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Topologically associating domain (TAD) boundaries are the flanking edges of TADs, also known as insulated neighborhoods, within the 3D structure of genomes. A prominent feature of TAD boundaries in mammalian genomes is the enrichment of clustered CTCF sites often with mixed orientations, which can either block or facilitate enhancer–promoter (E‐P) interactions within or across distinct TADs, respectively. We will discuss recent progress in the understanding of fundamental organizing principles of the clustered CTCF insulator codes at TAD boundaries. Specifically, both inward‐ and outward‐oriented CTCF sites function as topological chromatin insulators by asymmetrically blocking improper TAD‐boundary‐crossing cohesin loop extrusion. In addition, boundary stacking and enhancer clustering facilitate long‐distance E‐P interactions across multiple TADs. Finally, we provide a unified mechanism for RNA‐mediated TAD boundary function via R‐loop formation for both insulation and facilitation. This mechanism of TAD boundary formation and insulation has interesting implications not only on how the 3D genome folds in the Euclidean nuclear space but also on how the specificity of E‐P interactions is developmentally regulated. [ABSTRACT FROM AUTHOR]

Aiming at the problems of a large volume, slow processing speed, and difficult deployment in the edge terminal, this paper proposes a lightweight insulator detection algorithm based on an improved SSD. Firstly, the original feature extraction network VGG-16 is replaced by a lightweight Ghost Module network to initially achieve the lightweight model. A Feature Pyramid structure and Feature Pyramid Network (FPN+PAN) are integrated into the Neck part and a Simplified Spatial Pyramid Pooling Fast (SimSPPF) module is introduced to realize the integration of local features and global features. Secondly, multiple Spatial and Channel Squeeze-and-Excitation (scSE) attention mechanisms are introduced in the Neck part to make the model pay more attention to the channels containing important feature information. The original six detection heads are reduced to four to improve the inference speed of the network. In order to improve the recognition performance of occluded and overlapping targets, DIoU-NMS was used to replace the original non-maximum suppression (NMS). Furthermore, the channel pruning strategy is used to reduce the unimportant weight matrix of the model, and the knowledge distillation strategy is used to fine-adjust the network model after pruning, so as to ensure the detection accuracy. The experimental results show that the parameter number of the proposed model is reduced from 26.15 M to 0.61 M, the computational load is reduced from 118.95 G to 1.49 G, and the mAP is increased from 96.8% to 98%. Compared with other models, the proposed model not only guarantees the detection accuracy of the algorithm, but also greatly reduces the model volume, which provides support for the realization of visible light insulator target detection based on edge intelligence. [ABSTRACT FROM AUTHOR]

Outdoor, real-time, and accurate detection of insulator defect locations can effectively avoid the occurrence of power grid security accidents. This paper proposes an improved GhostNet-YOLOv5s algorithm based on GhostNet and YOLOv5 models. First, the backbone feature extraction network of YOLOv5 was reconstructed with the lightweight GhostNet module to reduce the number of parameters and floating point operations of the model, so as to achieve the purpose of being lightweight. Then, a 160 × 160 feature layer was added to the YOLOv5 network to extract more feature information of small targets and fuzzy targets. In addition, the introduction of lightweight GSConv convolution in the neck network further reduced the computing cost of the entire network. Finally, Focal-EIoU was introduced to optimize the CIoU bounding box regression loss function in the original algorithm to improve the convergence speed and target location accuracy of the model. The experimental results show that the parameter number, computation amount, and model size of the GhostNet-YOLOv5s model are reduced by 40%, 25%, and 36%, respectively, compared with the unimproved YOLOv5s model. The proposed method not only ensures the precision of insulator defect detection, but also greatly decreases the complexity of the model. Therefore, the GhostNet-YOLOv5s algorithm can meet the requirements of real-time detection in complex outdoor environments. [ABSTRACT FROM AUTHOR]

In this paper, a method for detection of crack locations and the width of basin insulators is proposed. Based on the optical frequency domain reflectometry (OFDR) system, the system utilizes an FBG with high feedback for strain as well as temperature, which is affixed to the surface of the tub insulator, and a common single-mode optical fiber, which is used for transmitting data and connected to the optical backscattering reflectometry interrogator. The interrogator measures the backscattered light from the FBG, which varies with temperature or strain. The method has been used to measure the location and width of several different cracks and can locate the crack position with a spatial resolution of 1 mm and measure the crack width with a resolution of 0.77 mm. The method has been used to measure the position and width of insulators. This method provides a simple and fast approach to crack detection in insulators. [ABSTRACT FROM AUTHOR]

