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Edge devices are increasingly utilized for defect detection in power line inspection, necessitating algorithms that optimize model size and accuracy. This study introduces a lightweight detection method using the Optimized Feature Network with MobileOne‐FPN‐NASHead (OFN network) OFN network and distillation technique. The OFN incorporates a lightweight backbone, neural network search, re‐parametrization, and a feature pyramid to create a compact yet effective detection network. To enhance feature learning, a heterogeneous distillation approach is applied, leveraging a modified YOLOv8 as a teacher network. This modification includes an explicit visual centre for improved global and local information extraction, crucial for dense target detection in power line inspections. Additionally, the Minimize the points distance IoU (MPDloU) loss function is used to improve localization accuracy over the the Complete‐Intersection Over Union (CIoU) loss. Experimental results show a 1.1% mean Average Precision (mAP) increase for the enhanced YOLOv8 and a 70.2% mAP for the OFN network with 18.95 GFLOPs and 343 FPS, achieving a commendable balance between model efficiency and detection performance. The research underscores the viability of the OFN for edge computing in power line defect detection, highlighting the potential of innovative algorithmic structures in this application. [ABSTRACT FROM AUTHOR]

Abstract Edge devices are increasingly utilized for defect detection in power line inspection, necessitating algorithms that optimize model size and accuracy. This study introduces a lightweight detection method using the Optimized Feature Network with MobileOne‐FPN‐NASHead (OFN network) OFN network and distillation technique. The OFN incorporates a lightweight backbone, neural network search, re‐parametrization, and a feature pyramid to create a compact yet effective detection network. To enhance feature learning, a heterogeneous distillation approach is applied, leveraging a modified YOLOv8 as a teacher network. This modification includes an explicit visual centre for improved global and local information extraction, crucial for dense target detection in power line inspections. Additionally, the Minimize the points distance IoU (MPDloU) loss function is used to improve localization accuracy over the the Complete‐Intersection Over Union (CIoU) loss. Experimental results show a 1.1% mean Average Precision (mAP) increase for the enhanced YOLOv8 and a 70.2% mAP for the OFN network with 18.95 GFLOPs and 343 FPS, achieving a commendable balance between model efficiency and detection performance. The research underscores the viability of the OFN for edge computing in power line defect detection, highlighting the potential of innovative algorithmic structures in this application.

Abstract The insulators of overhead power lines play a crucial role in maintaining the reliability of transmission and distribution networks. Because they are exposed to harsh and dynamic environmental conditions, it is essential to investigate the impact of environmental parameters such as pollution, inclined angle with the cross arm, and temperature on the dielectric performance of the insulators of overhead lines. Conventionally, the effect of such parameters can be investigated through experimental measurements of the insulator flashover voltage. However, this approach is costly and time-consuming and calls for the isolation of the lines to conduct the test, causing interruption to the entire grid. As such, there is an essential need to develop a new methodology to quantify the flashover voltage of overhead insulators operating under various environmental conditions, which is the main aim of this paper. The Central Composite Design is employed to develop a mathematical correlation between the insulator flash over voltage as a dependent variable and three environmental parameters: pollution level, inclined angle, and temperature as independent variables. The robustness of the developed equation is validated through extensive experimental measurements of the insulator’s flash overvoltage under various conditions. Results reveal a good agreement between the actual and predicted flashover voltage using the developed correlation, as the absolute error for all investigated samples is less than 6%.

Abstract The leakage current characteristic monitoring of transmission line insulators is considered a worthy technique for insulator state prediction. In this paper, the harmonic analysis of leakage current signals has been performed to analyze the composite insulator condition. Experimental studies of asymmetrically aged, fully aged, and virgin insulators have been conducted. The effects of different aging types, pollution, and humidity on leakage current components, such as harmonics and the phase differences with the applied voltage, have been investigated. The analysis of the results shows that the effect of asymmetric aging on leakage current components is non‐linear, and this non‐linearity increases with the degree of pollution. Also, the type of insulator aging can be determined by the ratio of the third‐ and fifth‐order leakage current harmonics. For instance, the classification of insulators into virgin, asymmetrically aged, and fully aged can be determined by examining the corresponding value ranges of 0–0.5, 0.5–0.9, and > 0.9, respectively. Similarly, the phase difference values of 15% indicate the clean and polluted operating conditions individually.

Decades ago, Gurzhi proposed that if momentum-conserving collisions prevail among heat-carrying phonons in insulators and charge-carrying electrons in metals, hydrodynamic features will become detectable. In this paper, we will review the experimental evidence emerging in the last few years supporting this viewpoint and raising new questions. The focus of the paper will be bulk crystals without (or with a very dilute concentration of) mobile electrons and steady-state thermal transport. We also discuss the possible link between this field of investigation and other phenomena, such as the hybridization of phonon modes and the phonon thermal Hall effect. [ABSTRACT FROM AUTHOR]

