libraries, particularly in the East, within today's digitized economy.

This study is among the first to integrate the technology acceptance model (TAM) and value-based adoption model (VAM) in the context of augmented reality (AR) shopping. It assesses how consumers' rational (TAM) and emotional (VAM) factors influence their intention to use AR in online shopping via perceived value and consumer engagement.

This study uses a quantitative research approach and employs a standardized survey questionnaire distributed on social media platforms to recruit Gen Z members who are potential buyers or users of AR technology. SmartPLS 4.0 was used to test the responses of 204 respondents.

The results indicate that consumers who perceive a higher value of AR in shopping are inclined to use AR in their future shopping when AR shopping is easy to use, useful, personalized, innovative and provides a highly engaging experience. Interestingly, perceived sacrifice did not influence perceived value. This study confirms that integrating TAM and VAM is instrumental in capturing value, which in turn influences engagement and the intention to use AR in online shopping.

This study further extends the conceptualization of AR perceived value by combining rational components derived from TAM and VAM, thus leading to a sturdy and theoretically grounded framework. In addition, this study contributes to the literature on extended reality, namely AR shopping, and helps brand managers manage highly evolving AR experience for Gen Z.

Workers' unsafe behavior is the main cause of construction safety accidents, thereby highlighting the critical importance of behavior-based management. To compensate for the limitations of computer vision in tackling knowledge-intensive issues, semantic-based methods have gained increasing attention in the field of construction safety management. Knowledge graph provides an efficient and visualized method for the identification of various unsafe behaviors.

This study proposes an unsafe behavior identification framework by integrating computer vision and knowledge graph–based reasoning. An enhanced ontology model anchors our framework, with image features from YOLOv5, COCO Panoptic Segmentation and DeepSORT integrated into the graph database, culminating in a structured knowledge graph. An inference module is also developed, enabling automated the extraction of unsafe behavior knowledge through rule-based reasoning.

A case application is implemented to demonstrate the feasibility and effectiveness of the proposed method. Results show that the method can identify various unsafe behaviors from images of construction sites and provide mitigation recommendations for safety managers by automated reasoning, thus supporting on-site safety management and safety education.

Existing studies focus on spatial relationships, often neglecting the diversified spatiotemporal information in images. Besides, previous research in construction safety only partially automated knowledge graph construction and reasoning processes. In contrast, this study constructs an enhanced knowledge graph integrating static and dynamic data, coupled with an inference module for fully automated knowledge-based unsafe behavior identification. It can help managers grasp the workers’ behavior dynamics and timely implement measures to correct violations.

The purpose of this paper is to analyze the symbiotic evolution decisions of digital innovation enterprises, research institutes and the government in the digital innovation ecosystem.

Based on innovation ecosystem theory and an evolutionary game model, this study constructs a tripartite symbiotic evolution game model of digital innovation ecosystems with digital innovation enterprises, research institutes and the government as the main bodies and analyzes the influencing factors as well as the evolution paths of the different behavioral strategies of each subject through numerical simulation.

The research shows that the digital innovation ecosystem has the characteristic of self-organization, which requires the symbiotic cooperation of each subject. The government plays an active role in any stage of symbiotic evolution, and the system cannot enter symbiosis under a low level of subsidies and penalties. Only when the initial willingness to cooperate of digital innovation enterprises and scientific research institutes is at a medium or high level is the system likely to become symbiotic. While digital innovation enterprises are more sensitive to government subsidies and punishments, scientific research institutes are more sensitive to the distribution proportion of cooperation income.

This study includes government regulation into the research scope, expands the research mode of the digital innovation ecosystem and overcomes the difficulties of empirical research in collecting dynamic large sample data. It vividly and systematically simulates the symbiotic evolution process of the digital innovation ecosystem, which provides a theoretical and practical reference for digital innovation ecosystem governance.

The coupling process between the loading mechanism and the tank car mouth is a crucial step in the tank car loading process. The purpose of this paper is to design a method to accurately measure the pose of the tanker car.

The collected image is first subjected to a gray enhancement operation, and the black parts of the image are extracted using Otsu’s threshold segmentation and morphological processing. The edge pixels are then filtered to remove outliers and noise, and the remaining effective points are used to fit the contour information of the tank car mouth. Using the successfully extracted contour information, the pose information of the tank car mouth in the camera coordinate system is obtained by establishing a binocular projection elliptical cone model, and the pixel position of the real circle center is obtained through the projection section. Finally, the binocular triangulation method is used to determine the position information of the tank car mouth in space.