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Transvection is a phenomenon of interallelic communication in which enhancers can activate a specific promoter located on a homologous chromosome. Insulators play a significant role in ensuring functional interactions between enhancers and promoters. In the presented work, we created a model where two or three copies of the insulator are located next to enhancers and promoters localized on homologous chromosomes. Using the Su(Hw) insulator as a model, we showed that the functional interaction between a pair of insulators promotes enhancer–promoter trans-interactions. The interaction between the three insulators, on the contrary, can lead to the formation of chromatin loops that sterically hinder the full enhancer–promoter interaction. The results of the work suggest the participation of insulators in the regulation of homologous chromosome pairing and in communication between distant genomic loci. [ABSTRACT FROM AUTHOR]

Currently, the use of deep learning technologies for detecting defects in transmission line insulators based on images obtained through unmanned aerial vehicle inspection simultaneously presents the problems of insufficient detection accuracy and speed. Therefore, this study first introduced the bidirectional feature pyramid network (BiFPN) module into YOLOv5 to achieve high detection speed as well as enable the combination of image features at different scales, enhance information representation, and allow accurate detection of insulator defect at different scales. Subsequently, the BiFPN module and simple parameter-free attention module (SimAM) were combined to improve the feature representation ability and object detection accuracy. The SimAM also enabled fusion of features at multiple scales, further improving the insulator defect detection performance. Finally, multiple experimental controls were designed to verify the effectiveness and efficiency of the proposed model. The experimental results obtained using self-made datasets show that the combined BiFPN and SimAM model (i.e., the improved BaS-YOLOv5 model) performs better than the original YOLOv5 model; the precision, recall, average precision and F1 score increased by 6.2%, 5%, 5.9%, and 6%, respectively. Therefore, BaS-YOLOv5 substantially improves detection accuracy while maintaining a high detection speed, meeting the requirements for real-time insulator defect detection. [ABSTRACT FROM AUTHOR]

The zero-value piece of insulator string can be detected by measuring its electric field strength. The traditional way is always by measuring the electric field distribution curve of the whole string to judge out the zero-value piece position, which is inefficient and difficult to operate in field. In this paper, an improved method of identifying zero-value insulator by just measuring the partial electric field of insulator unit is proposed. Firstly, the simulation model of insulator string zero-value unit detection is established, and the partial electric field distortion characteristic caused by zero-value piece is analysed. Then the partial electric field variation criterion for zero-value identifying is obtained basing on the simulation result. Finally, the identification method is verified by designing the detection device and carrying out the point to point zero-value piece evaluating experiments. The results show that the point to point zero-value insulator detection can be realized using the three-array-probe electric field measuring method. The length of the array should be larger than the length of the electric field distortion interval, which can be taken as 130 mm for the research object in this paper. The identification criteria is that the deviation between probe 1 and 3 is less than 10%, and the deviation between probe 2 and the average is less than 50% at the same time. When detecting zero-value piece at the high-voltage and medium-voltage end, the best position for the sensor device identification is directly facing the steel foot, while for the low-voltage end, the best position is facing the shed edge. The results of the study can provide theoretical support for the intelligent operation and maintenance of external insulation in power transmission and distribution.

Insulators are vital components used in electrical transmission lines. It's crucial to keep their production cost low since they are widely used in electricity transmission. Instead of expensive alumina-based insulators, we can make cheaper ones using clay and bauxite found in nature. Adding more alumina makes them stronger, which is important for high voltage transmission. This paper aims to study the production process of bauxite-based insulators. We examined the insulators' structure using SEM images and EDAX spectrum analysis to see their formations, porosity ratio, and density. This research helps us learn more about these insulators and how they can be used effectively for high voltage transmission lines. [ABSTRACT FROM AUTHOR]

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Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Integrated Intelligent Energy is the property of Editorial Department of Integrated Intelligent Energy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the process of power system inspection, manual inspection is difficult and poses safety hazards. The unmanned aerial vehicle platform equipped with intelligent algorithms has a promising prospect of replacing manual insulator detection methods. In response to the shortcomings of slow speed and low accuracy in the detection process of insulator defect targets, an improved YOLOv5 insulator defect fault detection method integrating attention mechanism was proposed. This method integrates the SE attention module and CBAM attention module in the YOLOv5s network, and combines the SE attention module with the C3 module in the network structure to enhance the network’s feature extraction ability. The construction of the self-built insulator dataset was completed through relevant image processing methods. The K-means + + clustering algorithm was used to construct the prior frame of the self-built dataset, and the Mosaic9 data augmentation strategy was introduced, effectively solving the problem of insufficient training data and difficulty in ensuring training effectiveness. Experimental verification shows that the improved detection method improves the accuracy of insulator detection by 9. 7% without affecting the detection time, which has certain reference significance for power system inspection methods. [ABSTRACT FROM AUTHOR]

