PMU measurements based short-term voltage stability assessment of power systems via deep transfer learning

Deep learning has emerged as an effective solution for addressing the challenges of short-term voltage stability assessment (STVSA) in power systems. However, existing deep learning-based STVSA approaches face limitations in adapting to topological changes, sample labeling, and handling small datasets. To overcome these challenges, this paper proposes a novel phasor measurement unit (PMU) measurements-based STVSA method by using deep transfer learning. The method leverages the real-time dynamic information captured by PMUs to create an initial dataset. It employs temporal ensembling for sample labeling and utilizes least squares generative adversarial networks (LSGAN) for data augmentation, enabling effective deep learning on small-scale datasets. Additionally, the method enhances adaptability to topological changes by exploring connections between different faults. Experimental results on the IEEE 39-bus test system demonstrate that the proposed method improves model evaluation accuracy by approximately 20% through transfer learning, exhibiting strong adaptability to topological changes. Leveraging the self-attention mechanism of the Transformer model, this approach offers significant advantages over shallow learning methods and other deep learning-based approaches.Comment: Accepted by IEEE Transactions on Instrumentation & Measuremen

GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals

Towards developing effective and efficient brain-computer interface (BCI) systems, precise decoding of brain activity measured by electroencephalogram (EEG), is highly demanded. Traditional works classify EEG signals without considering the topological relationship among electrodes. However, neuroscience research has increasingly emphasized network patterns of brain dynamics. Thus, the Euclidean structure of electrodes might not adequately reflect the interaction between signals. To fill the gap, a novel deep learning framework based on the graph convolutional neural networks (GCNs) was presented to enhance the decoding performance of raw EEG signals during different types of motor imagery (MI) tasks while cooperating with the functional topological relationship of electrodes. Based on the absolute Pearson's matrix of overall signals, the graph Laplacian of EEG electrodes was built up. The GCNs-Net constructed by graph convolutional layers learns the generalized features. The followed pooling layers reduce dimensionality, and the fully-connected softmax layer derives the final prediction. The introduced approach has been shown to converge for both personalized and group-wise predictions. It has achieved the highest averaged accuracy, 93.056% and 88.57% (PhysioNet Dataset), 96.24% and 80.89% (High Gamma Dataset), at the subject and group level, respectively, compared with existing studies, which suggests adaptability and robustness to individual variability. Moreover, the performance was stably reproducible among repetitive experiments for cross-validation. To conclude, the GCNs-Net filters EEG signals based on the functional topological relationship, which manages to decode relevant features for brain motor imagery

End-of-Life Photovoltaic Modules

More than 78 million tons of photovoltaic modules (PVMs) will reach their end of life (EOL) by 2050. If they are not responsibly managed, they can (a) pollute our terrestrial ecosystem, (b) indirectly encourage continuous mining and extraction of Earth’s finite resources, and (c) diminish the net environmental benefit of harvesting solar energy. Conversely, successfully recovering them could reduce resource extraction and waste and generate sufficient economic return and value to finance the production of another 2 billion PVMs by 2050. Therefore, EOL PVMs must participate in the circular economy, and business and political leaders are actively devising strategies to enable their participation. This article aims to facilitate and expedite their efforts by comprehensively reviewing and presenting the latest progress and developments in EOL PVM recovery methods and processes. It also identifies and thoroughly discusses several interrelated observations that impede or accelerate their efforts. Overall, our approach to this article differs but synergistically complements and builds upon existing life cycle assessment-based (LCA-based) contributions

End-of-Life Photovoltaic Modules

More than 78 million tons of photovoltaic modules (PVMs) will reach their end of life (EOL) by 2050. If they are not responsibly managed, they can (a) pollute our terrestrial ecosystem, (b) indirectly encourage continuous mining and extraction of Earth’s finite resources, and (c) diminish the net environmental benefit of harvesting solar energy. Conversely, successfully recovering them could reduce resource extraction and waste and generate sufficient economic return and value to finance the production of another 2 billion PVMs by 2050. Therefore, EOL PVMs must participate in the circular economy, and business and political leaders are actively devising strategies to enable their participation. This article aims to facilitate and expedite their efforts by comprehensively reviewing and presenting the latest progress and developments in EOL PVM recovery methods and processes. It also identifies and thoroughly discusses several interrelated observations that impede or accelerate their efforts. Overall, our approach to this article differs but synergistically complements and builds upon existing life cycle assessment-based (LCA-based) contributions

