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This paper introduces a methodology to identify, analyse and represent heritage site attributes, emphasizing their impact on value, authenticity, integrity and management, with a case study on Ghadames, Libya. Inscribed in 1986 and moved to the In-Danger List in 2016 due to conflict, this work seeks to update the site's attributes and values for improved management.

This methodology, focusing on Ghadames, leverages recent heritage management advancements to monitor conflict-induced changes, aiming to enhance decision-making through a detailed analysis of the site's natural and cultural attributes.

Our findings highlight the need for systematic and holistic assessments of heritage site attributes and values, crucial for managing sites of both local and global significance. This approach is a key to understanding their identity, guiding interpretation, management and preserving cultural significance.

Developed for Ghadames, the methodology requires adaptation for other sites, underscoring the importance of identifying core tangible and intangible attributes that define a site's uniqueness.

Our developed methodology offers a replicable framework that can be modified by local heritage professionals to map attributes and assess the direct and indirect impact of conflict on heritage sites.

The detailed assessment provides a foundation for crafting informed policies and effective management strategies. It specifically targets minimizing the adverse effects of conflict on heritage sites' attributes. This effort is instrumental in preparing the necessary documentation to support the delisting of these sites from the UNESCO World Heritage Site In-Danger List, promoting their preservation and recovery.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

The social acceptance of wind energy is increasingly conditioning the Italian Government and regions to authorize the construction of these plants. The proposal of offshore wind farm in the south-west of Sardinia has raised many perplexities both from the marine environmental point of view and from concern about increasing the electricity production in a region that already exports electricity to the peninsula. The purpose of this study is to evaluate what are the factors that most affect the coastal residents’ acceptability of an offshore wind farm.

The data is based on a Sardinia-wide in-person survey with about 512 participants in the period between May and June 2023. Respondents were selected randomly from five different locations in Carloforte region. Multiple regression analysis investigates the factors that influenced acceptability to construction and development of offshore wind power plant. Four independent variables were selected in the regression models such as (i) interest towards wind energy, (ii) attitude towards renewable energy production, (iii) perceived regional energy policy and (iv) attitude towards offshore wind farm. The dependent variables are the rates of coastal local residents’ acceptance of offshore wind farm for respondents in five coastal towns.

Fishermen and summer tourism operators’ respondents expressed significant reluctance to accept the new project because they perceive a strong risk of environmental impact on both tuna fishing and marine recreational activities in the vicinity. The distance between the turbines at sea and local residents along the coast and surrounding small islands does not have an influence to accept wind energy. The energy policy of the Region of Sardinia, which is perceived by respondents as an expansive energy policy in the production of electricity that exceeds their needs, not being accompanied by a scenario of reducing that from fossil fuels, is the variable that led about 70% of respondents to express a negative opinion towards this project.

A potential problem associated with the survey is that local response produced sample selection bias. The proportion of respondents with secondary education and no school certificate is quite high. It is possible that respondents who provided valid questionnaires were more low-educated and therefore, the sample may be biased towards lower-educated people. While this does not invalidate the results of this study, it is important to note that the sample was on average less educated than the overall population in Italy. In addition, people with lower incomes were more likely to complete the questionnaires; the results are more representative of a portion of the population with incomes below the national average.

Results showed how coastal communities are in favour of wind energy within their island, but not within a marine protected area that is considered unique for its beauty and marine ecosystem. Wind energy developers should, before submitting a proposal for an offshore wind farm project, consider among other things the long approval time and understand the activities carried out by local communities and their attachment to those places. Politicians and developers should develop a coherent energy transition policy based on a long-term vision of zero emissions, because according to the findings of this study, it was the regional energy policy that is the most controversial reason for residents to reject the project.

The contribution of this study is to fill a part of the research gap linking to ongoing energy transitions. In particular, this study analysed for the first time in Italy the acceptability level of an offshore renewable energy project considering the environmental implications and risks in the fragile marine ecosystem of Carloforte waters. This study also made it possible to analyse the impact of the regional energy policy on the acceptability of residents towards the development of offshore wind farms, which is scarcely discussed in the literature.