Introduction: Gene expression is often controlled via cis-regulatory elements (CREs) that modulate the production of transcripts. For multi-gene genetic engineering and synthetic biology, precise control of transcription is crucial, both to insulate the transgenes from unwanted native regulation and to prevent readthrough or cross-regulation of transgenes within a multi-gene cassette. To prevent this activity, insulator-like elements, more properly referred to as transcriptional blockers, could be inserted to separate the transgenes so that they are independently regulated. However, only a few validated insulator-like elements are available for plants, and they tend to be larger than ideal. Methods: To identify additional potential insulator-like sequences, we conducted a genome-wide analysis of Utricularia gibba (humped bladderwort), one of the smallest known plant genomes, with genes that are naturally close together. The 10 best insulator-like candidates were evaluated in vivo for insulator-like activity. Results: We identified a total of 4,656 intergenic regions with expression profiles suggesting insulator-like activity. Comparisons of these regions across 45 other plant species (representing Monocots, Asterids, and Rosids) show low levels of syntenic conservation of these regions. Genome-wide analysis of unmethylated regions (UMRs) indicates ~87% of the targeted regions are unmethylated; however, interpretation of this is complicated because U. gibba has remarkably low levels of methylation across the genome, so that large UMRs frequently extend over multiple genes and intergenic spaces. We also could not identify any conserved motifs among our selected intergenic regions or shared with existing insulator-like elements for plants. Despite this lack of conservation, however, testing of 10 selected intergenic regions for insulator-like activity found two elements on par with a previously published element (EXOB) while being significantly smaller. Discussion: Given the small number of insulator-like elements currently available for plants, our results make a significant addition to available tools. The high hit rate (2 out of 10) also implies that more useful sequences are likely present in our selected intergenic regions; additional validation work will be required to identify which will be most useful for plant genetic engineering. [ABSTRACT FROM AUTHOR]

Chromatin architecture is critical for the temporal and tissue-specific activation of genes that determine eukaryotic development. The functional interaction between enhancers and promoters is controlled by insulators and tethering elements that support specific long-distance interactions. However, the mechanisms of the formation and maintenance of long-range interactions between genome regulatory elements remain poorly understood, primarily due to the lack of convenient model systems. Drosophila became the first model organism in which architectural proteins that determine the activity of insulators were described. In Drosophila, one of the best-studied DNA-binding architectural proteins, Su(Hw), forms a complex with Mod(mdg4)-67.2 and CP190 proteins. Using a combination of CRISPR/Cas9 genome editing and attP-dependent integration technologies, we created a model system in which the promoters and enhancers of two reporter genes are separated by 28 kb. In this case, enhancers effectively stimulate reporter gene promoters in cis and trans only in the presence of artificial Su(Hw) binding sites (SBS), in both constructs. The expression of the mutant Su(Hw) protein, which cannot interact with CP190, and the mutation inactivating Mod(mdg4)-67.2, lead to the complete loss or significant weakening of enhancer–promoter interactions, respectively. The results indicate that the new model system effectively identifies the role of individual subunits of architectural protein complexes in forming and maintaining specific long-distance interactions in the D. melanogaster model. [ABSTRACT FROM AUTHOR]

In the past few years, considerable focus has been directed toward the employment of appropriate coatings with the intention of mitigating imperfections brought about by various factors including UV radiation, humidity, and particulate matter within the insulation framework. Employing an appropriate polymer coating to preserve the characteristics of insulating materials can prove to be an effective remedy within this domain. Polymer composites have emerged as viable substitutes for ceramic insulation, owing to their distinctive characteristics such as lightweight composition, hydrophobic surface properties, thermal and dielectric attributes, as well as superior resistance to vandalism. Typically, polymer insulation comprises two primary components: the insulating core, commonly composed of materials like fiber or epoxy, and the secondary part, which incorporates elastomeric substances such as ethylene propylene diene monomer (EPDM) and Polydimethylsiloxane (PDMS). The use of silicone contributes to the prolonged retention of surface properties, primarily due to the chemical stability inherent in the polymer chains. Silicone-based insulators, characterized by their low surface energy and resulting hydrophobic surface, prove to be more suitable in wet conditions. However, these materials are susceptible to degradation from environmental factors such as temperature, humidity, and Ultraviolet (UV) radiation. An inherent challenge with composite insulators lies in their aging process. Various strategies are employed to enhance polymer properties, involving mixing, composition adjustments, and the incorporation of diverse additives to improve specific material characteristics. This discussion outlines factors influencing insulator efficiency and explores various methods for enhancing the performance of polymer insulators, with a focus on hydrophobic and superhydrophobic surfaces. [ABSTRACT FROM AUTHOR]

