Enhancing Healthcare through Sensor-Enabled Digital Twins in Smart Environments: A Comprehensive Analysis.

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.

The development of an integrated BIM-based visual demolition waste management planning system for sustainability-oriented decision-making.

In light of the suboptimal resource efficiency persisting in current demolition waste management (DWM) practices caused by inattentive and profit-driven decision-making due to the lack of tailored stringent legislation, monetary incentives, and benchmarking frameworks, this study aims to facilitate sustainability-oriented decision-making at the demolition planning stage. A practical Building Information Modelling (BIM)-based visual DWM planning system is designed, wherein the system seamlessly accommodates inventory analysis and Multi-Criteria Decision-Aiding (MCDA) algorithms into various interconnected modules. Moreover, this research proposes bespoke algorithms and colour coding schemes to quantify and visualise the recycling value of building components for augmenting the visual guidance of sustainable building design and selective demolition planning. Furthermore, a pilot case study demonstrates the system's applicability in a real-world demolition project. The findings unveil that improving the recycling rate substantially offsets carbon emissions and demolition waste disposal expenditures. The increment in beneficial impacts outweighs the additional energy consumption and costs for implementing sustainable DWM strategies based on the predefined geographical settings. This BIM-based system reforms the conventional demolition planning and DWM decision-making workflow by tackling technical barriers concerning data richness, interoperability, and result interpretation. It equips the users with intuitive visual design guidance and parallel scenario analysis when crafting sustainability-oriented DWM schemes. In summary, this research contributes to familiarising industry practitioners with sustainable DWM schematic design and circular economy principles. Moreover, it prompts the development of customised BIM libraries as repositories for updating and capitalising on DWM-related information that can be adapted to different regional contexts.

Identifying and prioritizing sustainability indicators for China's assessing demolition waste management using modified Delphi-analytic hierarchy process method.

Addressing the sustainability issues arising from construction and demolition waste management (DWM) has gained little traction due to the lack of incentives, stringent regulations, and systematic guidance. This study aims to empower systematic decision-making concerning DWM alternative selection by developing a sustainability assessment framework by coupling a modified Delphi method with the multicriteria decision analysis technique. First, the study identifies a comprehensive inventory of indicators across three dimensions of sustainability in the context of DWM. Next, the study combines a modified Delphi method with the analytic hierarchy process to validate and prioritize the selected sustainability indicators. For the first time, insights regarding the DWM sustainability indicators from China's construction industry practitioners' perspectives are elicited using a mixed method comprising online semistructured interviews and two rounds of questionnaire surveys. Experts participating in the research are mostly based in Guangzhou and Shenzhen, where local governments exhaust all efforts in promoting carbon-neutral and sustainable development. The findings reveal that eight sustainability indicators were regarded as the determinants for the sustainability performance of DWM, with the global warming potential (32%), energy efficiency (16.1%) and land use (13.5%) receiving the highest preference scores (weights) based on the experts' judgment. Notably, the economic factors like the total cost (6.54%) appeared not highly prioritized by the local experts as typically did in the previous studies from developing countries.

Region-income-based prioritisation of Sustainable Development Goals by Gradient Boosting Machine.

The Sustainable Development Goals (SDGs) seek to address complex global challenges and cover aspects of social development, environmental protection, and economic growth. However, the holistic and complicated nature of the goals has made their attainment difficult. Achieving all goals by 2030 given countries' limited budgets with the economic and social disruption that the COVID-19 pandemic has caused is over-optimistic. To have the most profound impact on the SDGs achievement, prioritising and improving co-beneficial goals is an effective solution. This study confirms that countries' geographic location and income level have a significant relationship with overall SDGs achievement. This article applies the Gradient Boosting Machine (GBM) algorithm to identify the top five SDGs that drive the overall SDG score. The results show that the influential SDGs vary for countries with a specific income level located in different regions. In Europe and Central Asia, SDG10 is among the most influential goals for high-income countries, SDG9 for upper-middle-income, SDG3 in low and lower-middle-income countries of Sub-Saharan Africa, and SDG5 in Latin America and the Caribbean upper-middle-income countries. This systematic and exploratory data-driven study generates new insights that confirm the uniqueness, and non-linearity of the relationship between goals and overall SDGs achievement.

Urban drought challenge to 2030 sustainable development goals.

