Sustainable AI considers both the environmental impact of AI technologies and their role in advancing global sustainability. This study provides a comprehensive analysis of emerging trends in sustainable AI, highlighting energy-efficient algorithms, green data centers, AI-driven resource management, and ethical governance. It emphasizes AI's dual nature, its potential to drive climate action and socio-economic progress, alongside risks such as carbon emissions, e-waste, and digital inequality. The study underscores the need for interdisciplinary collaboration, inclusive policies, and standardized sustainability metrics to ensure responsible AI deployment. By identifying barriers and proposing strategies, it offers guidance to researchers, policymakers, and industry leaders on aligning AI with long-term environmental, ethical, and social objectives.

The Metaverse, as a paradigm continuously evolving in the next generation of the Internet, aims to integrate various network applications. However, existing applications on the Internet, such as serve computing, and edge computing, have highly complex technical requirements. These applications face compatibility issues with the Metaverse in terms of protocols, applications, and services. So they can’t be directly integrated into the Metaverse. How to efficiently deploy service computing in the Metaverse has become a hotspot research area. Moreover, Metaverse implements innovative services to offer individuals more immersive experiences, such as virtual reality services and augmented Reality services. These new services demand high computational resources including computing power, network performance, data security, etc. Ensuring optimal service quality for these new services in the Metaverse is another critical aspect of Metaverse research. To address the aforementioned challenges, Meta-services, designed to describe, discover, compose, and manage other services, are gradually attracting widespread attention and research. In this paper, we provide a comprehensive review, analysis, and discussion of existing research work. We summarize the services computing and novel services in Metaverse and categorize the meta-services framework into three layers: meta-bottom layer (meta-data), meta-middle layer (meta-models, meta-objects, and meta-languages), and meta-top layer (meta-programming). Based on the meta-services framework, we discuss some current challenges, as well as provide future research directions. We hope that this paper can enable readers to quickly understand the reasons for each problem and the current research progress, thereby providing guidance and motivation for further research in this field.

Acupoints (APs) prove to have positive effects on disease diagnosis and treatment, while intelligent techniques for the automatic detection of APs are not yet mature, making them more dependent on manual positioning. In this paper, we realize the skin conductance-based APs and non-APs recognition with machine learning, which could assist in APs detection and localization in clinical practice. Firstly, we collect skin conductance of traditional Five-Shu Point and their corresponding non-APs with wearable sensors, establishing a dataset containing over 36000 samples of 12 different AP types. Then, electrical features are extracted from the time domain, frequency domain, and nonlinear perspective respectively, following which typical machine learning algorithms (SVM, RF, KNN, NB, and XGBoost) are demonstrated to recognize APs and non-APs. The results demonstrate XGBoost with the best precision of 66.38%. Moreover, we also quantify the impacts of the differences among AP types and individuals, and propose a pairwise feature generation method to weaken the impacts on recognition precision. By using generated pairwise features, the recognition precision could be improved by 7.17%. The research systematically realizes the automatic recognition of APs and non-APs, and is conducive to pushing forward the intelligent development of APs and Traditional Chinese Medicine theories.

It is well known that the metaverse, also named general cyberspace (GC), is virtual-real fusion spaces, consisting of a virtual space, namely, cyberspace and virtual-real spaces, namely, cyber-enabled physical, social, and thinking (cyber-enabled) spaces. This article discusses the open issues of the metaverse in terms of science and technology and proposes a new discipline and interdiscipline hierarchy for the metaverse (GC), namely, cyberology first. Then, it explores various relevant standards of discipline classification and a discipline and interdiscipline hierarchy based on physical, social, and thinking spaces, and investigates the cyberspace and cyber-enabled spaces. On the basis of the above research, this article enriches the contents of cyberology in two terms: 1) the disciplines in cyberspace and 2) the interdisciplines in cyber-enabled spaces. Finally, this article gives predictions of cyberology on the future development of the metaverse from the aspects of cyber–physical space, cyber–social space and cyber-thinking space.

The rapid growth of the Internet of Things (IoT) and the increasing number of connected devices have propelled the proliferation of offered applications, causing “applications exploding.” In the context of IoT, filtering and selecting the most relevant applications in a given situation is a challenging task. Thus, developing techniques that can alleviate applications exploding and meet users’ requirements is highly demanded for IoT development. This survey focuses on applications exploding in the IoT and reviews some of the existing techniques, such as intelligent sensing, content distribution network, microservices, and 5G, which help mitigate the effects of applications exploding. Furthermore, the survey discusses how to describe user requirements and offer application services to better match the two. In addition, this survey presents the smart home as an instance of typical IoT applications and explores how adaptive users’ requirements for food ordering can be better met when various food provider applications are available for choice. Finally, partially resolved and unresolved bottlenecks brought by applications exploding are put forward to be further researched by the technical and scientific community.