This article summarizes the research progress of the application of silicone materials in composite insulators, anti-pollution flashover coatings, cable accessories, anti-corrosion coatings, electric insulating oil and other materials, and looks forward to the development prospects of silicone materials in the field of power. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Insulators are crucial components of power grid systems, safeguarding against electrical conductor breaks. However, their prolonged exposure to complex outdoor environments renders them susceptible to defects. In this study, we address the importance of accurate insulator defect detection and propose an approach using the You Only Look Once (YOLO) object detection framework. In particular, we compare the performance of YOLO v8 against YOLO v7 in detecting two specific types of insulator defects—broken insulators and flashover damaged insulators. Leveraging the Insulator Defect Image Dataset, our results demonstrate that YOLO v8 achieves superior accuracy with a rate of 98.99 percent along with a mean average precision (mAP) of 99.10 percent. The findings underscore the efficacy of YOLO v8 in improving the reliability and resilience of power grid systems by allowing timely and accurate detection of insulator defects in complex outdoor environments. This research contributes to advancing the field of power grid infrastructure monitoring and maintenance, ultimately facilitating more effective strategies for mitigating the consequence of insulator defects on power grid system performance and reliability.

Insulators are essential equipment to ensure the safety and reliability of power transmission systems. Defective insulators may cause partial discharge and even lead to serious safety accidents. Hence it is necessary to accurately identify the defective insulator from a string of insulators. However, small defect poses significant challenges for recognizing the defective insulator from a large number of insulators. To address these issues, we collect and annotate the randomly generated defect dataset (RGDD). Further, the discriminative feature learning-based detector (DFLD) is constructed based on the pattern of backbone-neck-head. Specifically, considering the simultaneous existence of many insulators, attention-based bidirectional feature pyramid (ABFP) is designed to capture the discriminative information. Considering the small size of defective part, the efficient receptive field adaptation (ERFA) module is constructed to enhance the perception of contextual information related to defective insulators. Meanwhile, the two-stage detection head is designed to correct the location of defective insulators. It also adapts to the shape variation of insulators by the deformable convolution. On this basis, the keypoints method is introduced to more accurately represent the location of defective insulators. Due to the imbalance between positive and negative samples, the Adaptive Threshold Sample Assignment (ATSA) Strategy is proposed for selecting the best positive samples. DFLD has achieved good detection performance compared with classical object detection networks on the RGDD dataset and CPLID dataset. The ablation experiments are conducted on the RGDD dataset. It is verified that the discriminative features from DFLD can effectively recognize the small defect from insulators.

Resilience of the power system against natural disasters is a vital need for sustainable energy supply. As a result of global warming, lakes and rivers have dried out, resulting in dust hubs that threaten the normal operation of outdoor power system equipment. Unlike other events like hurricanes and blizzards, the impact of extreme salt dust on power system insulator failures and network resilience in affected areas remains unexamined. In this paper, to avoid power curtailment caused by insulators breakdown in electricity distribution networks, the resiliency assessment and enhancement of these networks against salt dust is investigated. Failure mechanism analysis of insulators and fragility curves extraction of them in face of salt pollution and relative humidity are done using mathematical modelling and experimental tests to extract the breakdown probability; Experimental tests are conducted in the High Voltage Laboratory, University of Tehran (HVLUT) and a novel method is proposed to extract 3-dimensional fragility curves of insulators. A Monte Carlo-based resiliency assessment method is then employed to obtain resiliency curve against the salt dust. Some suitable indicators are introduced for this purpose. In addition, several resiliency enhancement measures are proposed and ranked using a benefit to cost ratio (BCR) index. Numerical simulations are conducted on two real distribution feeders in a distribution grid around Urmia Salt Lake, Iran. Numerical results confirm the effectiveness and applicability of the proposed method.

Insulators are crucial insulation components and structural supports in power grids, playing a vital role in the transmission lines. Due to temperature fluctuations, internal stress, or damage from hail, insulators are prone to injury. Automatic detection of damaged insulators faces challenges such as diverse types, small defect targets, and complex backgrounds and shapes. Most research for detecting insulator defects has focused on a single defect type or a specific material. However, the insulators in the grid's transmission lines have different colors and materials. Various insulator defects coexist, and the existing methods have difficulty meeting the practical application requirements. Current methods suffer from low detection accuracy and mAP0.5 cannot meet application requirements. This paper proposes an improved you only look once version 7 (YOLOv7) model for multi-type insulator defect detection. First, our model replaces the spatial pyramid pooling cross stage partial network (SPPCSPC) module with the receptive filed block (RFB) module to enhance the network's feature extraction capability. Second, a coordinate attention (CA) mechanism is introduced into the head part to enhance the network's feature representation ability and to improve detection accuracy. Third, a wise intersection over union (WIoU) loss function is employed to address the low-quality samples hindering model generalization during training, thereby improving the model's overall performance. The experimental results indicate that the proposed model exhibits enhancements across various performance metrics. Specifically, there is a 1.6% advancement in mAP_0.5, a corresponding 1.6% enhancement in mAP_0.5:0.95, a 1.3% elevation in precision, and a 1% increase in recall. Moreover, the model achieves parameter reduction by 3.2 million, leading to a decrease of 2.5 GFLOPS in computational cost. Notably, there is also an improvement of 2.81 milliseconds in single-image detection speed. This improved model can detect insulator defects for diverse materials, color insulators, and partial damage shapes in complex backgrounds.