Experimental results have shown that this method for measuring the position and orientation of the tank car mouth is highly accurate and can meet the requirements for industrial loading accuracy.

A method for extracting the contours of various types of complex tanker mouth is proposed. This method can accurately extract the contour of the tanker mouth when the contour is occluded or disturbed. Based on the binocular elliptic conical model and perspective projection theory, an innovative method for measuring the pose of the tanker mouth is proposed, and according to the space characteristics of the tanker mouth itself, the ambiguity of understanding is removed. This provides a new idea for the automatic loading of ash tank cars.

Manual monitoring is a conventional method for monitoring and managing construction safety risks. However, construction sites involve risk coupling - a phenomenon in which multiple safety risk factors occur at the same time and amplify the probability of construction accidents. It is challenging to manually monitor safety risks that occur simultaneously at different times and locations, especially considering the limitations of risk manager’s expertise and human capacity.

To address this challenge, an automatic approach that integrates point cloud, computer vision technologies, and Bayesian networks for simultaneous monitoring and evaluation of multiple on-site construction risks is proposed. This approach supports the identification of risk couplings and decision-making process through a system that combines real-time monitoring of multiple safety risks with expert knowledge. The proposed approach was applied to a foundation project, from laboratory experiments to a real-world case application.

In the laboratory experiment, the proposed approach effectively monitored and assessed the interdependent risks coupling in foundation pit construction. In the real-world case, the proposed approach shows good adaptability to the actual construction application.

The core contribution of this study lies in the combination of an automatic monitoring method with an expert knowledge system to quantitatively assess the impact of risk coupling. This approach offers a valuable tool for risk managers in foundation pit construction, promoting a proactive and informed risk coupling management strategy.

Surface acoustic wave (SAW) sensors have attracted great attention worldwide for a variety of applications in measuring physical, chemical and biological parameters. However, stability has been one of the key issues which have limited their effective commercial applications. To fully understand this challenge of operation stability, this paper aims to systematically review mechanisms, stability issues and future challenges of SAW sensors for various applications.

This review paper starts with different types of SAWs, advantages and disadvantages of different types of SAW sensors and then the stability issues of SAW sensors. Subsequently, recent efforts made by researchers for improving working stability of SAW sensors are reviewed. Finally, it discusses the existing challenges and future prospects of SAW sensors in the rapidly growing Internet of Things-enabled application market.

A large number of scientific articles related to SAW technologies were found, and a number of opportunities for future researchers were identified. Over the past 20 years, SAW-related research has gained a growing interest of researchers. SAW sensors have attracted more and more researchers worldwide over the years, but the research topics of SAW sensor stability only own an extremely poor percentage in the total researc topics of SAWs or SAW sensors.

Although SAW sensors have been attracting researchers worldwide for decades, researchers mainly focused on the new materials and design strategies for SAW sensors to achieve good sensitivity and selectivity, and little work can be found on the stability issues of SAW sensors, which are so important for SAW sensor industries and one of the key factors to be mature products. Therefore, this paper systematically reviewed the SAW sensors from their fundamental mechanisms to stability issues and indicated their future challenges for various applications.

With the uptake of prefabricated construction (PC) facing serious obstacles in China, networked innovation can break the technical constraints while also containing the risks in individual innovation. However, the construction community has paid little attention to PC innovation, especially networked innovation. This study aims to gain deep insights into what impacts the formation and dynamics of a prefabricated construction innovation network (PCIN). With the uptake of PC facing serious obstacles in China, networked innovation can break the technical constraints while also containing the risks in individual innovation.

The research design follows a sequential mixed methodology of qualitative and quantitative data collection and analysis. It first conceptualizes the PCIN based on the quadruple helix model and formulates a corresponding system dynamics model based on causality analysis. After validating the PCIN model using empirical data, simulations are carried out using Vensim PLE software. Finally, this study identifies key factors that promote the formation of PCIN in China through sensitivity analysis.

The results show that PC predicts a continuous increase in practice as of 2030. The tested drivers all positively influence the formation of the PCIN, with market demand and risk sharing having the greatest influence, followed by competitive pressure, profit government support, scientific and technological advancement and collaborative innovation strategy.

The study makes three major contributions. First, it provides a novel angle for a deeper understanding of the PC innovation. Second, it proposes a new approach for probing the formation and dynamics of the PCIN. Finally, it offers a theoretical reference for promoting the formation of innovation networks and the development of PC.