Discoveries in the field of genomics have revealed that non-coding genomic regions are not merely "junk DNA", but rather comprise critical elements involved in gene expression. These gene regulatory elements (GREs) include enhancers, insulators, silencers, and gene promoters. Notably, new evidence shows how mutations within these regions substantially influence gene expression programs, especially in the context of cancer. Advances in high-throughput sequencing technologies have accelerated the identification of somatic and germline single nucleotide mutations in non-coding genomic regions. This review provides an overview of somatic and germline non-coding single nucleotide alterations affecting transcription factor binding sites in GREs, specifically involved in cancer biology. It also summarizes the technologies available for exploring GREs and the challenges associated with studying and characterizing non-coding single nucleotide mutations. Understanding the role of GRE alterations in cancer is essential for improving diagnostic and prognostic capabilities in the precision medicine era, leading to enhanced patient-centered clinical outcomes. [ABSTRACT FROM AUTHOR]

The leakage current characteristic monitoring of transmission line insulators is considered a worthy technique for insulator state prediction. In this paper, the harmonic analysis of leakage current signals has been performed to analyze the composite insulator condition. Experimental studies of asymmetrically aged, fully aged, and virgin insulators have been conducted. The effects of different aging types, pollution, and humidity on leakage current components, such as harmonics and the phase differences with the applied voltage, have been investigated. The analysis of the results shows that the effect of asymmetric aging on leakage current components is non‐linear, and this non‐linearity increases with the degree of pollution. Also, the type of insulator aging can be determined by the ratio of the third‐ and fifth‐order leakage current harmonics. For instance, the classification of insulators into virgin, asymmetrically aged, and fully aged can be determined by examining the corresponding value ranges of 0–0.5, 0.5–0.9, and > 0.9, respectively. Similarly, the phase difference values of < 15% and those > 15% indicate the clean and polluted operating conditions individually. [ABSTRACT FROM AUTHOR]

Abstract The YOLO series of algorithms have made substantial contributions to the detection of insulator defects in power transmission line operations. However, existing target detection algorithms for the small target detection and low‐quality insulator images encounter difficulties in effectively capturing relevant features, resulting in a higher probability of target loss. To identify and classify defects in the operational state of insulators, an improved YOLOv8 target identification algorithm called DGW‐YOLOv8 is proposed in this paper. The deformable attention backbone of the DGW‐YOLOv8 target identification algorithm is designed by adding the deformable ConvNets v2 module and the global attention mechanism. This addition reduces the feature loss caused by the network feature processing, enhances the sensitivity of the algorithm to small‐scale targets, and reduces the impact caused by the different global positions of the targets. Additionally, to address the problem of low quality of captured images, WIoU v3 is used to replace CIoU in the original YOLOv8 target identification algorithm to optimize the loss function, reduce the degrees of freedom, and improve the network robustness. Experimental results demonstrate that the enhanced YOLOv8 algorithm can achieve an improvement of 2.4% and 5.5% in mAP and mAP50‐95, respectively, compared with the original algorithm.

Ensuring the safety of transmission lines necessitates effective insulator defect detection. Traditional methods often need more efficiency and accuracy, particularly for tiny defects. This paper proposes an innovative insulator defect recognition method leveraging YOLOv8s-SwinT. Combining Swin Transformer and Convolutional Neural Network (CNN) enhances the model's understanding of multi-scale global semantic information through cross-layer interactions. The improved BiFPN structure in the neck achieves bidirectional cross-scale connections and weighted feature fusion during feature extraction. Additionally, a new small-target detection layer enhances the capability to detect tiny defects. The experimental results showcase outstanding performance, with precision, recall, and mAP reaching 95.6%, 95.3%, and 97.7%, respectively. This boosts detection efficiency and ensures high accuracy, providing robust support for real-time detection of tiny insulator defects. [ABSTRACT FROM AUTHOR]

The YOLO series of algorithms have made substantial contributions to the detection of insulator defects in power transmission line operations. However, existing target detection algorithms for the small target detection and low‐quality insulator images encounter difficulties in effectively capturing relevant features, resulting in a higher probability of target loss. To identify and classify defects in the operational state of insulators, an improved YOLOv8 target identification algorithm called DGW‐YOLOv8 is proposed in this paper. The deformable attention backbone of the DGW‐YOLOv8 target identification algorithm is designed by adding the deformable ConvNets v2 module and the global attention mechanism. This addition reduces the feature loss caused by the network feature processing, enhances the sensitivity of the algorithm to small‐scale targets, and reduces the impact caused by the different global positions of the targets. Additionally, to address the problem of low quality of captured images, WIoU v3 is used to replace CIoU in the original YOLOv8 target identification algorithm to optimize the loss function, reduce the degrees of freedom, and improve the network robustness. Experimental results demonstrate that the enhanced YOLOv8 algorithm can achieve an improvement of 2.4% and 5.5% in mAP and mAP50‐95, respectively, compared with the original algorithm. [ABSTRACT FROM AUTHOR]

The stable operation of a power supply system is inseparable from the work of detecting defects in transmission lines. However, the insulator defect detection model based on deep learning is widely used in wire inspection work. Therefore, this paper proposes an improved YOLOv5s insulator defect detection model in order to solve the problems of insufficient training data and low recognition accuracy of the target detection model in the real-time detection of small target insulator defects. To expand and enhance the training data, experiments were conducted using the addition of noise and random black blocks. The spatial and channel weight coefficients were obtained by adding an attention mechanism (Convolutional Block Attention Module, CBAM), and the dimensions of the input feature maps were transformed to enhance the model's ability to extract and fuse small target defect features. Experiments show that with Faster RCNN, YOLOv3, SSD and YOLOv4 comparison experiments verified that the algorithm achieves 97.38% detection accuracy for insulators and 93.32% detection accuracy for small target insulator defects with a fast detection speed, which is a better solution to the problem of detecting insulator defects with too small a proportion in the image. [ABSTRACT FROM AUTHOR]