In the first two decades of the 21st century, 79 global big cities have suffered extensively from drought disaster. Meanwhile, climate change has magnified urban drought in both frequency and severity, putting tremendous pressure on a city's water supply. Therefore, tackling the challenges of urban drought is an integral part of achieving the targets set in at least 5 different Sustainable Development Goals (SDGs). Yet, the current literatures on drought have not placed sufficient emphasis on urban drought challenge in achieving the United Nations' 2030 Agenda for Sustainable Development. This review is intended to fill this knowledge gap by identifying the key concepts behind urban drought, including the definition, occurrence, characteristics, formation, and impacts. Then, four sub-categories of urban drought are proposed, including precipitation-induced, runoff-induced, pollution-induced, and demand-induced urban droughts. These sub-categories can support city stakeholders in taking drought mitigation actions and advancing the following SDGs: SDG 6 "Clean water and sanitation", SDG 11 "Sustainable cities and communities", SDG 12 "Responsible production and consumption", SDG 13 "Climate actions", and SDG 15 "Life on land". To further support cities in taking concrete actions in reaching the listed SDGs, this perspective proposes five actions that city stakeholders can undertake in enhancing drought resilience and preparedness:1) Raising public awareness on water right and water saving; 2) Fostering flexible reliable, and integrated urban water supply; 3) Improving efficiency of urban water management; 4) Investing in sustainability science research for urban drought; and 5) Strengthening resilience efforts via international cooperation. In short, this review contains a wealth of insights on urban drought and highlights the intrinsic connections between drought resilience and the 2030 SDGs. It also proposes five action steps for policymakers and city stakeholders that would support them in taking the first step to combat and mitigate the impacts of urban droughts.

Influence of pedestrian age and gender on spatial and temporal distribution of pedestrian crashes.

OBJECTIVES: Every year, about 1.24 million people are killed in traffic crashes worldwide and more than 22% of these deaths are pedestrians. Therefore, pedestrian safety has become a significant traffic safety issue worldwide. In order to develop effective and targeted safety programs, the location- and time-specific influences on vehicle-pedestrian crashes must be assessed. The main purpose of this research is to explore the influence of pedestrian age and gender on the temporal and spatial distribution of vehicle-pedestrian crashes to identify the hotspots and hot times. METHODS: Data for all vehicle-pedestrian crashes on public roadways in the Melbourne metropolitan area from 2004 to 2013 are used in this research. Spatial autocorrelation is applied in examining the vehicle-pedestrian crashes in geographic information systems (GIS) to identify any dependency between time and location of these crashes. Spider plots and kernel density estimation (KDE) are then used to determine the temporal and spatial patterns of vehicle-pedestrian crashes for different age groups and genders. RESULTS: Temporal analysis shows that pedestrian age has a significant influence on the temporal distribution of vehicle-pedestrian crashes. Furthermore, men and women have different crash patterns. In addition, results of the spatial analysis shows that areas with high risk of vehicle-pedestrian crashes can vary during different times of the day for different age groups and genders. For example, for those between ages 18 and 65, most vehicle-pedestrian crashes occur in the central business district (CBD) during the day, but between 7:00 p.m. and 6:00 a.m., crashes among this age group occur mostly around hotels, clubs, and bars. CONCLUSIONS: This research reveals that temporal and spatial distributions of vehicle-pedestrian crashes vary for different pedestrian age groups and genders. Therefore, specific safety measures should be in place during high crash times at different locations for different age groups and genders to increase the effectiveness of the countermeasures in preventing and reducing vehicle-pedestrian crashes.

Neighborhood Influences on Vehicle-Pedestrian Crash Severity.

Socioeconomic factors are known to be contributing factors for vehicle-pedestrian crashes. Although several studies have examined the socioeconomic factors related to the location of the crashes, limited studies have considered the socioeconomic factors of the neighborhood where the road users live in vehicle-pedestrian crash modelling. This research aims to identify the socioeconomic factors related to both the neighborhoods where the road users live and where crashes occur that have an influence on vehicle-pedestrian crash severity. Data on vehicle-pedestrian crashes that occurred at mid-blocks in Melbourne, Australia, was analyzed. Neighborhood factors associated with road users' residents and location of crash were investigated using boosted regression tree (BRT). Furthermore, partial dependence plots were applied to illustrate the interactions between these factors. We found that socioeconomic factors accounted for 60% of the 20 top contributing factors to vehicle-pedestrian crashes. This research reveals that socioeconomic factors of the neighborhoods where the road users live and where the crashes occur are important in determining the severity of the crashes, with the former having a greater influence. Hence, road safety countermeasures, especially those focussing on the road users, should be targeted at these high-risk neighborhoods.

Supporting the risk management process with land information: a case study of Australia.

It is frequently argued that, at the parcel level, stakeholders are capable of and well supported in managing their land-related risks. Yet, evidence from the contemporary Australian context suggests otherwise: numerous large-scale disaster events have revealed that citizens are ill-prepared to respond and recover adequately. This paper begins with the premise that information, specifically land information, could better support parcel-level risk preparation, mitigation, response, and recovery. State land administration organisations in Australia primarily maintain this information and make it accessible. Land information is used regularly across all levels of government to support risk management activities; however, such application has not always occurred at the parcel and citizen level. Via a case study approach, this paper initially explores the land information available in Australia to stakeholders interested in parcel-level detail, and then goes on to propose how the utilisation of parcel-level land information could serve to enhance risk management practices.