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The current state evaluation of power equipment often focuses solely on changes in electrical quantities while neglecting basic equipment information as well as textual information such as system alerts, operation records, and defect records. Constructing a device-centric knowledge graph by extracting information from multiple sources related to power equipment is a valuable approach to enhance the intelligence level of asset management. Through the collection of pertinent authentic datasets, we have established a dataset for the state evaluation of power equipment, encompassing 35 types of relationships. To better suit the characteristics of concentrated relationship representations and varying lengths in textual descriptions, we propose a generative model called RoUIE, which is a method for constructing a knowledge graph of power equipment based on improved Universal Information Extraction (UIE). This model first utilizes a pre-trained language model based on rotational position encoding as the text encoder in the fine-tuning stage. Subsequently, we innovatively leverage the Distribution Focal Loss (DFL) to replace Binary Cross-Entropy Loss (BCE) as the loss function, further enhancing the model's extraction performance. The experimental results demonstrate that compared to the UIE model and mainstream joint extraction benchmark models, RoUIE exhibits superior performance on the dataset we constructed. On a general Chinese dataset, the proposed model also outperforms baseline models, showcasing the model's universal applicability. [ABSTRACT FROM AUTHOR]

The decipherment of ancient Chinese scripts, such as oracle bone and bronze inscriptions, holds immense significance for understanding ancient Chinese history, culture, and civilization. Despite substantial progress in recognizing oracle bone script, research on the overall recognition of ancient Chinese characters remains somewhat lacking. To tackle this issue, we pioneered the construction of a large-scale image dataset comprising 9233 distinct ancient Chinese characters sourced from images obtained through archaeological excavations. We propose the first model for recognizing the common ancient Chinese characters. This model consists of four stages with Linear Embedding and Swin-Transformer blocks, each supplemented by a CoT Block to enhance local feature extraction. We also advocate for an enhancement strategy, which involves two steps: firstly, conducting adaptive data enhancement on the original data, and secondly, randomly resampling the data. The experimental results, with a top-one accuracy of 87.25% and a top-five accuracy of 95.81%, demonstrate that our proposed method achieves remarkable performance. Furthermore, through the visualizing of model attention, it can be observed that the proposed model, trained on a large number of images, is able to capture the morphological characteristics of ancient Chinese characters to a certain extent. [ABSTRACT FROM AUTHOR]

A hybrid type of polyimide fibers (PIF) grafted with multi-walled carbon nanotubes (MWCNTs) was developed for high-temperature tribological applications. Compared to pure PI samples, the mechanical properties (i.e., Young's modulus and hardness) of the PIF-based composite were enhanced following a rule of mixture prediction; the onset decomposition temperature of the MWCNT-PIF-based composite was increased by 14.5 °C and the wear rate at 300 °C decreased by 34.5%. To understand the grafting mechanism, Dmol3 simulation was performed, which revealed that the benzene ring and the hydroxyl group of diene-acceptor (DA) donated electrons to the oxygen atom in the nitrogen-containing five-membered ring of PIF and the straight chain structures had higher reactivity than a branched chain structure. [ABSTRACT FROM AUTHOR]

As an unsupervised data representation neural network, auto-encoder (AE) has shown great potential in denoising, dimensionality reduction, and data reconstruction. Many AE-based background (BKG) modeling methods have been developed for hyperspectral anomaly detection (HAD). However, their performance is subject to their unbiased reconstruction of BKG and target pixels. This article presents a rather different low rank and sparse matrix decomposition (LRaSMD) method based on AE, named auto-encoder and independent target (AE-IT), for hyperspectral anomaly detection. First, the encoder weight matrix, obtained by a designed AE network, is utilized to construct a projector for generating a low-rank component in the encoder subspace. By adaptively and reasonably determining the number of neurons in the latent layer, the designed AE-based method can promote the reconstruction of BKG. Second, to ensure independence and representativeness, the component in the encoder orthogonal subspace is made into a sphere and followed by finding of unsupervised targets to construct an anomaly space. In order to mitigate the influence of noise on anomaly detection, sparse cardinality (SC) constraint is enforced on the component in the anomaly space for obtaining the sparse anomaly component. Finally, anomaly detector is constructed by combining Mahalanobi distance and multi-components, which include encoder component and sparse anomaly component, to detect anomalies. The experimental results demonstrate that AE-IT performs competitively compared to the LRaSMD-based models and AE-based approaches. [ABSTRACT FROM AUTHOR]

The perception of anomalies in power scenarios plays a crucial role in the safe operation and fault prediction of power systems. However, traditional anomaly detection methods face challenges in identifying difficult samples due to the complexity and uneven distribution of power scenarios. This paper proposes a power scene anomaly perception method based on high-resolution networks and difficult sample mining. Firstly, a high-resolution network is introduced as the backbone for feature extraction, enhancing the ability to express fine details in power scenarios and capturing information on small target anomaly regions. Secondly, a strategy for mining difficult samples is employed to focus on learning and handling challenging and hard-to-recognize anomaly samples, thereby improving the overall anomaly detection performance. Lastly, the method incorporates GIOU loss and a flexible non-maximum suppression strategy to better adapt to the varying sizes and dense characteristics of power anomaly targets. This improvement enables higher adaptability in detecting anomalies in power scenarios. Experimental results demonstrate significant improvements in power scene anomaly perception and superior performance in handling challenging samples. This study holds practical value for fault diagnosis and safe operation in power systems. [ABSTRACT FROM AUTHOR]

