Search results

This study aims to streamline and enhance the assembly defect inspection process in diesel engine production. Traditional manual inspection methods are labor-intensive and time-consuming because of the complex structures of the engines and the noisy workshop environment. This study’s robotic system aims to alleviate these challenges by automating the inspection process and enabling easy remote inspection, thereby freeing workers from heavy fieldwork.

This study’s system uses a robotic arm to traverse and capture images of key components of the engine. This study uses anomaly detection algorithms to automatically identify defects in the captured images. Additionally, this system is enhanced by digital twin technology, which provides inspectors with various tools to designate components of interest in the engine and assist in defect checking and annotation. This integration facilitates smooth transitions from manual to automatic inspection within a short period.

Through evaluations and user studies conducted over a relatively long period, the authors found that the system accelerates and improves the accuracy of engine inspections. The results indicate that the system significantly enhances the efficiency of production processes for manufacturers.

The system represents a novel approach to engine inspection, leveraging robotic technology and digital twin enhancements to address the limitations of traditional manual inspection methods. By automating and enhancing the inspection process, the system offers manufacturers the opportunity to improve production efficiency and ensure the quality of diesel engines.

This paper aims to present the fabrication of 6061 aluminum alloy (AA6061) using a promising laser additive manufacturing process, called the laser-foil-printing (LFP) process. The process window of AA6061 in LFP was established to optimize process parameters for the fabrication of high strength, dense and crack-free parts even though AA6061 is challenging for laser additive manufacturing processes due to hot-cracking issues.

The multilayers AA6061 parts were fabricated by LFP to characterize for cracks and porosity. Mechanical properties of the LFP-fabricated AA6061 parts were tested using Vicker’s microhardness and tensile testes. The electron backscattered diffraction (EBSD) technique was used to reveal the grain structure and preferred orientation of AA6061 parts.

The crack-free AA6061 parts with a high relative density of 99.8% were successfully fabricated using the optimal process parameters in LFP. The LFP-fabricated parts exhibited exceptional tensile strength and comparable ductility compared to AA6061 samples fabricated by conventional laser powder bed fusion (LPBF) processes. The EBSD result shows the formation of cracks was correlated with the cooling rate of the melt pool as cracks tended to develop within finer grain structures, which were formed in a shorter solidification time and higher cooling rate.

This study presents the pioneering achievement of fabricating crack-free AA6061 parts using LFP without the necessity of preheating the substrate or mixing nanoparticles into the melt pool during the laser melting. The study includes a comprehensive examination of both the mechanical properties and grain structures, with comparisons made to parts produced through the traditional LPBF method.

Innovation in service, procedure and product design is essential for long-term success in today's fast-paced and cutthroat hospitality sector. This study aims to learn how innovation may revolutionise the hospitality sector and lead to memorable guest experiences.

The research delves into new ways of thinking about service design, emphasizing how to create engaging and individual customer experiences (CXs). In order to stand out in a crowded hospitality market and keep up with customers' ever-changing demands, businesses in the industry are experimenting with new approaches to service, like co-creation, personalisation and experience design.

The study also digs into process innovation in the hotel industry, looking at how the latest tech and automation are helping to streamline processes and boost productivity. Reservation systems, guest check-in and check-out, cleaning and supply chain management are just a few areas that get studied. The study delves into how thoughtful product design may enrich visitors' hotel stays. It explores new and interesting services like in-room entertainment, eco-friendly building techniques and creative cuisine. The study investigates how these unique items affect customers' opinions of the products' worth, satisfaction and loyalty.

Methods such as in-depth interviews with experts, guest surveys and the examination of case studies highlighting cutting-edge design in the hospitality industry are all part of the research strategy. This project seeks to provide useful insights and recommendations for hospitality firms that want to adopt innovative service, process and product design methods by analysing real-world instances and gathering empirical data.

