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The need to digitise is an awareness that is shared across our community globally, and yet the probability of the intersection between resources, expertise and institutions are not as prospective. A strategic view towards the long-term goal of cultivating and digitally upskilling the younger generation, building a community and creating awareness with digital activities that can be beneficial for cultural heritage is necessary.

The work involves distributing tasks between stakeholders and local volunteers. It uses close-range photogrammetry for reconstructing the entire heritage site in 3D, and outlines achievable digitisation activities in the crowdsourced, close-range photogrammetry of a 19th century Cheah Kongsi clan temple located in George Town, a UNESCO World Heritage Site in Penang, Malaysia.

The research explores whether loosely distributing photogrammetry work that partially simulates an unorganised crowdsourcing activity can generate complete models of a site that meets the criteria set by the needs of the clan temple. The data acquired were able to provide a complete visual record of the site, but the 3D models that was generated through the distributed task revealed gaps that needed further measurements.

Key lessons learned in this activity is transferable. Furthermore, the involvement of volunteers can also raise awareness of ownership, identity and care for local cultural heritage.

Key lessons learned in this activity is transferable. Furthermore, the involvement of volunteers can also raise awareness of identity, ownership, cultural understanding, and care for local cultural heritage.

The value of semi-formal activities indicated that set goals can be achieved through crowdsourcing and that the new generation can be taught both to care for their heritage, and that the transfer of digital skills is made possible through such activities. The mass crowdsourcing activity is the first of its kind that attempts to completely digitise a cultural heritage site in 3D via distributed activities.

Accurate diagnosis of brain tumors is crucial for effective treatment and improved patient outcomes. Magnetic resonance imaging (MRI) is a common method for detecting brain malignancies, but interpreting MRI data can be challenging and time-consuming for healthcare professionals.

An innovative method is presented that combines deep learning (DL) models with natural language processing (NLP) from ChatGPT to enhance the accuracy of brain tumor detection in MRI scans. The method generates textual descriptions of brain tumor regions, providing clinicians with valuable insights into tumor characteristics for informed decision-making and personalized treatment planning.

The evaluation of this approach demonstrates promising outcomes, achieving a notable Dice coefficient score of 0.93 for tumor segmentation, outperforming current state-of-the-art methods. Human validation of the generated descriptions confirms their precision and conciseness.

While the method showcased advancements in accuracy and understandability, ongoing research is essential for refining the model and addressing limitations in segmenting smaller or atypical tumors.

These results emphasized the potential of this innovative method in advancing neuroimaging practices and contributing to the effective detection and management of brain tumors.

This study aims to explore how libraries in the United Arab Emirates use technology to preserve and digitize cultural and historical documents. It examined how these institutions use different technology models to facilitate the dissemination of UAE’s cultural traditions, practices, historical experiences and expressions to the local and global populations interested in learning about the country.

This study relied heavily on a review of the relevant literature and case studies covering how UAE libraries use technology to preserve, document and share tangible and intangible cultural heritage. The methodology entailed gathering and synthesizing relevant information from scholarly journal articles, government and reputable institutional resources online and reports. Collectively, it led to a close analysis of the impact of technology on cultural preservation and an assessment of the specific technology models preferred for optimal outcomes in preserving and disseminating cultural heritage information of the UAE.

Multiple UAE libraries rely heavily on technology to collect, record, translate and store cultural heritage information, including releasing it to users when required. The National Archives of the United Arab Emirates, the Arabian Gulf Digital Archive, Mohammed Bin Rashid Library, the UAE National Library and Archives, New York University Abu Dhabi and Khalifa University of Science and Technology and Research libraries have leveraged different technological models and tools to make UAE’s cultural heritage information available and accessible globally. Artificial intelligence and machine learning algorithms, three-dimensional imaging and scanning, electronic archiving systems, document management systems and ICT storage systems have helped the UAE libraries to promote and disseminate the nation’s tangible and intangible cultural heritage.

By relying on scholarly and authoritative sources of information and evidence to draw conclusions, this study contributes to the existing literature by offering insights into the innovative strategies used by UAE libraries to leverage technology for cultural preservation and promotion. In underlining the value of digital approaches to safeguard tangible and intangible cultural heritage, the research highlights the instrumentalism of technology in preserving the UAE’s cultural heritage and identity.

Monitoring of the quality of precast concrete (PC) components is crucial for the success of prefabricated construction projects. Currently, quality monitoring of PC components during the construction phase is predominantly done manually, resulting in low efficiency and hindering the progress of intelligent construction. This paper presents an intelligent inspection method for assessing the appearance quality of PC components, utilizing an enhanced you look only once (YOLO) model and multi-source data. The aim of this research is to achieve automated management of the appearance quality of precast components in the prefabricated construction process through digital means.

