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The Ottoman cultural legacy in Algeria is made up of a diverse range of architectural structures. The Algerian government strategy in connection with the Ottoman old buildings is to restore them into museums. This study will attempt to present a contrastive analysis between two old palaces being under restoration (refurbishment), and the goal is to propose a museum route by calculating the ambiance aimed at circulating the rooms by visitors.

The authors analyzed the architectural components of the various entities by observing in situ and taking measurements for a single case study to get a sense of the results for the mid-season (spring and fall). The configuration was next evaluated by modeling the space syntax and combining it with a simulation of daylight luminance, for the period when the authors could not make measurements on-site. The ultimate goal is to combine these findings to suggest the ideal in-route for the future museum.

This research allowed the authors to propose a museum itinerary adapted to the new vocation of the palaces, which considers the daylight as an element of composition in the spaces of circulation.

The paper proposes solutions to a flow management problem encountered in several similar palaces converted into museums.

The study aims to raise questions on the museum, and to preserve such heritage from neglect by giving it a new life more adapted to the needs of the Algerian society.

The authors believe that this contribution will be a creative solution for issues related to the operation of palaces that have been converted into museums.

Industrial heritage is considered an essential part of cultural heritage in the world. This heritage suffers from continued marginalisation in the Arab world, particularly in Jordan, where many industrial heritage sites have not been protected or studied well due to the lack of a clear definition of cultural heritage. Most of these sites, built in the 20th century, are gradually disappearing or scheduled for demolition. This paper explores the de-industrialisation discourse and the loss of modern industrial heritage in the Arab world, especially in Jordan.

This research investigates the modern industrial heritage in Jordan as a case study in the Arab world. A comprehensive understanding of the industrial heritage has been obtained by adopting a case study approach and using a reconnaissance survey of potential industrial heritage sites in Jordan.

Seven categories were used in the analysis of the de-industrialisation phenomenon of heritage sites: ownership, location, design and types; structure, significance, deterioration and physical condition and conservation attempts and alterations. Three main approaches to industrial heritage were identified: demolition, occasional maintenance and rare examples of conservation and adaptive reuse.

This study sheds light on the ownership issue of industrial structures in Jordan and invites policymakers, relevant authorities, private organisations and the public to consider the challenges and impact of de-industrialisation of such sites.

This research raises awareness of the de-industrialisation discourse, and highlights the value of industrial architecture dating back to the modernity period, which was short-lived in Jordan. It also calls for serious consideration of these sites to support sustainable development in the Arab World.

This study aims to apply the appreciative inquiry approach (AI) to develop a tourism strategy for poverty alleviation in marginalised communities. The focus is to provide practical insights for leveraging tourism to drive positive socio-economic change for the impoverished, using Rosetta, a port city in Egypt with cultural and historical significance, as a case study.

This qualitative applied study uses the four-D phases of AI and thematic analysis to strategise tourism development in Rosetta. Through interviews, focus groups and field visits, the study identifies tourism potential, stakeholder aspirations and actionable strategies for sustainable development. The approach prioritises a bottom-up, community-centric and stakeholder-involved process, aiming for inclusive and equitable growth.

The study revealed Rosetta’s underutilised tourism potential, emphasising heritage tourism. Although tourism offers some economic benefits, its impact on alleviating poverty in Rosetta remains limited. A holistic strategy for tourism development in Rosetta is proposed for economic growth and poverty reduction, focusing on sustainable management, local empowerment, enhanced marketing, improved infrastructure and diversified tourism offerings.

While AI is not new in qualitative studies, the novelty of this study lies in its application to tourism planning for poverty alleviation in a marginalised community like Rosetta, introducing a comprehensive tourism strategy with an original framework applicable to comparable destinations. The study’s significance is emphasised by providing actionable strategies for policymakers, valuable insights for practitioners and enriching the discourse and methodology on pro-poor tourism for academics, representing a step towards filling the gap between theoretical concepts and practical strategies.

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 aim of the study is to capture consumers' conceptualization of ethnic food and ethnic restaurants, focusing on what it takes for ethnic restaurants to be perceived authentic.

Qualitative research, based on semi-structured in-depth interviews which were transcribed and subjected to inductive thematic analysis.

The findings conceptualize what ethnic food and ethnic-authentic restaurant are, leading to formulating the criteria that authentic ethnic restaurant should meet.

Findings have practical implications for restaurateurs interested in establishing themselves as ethnically authentic and for businesses focusing on ethnic food. This study was based on Israeli-Jewish participants; studies with other populations will contribute to the findings' trustability.

This study provides novel and critical insights into ethnic restaurant managements and to the customer conceptualization of the concept of ethnic authentic, demonstrating it being a multifaceted concept.

This paper aims to view the tradition of the Prophet in zakat payment, especially the attributed measurements, and adapt these measures to current zakat measurement practices.

This study is designed with an exploratory sequential mixed method where qualitative data were collected from historical archives and also from the measurement of available ṣāʿ صاع and mud_ مد. These standard measures were collected from Malaysia and other Muslim countries and calibrated at the National Measurement Standards Laboratory (currently carried out by the National Metrology Institute of Malaysia). The volume of the ṣāʿ were tabulated, charted, analyzed and determined.

Prophet Muḥammad ordered for zakat al-fiṭr زكاة الفطر to measure the crops using ṣāʿ (gallon: volume measure), not raṭl رطل (pound: standard weight). This paper recommends a reference volume for the ṣāʿ, and from this value, the volume for the smaller measure mud and much larger measure wasq وسق were calculated. The agricultural niṣāb (5 wasq) is one of the key parameters in ṣāʿ volume analysis.

This study covers standard measures acquired from Malaysia, Saudi Arabia and the United Kingdom (traceable to India). Several empirical data are attained from previous studies from Saudi Arabia and North Africa. Also, some theoretical data are obtained from kitab tafsir, hadith and kitab turath (books and records written by early scholars). A concern in this study is the ratio of de-husked grains currently approximated at half of un-hulled grains. This might not necessarily be accurate and may differ by different types of grains.

After the introduction of the modern measurement system known as the International System of Units (SI) in 1971, ṣāʿ became obsolete, leaving each state in Malaysia to define and convert ṣāʿ to SI units variously. This paper contributes to standardizing the conversion to one value with an appropriate SI unit, that has traceability to the Prophet’s ṣāʿ. This national reference standard ṣāʿ is essential for zakat al-fiṭr measurement, important for zakat centers and impacting every Muslim in Malaysia. Often overlooked, ṣāʿ is more crucial as a basis in determining and standardizing the seasonal crops niṣāb, unfolding the reasons why the poor are paying the paddy zakat in Malaysia.

The Prophet’s tradition for ablution is to use water not more than one mud, this is aligned with the 12th Sustainable Development Goals by the United Nations which promoted the responsible consumption of water. Considering half (adults) of the 1.9 billion Muslims in the world, multiplied by 5 times of prayer, the Muslims will at least use 4.75 billion muds of water for ablution alone in 1 day. This is approximately 3.5 billion liters of water daily, and if they limit their water usage to 1 mud for each ablution, they collectively will save at least four times more water (about 14 billion liters) daily than performing ablution from a running tap. Though for ablution, accuracy is not an issue, the mud impact on all Muslims is vital on a daily basis, literally by volume.

Ṣāʿ is essential in measuring food for the annually obligated zakat al-fiṭr and more crucial as a basis in determining the seasonal crops niṣāb. This paper contributes to the development of a national reference standard ṣāʿ for Malaysia that has traceability to the Prophet’s ṣāʿ.