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This study analyzes the optimization of Extra High Voltage (EHV) transmission line design parameters by Uttar Pradesh Power Corporation Limited (UPPCL) in India to improve power grid efficiency and reliability. UPPCL reduced insulation specifications for 400 kV lines from 27 to 21 disc insulators and decreased live metal clearance from 2600 to 2400 mm, resulting in 7–9% cost savings per line and reduced right-of-way requirements. While initial test lines performed satisfactorily for over 5 years, longer lines experienced operational challenges related to insulator quality, bird activity, and design margins. Tripping issues emerged during adverse weather conditions, particularly in foggy winter periods. Root cause analysis revealed the need for stringent insulator quality control, regular cleaning to mitigate bird dropping contamination, and potential corona ring design improvements. This case study demonstrates how targeted EHV transmission line optimization can yield significant cost and environmental benefits, while highlighting the importance of balancing economic gains with reliability and safety considerations. The findings provide valuable insights for future EHV transmission line designs and upgrades across India's power grid. [ABSTRACT FROM AUTHOR]

The negative influence of single-phase-to-ground faults in networks with an isolated neutral, resulting from a breakdown of pin insulation, is analyzed. A concept for early detection of damage to pin insulators is proposed, based on measuring changes in leakage current through the insulators caused by changes in the capacitance of the “pininsulator-wire” system. For this purpose, a simulation of the system was done. A model of the geometric dimensions of the insulator is created with taking into account the design features of the installation of the line wire in the AutoCAD software package and the distribution of the electric field is simulated in the Comsol Multiphysics 5.6 package, which makes it possible to estimate the electrical capacitance. The dependence of the calculated electrical capacitance of ShF-20G insulators on the dielectric constant, diameters and grades of wires used on 6-10 kV overhead power lines and options for wire bundles is considered. Numerical values of the capacitance of the “pin-insulator-wire” system were obtained for different configuration options. It has been shown that the state of the insulator material has a decisive influence on the capacity of the system. References 11, figures 2, tables 2. [ABSTRACT FROM AUTHOR]

With the implementation of intelligent unmanned aerial vehicles (UAVs) in power equipment inspection, managing the obtained inspection results through information technology is increasingly crucial. This paper collected insulator images, including images of standard and self-exploding insulators, during the inspection process using intelligent UAVs. Then, an optimized you only look once version 5 (YOLOv5) model was developed by incorporating the convolutional block attention module and utilizing the efficient intersection-over-union loss function. The detection performance of the designed algorithm was analyzed. It was found that among different models, the YOLOv5s model exhibited the smallest size and the highest detection speed. Moreover, the optimized YOLOv5 model showed a significant improvement in speed and accuracy for insulator detection, surpassing other methods with a mean average precision of 93.81% and 145.64 frames per second. These results demonstrate the reliability of the improved YOLOv5 model and its practical applicability. [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]

Ceramic and glass insulators as the first generation of insulators are often affected by flashover disturbance during operation. Ceramic insulators coated with semiconducting glaze (SCG) material are then proposed to improve the insulation performance due to warmer surface characteristics that can trigger evaporation on the surface. In this paper, a comparison of insulation performances of semiconducting glaze, ceramic, and glass insulators is discussed from both electrical and non-electrical aspects under various conditions. This comparison aims to distinguish the leakage current characteristics of each insulator during operation. Leakage current tests are conducted to obtain leakage current characteristics of each insulator under artificial conditions of no fog, clean fog, and salt fog. Furthermore, pollutants that are a mixture of NaCl (as equivalent salt deposit density (ESDD)) and kaolin (as non-soluble deposit density (NSDD)) are sprayed onto the insulator surface to see how the pollutant affects the leakage current characteristics of each insulator. The properties of measured leakage current waves are then analyzed, including the waveform, magnitude, and harmonic components. In addition, the non-electrical tests consist of hydrophobicity tests and thermal tests. In the hydrophobicity test, the contact angle formed by a water droplet on the insulator surface is measured, and in the thermal test, temperature differences on the insulator surface before and after voltage applications are compared. The experiment results show that the SCG insulator has a larger leakage current than the other insulators, while the leakage current magnitude of the ceramic and glass insulators is similar. The surface temperature of the SCG insulator is also higher than that of the other insulators due to a larger leakage current. Additionally, the SCG insulator shows better hydrophobicity compared to the other insulators, where all types of insulators show decreased contact angle when pollutants are given. [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]