The purpose of this study investigate the antecedents the adoption of tour itineraries from smart travel apps. Travelers are progressively expanding their smart travel planning applications to organize their trip-related activities. With the help of these apps, users achieve their favorite tour itineraries and choose their preferred destinations.

This study aimed to examine the results of smart tour itineraries on travelers and elucidate the motivations for their continual use and why travel experts are increasingly using smart tour itineraries. Innovation resistance and experiential consumption theories were used in this study. SmartPLS 3.2.8 was used to consider 682 valid samples using structural equation modeling (SEM).

This analysis identified the following crucial factors: usage, value, risk and traditional barriers. Moreover, utilitarian and hedonic values significantly affected barriers. Finally, theoretical and practical suggestions are presented along with future research directions.

This study encompasses the tender of innovation resistance theory to travel itineraries by integrating experiential consumption theory in the context of smart tourism apps.

Light field images (LFIs) have gained popularity as a technology to increase the field of view (FoV) of plenoptic cameras since they can capture information about light rays with a large FoV. Wide FoV causes light field (LF) data to increase rapidly, which restricts the use of LF imaging in image processing, visual analysis and user interface. Effective LFI coding methods become of paramount importance. This paper aims to eliminate more redundancy by exploring sparsity and correlation in the angular domain of LFIs, as well as mitigate the loss of perceptual quality of LFIs caused by encoding.

This work proposes a new efficient LF coding framework. On the coding side, a new sampling scheme and a hierarchical prediction structure are used to eliminate redundancy in the LFI's angular and spatial domains. At the decoding side, high-quality dense LF is reconstructed using a view synthesis method based on the residual channel attention network (RCAN).

In three different LF datasets, our proposed coding framework not only reduces the transmitted bit rate but also maintains a higher view quality than the current more advanced methods.

(1) A new sampling scheme is designed to synthesize high-quality LFIs while better ensuring LF angular domain sparsity. (2) To further eliminate redundancy in the spatial domain, new ranking schemes and hierarchical prediction structures are designed. (3) A synthetic network based on RCAN and a novel loss function is designed to mitigate the perceptual quality loss due to the coding process.

The development of ‘smart destinations’ has fundamentally altered the travel and tourism sector by making trips more individualised and enhancing overall operational effectiveness. Using (AI) technology in the creation of smart destinations has provided the tourism industry with the opportunity to reimagine and redevelop tourism in a way that is both environmentally responsible and technologically advanced. This chapter investigates the use of AI and smart destinations in China and Hong Kong with the goals of enhancing tourist experiences, boosting environmental sustainability, and propelling economic expansion. AI and smart destinations have greatly impacted China and Hong Kong tourism. AI has improved tourism, customer service, and trip recommendations while intelligent destinations reduce carbon emissions and promote ecotourism to sustain tourism. AI and intelligent locations boost visitor satisfaction and economic growth. As the tourism industry faces future challenges, AI technology and smart destinations will be crucial to creatively and sustainably rebuild tourism. Smart locations and AI have transformed tourism by offering customised, efficient, and environmentally responsible travel experiences.

Agriculture is one sector where the Internet of things (IoT) is expected to make a major impact. Yet, its adoption in the sector falls behind expectations. The purpose of this paper is to present the state-of-the-art of IoT in agriculture and investigate its slow adoption in the sector.

The authors have undertaken a systematic review and a synthesis of 1355 relevant publications over the last decade.

This literature review reveals that the “big three” barriers for the overall sector are cost, skills and standardization. The lack of connectivity and data governance are two key reasons why most of the proposed IoT solutions are standalone systems of limited scope, while the majority of commercial IoT efforts focus on practices in the protected indoor environment. Lastly, the analysis of past research along the five layers of the IoT system architecture reveals limited attention to barriers and solutions at the business layer, which represents a research opportunity for information systems scholars.