This study was conducted with the main purpose of recording primary data related to environmental factors, which has become the main criteria in the selection of the Sungai Batu Archaeological Complex (SBAC) as the center of the iron smelting industry and trade in ancient Kedah.

To fulfill this purpose, field studies involving drone photogrammetry mapping, augering, core drilling and geophysical mapping methods were carried out.

The results obtained through the application of the method have shown that SBAC has a good environment, which has a wide and deep river flow, the existence of Mount Jerai and the abundance of iron ores, mangrove Merbok and clay.

Resources did not allow for environment studies of the by-products tourism sites as part of the current study.

The study also included a survey and mapping to obtain potential primary data around SBAC in the process of developing it as the center of the world iron industry.

One finding is that attention to heritage policy and protection must be ongoing at all levels of government and the local community to ensure that the survey and mapping data carried out can be developed as a sustainable heritage tourism product.

This study reveals primary data related to the suitability of paleoenvironment in the SBAC development process as a world iron smelting industry area.

In the context of the Covid-19 pandemic and the trend in industrial development 4.0, the problem is how to conduct transactions of goods and services in the market using cashless payment in Vietnam, as in developed countries. This study aims to investigate, analyze and evaluate the factors affecting cashless payment behavior in Ho Chi Minh City, Vietnam.

The QR codes are used for payments because many factors affect the behavior of cashless payments made using electronic payment tools, including QR code tools. To achieve this goal, this study applied behavioral theory and the importance-performance analysis (IPA) model to measure service quality based on the difference between customer opinions on the importance and performance level of service providers’ targets. Survey results were obtained from 111 people living in HCMC, Vietnam, including 47 men and 64 women, which was a survey for 15 criteria.

According to the IPA results of the first quadrant, managers should focus on allocating their resources toward improving their performance across five key criteria. These criteria are crucial for meeting customers’ expectations and include factors such as product quality, responsiveness to customer queries or complaints, delivery times, pricing and customer service. In addition, the second quadrant of the IPA highlights another set of five criteria that perform well and are essential to the success of the business. These criteria could consist of customer loyalty, employee satisfaction, profitability, market share and innovation

These results provide a basis for solutions and recommendations for managers to refer to and apply consistent practices. Therefore, this study examines the cashless payment in Vietnam. Empirical results offer solutions to financial technology policy, marketing policy, cashless payment services and technology, which can help managers provide online payments using QR codes and contribute to monetary policy solutions.

This article examines curatorial practices, both traditional and digital, in the Guizhou Provincial Museum’s ethnic exhibition to assess their effectiveness in representing ethnic minority cultures, fostering learning and inspiring curiosity about ethnic textiles and costumes and associated cultures. It also explores audience expectations concerning digital technology use in future exhibitions.

A mixed-methods approach was employed, where visitor data were collected through questionnaires, together with interviews with expert, museum professionals and ethnic minority textile practitioners. Their expertise proved instrumental in shaping the design of the study and enhancing the overall visitor experience, and thus fostering a deeper appreciation and understanding of ethnic minority cultures.

Visitors were generally satisfied with the exhibition, valuing their educational experience on ethnic textiles and cultures. There is a notable demand for more immersive digital technologies in museum exhibitions. The study underscores the importance of participatory design with stakeholders, especially ethnic minority groups, for genuine and compelling cultural representation.

This study delves into the potentials of digital technologies in the curation of ethnic minority textiles, particularly for enhancing education and cultural communication. Ethnic textiles and costumes provide rich sensory experience, and they carry deep cultural significance, especially during festive occasions. Our findings bridge this gap; they offer insights for museums aiming to deepen the visitor experiences and understanding of ethnic cultures through the use of digital technologies.

This study aims to stimulate scholarly attention and practical application pertaining to individuals recognized as “Docs.” Through conducting a comprehensive analysis and acquiring a profound understanding of its many connotations, the objective is to shift attitudes and approaches concerning those who are seen to possess knowledge and value within society.

For this study, culturally relevant pedagogies were used as theoretical frameworks in addition to Sankofa and concept explication being used as methodologies.

The authors identified three themes: (1) honorary cultural practice-community nomination of “professahs” and “docs,” (2) (Black) robinhoods – cultural signifiers of distinction and relatability and (3) docs as catalysts – elevating community via consciousness, trust and mentorship as significant understandings of this distinction.

