This paper proposes a fault location method for DC distribution networks (DCDNs) based on graph neural networks (GNNs), which integrates the fault line selection (FLS) and fault distance estimation (FDE) that are conventionally handled independently. The proposed method focuses on the analysis of feeders, including FLS of multiple feeders and FDE of a single feeder. Specifically, a feeder-as-node graph is constructed, where synchronous measurement data are extracted as node features, ensuring consistent dimensionality and enhanced learning efficiency. Moreover, the proposed method explicitly embeds the logical interdependencies between FLS and FDE into the structural design and parameter updating mechanism. An output processing module is designed to estimate the fault distance by analyzing the FLS results, ensuring the model utilizes the data of the DCDN system-level information rather than a single feeder. Furthermore, a two-stage pre-training strategy is introduced to improve stability and generalization, in which partial parameters are frozen. The effectiveness and generalization of the proposed method are verified by hardware-in-the-loop experiments.
Lai Wei, Kai Liao, Bo Li et al.· IEEE Transactions on Smart G...· 0 citations
An integrated computational framework combining machine learning (ML), deep learning (DL), and structure-based docking with experimental validation identifies AO65 as a promising lead for further TDP1-focused investigation.
Huang Zeng, Manyi Zhang, Bo Qiu et al.· RSC Advances· 0 citations
Terahertz (THz) communication is an essential component of sixth-generation (6G) wireless networks; however, its use is constrained by molecular absorption, ultrawideband beam squint, high- $\kappa $ Rician fading, and beam misalignment. The multiplicative fading penalty of passive RIS design can be avoided, and reliable coverage can be extended into the THz band using active, reconfigurable intelligent surfaces (A-RISs) that integrate per-element amplifiers. However, the simultaneous optimization of discrete phase shifts and continuous per-element amplification factors is a nonconvex, high-dimensional problem that is difficult to solve with conventional iterative solvers for the submillisecond coherence times of mobile THz users. A hardware-aware A-RIS-assisted THz communication framework is presented in this article, with an extensive channel model that accounts for frequency-selective molecular absorption, ultrawideband beam squint, high- $\kappa $ Rician fading ( $\kappa \in [{10,25}]$ dB), and stochastic beam misalignment. We present an unsupervised graph neural network-Transformer (GNN-Transformer) architecture to solve the resulting joint optimization problem in real time. Graph convolutional layers exploit local spatial dependencies among RIS elements, while Transformer attention layers capture global channel dependencies across the entire RIS panel. The network is trained end-to-end on channel realizations but, unlike optimal labeling, does not require optimal discrete-phase and continuous-amplification configurations, which are obtained in a single forward pass. The results of the simulations show that the proposed method, which performs an exhaustive search, achieves a higher SNR, meets the power budget without violating it, and reduces inference latency compared to successive convex approximation (SCA), thereby enabling large-scale deployments of THz A-RIS in real time.
Mian Muhammad Kamal, Syed Zain Ul Abideen, Ijaz Khan et al.· IEEE Internet of Things Jour...· 0 citations
Urban Internet of Thing (IoT) networks face severe reliability threats from diverse wireless interference, including jamming and spoofing, which are difficult to detect and localize in multipath-rich environments. Existing schemes often suffer from high false alarms, poor generalization, and low localization accuracy. This article presents an end–edge–cloud interference detection and localization framework integrating zero-shot detection, game-theoretic collaborative sensing, and GNN-based localization. Experiments on a city-scale prototype show that the system achieves 98.5% detection accuracy with recall of 96.7%, while reducing false alarms to 3.2%. The proposed graph neural network (GNN) reduces median localization error to 12.3 m, significantly outperforming baseline methods. Furthermore, the architecture reduces energy consumption by nearly 40% compared with cloud-only designs and maintains end-to-end latency under 120 ms. These results demonstrate that the proposed system enables robust, real-time interference awareness for large-scale IoT deployments, paving the way toward resilient 6G smart cities.
