Ky Nam Nguyen, Quang Anh Phan and Ngoc Minh Nguyen
This paper aims to examine the management status quo of archaeological heritage in Vietnam seen in the case of Vuon Chuoi, a complex of Bronze Age sites located in Central Hanoi…
Abstract
Purpose
This paper aims to examine the management status quo of archaeological heritage in Vietnam seen in the case of Vuon Chuoi, a complex of Bronze Age sites located in Central Hanoi, which has been believed to be Hanoi’s first human settlement. Like other archaeological sites located in urban areas, this site has been under threat of destruction caused by land encroachment pressure. Although researchers have long waged a campaign for preservation, the dissensus among key stakeholders and the dispute over responsibility have left this site at the heart of an interminable polemic over legislation.
Design/methodology/approach
This research utilises a qualitative approach, and the primary data were collected throughout multiple field trips in 2019 and 2020. Several open-ended interviews were conducted with various state and nonstate actors involved in the Vuon Chuoi Complex’s management process. The discussion was also supported by analysing related legal documents retrieved from national archives and official online directories.
Findings
This paper dissects the current legislative and administrative framework applied in governing heritage in general and archaeological sites in Vietnam, in particular. The results indicate that existing flaws in Vietnam’s legal system are detectable, and the unsystematic organisation has led to deferment of the decision-making processes. Also, there is an apparent difference found in the attitude of the bodies in charge toward the treatment of listed and unlisted sites.
Originality/value
This research outlines that in the wake of urbanisation and industrialisation in Vietnam, a consensus among key stakeholders and an inclusive legal system are required to help preserve archaeological sites in urgent need of attention. Although several Vietnamese laws and regulations have been put into practice, they have shown critical barriers and gaps in conserving Vietnamese cultural heritage.
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Keywords
Cong Doanh Duong, Duc Tho Bui, Huong Thao Pham, Anh Trong Vu and Van Hoang Nguyen
The emergence of artificial intelligence technologies, like ChatGPT, has taken the world by storm, particularly in the education sector. This study aims to adopt the unified…
Abstract
Purpose
The emergence of artificial intelligence technologies, like ChatGPT, has taken the world by storm, particularly in the education sector. This study aims to adopt the unified theory of acceptance and use of technology to explore how effort expectancy (EEC) and performance expectancy (PEE) individually, jointly, congruently and incongruently affect higher education students’ intentions and actual uses of ChatGPT for their learning.
Design/methodology/approach
An advanced methodology – polynomial regression with response surface analysis – and a sample of 1,461 higher education students recruited in Vietnam through three-phase stratified random sampling approach were adopted to test developed hypotheses.
Findings
Both EEC and PEE were found to have a direct positive impact on the likelihood of higher education students’ intention to use ChatGPT, which in turn promotes them actually use this tool for learning purposes. Conversely, a large incongruence between EEC and PEE will lower the level of intentions and actual uses of ChatGPT for learning. However, when there is a growing incongruence between EEC and PEE, either in a positive or negative direction, the likelihood of students’ intentions to use ChatGPT for learning decreases.
Practical implications
Some practical implications are subsequently recommended to obtain advantages and address potential threats arising from the implementation of this novel technology in the education context.
Originality/value
This study shed the new light on the educational setting by testing how higher education students’ intentions to use ChatGPT and subsequent actual uses of ChatGPT are synthesized from the balance between high EEC and PEE.
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Sandra Matarneh, Faris Elghaish, Amani Al-Ghraibah, Essam Abdellatef and David John Edwards
Incipient detection of pavement deterioration (such as crack identification) is critical to optimizing road maintenance because it enables preventative steps to be implemented to…
Abstract
Purpose
Incipient detection of pavement deterioration (such as crack identification) is critical to optimizing road maintenance because it enables preventative steps to be implemented to mitigate damage and possible failure. Traditional visual inspection has been largely superseded by semi-automatic/automatic procedures given significant advancements in image processing. Therefore, there is a need to develop automated tools to detect and classify cracks.
Design/methodology/approach
The literature review is employed to evaluate existing attempts to use Hough transform algorithm and highlight issues that should be improved. Then, developing a simple low-cost crack detection method based on the Hough transform algorithm for pavement crack detection and classification.
Findings
Analysis results reveal that model accuracy reaches 92.14% for vertical cracks, 93.03% for diagonal cracks and 95.61% for horizontal cracks. The time lapse for detecting the crack type for one image is circa 0.98 s for vertical cracks, 0.79 s for horizontal cracks and 0.83 s for diagonal cracks. Ensuing discourse serves to illustrate the inherent potential of a simple low-cost image processing method in automated pavement crack detection. Moreover, this method provides direct guidance for long-term pavement optimal maintenance decisions.
Research limitations/implications
The outcome of this research can help highway agencies to detect and classify cracks accurately for a very long highway without a need for manual inspection, which can significantly minimize cost.
Originality/value
Hough transform algorithm was tested in terms of detect and classify a large dataset of highway images, and the accuracy reaches 92.14%, which can be considered as a very accurate percentage regarding automated cracks and distresses classification.