Insulator, electrical device designed for electrical isolation and mechanical fastening of electrical installations or their separate parts, which are under different electrical potentials. As an element of the power system, it plays an important role in ensuring its reliable operation. At present the issue of diagnostics and monitoring of insulators is still topical. The article provides statistical data of failures on overhead lines and lists methods and devices for detecting insulation damage. The research problem is the lack of a reliable and cost-effective device for early detection of insulator failure under voltage. The solution is the previously proposed method of diagnosing single-element insulators by temperature. The author proposed a scheme of the diagnostic device, described its principle of operation in normal and emergency modes. The diagnostic device made according to the proposed scheme is simple, works under voltage and without the intervention of operating personnel.

The purpose of the work is to develop the design of a sensor for monitoring the condition of high-voltage power lines suspended insulation. To achieve this goal, a diagnostic criterion was selected, the operating principle and design of the insulation monitoring sensor were determined. As a diagnostic criterion is considered a changing of voltage on the insulator plate due to a change in the distribution of electrical potential on the insulators, caused of damage in the insu-lators string. For the diagnostic system implementation was adopted a sensor based on a capaci-tive divider. The calculations confirm the possibility of registration a damaged element in a insu-lators string by getting of voltage change at the sensors output. The calculation results were veri-fied by modeling in the Multisim software. The parameters of the sensor were calculated, cor-rectness of it is confirmed by the results of laboratory measurements of the new insulators plate capacitances, as well as insulators destroyed as a result of operation. The most important results of the research are the selection of the optimal diagnostic criterion and researching of the pro-posed design sensor effectiveness for assessing the suspended insulation condition. The signifi-cance of the results obtained lies in the fact that the developed design of a sensor allows one to obtain prompt information about the presence of damage in the insulation. The use of the pro-posed sensor design will make possible creation a continuous diagnostic system and identify damage at an early stage, which will ensure timely maintenance.

The development of an environment-friendly process for synthesizing nanoparticles in the field of bionanotechnology is growing day-by-day. Increasing drug resistance in microbes has compelled researchers to synthesize biologically active nanoparticles. In this study, we used Tribulus terrestris plant extract to synthesize CuO/NaCuSO4 nanocomposite. The synthesized samples were characterized through functional groups analysis of FTIR and morphological analysis of SEM. The X-ray diffraction (XRD) and ultra-visible light absorbance analysis (UV–vis) were used to find the nanocrystalline nature and bandgap energy of the biosynthesized copper oxide (CuO) nanoparticles, respectively. In the metal oxide region of Fourier transform infrared (FTIR) spectroscopy the copper oxide nanoparticles were confirmed at 523 cm−1 which showed the nature of plant extracts to control over the reduction and stabilizing of the nanoparticles. The Rietveld refinement analysis yielded unit cell compositions of 53.39% for CuO and 46.61% for NaCuSO4, respectively. The monoclinic structure of CuO was confirmed, and the crystalline size was calculated as 37 nm by XRD analysis. From UV–vis spectroscopy, the absorption peak was observed at 277 and 337 nm, revealing the presence of CuO nanoparticles. In fact, the cluster-like morphology patterns are captured by scanning electron microscopy (SEM) and particle sizes were observed around 102 nm. Finally, the dielectric properties of the synthesized copper oxide nanoparticles have been examined and reported in detail. [ABSTRACT FROM AUTHOR]

In this work, a study on a cap and pin insulator (1512L) is proposed to evaluate the distribution of the electric field and the potential along the insulator under different conditions. A computational and experimental study for the examination of a real insulators model is assessed. Tests on contaminated insulators in the laboratory under the suggested conditions have been carried out. Finite element methods (FEM) have been employed in the numerical analysis to assess the electrical properties of the insulator under the suggested contamination profiles, including potential and electric field. The study proposed in this paper provides an effective and practical tool for analysis and enhance the dielectric properties of the studied insulator. [ABSTRACT FROM AUTHOR]