Abstract This study analyzes the optimization of Extra High Voltage (EHV) transmission line design parameters by Uttar Pradesh Power Corporation Limited (UPPCL) in India to improve power grid efficiency and reliability. UPPCL reduced insulation specifications for 400 kV lines from 27 to 21 disc insulators and decreased live metal clearance from 2600 to 2400 mm, resulting in 7–9% cost savings per line and reduced right-of-way requirements. While initial test lines performed satisfactorily for over 5 years, longer lines experienced operational challenges related to insulator quality, bird activity, and design margins. Tripping issues emerged during adverse weather conditions, particularly in foggy winter periods. Root cause analysis revealed the need for stringent insulator quality control, regular cleaning to mitigate bird dropping contamination, and potential corona ring design improvements. This case study demonstrates how targeted EHV transmission line optimization can yield significant cost and environmental benefits, while highlighting the importance of balancing economic gains with reliability and safety considerations. The findings provide valuable insights for future EHV transmission line designs and upgrades across India’s power grid.

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.)

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.

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.

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]

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]

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.

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.

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.

This research develops a simulation model, aiming to study the breakdown process of the tri-post insulator when exposed to electric field and mechanical loadings. The insulator is subjected to external forces of varying magnitudes and directions, along with an applied 1100 kV AC voltage. Results show that the breakdown process is driven by the electrostatic energy density (Wele) under an AC voltage. As the length of the breakdown channel increases, Wele, also rises, thereby accelerating the extension of the breakdown channel. After applying a straight tension along the insulator post, the tensile stress and the corresponding strain energy density become concentrated near the tips of the lateral branches, thus promoting their expansion and even causing a transverse fracture under the condition of 100 kN. In the case of a small straight compression below 25 kN, the breakdown process can be restrained. But when it comes to 75 kN, the breakdown process is promoted again, and a shear fracture can be caused above 100 kN. Under diagonal tensile or compressive stress, the breakdown channel deviates towards the applied force due to asymmetric strain energy density distribution. The objective of this research is to improve the comprehension and forecasting of breakdown in dielectric materials.

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.

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.

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.

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]

Insulators play a pivotal role in the reliability of power distribution networks, necessitating precise defect detection. However, compared with aerial insulator images of transmission network, insulator images of power distribution network contain more complex backgrounds and subtle insulator defects, it leads to high false detection rates and omission rates in current mainstream detection algorithms. In response, this study presents ID-YOLOv7, a tailored convolutional neural network. First, we design a novel Edge Detailed Shape Data Augmentation (EDSDA) method to enhance the model's sensitivity to insulator's edge shapes. Meanwhile, a Cross-Channel and Spatial Multi-Scale Attention (CCSMA) module is proposed, which can interactively model across di􀀀erent channels and spatial domains, to augment the network's attention to high-level insulator defect features. Second, we design a Re-BiC module to fuse multi-scale contextual features and reconstruct the Neck component, alleviating the issue of critical feature loss during inter-feature layer interaction in traditional FPN structures. Finally, we utilize the MPDIoU function to calculate the model's localization loss, e􀀀ectively reducing redundant computational costs. We perform comprehensive experiments using the Su22kV_broken and PASCAL VOC 2007 datasets to validate our algorithm's e􀀀ectiveness. On the Su22kV_broken dataset, our approach attains an 85.7% mAP on a single NVIDIA RTX 2080ti graphics card, marking a 7.2% increase over the original YOLOv7. On the PASCAL VOC 2007 dataset, we achieve an impressive 90.3%mAP at a processing speed of 53 FPS, showing a 2.9% improvement compared to the original YOLOv7. [ABSTRACT FROM AUTHOR]

