Developing a dynamic digital twin at a building level: Using Cambridge campus as case study

A Digital Twin (DT) refers to a digital replica of physical assets, processes and systems. DTs integrate artificial intelligence, machine learning and data analytics to create dynamic digital models that are able to learn and update the status of the physical counterpart from multiple sources. A DT, if equipped with appropriate algorithms will represent and predict future condition and performance of their physical counterparts. Current developments related to DTs are still at an early stage with respect to buildings and other infrastructure assets. Most of these developments focus on the architectural and engineering/construction point of view. Less attention has been paid to the operation & maintenance (O&M) phase, where the value potential is immense. A systematic and clear architecture verified with practical use cases for constructing a DT is the foremost step for effective operation and maintenance of assets. This paper presents a system architecture for developing dynamic DTs in building levels for integrating heterogeneous data sources, support intelligent data query, and provide smarter decision-making processes. This will further bridge the gaps between human relationships with buildings/regions via a more intelligent, visual and sustainable channels. This architecture is brought to life through the development of a dynamic DT demonstrator of the West Cambridge site of the University of Cambridge. Specifically, this demonstrator integrates an as-is multi-layered IFC Building Information Model (BIM), building management system data, space management data, real-time Internet of Things (IoT)-based sensor data, asset registry data, and an asset tagging platform. The demonstrator also includes two applications: (1) improving asset maintenance and asset tracking using Augmented Reality (AR); and (2) equipment failure prediction. The long-term goals of this demonstrator are also discussed in this paper

Latent provisions for building information modeling (BIM) contracts: a social network analysis approach

The effective adoption and use of Building Information Modeling (BIM) require appropriate contract design to fairly allocate the contracting parties’ rights and responsibilities. Several standards for BIM protocols and contracts have been developed for the industry. However, the awareness and the use of these are rather limited, leading to unclear provisions in BIM contracts. Therefore, the research aims to identify the influential legal aspects that serve as the latent contract provisions in BIM contracts. A questionnaire survey was conducted to survey experts and active BIM users in construction projects. The data were analyzed using social network analysis (SNA) by assuming interdependent relationships among various the legal aspects in BIM contacts. The key legal aspects associated with BIM contracts pertain to the roles and responsibilities of the project participants. The results also reveal that data security is the center of all latent legal aspects in the contracts. The study provides significant new insights into clarifying the required contract provisions in BIM contracts

Somatostatin receptor imaging of thyroid tissue and differentiated thyroid cancer using gallium-68-labeled radiotracers-a review of clinical studies.

The rise in thyroid cancer incidence, especially papillary thyroid cancer (PTC), has underscored the need for improved diagnostic methods and management strategies. Herein, we aim to comprehensively review the evolving landscape in thyroid cancer diagnosis and the potential utility of Gallium-68 (Ga-68) based somatostatin receptor imaging. We reviewed the clinical studies involving Ga-68 based radiotracers by looking at the following literature databases -PUBMED, EMBASE, WEB OF SCIENCE and COCHRANE. We employed a detailed search strategy with the following search terms; PubMed: ("gallium Ga 68 dotatate" [Supplementary Concept]) AND ("Thyroid Gland"[Mesh] OR "Thyroid Nodule"[Mesh] OR "Thyroid Neoplasms"[Mesh]), Embase ("gallium 68" AND "Thyroid Disease"), Web of Science: ("Gallium 68 and Thyroid"). A comparison between Ga-68 DOTATATE and Ga-68 DOTANOC showed similar sensitivities but a higher uptake for Ga-68 DOTATATE. Studies comparing Ga-68-based SSTR PET with FDG PET highlighted the potential advantages of both approaches, with Ga-68-based SSTR PET being more specific in certain cases. Ga-68-based somatostatin receptor imaging displays clinical utility in RAI-R DTC, offering valuable insight into detecting skeletal lymph node metastases. Notably, it shows potential as a primary imaging tool, potentially augmenting the role of FDG PET. However, SSTR PET imaging's efficacy in distinguishing benign from malignant thyroid nodules varies, with a complex interplay of factors influencing its specificity, indicating its value as an adjunct to existing methods, warranting further research for a refined role in thyroid cancer management. Although study variations exist, Ga-based somatostatin receptor imaging holds potential as a complementary tool alongside diagnostic methods in thyroid cancer diagnosis, with particular relevance to RAI-R DTC. In carefully selected patients demonstrating the presence of Ga-68 DOTATATE avid lesions, further exploration, and investigation into the potential utilization of Lu177 DOTATATE are warranted