Insulators are crucial insulation components and structural supports in power grids, playing a vital role in the transmission lines. Due to temperature fluctuations, internal stress, or damage from hail, insulators are prone to injury. Automatic detection of damaged insulators faces challenges such as diverse types, small defect targets, and complex backgrounds and shapes. Most research for detecting insulator defects has focused on a single defect type or a specific material. However, the insulators in the grid's transmission lines have different colors and materials. Various insulator defects coexist, and the existing methods have difficulty meeting the practical application requirements. Current methods suffer from low detection accuracy and mAP0.5 cannot meet application requirements. This paper proposes an improved you only look once version 7 (YOLOv7) model for multi-type insulator defect detection. First, our model replaces the spatial pyramid pooling cross stage partial network (SPPCSPC) module with the receptive filed block (RFB) module to enhance the network's feature extraction capability. Second, a coordinate attention (CA) mechanism is introduced into the head part to enhance the network's feature representation ability and to improve detection accuracy. Third, a wise intersection over union (WIoU) loss function is employed to address the low-quality samples hindering model generalization during training, thereby improving the model's overall performance. The experimental results indicate that the proposed model exhibits enhancements across various performance metrics. Specifically, there is a 1.6% advancement in mAP_0.5, a corresponding 1.6% enhancement in mAP_0.5:0.95, a 1.3% elevation in precision, and a 1% increase in recall. Moreover, the model achieves parameter reduction by 3.2 million, leading to a decrease of 2.5 GFLOPS in computational cost. Notably, there is also an improvement of 2.81 milliseconds in single-image detection speed. This improved model can detect insulator defects for diverse materials, color insulators, and partial damage shapes in complex backgrounds. [ABSTRACT FROM AUTHOR]

The present paper deals with new modeling to reproduce the electric discharge in the rod-plane air gap system with rubber insulating barrier under AC and impulse voltage. This model considers the randomness character of discharge evolution which is governed by the electric field. The discharges shape obtained by this model are compared with ones given by experimental tests. The established model reproduces correctly the forms of discharges obtained by experimental tests under AC voltage. It is found that the behavior of the electrical discharge depends not only on the dimension (thickness and width) of the insulating barriers but on its positions in the air gap as well. It is to highlight that the mode of applied voltage is of key importance barrier. Experimental investigation shows that the developed arc can evolve on 1 to 4 channels. The generated discharges in AC voltage distinguish by the formation of a multiple-channel arc. Whereas, the discharge under lightning impulse voltage found to progress in a single channel whatever the barrier position and dimensions. The model confirms that electric field is the most important factor in the behavior of the rodinsulating barrier-plane system submitted to high voltage. [ABSTRACT FROM AUTHOR]

Insulators on overhead power lines have long been exposed to the outdoors and are susceptible to pollution and salt contamination. Due to factors such as wind and gravity, pollution in the atmosphere gradually deposits on the surface of the insulator. In humid and windy conditions, conductive pollutants begin to dissolve in the water on the surface of the insulator, increasing the leakage current and affecting insulation performance. This study mainly uses a data acquisition system to measure the leakage current of the insulator and weather parameters (including temperature, relative humidity, pressure, wind speed, and ultraviolet) around the insulator. Artificial intelligence is then applied to establish a prediction model for leakage current based on weather parameters. The established model accurately predicts insulator leakage current through weather parameters. In order to observe the real-time status of the insulator, this study establishes a monitoring platform that integrates the predicted leakage current with weather parameters. It allows users or maintenance personnel to connect to the server through the network to observe the predicted results and weather parameters. The results can establish a real-time salt contamination monitoring system for insulators on transmission lines, enabling operation and maintenance personnel to understand the actual insulation situation of the insulator in real-time. This can not only prevent power outages due to salt contamination or pollution but also reduce the workload for maintenance personnel. Moreover, the maintenance strategy is upgraded from time-base maintenance to condition-base maintenance, significantly improving the efficiency of operation and maintenance for power lines. [ABSTRACT FROM AUTHOR]

Using density functional theory, we have studied the intrinsic properties of styrene. First, we determine the optimized structures, structural parameters and thermodynamic properties to make our simulations more realistic to experimental results and check the stability. Second, we investigate optoelectronic, electronic and global descriptors, transport properties of holes and electrons, natural bond orbital analysis, absorption and fluorescence properties. Finally, we study nonlinear optical (NLO) properties: first- and second-order hyperpolarizability, second and third-order optical susceptibilities, hyper-Rayleigh scattering hyperpolarizability, electro-optical Pockel effect, direct current Kerr effects and quadratic refractive index. The bandgap energy E g = 5.146 eV and dielectric constant [Formula: see text] show that styrene is a good insulator with an average electric field value of 4.43 × 108 Vm−1. Thermodynamic findings show that our molecule is thermodynamically and chemically stable. Electron and hole reorganization energies of 0.393 and 0.295 eV, respectively, show that styrene is more favourable to hole transport than electron transport. Styrene is transparent with linear refractive index n = 1.750 and quadratic n 2 = 1.748 × 10−20 m2 W−1. At the NLO, styrene has a non-zero value of [Formula: see text] which confirms the existence of first-order NLO activity. Globally the study shows that the styrene monomer is suitable for the architecture design of new polymer materials for NLO applications and optoelectronic by functionalization.