The paper begins by establishing an improved YOLO model and an image dataset for evaluating appearance quality. Through object detection in the images, a preliminary and efficient assessment of the precast components' appearance quality is achieved. Moreover, the detection results are mapped onto the point cloud for high-precision quality inspection. In the case of precast components with quality defects, precise quality inspection is conducted by combining the three-dimensional model data obtained from forward design conversion with the captured point cloud data through registration. Additionally, the paper proposes a framework for an automated inspection platform dedicated to assessing appearance quality in prefabricated buildings, encompassing the platform's hardware network.

The improved YOLO model achieved a best mean average precision of 85.02% on the VOC2007 dataset, surpassing the performance of most similar models. After targeted training, the model exhibits excellent recognition capabilities for the four common appearance quality defects. When mapped onto the point cloud, the accuracy of quality inspection based on point cloud data and forward design is within 0.1 mm. The appearance quality inspection platform enables feedback and optimization of quality issues.

The proposed method in this study enables high-precision, visualized and automated detection of the appearance quality of PC components. It effectively meets the demand for quality inspection of precast components on construction sites of prefabricated buildings, providing technological support for the development of intelligent construction. The design of the appearance quality inspection platform's logic and framework facilitates the integration of the method, laying the foundation for efficient quality management in the future.

With the advent of technology, a huge amount of data is being transmitted and received through the internet. Large bandwidth and storage are required for the exchange of data and storage, respectively. Hence, compression of the data which is to be transmitted over the channel is unavoidable. The main purpose of the proposed system is to use the bandwidth effectively. The videos are compressed at the transmitter’s end and reconstructed at the receiver’s end. Compression techniques even help for smaller storage requirements.

The paper proposes a novel compression technique for three-dimensional (3D) videos using a zig-zag 3D discrete cosine transform. The method operates a 3D discrete cosine transform on the videos, followed by a zig-zag scanning process. Finally, to convert the data into a single bit stream for transmission, a run-length encoding technique is used. The videos are reconstructed by using the inverse 3D discrete cosine transform, inverse zig-zag scanning (quantization) and inverse run length coding techniques. The proposed method is simple and reduces the complexity of the convolutional techniques.

Coding reduction, code word reduction, peak signal to noise ratio (PSNR), mean square error, compression percent and compression ratio values are calculated, and the dominance of the proposed method over the convolutional methods is seen.

With zig-zag quantization and run length encoding using 3D discrete cosine transform for 3D video compression, gives compression up to 90% with a PSNR of 41.98 dB. The proposed method can be used in multimedia applications where bandwidth, storage and data expenses are the major issues.

Several chaotic system-based encryption techniques have been presented in recent years to protect digital images using cryptography. The challenges of key distribution and administration make symmetric encryption difficult. The purpose of this paper is to address these concerns, the novel hybrid partial differential elliptical Rubik’s cube algorithm is developed in this study as an asymmetric image encryption approach. This novel algorithm generates a random weighted matrix, and uses the masking method on image pixels with Rubik’s cube principle. Security analysis has been conducted, it enhances and increases the reliability of the proposed algorithm against a variety of attacks including statistical and differential attacks.

In this light, a differential elliptical model is designed with two phases for image encryption and decryption. A modified image is achieved by rotating and mixing intensities of rows and columns with a masking matrix derived from the key generation technique using a unique approach based on the elliptic curve and Rubik’s cube principle.

To evaluate the security level, the proposed algorithm is tested with statistical and differential attacks on a different set of test images with peak signal-to-noise ratio, unified average changed intensity and number of pixel change rate performance metrics. These results proved that the proposed image encryption method is completely reliable and enhances image security during transmission.

The elliptic curve–based encryption is hard to break by hackers and adding a Rubik’s cube principle makes it even more complex and nearly impossible to decode. The proposed method provides reduced key size.

This study aims to investigate the effect of speckle pattern on displacement measurements using different speckle diameters and coverage ratios.

In order to compare the coverage ratio and speckle diameter during the evaluation of the correlation of digital images (DIC) study, template speckle plates were produced on a computer numerical control (CNC) punch press with 600 punches per minute. After the speckle plates were manufactured, the speckled pattern was randomly painted on a plain white side through the manufactured template plates, and then tensile tests were performed under the same loading conditions for each sample to observe displacement variation via correlation parameters.