It is possible that some of relevant publications were missed in the literature search, although the search queries were kept as broad as possible to avoid the exclusion of any relevant work. Any publication written in any other language other than English was excluded from the review. Given the geographical distribution of the reviewed English publications (see section 4.1), it is highly likely that important works written by Chinese and European scholars in their native language were overlooked.

This study provides practical insights into the technical and organisational challenges on the ground. It is the hope that this literature review lays the groundwork for IS researchers who are well positioned to investigate technology adoption challenges in the relatively understudied agriculture sector.

To the best of the authors’ knowledge, this is the first comprehensive review of adoption barriers and solutions across all five layers of the IoT system architecture.

The hospitality industry has been debating the merits of sustainable tourism for some time, but more and more people are becoming interested in regenerative tourism. The concept of regenerative tourism goes beyond that of sustainable tourism in that it seeks to restore and regenerate both the natural environment and the communities in which it operates. The Chinese hospitality industry has been expanding at a rapid rate and has the potential to adopt practices that are associated with regenerative tourism. This chapter investigated the hospitality industry of China and the shift from sustainable tourism to regenerative tourism. Consumer expectations, government programmes, and AI are driving this shift from sustainable to regenerative tourism. Despite the challenges of transitioning tourism from sustainable to regenerative, many Chinese initiatives and programmes are helping the environment and communities.

By studying the green development efficiency (GDE) of 33 cities in the provinces of Jiangsu, Zhejiang, and Fujian in China, this study strives to conduct an analysis of the sustainable practices implemented in these developed regions, and derive valuable insights that can foster the promotion of green transformation.

First, the urban green development system (GDS) was decomposed into the economic benefit subsystem (EBS), social benefit subsystem (SBS), and pollution control subsystem (PCS). Then, a mixed network SBM model was proposed to evaluate the GDE during 20152020, with Moran’s I and Bootstrap truncated regression model subsequently applied to measure the spatial characteristics and driving factors of efficiency.

Subsystem efficiency presents a distribution trend of PCS > EBS > SBS. There is a particular spatial aggregation effect in EBS efficiency, whereas SBS and PCS efficiencies have no significant spatial autocorrelation. Furthermore, urbanization level contributes significantly to the efficiency of all subsystems; industrial structure, energy consumption, and technological innovation play a crucial role in EBS and SBS; external openness is a pivotal factor in SBS; and environmental regulation has a significant effect on PCS.

This study further decomposes the black box of GDS into subsystems including the economy, society, and environment. Additionally, by employing a mixed network SBM model and Bootstrap truncated regression model to investigate efficiency and its driving factors from the subsystem perspective, it endeavors to derive more detailed research conclusions and policy implications.

The current study is a review-based analysis combined with some case studies that focuses on establishing a link between artificial intelligence (AI) and the emerging trends of regenerative tourism and green destinations. Regenerative tourism and green destinations are the new hallmark, promoting sustainability in the travel industry by restoring ecosystems and encouraging friendly practices. The incorporation of AI into sustainable tourism has a potential to revolutionize how one can approach tourism by providing customer experiences and to contribute towards a sustainable future. AI has naturally found its place in industries due to the advancements in data analysis and computing power. In the context of tourism, AI’s data-driven capabilities are discussed in the current review, to showcase how they enable recommendations for intelligent automation and efficient resource management. With the implementation of AI-powered technologies, tourism operations become more efficient, providing opportunities for sustainable development and conservation in green destinations. The integration of AI in destinations encompasses applications such as energy management, waste reduction, transportation optimization and sustainable resource management. These AI-driven solutions play an important role in minimizing the impact caused by tourism activities while conserving natural resources. Additionally, AI facilitates delivering experiences that align with eco-values through recommendation systems and virtual assistants. The chapter tackles issues related to AI such as protecting data privacy, addressing biases dealing with job displacement and ensuring cultural relevance. It emphasizes the significance of inclusive implementation of AI and explores the challenges faced when implementing AI solutions in developing regions that have limited resources.