Due to the ever-increasing growth of electric energy consumption, the production of high-quality, reliable and high-reliability electricity is very important. Therefore, it is essential to have distribution and sub-transmission networks with a good reliability factor. In power distribution and sub-transmission lines, it is necessary to somehow isolate the conductors under voltage from the towers, and insulators are used for this purpose. These insulators have two main tasks. One of the main tasks of insulators is to isolate (insulate) the line conductor from the body of the tower. The insulators must be able to isolate the high voltages of the lines from the body of the tower without having a leakage current, and on the other hand, the insulators must be able to withstand the mechanical forces resulting from the weight of the conductors and the applied forces caused by wind and ice. Also, leakage current is one of the important parameters for condition monitoring of insulators in power grid lines. Failure to inspect the insulation for contamination and health conditions will lead to insulator failure and will cause faults in the electrical system. Therefore, it is very important to monitor the condition of the insulator. Based on this, in this paper, according to the data related to leakage current and also according to the introduced wear out function, a procedure for measuring the condition of insulators has been obtained. Finally, the condition of each insulator will be determined according to the defined indicators. Also, the failure level of each monitored data will be obtained using sensitivity analysis.

Data augmentation is an important task of using existing data to expand data sets. Using generative countermeasure network technology to realize data augmentation has the advantages of high-quality generated samples, simple training, and fewer restrictions on the number of generated samples. However, in the field of transmission line insulator images, the freely synthesized samples are prone to produce fuzzy backgrounds and disordered samples of the main insulator features. To solve the above problems, this paper uses cycle generative adversarial network (Cycle-GAN) used for domain conversion in the generation countermeasure network as the initial framework and uses the self-attention mechanism and the channel attention mechanism to assist the conversion to realize the mutual conversion of different insulator samples. The attention module with prior knowledge is used to build the generation countermeasure network, and the GAN model with local controllable generation is built to realize the directional generation of insulator belt defect samples. The experimental results show that the samples obtained by this method are improved in a number of quality indicators, and the quality effect of the samples obtained is excellent, which has a reference value for the data expansion of insulator images.

To address the limitations of deep learning models in detecting aerial insulator images from Unmanned Aerial Vehicles, we present a framework dubbed DP-FedIOD, which utilizes differential privacy and federated learning techniques for identifying the aerial insulators. It tackles the issue posed by current algorithms for inspecting insulators, which employ horizontal anchor frames and are incapable of precisely identifying both insulators and their defective parts. In addition, this study addresses the issue of insulator data being safeguarded by laws and policies that impede its wide-scale collection and dissemination among electric power companies enterprises, resulting in insufficient data volume to train deep learning models. In DP-FedIOD, we have improved the YOLOv5 algorithm by refining its head structure and loss function for directional detection of insulators and their defective parts. Additionally, an attention mechanism module has been incorporated into its backbone to enhance feature extraction capabilities. Furthermore, DP-FedIOD collaboratively trains the global model through federated learning. To prevent privacy leakage in the federated learning process, we also incorporate Laplace noise according to the differential privacy mechanism before uploading the weight information. The experimental outcomes demonstrate that the improved YOLOv5 model attains a mAP@0.5 metric of 95.00%, while DP-FedIOD achieves over 75.10% and 77.90% in precision and recall, respectively. These results indicate that DP-FedIOD has significant practical value in constructing an intelligent grid equipment detection system.

In recent years, there has been a considerable increase in the use of DC power equipment in the industry, making it crucial for insulation materials to withstand high DC electric fields. The current integration method (Q(t) measurement) is one of the most useful and effective methods to measure the time dependency of charges. However, the mathematical/physical model of Q(t) measurement is not advanced because obtained data are not completely analyzed, particularly the methods in which delay parameters affect the charge saturation time of insulators. In this study, we analyze the delay property of Q(t) data by solving differential equations with some approximations. In addition, sample sheets of polyimide and polyethylene terephthalate are tested for experimental purposes. The results indicate that circuit properties can be estimated from the analysis of experimental Q(t) data based on the assumption of a circuit model. The proposed analysis method can be used for further discussions regarding the analytical estimation of timing of charge saturation, and the relationship between circuit models and the space charges phenomenon.