Defect detection in power scenarios is a critical task that plays a significant role in ensuring the safety, reliability, and efficiency of power systems. The existing technology requires enhancement in its learning ability from large volumes of data to achieve ideal detection effect results. Power scene data involve privacy and security issues, and there is an imbalance in the number of samples across different defect categories, all of which will affect the performance of defect detection models. With the emergence of the Internet of Things (IoT), the integration of IoT with machine learning offers a new direction for defect detection in power equipment. Meanwhile, a generative adversarial network based on multi-view fusion and self-attention is proposed for few-shot image generation, named MVSA-GAN. The IoT devices capture real-time data from the power scene, which are then used to train the MVSA-GAN model, enabling it to generate realistic and diverse defect data. The designed self-attention encoder focuses on the relevant features of different parts of the image to capture the contextual information of the input image and improve the authenticity and coherence of the image. A multi-view feature fusion module is proposed to capture the complex structure and texture of the power scene through the selective fusion of global and local features, and improve the authenticity and diversity of generated images. Experiments show that the few-shot image generation method proposed in this paper can generate real and diverse defect data for power scene defects. The proposed method achieved FID and LPIPS scores of 67.87 and 0.179, surpassing SOTA methods, such as FIGR and DAWSON. [ABSTRACT FROM AUTHOR]

Both catalase and peroxiredoxin show high activities of H2O2 decomposition and coexist in the same organism; however, their division of labor in defense against H2O2 is unclear. We focused on the major peroxiredoxin (PrxA) and catalase (CatB) in Aspergillus nidulans at different growth stages to discriminate their antioxidant roles. The dormant conidia lacking PrxA showed sensitivity to high concentrations of H2O2 (>100 mM), revealing that PrxA is one of the important antioxidants in dormant conidia. Once the conidia began to swell and germinate, or further develop to young hyphae (9 h to old age), PrxA-deficient cells (ΔprxA) did not survive on plates containing H2O2 concentrations higher than 1 mM, indicating that PrxA is an indispensable antioxidant in the early growth stage. During these early growth stages, absence of CatB did not affect fungal resistance to either high (>1 mM) or low (<1 mM) concentrations of H2O2. In the mature hyphae stage (24 h to old age), however, CatB fulfills the major antioxidant function, especially against high doses of H2O2. PrxA is constitutively expressed throughout the lifespan, whereas CatB levels are low in the early growth stage of the cells developing from swelling conidia to early growth hyphae, providing a molecular basis for their different contributions to H2O2 resistance in different growth stages. Further enzyme activity and cellular localization analysis indicated that CatB needs to be secreted to be functionalized, and this process is confined to the growth stage of mature hyphae. Our results revealed differences in effectiveness and timelines of two primary anti-H2O2 enzymes in fungus. [ABSTRACT FROM AUTHOR]

Despite the rapid development of deep learning in hyperspectral image classification (HSIC), most models require a large amount of labeled data, which are both time-consuming and laborious to obtain. However, contrastive learning can extract spatial–spectral features from samples without labels, which helps to solve the above problem. Our focus is on optimizing the contrastive learning process and improving feature extraction from all samples. In this study, we propose the Unlocking-the-Potential-of-Data-Augmentation (UPDA) strategy, which involves adding superior data augmentation methods to enhance the representation of features extracted by contrastive learning. Specifically, we introduce three augmentation methods—band erasure, gradient mask, and random occlusion—to the Bootstrap-Your-Own-Latent (BYOL) structure. Our experimental results demonstrate that our method can effectively improve feature representation and thus improve classification accuracy. Additionally, we conduct ablation experiments to explore the effectiveness of different data augmentation methods. [ABSTRACT FROM AUTHOR]

Memristors have been proved effective in intelligent computing systems owing to the advantages of non-volatility, nanometer size, low power consumption, compatibility with traditional CMOS technology, and rapid resistance transformation. In recent years, considerable work has been devoted to the question of how to design and optimize memristor models with different structures and physical mechanisms. Despite the fact that the optoelectronic effect inevitably makes the modelling process more complex and challenging, relatively few research works are dedicated to optoelectronic memristor modelling. Based on this, this paper develops an optoelectronic memristor model (containing mathematical model and circuit model). Moreover, the composite memristor circuit (series- and parallel-connected configuration) with a rotation mechanism is discussed. Further, a multi-valued logic circuit is designed, which is capable of performing multiple logic functions from 0–1, verifying the validity and effectiveness of the established memristor model, as well as opening up a new path for the circuit implementation of fuzzy logic. [ABSTRACT FROM AUTHOR]