Abstract Insulators are one of the key components in high‐voltage power systems that prevent transmission lines from grounding. Since they are exposed to different kinds of harsh environments and climates, periodic inspection is indispensable for the safety and high quality of power grid. Nowadays, unmanned aerial vehicle (UAV) inspection is more widely used, facilitating incorporation of convolutional neural network‐based detectors in the insulator detection task. However, these methods are generally based on the assumption that the image samples are balanced among different categories and possess completely ideal annotations. The problem of sample imbalance or incomplete annotation is rarely investigated in depth for insulator defect detection. Here, insulator defect detection with imbalanced data and incomplete annotations is focused on. The proposed framework, named Pi‐index, introduces positive unlabelled (PU) learning to solve the problem of incomplete annotation and designs a novel index the class prior, which is a key parameter in PU learning. Moreover, focal loss is integrated in our framework to alleviate the effect of sample imbalance. Experiment results demonstrate that the proposed framework achieves better performance than the baseline methods in situations of sample imbalance and missing annotation.

Insulators are one of the key components in high‐voltage power systems that prevent transmission lines from grounding. Since they are exposed to different kinds of harsh environments and climates, periodic inspection is indispensable for the safety and high quality of power grid. Nowadays, unmanned aerial vehicle (UAV) inspection is more widely used, facilitating incorporation of convolutional neural network‐based detectors in the insulator detection task. However, these methods are generally based on the assumption that the image samples are balanced among different categories and possess completely ideal annotations. The problem of sample imbalance or incomplete annotation is rarely investigated in depth for insulator defect detection. Here, insulator defect detection with imbalanced data and incomplete annotations is focused on. The proposed framework, named Pi‐index, introduces positive unlabelled (PU) learning to solve the problem of incomplete annotation and designs a novel index the class prior, which is a key parameter in PU learning. Moreover, focal loss is integrated in our framework to alleviate the effect of sample imbalance. Experiment results demonstrate that the proposed framework achieves better performance than the baseline methods in situations of sample imbalance and missing annotation. [ABSTRACT FROM AUTHOR]

Anorthite ceramics (CaAl 2 Si 2 O 8) was obtained by slip casting of water suspension of kaolin (Al 2 O 3 ·2SiO 2 ·2H 2 O) and CaCO 3 as a source of CaO. A relative density of 87 % of the theoretical density was achieved after a 3-h-long sintering of green samples at 1200 °C. It was found that slip casting allows the fabrication of anorthite with relatively small pores (<8 μm) which was beneficial for achieving a high fracture strength of 129 MPa and good thermal shock resistance. For a quantitative measurement of thermal shock resistance, a critical temperature interval (ΔT c) was determined by a reduction in the mean fracture strength of at least 30 %. The experimentally determined ΔT c of 300 °C was higher than the value calculated by the equation assuming that the thermal stress created during quenching causes the fracture. It was found that the discrepancy between experimental and calculated values of ΔT c could be explained by an introduction of cracks of various sizes by indentation method and careful observation of crack growth initiation after quenching from various temperatures. It is believed that the reason for the discrepancy lies in the different nature of stress that was used in the theoretical calculation and the stress that acts during sample quenching. [ABSTRACT FROM AUTHOR]

An electron transport was investigated both in Sr2IrO4 thin films and in Josephson heterostructures with Sr2IrO4 barrier. The band gap in Sr2IrO4 varied in wide range of 15–250 meV which depends on deposition conditions of thin films, or on nature of interfaces between the barrier and superconducting electrodes. [ABSTRACT FROM AUTHOR]

Lightweight ablative materials owing to their low density, high efficiency heat insulation and excellent designability, which are considered as the most promising candidate materials in the field of aerospace ablative thermal protection. In this work, a polycarbosilane (PCS) in-situ reinforced carbon felt/phenolic (PR/C f) aerogel composite is reported. The surface of aerogel and carbon fiber was coated with PCS protective layer by sol gel method and secondary vacuum impregnation process. The aerogel composite material possesses low density (0.264–0.307 g/cm3), hydrophobic, low thermal conductivity (0.121–0.178 W/(m·K)) and significant high compressive strength (12.89 MPa). When the oxygen acetylene flame with high heat flow density ablates for 20 s, it shows high efficiency in heat protection and insulation, and the linear ablation rate is as low as 0.089 mm/s. The PL/PR/C f aerogel composite shows competitive and broad application prospects in the field of ablation heat protection in the future. [ABSTRACT FROM AUTHOR]

