Review re-maps multi-view learning into four supervised scenarios and three granular sub-tiers, delivering the first unified blueprint for researchers to navigate classification, clustering, incomplete views and hybrid techniques.

DynIMTS replaces static graphs with instance-attention that updates edge weights on the fly, delivering SOTA imputation and P12 classification accuracy for streaming irregular multivariate time series.

 Anti-forgetting representation learning method reduces the weight aggregation interference on model memory and augments the representation performance.

Geothermal energy is clean and renewable, derived from the heat stored within accessible depths of the Earth’s crust. The adoption of a single-well system for medium-deep and deep geothermal energy extraction has attracted significant interest from the scientific and industrial communities because it effectively circumvents issues such as downhole inter-well connections and induced seismicity. However, the low heat transfer capacity in geothermal formations limits the heat extraction performance of single-well systems and hinders their commercial deployment. This review covers various enhancement concepts for optimizing the heat transfer within single-well systems, emphasizing critical parameters such as heat transfer area, heat transfer coefficient, and temperature difference. Additionally, it presents the thermo-economic evaluation of different configurations of single-well borehole heat exchangers and superlong gravity heat pipes (SLGHPs). The SLHGP, utilizing phase-change heat transfer, is recognized as a highly effective and continuously productive technology, capable of extracting over 1 MW of heat. Its pumpless operation and ease of installation in abandoned wells make it cost-effective, offering a promising economic advantage over traditional geothermal systems. It also highlights the challenges and potential research opportunities that can help identify gaps in research to enhance the performance of single-well geothermal systems.

Huawei’s new “Agentic-AI Core” lets 6G networks write their own control procedures: natural-language intents become executable missions, composed on the fly from verified, third-party building blocks and orchestrated by AI agents with no human coding required.

Discover how generative AI is transforming video communication in a groundbreaking new paradigm. Learn about the innovative techniques and future prospects of generative video communication, which promises more efficient, adaptable, and immersive video services. Explore the challenges and potential solutions in this emerging field.

Researchers from Southeast University have developed a novel amplifying and filtering reconfigurable intelligent surface (AF-RIS) that enhances wireless communication for 6G networks. The AF-RIS achieves significant signal amplification, frequency selectivity, and beam-steering capabilities while reducing hardware costs and power consumption. This innovation could revolutionize wireless relay systems in next-generation networks.