With the convergence of modern computing technology and social sciences, both theoretical research and practical applications of social computing have been extended to new domains. In particular, social computing was significantly influenced by the recent advances of artificial intelligence (AI). However, the conventional technologies of AI have various drawbacks in dealing with complicated and dynamic problems. Such deficiency can be rectified by hybrid human-artificial intelligence (H-AI), which integrates both human intelligence and AI into one unity, forming a new enhanced intelligence. H-AI in dealing with social problems shows some advantages over the conventional AI. This article firstly reviews the latest research progresses of AI in social computing. Secondly, it summarizes typical challenges AI faces in social computing, which motivate the necessity to introduce H-AI to tackle social-oriented problems. Finally, we discuss the concept of H-AI and propose a holistic architecture of H-AI in social computing, which consists of three layers: object layer, intelligent processing layer, and application layer. The proposed architecture shows that H-AI has significant advantages over AI in solving social problems.

With the continuous development of Artificial Intelligence (AI), autonomous driving has become a popular research area. AI enables the autonomous driving system to make a judgment, which makes studies on autonomous driving reaches a period of booming development. However, due to the defects of AI, it is not easy to realize a general intelligence, which also limits the research on autonomous driving. In this paper, we summarize the existing architectures of autonomous driving and make a taxonomy. Then we introduce the concept of hybrid human-artificial intelligence (H-AI) into a semi-autonomous driving system. For making better use of H-AI, we propose a theoretical architecture based on it. Given our architecture, we classify and overview the possible technologies and illustrate H-AI’s improvements, which provides a new perspective for the future development. Finally, we have identified several open research challenges to attract the researchers for presenting reliable solutions in this area of research.

As Internet of Things (IoT) is a fascinating paradigm in which all things and objects are connected together, it holds a significant position in fostering intelligent high-level services. However, the future IoT architecture is still under evolution profiting from the overwhelming development of cyberspace and cyber technologies. Based on the traditional physical-based IoT, social-inspired Internet of People (IoP) and brain-abstracted Internet of Thinking (IoTk), an intelligent embryo of cyber-enabled Internet of X (IoX) is being established where all things, entities, people and thinking are interacted seamlessly. In this article, we clearly introduce the cyber-enabled IoX from perspective of both ubiquitous connections and space convergence, and design an architecture with four pillars, namely, things, people, thinking and cyberentities in respective spaces. In addition, we analyze the fundamental issues in IoX development, such as information exploding, link exploding and application exploding from the view of ubiquitous connections, entity exploding and relationship exploding on the basis of space convergence, and service exploding from overall aspects, where potential solutions are discussed at the same time. The intelligent cyber-enabled IoX will be the cornerstone for future techniques and applications, and proves to be the solid foundation for upcoming intelligent and proactive era.

Continual learning can learn new tasks incrementally while avoiding catastrophic forgetting. Recent work has shown that packing multiple tasks into a single network incrementally by iterative pruning and re-training network is a promising method. We build upon this idea and propose an improved version of PackNet. Specifically, we propose a novel gradient-based threshold method to reuse the knowledge of the previous tasks selectively when learning new tasks. Our experiments on a variety of classification tasks and different network architectures demonstrate that our method obtains competitive results when compared to PackNet.

With the development of the Internet of Things (IoT), the physical space we are living in is experiencing unprecedented digitalization and virtualization. It is an overwhelming trend to achieve the convergence between the physical space and cyberspace, where the fundamental problem is to realize the accurate mapping between the two spaces. Therefore, identity modeling and identity addressing, which serve as the main bridge between the physical space and cyberspace, are regarded as important research areas. This article summarizes the related works regarding identity modeling and identity addressing in IoT, and makes a general comparison and analysis based on their respective features. Following that a flexible and low coupling framework, with strong independence between different modules is proposed, where both identity modeling and identity addressing are integrated. Meanwhile, we discuss and analyze the future development and challenges of identity modeling and addressing. It is proved that identity modeling and identity addressing are extremely significant topics in the era of IoT.