The contamination of insulators increases their surface conductivity, resulting in a higher chance of shutdowns occurring. To measure contamination, equivalent salt deposit density (ESDD) and non-soluble deposit density (NSDD) are used. In this paper, the VGG-11, VGG-13, VGG-16, VGG-19, ResNet-18, ResNet-34, ResNet-50, ResNet-152, DenseNet-121, DenseNet-161, DenseNet-169, and DenseNet-201 convolutional neural networks (CNNs) were considered to classify the visible contamination of pin-type distribution power grid insulators. The NSDD presents more visual variation than ESDD when artificial contamination is evaluated. Comparing the CNNs, the ResNet-50 had the best performance for classifying visible contamination using unbalanced data with an accuracy of 99.242% and an F1-score of 0.97436, respectively. In benchmarking, the ResNet-50 outperformed well-established classifiers such as the multilayer perceptron, support vector machine, k-nearest neighbors, decision tree, ensemble bagged trees, and quadratic discriminant. [ABSTRACT FROM AUTHOR]

Featured Application: The results presented in this paper highlight the chemical nature of the deposits measured on high power line insulators in Italy; the results also underline the significant role of inorganic ions in forming a conductive layer on insulators when exposed to moisture, leading to the risk of flashover events. Applications are related to the prevention of such phenomena by identifying the phase transitions of atmospheric aerosols deposited on insulators and their associated conductive effect. This is achieved through the generation of synthetic aerosols and the study of their phase transitions and conductance in an aerosol exposure chamber (AEC). The reliability of the national power grid is a key issue in modern society. Atmospheric aerosols are the main cause of the reduction in the performance of insulators and the increase in the possibility of flashovers, resulting in power line failures. Under high ambient humidity, the water-soluble compounds of atmospheric aerosols collected on the insulators' surface can dissociate in ions and form a conductive layer, which may lead to flashover events. With a view to investigating the processes that drive these phenomena, the chemical composition of aerosol deposits on insulators in Italy was determined by ion chromatography analysis and thermos-optical and X-ray techniques. In addition, a synthetic aerosol with the same analyzed chemical composition was generated in a laboratory and deposited on PTFE filters and glass specimens allowing us to determine the deliquescence and crystallization relative humidity and the conductive effect in an aerosol exposure chamber. The results evidenced the presence of a hazardous inorganic ion layer, which generates a sharp phase transition of the aerosol deposit as a function of the ambient relative humidity; this layer poses a dangerous threat to the reliability of the power grid, increasing the probability of flashover events where the conductive layer facilitates the flow of electrical current across the insulator surface, potentially causing power outages or damage to the power lines. [ABSTRACT FROM AUTHOR]

A novel Coulomb spike concept is applied to the radiation damage induced in LiF and SiO2 with about the same mass density (~2.65 g cm−3) by Ni2860 and Kr3684 ions of 1.0-MeV u−1 energy for about the same electronic energy loss (~10 MeV µm−1). This is an alternative concept to the already known models of the Coulomb spike and inelastic thermal spike for the damage induced by swift heavy ion irradiations. The distribution of ionizations and electrostatic energy gained in the electric field by the ionized atoms is computed with the PHITS code for both targets. Further, the atomic collision cascades induced by these low-energy hot ions of about 500 eV are simulated with the SRIM2013 code. It is found that melting is reached in a small volume for SiO2 due to the energy deposition in the subthreshold events of nuclear collisions induced by the Si and O ions. For LiF, the phonon contribution to the stopping power of the lighter Li and F ions is not sufficient to induce melting, even though the melting temperature is lower than for SiO2. The formation of amorphous domains in SiO2 is likely after fast quenching of the small molten pockets, whereas only point defects may be formed in LiF.

Abstract With the improvement of smart grid, utilizing unmanned aerial vehicles (UAV) to detect the operation status of insulators has attracted widespread attention. The insulator defects can lead to serious power loss, damage the service life of power lines, and even result in power outages in serious cases. The small‐scale object, complex background, and limited‐number collected data make insulator defect still a challenging problem. Benefitted by the advances in deep learning, deep learning‐based insulator defects have achieved great progress in recent years. In the paper, the authors present a novel systematic survey of these advances, where further analysis about different processing stages methods is introduced: (i) insulator processing stage methods exploit the specific image pre‐processing algorithm for data augmentation and low‐level vision information extraction; (ii) defect detection stage model can locate and classify diagnosis fault with different task targets, like sequential task strategy and multi‐task strategy. In addition, the authors also review publicly available benchmark and datasets. The future research direction and open problem are discussed to promote the development of the community.

Thermal conductivity is the amount of heat that can be passed through or conducted by any material. Evaluating the thermal conductivity of insulators is critical for many applications. The available thermal conductivity apparatuses are expensive, time-consuming, and tedious to operate. With the advent of nanotechnology, there is a tremendous increase in the research and development of new insulating materials to be used for various applications. So, there is an urgent need to develop a portable, simple, and cost-effective apparatus that can be used in research laboratories to measure the thermal conductivity of newly developed insulating materials. In this regard, a portable and cost-effective thermal conductivity apparatus has been indigenously developed in the laboratory. The apparatus has been used to measure the thermal conductivity of insulating slabs. The values obtained from the apparatus have been verified with known material and found to be in broad agreement with the value available in the literature. The results obtained have been deployed onto the cloud in real-time using the Internet of Things platform. A plot of the thermal conductivity of the insulating slab at different temperatures has been displayed in the cloud. It can be concluded that the developed apparatus can be used to measure the thermal conductivity of any insulating slab with reasonable accuracy. Furthermore, this apparatus gives the same accuracy for measuring the thermal conductivity of metals as long as their dimensions are known, such as metallic bars or slabs. The thermal conductivity of the metals is compared to the standard values and this can determine their purity and if composites such as nanomaterials have been added. It is very useful for the mining industry as well, to measure the thermal conductivity of metals and assess their purity. [ABSTRACT FROM AUTHOR]

