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Over the past four years the Historic Environment Department of the Department of Culture & Tourism (DCT Abu Dhabi) has recorded around sixty separate ancient aflāj or underground water channels in more than 150 different locations within the city of al-ʿAyn. These new discoveries have been made in three main areas of the city -- around Hīlī Oasis in the north, to the east of Qaṭṭāra and Jīmī Oases, and in the Central District of the downtown area to the east of al-ʿAyn Oasis. This paper will give a brief introduction to the context of ongoing aflāj discoveries, most of which have taken place during archaeological monitoring of infrastructure and construction projects, and to the archaeological methodologies employed to investigate and record them. The aflāj of al-ʿAyn present several distinct typologies and range in depth and mode of construction, from plastered brick surface channels to deep subterranean tunnels up to 15 m below modern street level. The paper will argue that these typologies reflect both the availability and exploitation of different water sources over time and will focus on case studies provided by two recently excavated archaeological sites -- in the areas of Falaj Al Mazmi (Falaj al-Māzmī) and Hai Al Murabba (Ḥayy al-murabbaʿ)-- to examine in detail new evidence for the various techniques used to excavate, construct, and cover the aflāj. It will also review the key stratigraphic relationships between the different typologies and use these to outline a chronology that reflects the relationship of the aflāj to the wider hydrological context of the region, their interface with changing agricultural practices and locations over time, and the role they have played in the development of the oasis landscape of al-ʿAyn. [ABSTRACT FROM AUTHOR]

The paper intends to build a drone detection system using machine learning and deep learning algorithms. The system could differentiate between a drone and other flying objects like a different type of bird and recognize it. It also covers the various algorithms implemented for the development of drone detection systems. The research work uses YOLO algorithm as it is very efficient and widely used algorithm. Using the YoloV4 model, 85% of accuracy is obtained by classifying the images of the military drones in the 'aeroplane' category. It is one of the classes 'coco' dataset files. YoloV4 algorithm detects the drones which are based on deep convolutional neural networks. YoloV4 model is one of the most tested and stable versions of the YOLO. Once the drone is identified by the model, it will switch on the alarm to indicate drone detection. [ABSTRACT FROM AUTHOR]

Over a six-month period from January to August 2021 the Historic Environment Department of DCT Abu Dhabi carried out archaeological monitoring and excavation along an 11.5 km section of a project to renew the existing border fence between the UAE and Oman. The project produced a wealth of new data on the development of the historic oasis landscape of al-'Ayn. Some of the most significant features include a monumental stone tomb, an apparently extensive Iron Age cemetery, and high-status tombs of late pre-Islamic (PIR) date. Along with this funerary landscape we have identified more than fifty ancient aflāj or underground water channels of various dates and techniques of construction, along with extensive evidence of distinct phases of Iron Age agriculture and the organization of their associated irrigation systems and agricultural plots. This paper provides an overview of the nature and progress of the fieldwork and initial study of the finds associated with the various feature groups, including a corpus of Iron Age and PIR ceramics from both funerary and agricultural contexts, a large number of chlorite vessels, PIR iron weaponry, extensive environmental samples, and information on the geology and wadi systems shaping this cultural landscape. [ABSTRACT FROM AUTHOR]

Solving duster identification problem on large amount of data ig known to be time consuming. Àlmoit au the state of art clustering techniques focuses on sequential algorithms which suffer from me problem of long runtime. So, parallel algorithms are needed. One of the attempts is a parallel minimum spanning tree (MST)-based clustering technique, called CLUMP, which identifies dense clusters in a noisy background. Although, CLUMP is efficient algorithm for clustering large data set, the MST construction is considered the time consuming phase of the algorithm. This paper presents and improved CLUMP algorithm CLUMP to enhance its speed. The experimental results showed that the proposed algorithm proved to be efficient than the original algorithm CLUMP in terms of complexity and runtime. [ABSTRACT FROM PUBLISHER]