Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: advances, challenges, and opportunities

Developing Campus Digital Twin Using Interactive Visual Analytics Approach

Digital Twins (DTs) are increasingly recognized for their potential to improve efficiency and decision-making in various domains of the built environment. Despite their promise, challenges like cost, complexity, interoperability, and data integration remain. This paper introduces a novel interactive visual analytics system that tackles these issues, using a case study of simulating class distribution and campus building capacity at a large public university. The system leverages enrollment data, converting it into a spatial-temporal format for interactive exploration and analysis of class distribution and resource utilization. Through case studies, we demonstrate the system's effectiveness, adaptability, and real-world applicability, highlighting its role in practical DT implementation for built environments.

Smart monitoring solution for dengue infection control: A digital twin-inspired approach

BACKGROUND AND OBJECTIVE

In the realm of smart healthcare, precise monitoring and prediction services are crucial for mitigating the impact of infectious diseases. This study introduces an innovative digital twin technology-inspired monitoring architecture that employs a similarity-based hybrid modeling scheme to significantly enhance accuracy in the smart healthcare domain. The research also delves into the potential of IoT technology in delivering advanced technological healthcare solutions, with a specific focus on the rapid expansion of dengue fever.

METHODS

The proposed digital twin-inspired healthcare system is designed to proactively combat the spread of dengue virus by enabling ubiquitous monitoring and forecasting of individuals' susceptibility to dengue infection. The system utilizes digital twin technology to observe the status of healthcare and generate likely predictions about the vulnerability to the virus by employing k-means Clustering and Artificial Neural Networks.

RESULTS

The proposed system has been validated and its effectiveness has been demonstrated through experimental evaluation using carefully defined methods. The results of the experimental assessment confirm that the system performs optimally in terms of Temporal Delay (14.15 s), Classification Accuracy (92.86%), Sensitivity (92.43%), Specificity (91.52%), F-measure (90.86%), and Prediction Effectiveness. Moreover, by integrating a hybrid model that corrects errors in physics-based predictions employing a model for error correction driven by data, this approach has demonstrated a noteworthy 48% reduction in prediction errors, particularly in health monitoring scenarios.

CONCLUSIONS

The digital twin-inspired healthcare system proposed in this study can assist healthcare providers in assessing the health vulnerability of the dengue virus, thereby reducing the likelihood of long-term or catastrophic health consequences. The integration of a hybrid modeling approach and the utilization of IoT technology has shown promising results in enhancing the accuracy and effectiveness of smart health monitoring and prediction services.

Enhancing visual communication through representation learning

Introduction

This research aims to address the challenges in model construction for the Extended Mind for the Design of the Human Environment. Specifically, we employ the ResNet-50, LSTM, and Object Tracking Algorithms approaches to achieve collaborative construction of high-quality virtual assets, image optimization, and intelligent agents, providing users with a virtual universe experience in the context of visual communication.

Methods

Firstly, we utilize ResNet-50 as a convolutional neural network model for generating virtual assets, including objects, characters, and environments. By training and fine-tuning ResNet-50, we can generate virtual elements with high realism and rich diversity. Next, we use LSTM (Long Short-Term Memory) for image processing and analysis of the generated virtual assets. LSTM can capture contextual information in image sequences and extract/improve the details and appearance of the images. By applying LSTM, we further enhance the quality and realism of the generated virtual assets. Finally, we adopt Object Tracking Algorithms to track and analyze the movement and behavior of virtual entities within the virtual environment. Object Tracking Algorithms enable us to accurately track the positions and trajectories of objects, characters, and other elements, allowing for realistic interactions and dynamic responses.

Results and discussion

By integrating the technologies of ResNet-50, LSTM, and Object Tracking Algorithms, we can generate realistic virtual assets, optimize image details, track and analyze virtual entities, and train intelligent agents, providing users with a more immersive and interactive visual communication-driven metaverse experience. These innovative solutions have important applications in the Extended Mind for the Design of the Human Environment, enabling the creation of more realistic and interactive virtual worlds.

Using Constrained-Disorder Principle-Based Systems to Improve the Performance of Digital Twins in Biological Systems

Digital twins are computer programs that use real-world data to create simulations that predict the performance of processes, products, and systems. Digital twins may integrate artificial intelligence to improve their outputs. Models for dealing with uncertainties and noise are used to improve the accuracy of digital twins. Most currently used systems aim to reduce noise to improve their outputs. Nevertheless, biological systems are characterized by inherent variability, which is necessary for their proper function. The constrained-disorder principle defines living systems as having a disorder as part of their existence and proper operation while kept within dynamic boundaries. In the present paper, we review the role of noise in complex systems and its use in bioengineering. We describe the use of digital twins for medical applications and current methods for dealing with noise and uncertainties in modeling. The paper presents methods to improve the accuracy and effectiveness of digital twin systems by continuously implementing variability signatures while simultaneously reducing unwanted noise in their inputs and outputs. Accounting for the noisy internal and external environments of complex biological systems is necessary for the future design of improved, more accurate digital twins.

A Novel Digital Twin Architecture with Similarity-Based Hybrid Modeling for Supporting Dependable Disaster Management Systems

Disaster management systems require accurate disaster monitoring and prediction services to reduce damages caused by natural disasters. Digital twins of natural environments can provide the services for the systems with physics-based and data-driven disaster models. However, the digital twins might generate erroneous disaster prediction due to the impracticability of defining high-fidelity physics-based models for complex natural disaster behavior and the dependency of data-driven models on the training dataset. This causes disaster management systems to inappropriately use disaster response resources, including medical personnel, rescue equipment and relief supplies, to ensure that it may increase the damages from the natural disasters. This study proposes a digital twin architecture to provide accurate disaster prediction services with a similarity-based hybrid modeling scheme. The hybrid modeling scheme creates a hybrid disaster model that compensates for the errors of physics-based prediction results with a data-driven error correction model to enhance the prediction accuracy. The similarity-based hybrid modeling scheme reduces errors from the data dependency of the hybrid model by constructing a training dataset using similarity assessments between the target disaster and the historical disasters. Evaluations in wildfire scenarios show that the digital twin decreases prediction errors by approximately 50% compared with those of the existing schemes.

Resilient City: Characterization, Challenges and Outlooks

The increasingly severe environmental pollution and the Earth’s ecological crisis make the concept of resilient cities (RCs) a hot topic in urban research. We ran a bibliometric analysis to analyze the research progress, areas, hotspots, and strategies pertaining to RCs. The core collection came from the Web of Science (WoS) database as the data source to explore 4462 literature works on RCs. The results revealed that development time series analysis is divided into three stages. Changes in the number of publications are linked to natural disasters, the ecological environment, and science policy. The top five issuing journals accounted for 24.15% of the total sample. Country cooperation mainly is concentrated in countries with good economic development trends, such as the United States, China, and the United Kingdom. There were 63 core authors. The most published research institution was the Chinese Academy of Sciences. The RC research hotspots included the definition of resilience and evolution, the study of resilience as an analytical framework for urban issues, and resilience assessment indicators. This paper shows that RCs should strengthen multi-country cooperation and interdisciplinary integration and should focus on comprehensive research on basic theories, evaluation systems, and action mechanisms to reference future research on RCs further.