The metaverse is a digital universe and a parallel world, which involves the application and development of Metaverse in interdisciplinary lenses, bridging fields such as social sciences, psychology, economics, communication, cultural studies, ethics, and the arts. Topics may include human behavior in virtual environments, economic systems in digital worlds, narrative and identity construction, immersive pedagogy, sociotechnical imaginaries, and the cultural impact of metaverse technologies. The metaverse is underpinned by six core technological domains: blockchain technology, interactivity technology (VR/AR/MR/XR/ER), game technology (game engine, game code, multimedia resources), AI technology, network communication technology, and Internet of Things. To support the operation of the huge metaverse, it also needs extremely powerful algorithms. Metaverse (eISSN: 2810-9791) is an open-access academic journal that dedicated to exploring a wide spectrum of metaverse-related topics, academic supported by Institute for Metaverse, School of Artificial Intelligence, Nanjing University of Information Science & Technology (NUIST). Metaverse also welcomes different types of articles including research article, case report, review, perspective, and commentary concerned with metaverse studies such as the concept definition and its development. Authors should read the focus and scope before submission.
Asia Pacific Academy of Science Pte. Ltd.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit, and that in the event of reuse or distribution, the terms of this license are made clear. With this license, authors hold the copyright without restrictions and are allowed to retain publishing rights without restrictions as long as this journal is the original publisher of the articles.
NFTS: The evaluation of standards and price volatility
The latest technology Non-Fungible Token (NFT) supports ownership of objects on the internet; everyone wants to reap the maximum of this opportunity. The price of the NFT shot up overnight, creating a market with trading volumes of millions worth, but there seem to be issues related to the legitimacy of this technology. Some countries define the legality of NFTs, cryptocurrencies, and cryptocurrency-based smart contracts, but they are just a handful of them; there requires the assessment of standards in NFT for full-fledged expansion throughout the world. The majority of the problems are related to the security of the users, price volatility of NFTs, and copyright issues. In this research, the evaluation is achieved by applying methods to identify the standards present in the current NFT ecosystem. The methods acquire quantitative and qualitative information to analyze it by designing models based on Correlation and Total Connectedness Index formulas to give the perspective of the inter relation between NFTs and other financial assets and deeply examine the technology's compliance with the regulations like KYC requirements and copyright registrations. The research uses numerical and non-numerical data from various sources, which are familiar with the crypto community. The results manifest the standards of NFTs, stabilization measures to the NFT market, and it guides investors, developers, and entrepreneurs. May be there is a prerequisite for the design change, viewpoint for alternative replacements for establishing smart contracts between the parties engaged in NFT ventures. Contemplating the level of centralization required on NFTs for protection of the stakeholders in the financial market.
Copyright (c) 2026 Authors http://creativecommons.org/licenses/by/4.0
AI-driven digital twins for resilient wind turbines under extreme conditions
The increasing deployment of wind turbines in extreme environmental conditions, like high-altitude icing plateaus, introduces significant structural and operational challenges. Harsh conditions, including corrosion fatigue, ice-induced dynamic loads, and fluctuating wind forces, accelerate component degradation and increase maintenance demands. Traditional operation and maintenance (O&M) strategies struggle to adapt to these conditions, demanding a shift towards more proactive, adaptive and intelligent solutions. AI-driven digital twins (DTs) offer a transformative approach by integrating real-time monitoring, predictive analytics, and adaptive control to enhance turbine resilience. This study focuses on enhancing the resilience of onshore wind turbine towers in challenging environments using a digital twin (DT) framework. The case study investigates a 5 MW onshore wind turbine with a lattice-tubular hybrid (LTH) tower, subjected to highly variable wind and environmental loads. Through a DT framework integrating OpenFAST and OpenSees, the study combines multi-physics simulations with supervisory control and data acquisition (SCADA) and structural health monitoring (SHM) data to reconstruct wind-induced loads and predict fatigue deterioration in critical components, such as bolted ring-flange connections. The results demonstrate that the DT-enabled model updating significantly reduces estimated fatigue damage, improving structural reliability and enabling proactive maintenance under fluctuating conditions. Beyond the advances, challenges still remain, including data integration, real-time processing, and cost-effective deployment. Future works are highly advised to focus on refining AI models, enhancing sensor data accuracy, and developing standardized frameworks for DT applications in renewable energy. By addressing these challenges, AI-driven DTs can play a crucial role in the long-term sustainability and resilience of wind energy systems under extreme conditions.
Adaptive Feature Fusion-Enhanced Vision Transformer Autoencoder for Realistic Image Inpainting
Image inpainting restores missing or corrupted image regions using contextual information from surrounding pixels. In the Metaverse integration of Virtual Reality (VR), Augmented Reality (AR), and Artificial Intelligence (AI)-realism and visual continuity are vital for immersive user experiences. This paper presents Vi-Trans, a Vision Transformer-based autoencoder for high-quality image inpainting. The model divides images into non-overlapping patches and reconstructs masked regions using global contextual learning through multi-head self-attention and feed-forward layers. To enhance structural integrity and edge preservation, a novel Adaptive Feature Fusion (AFF) module is introduced to fuse global transformer representations with local encoder features through an attention-weighted mechanism. This dynamic fusion balances semantic understanding with fine-grained spatial details, improving visual consistency. Experimental evaluations on the CelebA dataset demonstrate that Vi-Trans with AFF outperforms existing transformer-based inpainting methods in PSNR, SSIM, FID, and LPIPS metrics.
Augmented Reality Pose Estimation: A Systematic Review of Visual Localization and SLAM
Augmented Reality (AR) has attracted increasing attention as it enhances user perception and interaction by overlaying virtual content onto the physical world. Accurate and real-time six degrees of freedom (6-DoF) pose estimation is a core requirement for reliable spatial registration. To help researchers understand recent advances and select appropriate methods for AR applications, this survey provides a systematic overview of AR pose estimation algorithms from two complementary perspectives: visual localization with prior scene knowledge and simultaneous localization and mapping (SLAM), which performs online pose estimation and mapping in unknow environments. For visual localization, pose estimation methods can be grouped into three major lines, including feature-matching-based localization, scene coordinate regression, and pose regression; their evolution toward improved robustness and scalability is also discussed. For SLAM-based pose estimation, representative approaches are organized into traditional visual SLAM (VSLAM), deep learning–enhanced SLAM, rendering-based SLAM, highlighting key design choices as well as the strengths and limitations of each category under AR constraints. In addition, evaluation metrics and commonly used benchmarks are reviewed, and reported performance of representative algorithms on selected datasets is summarized. Finally, the review summarizes the current state of AR pose estimation and outlines future research directions.