The energy harvesting of ultra‐high voltage transmission lines and wireless power transmission technology are effective means to solve the stable energy supply of outdoor tower equipment. However, there are inevitably technical problems such as long transmission distance, weak coupling, and parameter sensitivity. In this paper, an effective design approach for a power demand‐oriented long‐distance wireless power transmission system is proposed. With the goal of transmitting power, a system equivalent parameter model is established to address the sensitivity of design parameters for the point‐to‐point long‐distance wireless power transmission system, and the main influencing factors that lead to system power transmission failure are analyzed. To effectively address the impact of high‐sensitivity parameters, a fast‐tuning method with redundant capacitance matrix compensation is proposed, which improves the deviation problem in system parameter design, improves the efficiency of offset parameter matching, and analyzes the impact of wireless energy transmission systems on high‐voltage insulator strings. Finally, an experimental platform was constructed to achieve power transmission at a distance of not less than 5 m at a resonant frequency of 50 kHz. The transmission efficiency met the design expectations and verified the effectiveness of the analysis. [ABSTRACT FROM AUTHOR]

Transmission line fault detection using drones provides real-time assessment of the operational status of transmission equipment, and therefore it has immense importance in ensuring stable functioning of the transmission lines. Currently, identification of transmission line equipment relies predominantly on manual inspections that are susceptible to the influence of natural surroundings, resulting in sluggishness and a high rate of false detections. In view of this, in this study, we propose an insulator defect recognition algorithm based on a YOLOv5 model with a new lightweight network as the backbone network, combining noise reduction and target detection. First, we propose a new noise reduction algorithm, i.e., the adaptive neighborhood-weighted median filtering (NW-AMF) algorithm. This algorithm employs a weighted summation technique to determine the median value of the pixel point's neighborhood, effectively filtering out noise from the captured aerial images. Consequently, this approach significantly mitigates the adverse effects of varying noise levels on target detection. Subsequently, the RepVGG lightweight network structure is improved to the newly proposed lightweight structure called RcpVGG-YOLOv5. This structure facilitates single-branch inference, multi-branch training, and branch normalization, thereby improving the quantization performance while simultaneously striking a balance between target detection accuracy and speed. Furthermore, we propose a new loss function, i.e., Focal EIOU, to replace the original CIOU loss function. This optimization incorporates a penalty on the edge length of the target frame, which improves the contribution of the high-quality target gradient. This modification effectively addresses the issue of imbalanced positive and negative samples for small targets, suppresses background positive samples, and ultimately enhances the accuracy of detection. Finally, to align more closely with real-world engineering applications, the dataset utilized in this study consists of machine patrol images captured by the Unmanned Aerial Systems (UAS) of the Yunnan Power Supply Bureau Company. The experimental findings demonstrate that the proposed algorithm yields notable improvements in accuracy and inference speed compared to YOLOv5s, YOLOv7, and YOLOv8. Specifically, the improved algorithm achieves a 3.7% increase in accuracy and a 48.2% enhancement in inference speed compared to those of YOLOv5s. Similarly, it achieves a 2.7% accuracy improvement and a 33.5% increase in inference speed compared to those of YOLOv7, as well as a 1.5% accuracy enhancement and a 13.1% improvement in inference speed compared to those of YOLOv8. These results validate the effectiveness of the proposed algorithm through ablation experiments. Consequently, the method presented in this paper exhibits practical applicability in the detection of aerial images of transmission lines within complex environments. In future research endeavors, it is recommended to continue collecting aerial images for continuous iterative training, to optimize the model further, and to conduct in-depth investigations into the challenges associated with detecting small targets. Such endeavors hold significant importance for the advancement of transmission line detection. [ABSTRACT FROM AUTHOR]

Abstract The proportion of insulators in aerial power patrol images is small and the background of overhead lines is complex, often leading to incomplete and inaccurate detection of insulators. Therefore, an algorithm for detecting insulator targets based on multi‐feature fusion is developed in this study. Firstly, a dynamic threshold oriented fast and rotated brief algorithm is proposed, which uses the bag‐of‐words dictionary model to determine local shape features of the image, applies gradient weighting to the global texture feature vector extracted by the histogram of oriented gradients algorithm and performs radial gradient transformations to get the improved HOG of features. Secondly, the feature vectors are fused serially, the learning machine is trained and the parameters of the support vector machine are optimized using the quantum particle swarm optimization algorithm. Finally, the target area is pre‐divided by the selective search algorithm, and the area is classified by the learning machine. The experimental results show that the proposed feature extraction method can describe the image details more accurately than the existing methods, and the average accuracy of the feature extraction classifier can reach 93.7%, which helps to overcome the incomplete detection problem of insulator detection at the aerial work site.