During the manufacturing of templates with thin plates, a punch diameter of less than 1.7 mm will cause tool failure; therefore, uniform speckle size can be assessed before operation. A higher coverage ratio resulted in more accurate and reliable results in displacement data. With smaller coverage, the facet size should be increased to achieve favorable results.

If thick template plates are selected, speckle painting cannot be done properly; therefore, template thickness shall also be assessed before operation.

For randomly distributed DIC templates, increasing coverage beyond 50% does not make sense due to difficulties in the production process in the punch press.

Evaluating DIC results via templates manufactured in a punch press with different speckle diameters and coverage ratios is a new topic in literature.

In an era overshadowed by the alarming consequences of climate change and the escalating peril of recurring floods for communities worldwide, the significance of proficient disaster risk management has reached unprecedented levels. The successful implementation of disaster risk management necessitates the ability to make informed decisions. To this end, the utilization of three-dimensional (3D) visualization and Web-based rendering offers decision-makers the opportunity to engage with interactive data representations. This study aims to focus on Thiruvananthapuram, India, where the analysis of flooding caused by the Karamana River aims to furnish valuable insights for facilitating well-informed decision-making in the realm of disaster management.

This work introduces a systematic procedure for evaluating the influence of flooding on 3D building models through the utilization of Web-based visualization and rendering techniques. To ensure precision, aerial light detection and ranging (LiDAR) data is used to generate accurate 3D building models in CityGML format, adhering to the standards set by the Open Geospatial Consortium. By using one-meter digital elevation models derived from LiDAR data, flood simulations are conducted to analyze flow patterns at different discharge levels. The integration of 3D building maps with geographic information system (GIS)-based vector maps and a flood risk map enables the assessment of the extent of inundation. To facilitate visualization and querying tasks, a Web-based graphical user interface (GUI) is developed.

The efficiency of comprehensive 3D building maps in evaluating flood consequences in Thiruvananthapuram has been established by the research. By merging with GIS-based vector maps and a flood risk map, it becomes possible to scrutinize the extent of inundation and the affected structures. Furthermore, the Web-based GUI facilitates interactive data exploration, visualization and querying, thereby assisting in decision-making.

The study introduces an innovative approach that merges LiDAR data, 3D building mapping, flood simulation and Web-based visualization, which can be advantageous for decision-makers in disaster risk management and may have practical use in various regions and urban areas.

This study evaluates machine learning (ML) classifiers for diagnosing Parkinson’s disease (PD) using subcortical brain region data from 3D T1 magnetic resonance imaging (MRI) Parkinson’s Progression Markers Initiative (PPMI database). We aim to identify top-performing algorithms and assess gender-related differences in accuracy.

Multiple ML algorithms will be compared for their ability to classify PD vs healthy controls using MRI scans of the brain structures like the putamen, thalamus, brainstem, accumbens, amygdala, caudate, hippocampus and pallidum. Analysis will include gender-specific performance comparisons.

The study reveals that ML classifier performance in diagnosing PD varies across subcortical brain regions and shows gender differences. The Extra Trees classifier performed best in men (86.36% accuracy in the putamen), while Naive Bayes performed best in women (69.23%, amygdala). Regions like the accumbens, hippocampus and caudate showed moderate accuracy (65–70%) in men and poor performance in women. The results point out a significant gender-based performance gap, highlighting the need for gender-specific models to improve diagnostic precision across complex brain structures.

This study highlights the significant impact of gender on machine learning diagnosis of PD using data from subcortical brain regions. Our novel focus on these regions uncovers their diagnostic potential, improves model accuracy and emphasizes the need for gender-specific approaches in medical AI. This work could ultimately lead to earlier PD detection and more personalized treatment.

Facility management gained profound importance due to the increasing complexity of different systems and the cost of operation and maintenance. However, due to the increasing complexity of different systems, facility managers may suffer from a lack of information. The purpose of this paper is to propose a new facility management approach that links segmented assets to the vital data required for managing facilities.

Automatic point cloud segmentation is one of the most crucial processes required for modelling building facilities. In this research, laser scanning is used for point cloud acquisition. The research utilises region growing algorithm, colour-based region-growing algorithm and Euclidean cluster algorithm.

A case study is worked out to test the accuracy of the considered point cloud segmentation algorithms utilising metrics precision, recall and F-score. The results indicate that Euclidean cluster extraction and region growing algorithm revealed high accuracy for segmentation.

The research presents a comparative approach for selecting the most appropriate segmentation approach required for accurate modelling. As such, the segmented assets can be linked easily with the data required for facility management.