Glycoproteomic Analysis of Urinary Extracellular Vesicles for Biomarkers of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) accounts for the most common form of primary liver cancer cases and constitutes a major health problem worldwide. The diagnosis of HCC is still challenging due to the low sensitivity and specificity of the serum α-fetoprotein (AFP) diagnostic method. Extracellular vesicles (EVs) are heterogeneous populations of phospholipid bilayer-enclosed vesicles that can be found in many biological fluids, and have great potential as circulating biomarkers for biomarker discovery and disease diagnosis. Protein glycosylation plays crucial roles in many biological processes and aberrant glycosylation is a hallmark of cancer. Herein, we performed a comprehensive glycoproteomic profiling of urinary EVs at the intact N-glycopeptide level to screen potential biomarkers for the diagnosis of HCC. With the control of the spectrum-level false discovery rate ≤1%, 756 intact N-glycopeptides with 154 N-glycosites, 158 peptide backbones, and 107 N-glycoproteins were identified. Out of 756 intact N-glycopeptides, 344 differentially expressed intact N-glycopeptides (DEGPs) were identified, corresponding to 308 upregulated and 36 downregulated N-glycopeptides, respectively. Compared to normal control (NC), the glycoproteins LG3BP, PIGR and KNG1 are upregulated in HCC-derived EVs, while ASPP2 is downregulated. The findings demonstrated that specific site-specific glycoforms in these glycoproteins from urinary EVs could be potential and efficient non-invasive candidate biomarkers for HCC diagnosis

Glycoproteomic Analysis of Urinary Extracellular Vesicles for Biomarkers of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) accounts for the most common form of primary liver cancer cases and constitutes a major health problem worldwide. The diagnosis of HCC is still challenging due to the low sensitivity and specificity of the serum α-fetoprotein (AFP) diagnostic method. Extracellular vesicles (EVs) are heterogeneous populations of phospholipid bilayer-enclosed vesicles that can be found in many biological fluids, and have great potential as circulating biomarkers for biomarker discovery and disease diagnosis. Protein glycosylation plays crucial roles in many biological processes and aberrant glycosylation is a hallmark of cancer. Herein, we performed a comprehensive glycoproteomic profiling of urinary EVs at the intact N-glycopeptide level to screen potential biomarkers for the diagnosis of HCC. With the control of the spectrum-level false discovery rate ≤1%, 756 intact N-glycopeptides with 154 N-glycosites, 158 peptide backbones, and 107 N-glycoproteins were identified. Out of 756 intact N-glycopeptides, 344 differentially expressed intact N-glycopeptides (DEGPs) were identified, corresponding to 308 upregulated and 36 downregulated N-glycopeptides, respectively. Compared to normal control (NC), the glycoproteins LG3BP, PIGR and KNG1 are upregulated in HCC-derived EVs, while ASPP2 is downregulated. The findings demonstrated that specific site-specific glycoforms in these glycoproteins from urinary EVs could be potential and efficient non-invasive candidate biomarkers for HCC diagnosis

State of knowledge in photoredox-catalysed direct difluoromethylation

International audienceThe combination of visible light photoredox catalysis with direct difluoromethylation has allowed the synthesis of a large choice of CF 2 H-containing value-added molecules under very mild reaction conditions

Quantification of the urbanization impacts on solar dimming and brightening over China

Metropolis’ contribution (anthropogenic aerosols) to solar dimming and brightening remains a hot topic of special concern over the past several decades. However, urbanization effects on surface incident solar radiation ( R _s ) have not been comprehensively investigated. In this study, the urbanization effects on solar dimming and brightening were addressed using the densely distributed reconstructed R _s data at 375 stations and 92 urban–rural station pairs over the time period of 1960–2019 in China. The results indicate that the impacts of urbanization on the monthly mean R _s is 0.86 ± 7.99 W m ^−2 during the study period, while the impact is 0.90 ± 8.30 W m ^−2 and 0.82 ± 8.26 W m ^−2 for the solar dimming (1960–1992) and brightening (1992–2019) phase, respectively. The urbanization effects on the trend of R _s is −0.39 and 0.16 W m ^−2 per decade during dimming and brightening, respectively. It also found that urbanization effects on R _s trend differs strikingly in magnitudes for specific regions in China. Generally, urbanization speeds up China’s dimming in the dimming phase and slows down China’s brightening in the brightening phase