Large-scale integration of PV generators in distribution grids will impair the voltage stability due to the stochastic and fluctuated PV power generation. To tame the volatile PV power generation, battery energy storage systems (BESS) are deployed as an effective yet expensive power buffering mechanism. In this paper, a dual ascent-based voltage optimization control is proposed to achieve the concurrent regulation of battery State-of-Charge (SoC), nodal voltages, and distribution loss. This control features the limited dependence on the communication network with information interaction between neighboring nodes. Besides, it can achieve the optimal power flow minimizing the distribution loss while maintaining the BESS SoC within a healthy range. The derivation of the control framework is provided, and comparative simulations in the IEEE 37-node distribution system are performed to validate the effectiveness of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

Polyethylenimine (PEI) has been widely used in gene delivery. However, its high cytotoxicity and undesired non-specific protein adsorption hinder the overall delivery efficacy and the practical applications of PEI-based gene delivery systems. In this study, we prepared hydrophobically modified PEIs (H-PEIs) via the reaction of octanal with 40% of primary amines in PEI25k and PEI10k, respectively. Two common zwitterionic molecules, 1,3-propanesultone and β-propiolactone, were then used for the modification of the resulting H-PEIs to construct polycationic gene carriers with zwitterionic properties (H-zPEIs). The siRNA delivery efficiency and cytotoxicity of these materials were evaluated in Hela-Luc and A549-Luc cell lines. Compared with their respective parental H-PEIs, different degrees of zwitterionic modification showed different effects in reducing cytotoxicity and delivery efficiency. All zwitterion-modified PEIs showed excellent siRNA binding capacity, reduced nonspecific protein adsorption, and enhanced stability upon nuclease degradation. It is concluded that zwitterionic molecular modification is an effective method to construct efficient vectors by preventing undesired interactions between polycationic carriers and biomacromolecules. It may offer insights into the modification of other cationic carriers of nucleic acid drugs. [ABSTRACT FROM AUTHOR]

As the acquisition, transmission, storage and conversion of images become more efficient, image data are increasing explosively. At the same time, the limitations of conventional computational processing systems based on the Von Neumann architecture continue to emerge, and thus, improving the efficiency of image processing has become a key issue that has bothered scholars working on images for a long time. Memristors with non-volatile, synapse-like, as well as integrated storage-and-computation properties can be used to build intelligent processing systems that are closer to the structure and function of biological brains. They are also of great significance when constructing new intelligent image processing systems with non-Von Neumann architecture and for achieving the integrated storage and computation of image data. Based on this, this paper analyses the mathematical models of memristors and discusses their applications in conventional image processing based on memristive systems as well as image processing based on memristive neural networks, to investigate the potential of memristive systems in image processing. In addition, recent advances and implications of memristive system-based image processing are presented comprehensively, and its development opportunities and challenges in different major areas are explored as well. By establishing a complete spectrum of image processing technologies based on memristive systems, this review attempts to provide a reference for future studies in the field, and it is hoped that scholars can promote its development through interdisciplinary academic exchanges and cooperation. [ABSTRACT FROM AUTHOR]

Amphiphilic copolymers with pendant functional groups in polyester segments are widely used in nanomedicine. These enriched functionalities are designed to form covalent conjugates with payloads or provide additional stabilization effects for encapsulated drugs. A general method is successfully developed for the efficient preparation of functional biodegradable PEG-polyester copolymers via click chemistry. Firstly, in the presence of mPEG as initiator, Sn(Oct)2-catalyzed ring-opening polymerization of the α-alkynyl functionalized lactone with D,L-lactide or ε-caprolactone afforded linear mPEG-polyesters bearing multiple pendant alkynyl groups. Kinetic studies indicated the formation of random copolymers. Through copper-catalyzed azide-alkyne cycloaddition reaction, various small azido molecules with different functionalities to polyester segments are efficiently grafted. The molecular weights, polydispersities and grafting efficiencies of azido molecules of these copolymers were investigated by NMR and GPC. Secondly, it is demonstrated that the resulting amphiphilic functional copolymers with low CMC values could self-assemble to form nanoparticles in aqueous media. In addition, the in vitro degradation study and cytotoxicity assays indicated the excellent biodegradability and low cytotoxicity of these copolymers. This work provides a general approach toward the preparation of functional PEG-polyester copolymers in a quite efficient way, which may further facilitate the application of functional PEG-polyesters as drug delivery materials. [ABSTRACT FROM AUTHOR]

Immunotherapy has recently become a promising strategy for the treatment of a wide range of cancers. However, the broad implementation of cancer immunotherapy suffers from inadequate efficacy and toxic side effects. Integrating pH-responsive nanoparticles into immunotherapy is a powerful approach to tackle these challenges because they are able to target the tumor tissues and organelles of antigen-presenting cells (APCs) which have a characteristic acidic microenvironment. The spatiotemporal control of immunotherapeutic drugs using pH-responsive nanoparticles endows cancer immunotherapy with enhanced antitumor immunity and reduced off-tumor immunity. In this review, we first discuss the cancer-immunity circle and how nanoparticles can modulate the key steps in this circle. Then, we highlight the recent advances in cancer immunotherapy with pH-responsive nanoparticles and discuss the perspective for this emerging area. [ABSTRACT FROM AUTHOR]