## 2 Materials and Methods
### Typical Applications  
Typical applications include adaptive monitoring and diagnostics, and adaptive response. Dynamic, evolving features of the physical world require a huge amount of data transmission/exchange to ensure synchronization between the physical world and its virtual image. Such a communication framework can be based on the \"look only once\" (YOLO) principle. The YOLOv7-X object detector in the case of an apple orchard was used to extract semantic information from captured images of edge devices, reducing the amount of data needed to be transmitted. The meaning of each piece of semantic information is determined based on the trust generated by the object detector. Two resource allocation schemes are proposed:  
* Trust-based scheme;* AI-generated scheme acrlong. Diffusion models generate an optimal allocation scheme that outperforms the results obtained from the schemes used separately. An additional improvement is provided by the attention modules of the ELAN-H and SimAM layer aggregation network that reduce model parameters and computational complexity when using edge devices with limited performance [63]. The complexity of the supply chain poses a particular challenge due to its lack of transparency, generally accepted standards, and regulations. A blockchain-based process data management solution for recycling and reuse of used electronic devices is proposed, combining DT and GenAI to solve the blockchain performance bottleneck by predicting future data flows. This improves the adaptability and throughput of the system, as well as traceability, prediction accuracy, and efficiency throughout the process [64]. GenAI-supported cellular network DT is also proposed to learn complex network data distribution (environmental, user, and service) from samples from the distribution [65]. GenAI uses these data to generate different scenarios, improving flexibility and practically solving network optimization [66; 67]. The integration of GenAI and urban DTs is used to address challenges in the planning and management of built environments, including various urban subsystems (transportation, energy, water, and construction and infrastructure) [68]. Adaptive Response  
Enabling sustainable development and efficient use of resources, especially expensive energy, is becoming critical for today's economy. This is due to a number of factors: climate change, resource depletion, and the need for decarbonization and increased innovation in solutions. GenAI, DTs, and big data can help the energy sector achieve greater efficiency, optimize operations, and facilitate decision-making to optimize energy use and reduce waste [69]. The confusion in construction stems from the need to differentiate between two technologies: building information modeling (BIM) and DT, which differ in terms of technologies, maturity levels, data layers, enablers, and functionalities. The research emphasis here is on the convergence of BIM and DT, data integrity, their integration and transmission, bidirectional interoperability, non-technical factors, and data security [12]. Asset Administration Shell (AAS) is a digital twin model in the context of Industry 4.0, assuming that semantic-based communication and meaningful textual data generation are directly related and that these processes are equivalent. An LLM-supported system is implemented to generate standard DT models as instances from raw textual data collected automatically from data sheets describing technical assets. The achievable effective generation rate was 62-79%. The resulting AAS model can be integrated with compliant DT software for data exchange and DT communication and interoperability in industrial applications [70]. OpenAI GPT-4 Turbo with Vision LLM can interpret images and provide textual answers to queries about those images by combining natural language processing and visual understanding. This allows for intelligent extraction of key metadata from images and videos to assess the state of real-world systems and propose sustainability measures.