Abstract The effects of uneven icing, number of icicles, inclination angle of icicle, ice pallets and arc ignition on the electric field are ignored for the two‐dimensional axisymmetric model of ice‐covered insulator. Therefore, a 3D simulation model was developed in this paper. The average electric field strength of air gap (Eav) and maximum field strength of icicle tip (Emax) were determined to characterize the influence of various factors on the electric field distortion degree. The results show that compared with the insulator without ice and with a dry ice, the supply voltage is almost entirely applied to all icicle air gaps for a wet ice‐covered insulator. Eav is independent of icicle diameter and icicle number but increases with the increment of icicle length and icicle inclination angle. Emax raises with the increase of icicle length, icicle number, icicle inclination angle and icicle diameter. As the source of electric field distortion, the ice pallets on the surface of the sheds rise the field strength of air gap. Based on the simulation analysis and natural icing test, the applied voltage is redistributed in the remaining air gaps when an arc occurs in the gap near the high‐voltage terminal, thereby causing the subsequent flashover.

Ensuring the safety of transmission lines necessitates effective insulator defect detection. Traditional methods often need more efficiency and accuracy, particularly for tiny defects. This paper proposes an innovative insulator defect recognition method leveraging YOLOv8s-SwinT. Combining Swin Transformer and Convolutional Neural Network (CNN) enhances the model’s understanding of multi-scale global semantic information through cross-layer interactions. The improved BiFPN structure in the neck achieves bidirectional cross-scale connections and weighted feature fusion during feature extraction. Additionally, a new small-target detection layer enhances the capability to detect tiny defects. The experimental results showcase outstanding performance, with precision, recall, and mAP reaching 95.6%, 95.3%, and 97.7%, respectively. This boosts detection efficiency and ensures high accuracy, providing robust support for real-time detection of tiny insulator defects.

Insulators are important components of transmission lines, serving as support for conductors and preventing current backflow. However, insulators exposed to natural environments for a long time are prone to failure and can cause huge economic losses. This article proposes a fast and accurate lightweight Fast and Accurate YOLOv5s (FA-YOLO) model based on YOLOv5s model. Firstly, attention mechanisms are integrated into the network module, improving the model’s ability to extract and fuse target features. Secondly, the backbone part of the network is lightweightened to reduce the number of parameters and computations at the cost of slightly reducing the accuracy of detecting a few objects. Finally, the loss function of the model is improved to accelerate the convergence of the network and improve detection accuracy. At the same time, a visual insulator detection interface is designed using PyQt5. The experimental results show that the algorithm in this paper reduces the number of parameters by 28.6%, the computational effort by 35.7%, and the mAP value by 1.7% compared with the original algorithm, and is able to identify defective insulators quickly and accurately in complex backgrounds.

With the improvement of smart grid, utilizing unmanned aerial vehicles (UAV) to detect the operation status of insulators has attracted widespread attention. The insulator defects can lead to serious power loss, damage the service life of power lines, and even result in power outages in serious cases. The small‐scale object, complex background, and limited‐number collected data make insulator defect still a challenging problem. Benefitted by the advances in deep learning, deep learning‐based insulator defects have achieved great progress in recent years. In the paper, the authors present a novel systematic survey of these advances, where further analysis about different processing stages methods is introduced: (i) insulator processing stage methods exploit the specific image pre‐processing algorithm for data augmentation and low‐level vision information extraction; (ii) defect detection stage model can locate and classify diagnosis fault with different task targets, like sequential task strategy and multi‐task strategy. In addition, the authors also review publicly available benchmark and datasets. The future research direction and open problem are discussed to promote the development of the community. [ABSTRACT FROM AUTHOR]

Hexagonal carbon allotropes C6, C9, and C12 with qtz, sta and lon topologies, respectively, were predicted on the basis of crystal chemistry and first principles (DFT) calculations. The new allotropes are mechanically (elastic properties) and dynamically (phonons) stable phases and are characterized by ultra-high Vickers hardness, exceptionally high for qtz C6 and C12, close to the previously studied qtz C3. The electronic band structures of all new allotropes show semi-conducting to insulating behavior. lon C12 can be considered as novel "superlonsdaleite." [ABSTRACT FROM AUTHOR]

Pollution usually accumulates on the surface of the insulator rapidly on high‐speed electric multiple units (EMUs) under operation condition, and the flashover can occur frequently, especially when the EMUs is running on a high‐altitude line. To solve the problem, this paper proposes the calculation method of insulator withstand voltage under the condition of non‐uniform fouling distribution of high‐speed airflow based on the characteristics of high altitude of Lanzhou‐Xin Line in China, and gives the design and test verification of insulator support insulator for 3500‐km altitude. The design and test verification of the supporting insulator of the rolling stock applicable to 3500 km altitude are presented. The result shows that the insulator should be reliable operation on 4430‐km high altitude area if the insulator structure height is 430 mm, the creep distance is 1250 mm. The anti‐pollution performance is fine in high‐speed wind if the radiuses of the large and small shed of the insulator are 196 and 145 mm, respectively, and the shed spacing is 45 mm. The 50‐Hz withstand voltage of the developed insulator is 131 kV and the light impulse voltage is 228 kV which is suitable for lines running on 3500 m. This research provides a theoretical basis and data to support the safety of high‐speed rolling stock in high‐altitude areas. [ABSTRACT FROM AUTHOR]