Qian Wang, Kai Cheng, Hong Mei et al.· IEEE Internet of Things Jour...· 0 citations
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The growing integration of renewable energy sources (RESs), such as photovoltaic (PV) and wind (WD), has significantly increased the variability of net load (NL), posing critical challenges on the net load forecasting. In this paper, a novel physics-informed series-aware graph Transformer (PISAGT) model is proposed for the net load forecasting, which synergistically combines the advantages of both direct and indirect forecasting methods to achieve superior forecasting performance. Firstly, a physics-informed loss module (PILM) is proposed, which introduces the physical characteristics of the net load into the loss function to incorporate physical knowledge in the gradient descent optimization, thereby enhancing the model’s generalization capability for more stable and trustworthy predictions. Secondly, a variable-scale patch embedding method segments time series into subsequence-level patches and transforms them into 2D representations, which enables the forecast model to simultaneously capture local temporal patterns across diverse forecasting horizons. Thirdly, a series-aware graph learning mechanism (SAGLM) which includes graph token encoding, adaptive adjacency matrix learning, and graph convolution, is proposed to integrate graph neural networks (GNNs) with Transformer within a synergistic framework to comprehensively capture intra-series and inter-series dependencies in net load datasets. Finally, case studies on the real-world datasets demonstrate that the PISAGT model can achieve a 14.4% improvement in prediction accuracy compared with direct forecasting methods, showing its effectiveness in net load forecasting.
Chang-Sen Feng, Shuai Zhang, Licheng Wang et al.· IEEE Transactions on Smart G...· 0 citations
Visible light positioning (VLP), due to its widespread infrastructure deployment and high accuracy, has emerged as a highly promising key technology for Internet of Things (IoT). Current research mainly relies on either data-driven methods based on fingerprint features or model-driven methods based on geometric localization to estimate position. Although data-driven approaches can effectively cope with complex environmental disturbances, their performance is limited by insufficient exploitation of latent signal features on the one hand and strong dependence on training data on the other, resulting in limited generalization capability. In contrast, model-driven methods can adapt to different scenarios by leveraging physical models and geometric constraints, but they struggle to characterize complex interference patterns in dynamic environments. To address these issues, this article proposes a data-model jointly driven VLP framework that integrates the complementary strengths of both paradigms. First, at the framework level, a tightly coupled joint optimization scheme is constructed to integrate data-driven ranging with model-driven localization, preserving physical interpretability while leveraging the representation capability of deep learning. In contrast to conventional methods that discard harmonics as detrimental components, this article introduces a harmonic-enhanced ranging module that uses selected harmonic components as auxiliary structured spectral cues to improve the robustness of VLP ranging. The fundamental received signal strength (RSS) and selected harmonic RSS values of the modulation signal are incorporated into the ranging process, and a graph neural network (GNN)-based data-driven ranging model is developed to capture the structured relationships between the fundamental component and its harmonics, thereby improving ranging robustness in complex environments. Finally, to enable end-to-end optimization of the joint framework, a geometry-aware loss function is designed, allowing the model to jointly consider data fitting and physical geometric constraints during training, thereby coupling learned signal representations with model-driven geometric constraints.
Hao Zhang, Xiansheng Yang, Xinyu Li et al.· IEEE Internet of Things Jour...· 0 citations
This work introduces a non-linear Laplacian operator specific to signed and directed networks (NLSD) and proposes an efficient spectral GNN framework (NLSD-GNN), which not only integrates signed and directional data effectively but also achieves superior performance across diverse datasets.
Graph Circuit Learning is introduced, a supervised, amortized framework that trains a GNN across multiple model--task pairs and applies it to unseen cases and preliminary results suggest that graph machine learning offers a natural and potentially powerful perspective on circuit localization.
Chester Tan, Moritz Lampert, Courtney Maynard et al.· 0 citations
The first comprehensive benchmarking framework specifically designed to accommodate inter-dataset heterogeneity is presented, finding that well-designed small datasets can match or even surpass the performance of larger benchmarks, suggesting that different metrics are applicable to different datasets/testing scenarios.
Yingjuan Cheng, Qing Ye, Linlong Jiang et al.· Journal of Cheminformatics· 0 citations
A novel committor learning framework grounded in the AlphaFold 3 paradigm is proposed that elucidates how ligand substituents regulate the ratio between distinct binding pathways, offering new perspectives for structure-based drug design.
Jintu Zhang, Zichang Jin, Huifeng Zhao et al.· 0 citations
CoRe-GNN is proposed, which performs both propagations in parallel at each layer: a coarsened inter-cluster term capturing long-range structure, and a local intra-cluster term preserving per-node discriminability.
Antonin Joly, Nicolas Keriven, Aline Roumy· 0 citations
This work establishes a scalable and physics guided candidate prioritization framework for inverse discovery of TMs beyond brute force electronic structure calculations and topological studies.
Tasneem U. Rehman, N. Yaqoob, A. A. Ganaie et al.· Physica Scripta· 0 citations
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
MIT News · Artificial Intelligence· news.mit.eduJul 6, 2026
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.