The study emphasizes the importance of “Docs” in both academic and social contexts. The role of “Docs” serves to alleviate potential conflicts of being a Black intellectual. This study further reveals the ways in which Docs align with, promote or possibly undermine established frameworks of thought. Finally, this study provides institutions with opportunities to consider strategies for the utilization, recognition and integration of individuals who are frequently overlooked or undervalued.

This study aims to explore the effect of whistleblowing and interpretation among practitioners in the Nigerian economy. The research puts a premium on understanding the relevance and critical issues in its practices and developing an improved model for the effective practice of whistleblowing and interpretation in Nigeria.

This study adopted a conceptual approach, relying on extant literature to understand the management of whistleblowing incidents and identify the managers’ and other stakeholders’ responsibility in the whistleblowing process. It applied systems dynamics conceptual modelling and presented an improvement approach to addressing the complexities associated with whistleblowing and interpretation among Nigerian organizations.

This study contributed to the extant literature by developing a model for proper management of whistleblowing in the Nigerian context and enhancing the robust practice of whistleblowing and interpretation in Nigeria. The findings highlighted critical factors such as managers’ skills development, legal system support, institutional stakeholders’ function and ethical balance as key factors to effective whistleblowing management and interpretation. This implies that the act of identifying and developing responses to an emerging case of whistleblowing requires a process of developing underpinning assumptions, engagement and consideration of stakeholders’ interests while driving the sustenance of organizational focus.

This study emphasized the departure from absolute best practice to developing idea approaches that reflect stakeholders’ interests and the context of whistleblowing and interpretation. While the study acknowledges limitations in the sole focus on the Nigerian private sector and the Nigerian economic background, it recommends further exploration of whistleblowing and its interpretation on a comparative approach, to improving the current understanding of the topic.

This study examines the public participation in the implementation of the Historic Urban Landscape (HUL), a United Nations Educational, Scientific and Cultural Organization (UNESCO) approach for urban development. HUL has six Critical Steps and four Tools Categories relating to community engagement, knowledge and planning, regulatory system and financial instruments. The HUL public participation component has materialized variably across the adopting cities, challenged by the local implementation capacity. In response, we have singled out two research questions revolving around the participants’ characteristics and participation methods.

This study instrumentalizes case studies as a research methodology and thematic literature as a theoretical context. The HUL projects for Ballarat, Australia and Cuenca, Ecuador have been selected as sources of published information because they exhibit comparative differences in completing community engagement. We have compared the community engagement accounts of the two cities.

The Ballarat and Cuenca HUL project accounts point out to commonalities and variances in responding to the HUL public participation mandate. The findings for the participants’ characteristics involve project setting, participants categories and participants empowerment; the findings for the participation methods involve initial preparation, domestic and international expertise and public conversation.

The results of the study help define public participation practices in HUL project implementations. The results present an opportunity for city officials, HUL project planners and field administrators for making informed decisions particularly in relation to the two public participation domains, the participants’ characteristics and the engagement methods.

This paper aims to examine and compare the export performance and competitiveness of Indian and Chinese textile and clothing industry in post-multifibre arrangement (MFA) era.

Balassa’s revealed comparative advantage Index is used to assess the competitiveness of Indian and Chinese textile and clothing exports.

The results indicate that China’s textiles and garments sector holds a greater proportion of the global market compared with India. India has a robust comparative advantage in silk, carpets and cotton post-MFA. Vegetable textile fibers, paper yarn and woven fabrics of paper yarn are also competitive. China had a strong comparative advantage in silk and fabrics; special woven fabrics, tafted textile fabrics, lace, tapestries, trimmings and embroidery in 2005. China also recorded comparative advantage in silk, man-made filaments: strip and the like of man-made textile materials, fabrics; special woven fabrics, tafted textile fabrics, lace, tapestries, trimmings and embroidery and fabrics; knitted or crocheted in 2021.

This study’s results and recommendations could assist the Indian and Chinese Governments develop policies to upgrade their garment industries.

Though vast literature reviews are available for textile and apparel export performance in India and China separately, there are few studies on comparisons. This study is a significant attempt to evaluate India and China’s competitiveness in the global market.