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Purpose: The achievement of the sustainable development goals (SDGs) for all communities and jurisdictions require a comprehensive roadmap that encompasses all dimensions of data infrastructure, social, economic, environmental and governance ecosystems. With this in mind, this paper aims to establish the link between the curriculum and intended learning outcomes of undergraduate and postgraduate subjects offered by the University and sustainability. This study is a part of a wider university strategy to embed sustainability knowledge and values in the university curricula. The 17 SDGs developed as a part of the 2030 Agenda for Sustainability Development was used as tool to measure and map how the subjects are linked with sustainability. Design/methodology/approach: To incorporate sustainability into the curriculum, this paper developed an interdisciplinary approach for analysing the interconnection between the SDGs, the expected subject learning points and the relevant aspects of sustainability (geospatial information, the legal, policies and institutional components). As part of the approach, in the first phase of the study, qualitative data were collected through a review of published information on the SDGs and the content of the subjects available in the subject handbook. Subject codes were assigned to the keywords and key phrases extracted from the SDGs and the subject content, and then compared and matched to establish the link between the subjects and the SDGs. Six university schools offering over 2,157 subjects were investigated. In the second phase, a survey was conducted involving subject coordinators with the purpose of validating the findings of the first phase and determining the strength of the linkages between the subjects and the SDGs. In the third phase, a plugin was designed to be used in the digital twin platform developed in the UoM, allowing visualisation of the research outcomes. Findings: Based on the interim findings, it was found that some subjects within the schools are linked to more than one SDG. However, not all of the subjects within the schools can be linked to the SDGs. There is a scope of improvement for embedding sustainability in more subjects within the schools. Some of the schools were also found to have weak linkages with sustainability, which demonstrate the challenge in technical subjects in linking their subject contents with sustainability. Originality/value: This study provides a methodology which enables the integration of sustainability into current state of the curricula at the university to be established. Further, with the advancement of geospatial technology and new visualisation opportunities through the use of the digital twin platform provides capabilities to communicate the outcomes of sustainability and involvement of each faculties and departments more effectively to the university community and wider stakeholders.

Increasing efforts have been devoted to promoting sustainable demolition waste management (DWM) from a life cycle thinking perspective. To this end, facilitating sustainability-oriented decision-making for DWM planning requires a sustainability assessment framework for assessing the trade-offs among multifaceted criteria. This study develops a BIM-based DWM sustainability assessment approach to facilitate the life cycle sustainability assessment (LCSA) and decision-making by integrating LCSA-related properties and hybrid Multi-Criteria Decision-Aiding (MCDA) methods into a BIM environment using Dynamo visual scripting. A dynamic linkage is developed in the streamlined BIM-based LCSA process, where the enriched Industry Foundation Class (IFC) models are coupled with custom LCSA data templates to achieve seamless data exchange between the BIM platform and external LCA tools. Subsequently, hybrid MCDA methods convert the assessment results into DWM scenario ranking. A pilot study verifies the applicability of the BIM-based framework. The results unveil that the sustainability score ascended with the recycling rate. The optimal DWM alternative with the highest recycling rate yields the highest sustainability score at 91.63. Conversely, a DWM alternative reflecting the 'status quo' in China's recycling industry has the lowest score at 8.37, significantly lower than the baseline scenario with a 50% recycling rate. It is worth noting that the 'growth curve' of the sustainability score continuously flattens as the target recycling rate escalates. The increment in recycling rate from the "Australian standard" scenario to the optimal scenario is 18.4%, whereas the sustainability score merely increases by 2.3%, implying that the former scenario arrived at an optimum point for maximising the cost-efficiency of DWM under the predefined settings. [ABSTRACT FROM AUTHOR]

Digital transformation is vital for modernising Land Administration Systems (LASs), ensuring efficient, transparent, and secure land services. This study evaluates Indonesia’s LAS using the Digital Government Readiness Assessment (DGRA) framework, identifying regulatory gaps, inefficient registration, validation issues, and resource constraints. Inspired by New Zealand, Singapore, and Australia, it recommends standardising parcel identification numbers, updating regulations, adopting new data formats, and enhancing address standards. Additionally, it suggests implementing digital workflows, automation services, and collaborating with the surveying industry. Institutional and organisational improvements are also proposed based on global initiatives. [ABSTRACT FROM AUTHOR]

This comprehensive review investigates the transformative potential of sensor-driven digital twin technology in enhancing healthcare delivery within smart environments. We explore the integration of smart environments with sensor technologies, digital health capabilities, and location-based services, focusing on their impacts on healthcare objectives and outcomes. This work analyzes the foundational technologies, encompassing the Internet of Things (IoT), Internet of Medical Things (IoMT), machine learning (ML), and artificial intelligence (AI), that underpin the functionalities within smart environments. We also examine the unique characteristics of smart homes and smart hospitals, highlighting their potential to revolutionize healthcare delivery through remote patient monitoring, telemedicine, and real-time data sharing. The review presents a novel solution framework leveraging sensor-driven digital twins to address both healthcare needs and user requirements. This framework incorporates wearable health devices, AI-driven health analytics, and a proof-of-concept digital twin application. Furthermore, we explore the role of location-based services (LBS) in smart environments, emphasizing their potential to enhance personalized healthcare interventions and emergency response capabilities. By analyzing the technical advancements in sensor technologies and digital twin applications, this review contributes valuable insights to the evolving landscape of smart environments for healthcare. We identify the opportunities and challenges associated with this emerging field and highlight the need for further research to fully realize its potential to improve healthcare delivery and patient well-being. [ABSTRACT FROM AUTHOR]

Enriching and updating maps are among the most important tasks of any urban management organization for informed decision making. Urban cadastral map enrichment is a time-consuming and costly process, which needs an expert's opinion for quality control. This research proposes a smart framework to enrich a cadastral base map using a more up-to-date map automatically by machine learning algorithms. The proposed framework has three main steps, including parcel matching, parcel change detection and base map enrichment. The matching step is performed by checking the center point of each parcel in the other map parcels. Support vector machine and random forest classification algorithms are used to detect the changed parcels in the base map. The proposed models employ the genetic algorithm for feature selection and grey wolf optimization and Harris hawks optimization for hyperparameter optimization to improve accuracy and performance. By assessing the accuracies of the models, the random forest model with feature selection and grey wolf optimization, with an F1-score of 0.9018, was selected for the parcel change detection method. Finally, the detected changed parcels in the base map are deleted and relocated automatically with corresponding parcels in the more up-to-date map by the affine transformation. [ABSTRACT FROM AUTHOR]