Super-enhancers are large, densely concentrated swaths of enhancers that regulate genes critical for cell identity. Tumorigenesis is accompanied by changes in the super-enhancer landscape. These aberrant super-enhancers commonly form to activate proto-oncogenes, or other genes upon which cancer cells depend, that initiate tumorigenesis, promote tumor proliferation, and increase the fitness of cancer cells to survive in the tumor microenvironment. These include well-recognized master regulators of proliferation in the setting of cancer, such as the transcription factor MYC which is under the control of numerous super-enhancers gained in cancer compared to normal tissues. This Review will cover the expanding cell-intrinsic and cell-extrinsic etiology of these super-enhancer changes in cancer, including somatic mutations, copy number variation, fusion events, extrachromosomal DNA, and 3D chromatin architecture, as well as those activated by inflammation, extra-cellular signaling, and the tumor microenvironment. [ABSTRACT FROM AUTHOR]

The inspection and maintenance of transmission systems are necessary for their proper functioning. In this way, among the line's critical points are the insulator chains, which are responsible for providing insulation between conductors and structures. The accumulation of pollutants on the insulator surface can cause failures in the power system, leading to power supply interruptions. Currently, the cleaning of insulator chains is performed manually by operators who climb towers and use cloths, high-pressure washers, or even helicopters. The use of robots and drones is also under study, presenting challenges to be overcome. This paper presents the development of a drone–robot for cleaning insulator chains. The drone–robot was designed to identify insulators by camera and perform cleaning through a robotic module. This module is attached to the drone and carries a battery-powered portable washer, a reservoir for demineralized water, a depth camera, and an electronic control system. This paper includes a literature review on the state of the art related to strategies used for cleaning insulator chains. Based on this review, the justification for the construction of the proposed system is presented. The methodology used in the development of the drone–robot is then described. The system was validated in a controlled environment and in field experimental tests, with the ensuing discussions and conclusions formulated, along with suggestions for future work. [ABSTRACT FROM AUTHOR]

In foggy environments, outdoor insulator detection is always with low visibility and unclear targets. Meanwhile, the scale of haze simulation insulator datasets is insufficient. Aiming to solve these problems, this paper proposes a novel Dark-Center algorithm, which is a joint learning framework based on image defogging and target detection. Firstly, the dark channel prior algorithm is used to calculate the foggy sky image transmittance and then transpose it to the original image to generate a foggy-simulated insulator dataset; secondly, the defogging and restoration modules and an optimized defogging module are combined to improve the robustness of the defogging algorithm; then, for small insulator detection, the CenterNet network structure is improved to enhance the feature extraction capability for small targets; finally, the target detection accuracy in foggy environments is improved by jointly learning the structure details and color features recovered in image defogging via the defogging model and the target detection model, which effectively learn the structure details and color features recovered in image defogging. The experimental results on the CPILD dataset show that the proposed Dark-Center algorithm based on image defogging and target detection can effectively improve the performance of the target detector in foggy scenes, with a detection accuracy of 96.76%. [ABSTRACT FROM AUTHOR]

The antiferromagnetic order in heterostructures of NiO/Pt thin films can be modified by optical pulses. After the irradiation with laser light, the optically induced creation of antiferromagnetic domains can be observed by imaging the created domain structure utilizing the X‐ray magnetic linear dichroism effect. The effect of different laser polarizations on the domain formation can be studied and used to identify a polarization‐independent creation of 180° domain walls and domains with 180° different Néel vector orientation. By varying the irradiation parameters, the switching mechanism can be determined to be thermally induced. This study demonstrates experimentally the possibility to optically create antiferromagnetic domains, an important step towards future functionalization of all optical switching mechanisms in antiferromagnets. [ABSTRACT FROM AUTHOR]

The proportion of insulators in aerial power patrol images is small and the background of overhead lines is complex, often leading to incomplete and inaccurate detection of insulators. Therefore, an algorithm for detecting insulator targets based on multi‐feature fusion is developed in this study. Firstly, a dynamic threshold oriented fast and rotated brief algorithm is proposed, which uses the bag‐of‐words dictionary model to determine local shape features of the image, applies gradient weighting to the global texture feature vector extracted by the histogram of oriented gradients algorithm and performs radial gradient transformations to get the improved HOG of features. Secondly, the feature vectors are fused serially, the learning machine is trained and the parameters of the support vector machine are optimized using the quantum particle swarm optimization algorithm. Finally, the target area is pre‐divided by the selective search algorithm, and the area is classified by the learning machine. The experimental results show that the proposed feature extraction method can describe the image details more accurately than the existing methods, and the average accuracy of the feature extraction classifier can reach 93.7%, which helps to overcome the incomplete detection problem of insulator detection at the aerial work site. [ABSTRACT FROM AUTHOR]

Insulators installed outdoors are vulnerable to the accumulation of contaminants on their surface, which raise their conductivity and increase leakage current until a flashover occurs. To improve the reliability of the electrical power system, it is possible to evaluate the development of the fault in relation to the increase in leakage current and thus predict whether a shutdown may occur. This paper proposes the use of empirical wavelet transform (EWT) to reduce the influence of non-representative variations and combines the attention mechanism with a long short-term memory (LSTM) recurrent network for prediction. The Optuna framework has been applied for hyperparameter optimization, resulting in a method called optimized EWT-Seq2Seq-LSTM with attention. The proposed model had a 10.17% lower mean square error (MSE) than the standard LSTM and a 5.36% lower MSE than the model without optimization, showing that the attention mechanism and hyperparameter optimization is a promising strategy. [ABSTRACT FROM AUTHOR]