UAV aerial images of insulators have problems such as complex backgrounds, small targets, and obscured targets, resulting in low detection accuracy of insulator targets. In order to solve the above problems, a HRD-YOLOX (Hybrid Attention Mechanisms, Regularization, and Depth Separable Convolution for YOLOX) algorithm suitable for recognizing insulators and detecting defects is proposed based on the YOLOX network. First, the hybrid attention module HAM-CSP (Communication Sequential Processes for Hybrid Attention Mechanisms) is incorporated into the feature extraction network to suppress the interference from the complex background of insulator pictures. Second, the PANet (Path Aggregation Network) structure is replaced with the R-BiFPN(Regularization-Bidirectional Feature Pyramid Network) structure for feature fusion, and an improved regularization module is introduced into the convolutional layer of the BiFPN structure to reduce the overfitting problem of the model and the inference speed of the network. Then, in order to solve the computationally intensive and overfitting phenomenon generated by the detection of small targets such as insulator discharge traces, the DHConv (Deep Separable and Hybrid Attention Mechanisms Convolution) convolution is proposed to be applied to the Conv $3\times 3$ convolutional layer in order to reduce the number of parameters and improve the generalization ability of the model. Finally, the binary cross-entropy function for confidence loss is changed to the Focal Loss function to solve the problem of missed detection and positive and negative sample imbalance due to the image overlap problem The experimental results show that the HRD-YOLOX algorithm improves the average accuracy of insulator detection from 86.23% to 91.34%, and at the same time improves the detection speed from 70.6FPS/S to 82.23FPS/S, which allows real-time detection of insulators, and improves the performance of complex background targets as well as small targets detection.

The use of DC power equipment in the industry has significantly increased in recent years. Consequently, it has become imperative for insulation materials to withstand high DC electric fields. The current integration method, Q(t) measurement, is one of the most useful and effective methods for measuring the time dependency of charges. However, the mathematical and physical models of Q(t) measurement remain undeveloped because acquired data are not completely analyzed, particularly the effect of delay parameters on the charge saturation time of insulators. Therefore, this study analyzes the delay characteristics of Q(t) data at a high temperature by solving differential equations with some approximations. In addition, experiments are performed using polyimide sample sheets. Consequently, the parameters defining the charging dynamics across different stages are determined, and the proposed approach can effectively estimate circuit properties data. Moreover, the results indicate that actual experimental data can be reconstructed using adequate parameters. Therefore, the proposed analysis method can be used for further discussions regarding the analytical estimation of charge saturation time, marking a significant advancement in the industry.

Unmanned aerial vehicle （UAV） inspections now have emerged as a predominant approach for the examination of transmission lines， with a pivotal focus on the detection of insulator defects. In this context， a lightweight insulator defect detection algorithm， founded on the improved YOLOv5， is introduced. Firstly， the lightweight Ghost convolution is employed to replace conventional convolution. Subsequently， the original feature extraction network is replaced with a repeated weighted bidirectional feature pyramid network （BiFPN） to bolster feature extraction capability across various scales. Finally， the coordinate attention （CA） mechanism is introduced to enhance the efficiency of backbone feature extraction. Experimental results reveal a significant 1.7% enhancement in the average precision of insulator detection， accompanied by a 13.1% reduction in the model’s size. This refined algorithm model not only elevates detection accuracy but also streamlines its footprint， thereby enabling more efficient and rapid insulator defect detection.

Abstract Background Triple-negative breast cancer (TNBC) is an aggressive subtype that exhibits a high incidence of distant metastases and lacks targeted therapeutic options. Here we explored how the epigenome contributes to matrix metalloprotease (MMP) dysregulation impacting tumor invasion, which is the first step of the metastatic process. Methods We combined RNA expression and chromatin interaction data to identify insulator elements potentially associated with MMP gene expression and invasion. We employed CRISPR/Cas9 to disrupt the CCCTC-Binding Factor (CTCF) binding site on an insulator element downstream of the MMP8 gene (IE8) in two TNBC cellular models. We characterized these models by combining Hi-C, ATAC-seq, and RNA-seq with functional experiments to determine invasive ability. The potential of our findings to predict the progression of ductal carcinoma in situ (DCIS), was tested in data from clinical specimens. Results We explored the clinical relevance of an insulator element located within the Chr11q22.2 locus, downstream of the MMP8 gene (IE8). This regulatory element resulted in a topologically associating domain (TAD) boundary that isolated nine MMP genes into two anti-correlated expression clusters. This expression pattern was associated with worse relapse-free (HR = 1.57 [1.06 − 2.33]; p = 0.023) and overall (HR = 2.65 [1.31 − 5.37], p = 0.005) survival of TNBC patients. After CRISPR/Cas9-mediated disruption of IE8, cancer cells showed a switch in the MMP expression signature, specifically downregulating the pro-invasive MMP1 gene and upregulating the antitumorigenic MMP8 gene, resulting in reduced invasive ability and collagen degradation. We observed that the MMP expression pattern predicts DCIS that eventually progresses into invasive ductal carcinomas (AUC = 0.77, p