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

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.