The effects of uneven icing, number of icicles, inclination angle of icicle, ice pallets and arc ignition on the electric field are ignored for the two‐dimensional axisymmetric model of ice‐covered insulator. Therefore, a 3D simulation model was developed in this paper. The average electric field strength of air gap (Eav) and maximum field strength of icicle tip (Emax) were determined to characterize the influence of various factors on the electric field distortion degree. The results show that compared with the insulator without ice and with a dry ice, the supply voltage is almost entirely applied to all icicle air gaps for a wet ice‐covered insulator. Eav is independent of icicle diameter and icicle number but increases with the increment of icicle length and icicle inclination angle. Emax raises with the increase of icicle length, icicle number, icicle inclination angle and icicle diameter. As the source of electric field distortion, the ice pallets on the surface of the sheds rise the field strength of air gap. Based on the simulation analysis and natural icing test, the applied voltage is redistributed in the remaining air gaps when an arc occurs in the gap near the high‐voltage terminal, thereby causing the subsequent flashover. [ABSTRACT FROM AUTHOR]

Concepts involving conductors and insulators are challenging at all levels of instruction. Here, we summarized an investigation of the difficulties that introductory students have pertaining to the charging of conductors and insulators and how that research was used as a resource to develop, validate, and evaluate a conceptual tutorial on this challenging topic. The tutorial uses guided inquiry-based teaching–learning sequences and focuses on helping students develop conceptual understanding of charging conductors and insulators using concrete examples. At a large university in the US, we first evaluated whether there was any statistically significant difference on the pretest (before college instruction) between the performance of students who had any high school physics instruction and those who did not on relevant questions. Then, we compared the performance of introductory physics students in the experimental group who engaged with the tutorial and the control group who did not engage with the tutorial and only had traditional, lecture-based instruction. Our analysis shows large improvements from pre- to post-tests (i.e., from before to after instruction) for the tutorial group and large gaps in post-test scores between the nontutorial and tutorial groups. [ABSTRACT FROM AUTHOR]

Abstract In Ecuador, a country with several active volcanoes and with four eruptions in the last decade in the continental arc, it is very likely that high-voltage transmission lines cross volcanic hazard zones on their routes. Here, we quantify the impact of fresh volcanic ash from the hydromagmatic Cotopaxi-2015 and the magmatic Tungurahua-2016 eruptions on the dielectric characteristics of ANSI 52–3 suspension insulators made of porcelain and glass, under moist conditions. The experiments include two methodologies to measure the performance of the insulators in real-time: the minimum insulator flashover voltage (FOVmin) and the dielectric loss factor angle. Both allow quantifying i) the critical voltage that the insulators can withstand prior to flashover occurrence and, ii) the strong fluctuating behavior that the insulators undergo in an ashy environment. Based on six contamination scenarios, we found that there is a higher chance of flashover if the insulators are completely blanketed (top and bottom) even with a fine ash layer (1 mm), than if they are covered just at the top. Our results further show that the ash of Cotopaxi-2015 eruption has a higher chance of leading to insulator failure because of its higher conductivity (i.e. higher leachate content) than that of Tungurahua-2016. Additionally, we identify two critical voltages prior to electrical flashover on the insulators of 28.25 kV and 17.01 kV for the 230 kV and 138 kV Ecuadorian transmission lines, respectively. Finally, we present a simple impact evaluation for the main Ecuadorian transmission lines based on the outcomes of this research and the official volcanic hazard maps for Cotopaxi and Tungurahua volcanoes.

The article shows one of the factors influencing the formation of corrosion of hollow reinforced concrete structures — leakage currents in insulators. The development of corrosion depends on the amount of electricity that has passed through the reinforced concrete. A method for controlling the corrosion state by changing the color parameters on the video image of the reinforced concrete surface is proposed. A full-scale experiment is carried out on samples simulating various degrees of corrosion, as well as on a sample with an unknown level of corrosion. The images are obtained using an industrial endoscope.

Background: KDM5 family proteins are multi-domain regulators of transcription that when dysregulated contribute to cancer and intellectual disability. KDM5 proteins can regulate transcription through their histone demethylase activity in addition to demethylase-independent gene regulatory functions that remain less characterized. To expand our understanding of the mechanisms that contribute to KDM5-mediated transcription regulation, we used TurboID proximity labeling to identify KDM5-interacting proteins. Results: Using Drosophila melanogaster, we enriched for biotinylated proteins from KDM5-TurboID-expressing adult heads using a newly generated control for DNA-adjacent background in the form of dCas9:TurboID. Mass spectrometry analyses of biotinylated proteins identified both known and novel candidate KDM5 interactors, including members of the SWI/SNF and NURF chromatin remodeling complexes, the NSL complex, Mediator, and several insulator proteins. Conclusions: Combined, our data shed new light on potential demethylase-independent activities of KDM5. In the context of KDM5 dysregulation, these interactions may play key roles in the alteration of evolutionarily conserved transcriptional programs implicated in human disorders. [ABSTRACT FROM AUTHOR]