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Insulators find extensive use across diverse facets of power systems, playing a pivotal role in ensuring the security and stability of electrical transmission. Detecting insulators is a fundamental measure to secure the safety and stability of power transmission, with precise insulator positioning being a prerequisite for successful detection. To overcome challenges such as intricate insulator backgrounds, small defect scales, and notable differences in target scales that reduce detection accuracy, we propose the AC-YOLO insulator multi-defect detection network based on adaptive attention fusion. To elaborate, we introduce an adaptive weight distribution multi-head self-attention module designed to concentrate on intricacies in the features, effectively discerning between insulators and various defects. Additionally, an adaptive memory fusion detection head is incorporated to amalgamate multi-scale target features, augmenting the network's capability to extract insulator defect characteristics. Furthermore, a CBAM attention mechanism is integrated into the backbone network to enhance the detection performance for smaller target defects. Lastly, improvements to the loss function expedite model convergence. This study involved training and evaluation using publicly available datasets for insulator defects. The experimental results reveal that the AC-YOLO model achieves a notable 5.1% enhancement in detection accuracy compared to the baseline. This approach significantly boosts detection precision, diminishes false positive rates, and fulfills real-time insulator localization requirements in power system inspections. [ABSTRACT FROM AUTHOR]

Insulators, pivotal to the integrity of railway catenaries, demand impeccable functioning to prevent system failures. Their consistent assessment is vital for railway safety. Current insulator evaluations in subways predominantly involve human intervention, a method fraught with inefficiencies, inaccuracies, and oversights, exacerbated by the complex backdrop of subway tunnels and minuscule defect dimensions. This study introduces an enhanced algorithm, anchored in the YOLOv5 framework, to refine insulator defect identification. Challenges in defect detection include limited, imbalanced data samples and adaptability. Addressing this, an accurate catenary model mirrors the subway line's architecture, facilitating the creation of synthetic instances of both intact and impaired insulators. An atomization technique augments the dataset volume, fortifying the algorithm's resilience in reduced visibility conditions, such as fog. Tackling sample equilibrium, the study introduces an equilibrium loss function, assigning disparate weights to various sample categories during training, thereby sharpening the algorithm's focus on positive instances, particularly those that are challenging to discern, and rectifying the disproportion in sample categories. Incorporating lightweight structures like GhostNet and the Efficient Channel Attention Network (ECA-Net) channel attention scheme not only diminishes the network's computational demands, thereby elevating the detection capabilities, but also minimizes superfluous data processing, enhancing the accuracy in identifying smaller targets. Empirical analyses indicate substantial model optimization: a size reduction to 60 pp of its original (from 15 MB to 9 MB), a near 1.4 pp increase in mean average precision (mAP) to 96.57%, and a tripling of the detection speed (from 30 to 90 FPS). Real-world image assessments further reveal a mAP improvement of approximately 2.5 pp (reaching 98.43%), confirming the model's suitability for real-time applications. [ABSTRACT FROM AUTHOR]

The transcriptome data of skin cells from domestic cats with brown, orange, and white coats were analyzed using a public database to investigate the possible relationship between coat color‐related gene expression and squamous cell carcinoma risk, as well as the mechanism of deafness in white cats. We found that the ratio of the expression level of genes suppressing squamous cell carcinoma to that of genes promoting squamous cell carcinoma might be considerably lower than the theoretical estimation in skin cells with orange and white coats in white‐spotted cat. We also found the possibility of the frequent production of KIT lacking the first exon (d1KIT) in skin cells with white coats, and d1KIT production exhibited a substantial negative correlation with the expression of SOX10, which is essential for melanocyte formation and adjustment of hearing function. Additionally, the production of d1KIT was expected to be due to the insulating activity of the feline endogenous retrovirus 1 (FERV1) LTR in the first intron of KIT by its CTCF binding sequence repeat. These results contribute to basic veterinary research to understand the relationship between cat skin coat and disease risk, as well as the underlying mechanism. [ABSTRACT FROM AUTHOR]

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

Among major nondestructive testing technologies (NDT), such as X-ray and ultra-sound, Terahertz (THz) waves present significant transmission rate through most of nonconductive materials without the need of applying couplant while posing zero ionized radiation hazard. In comparison to microwave NDT, terahertz technology boasts a superior spatial resolution and measurement precision due to its shorter wavelength. As such, it is considered to be the most promising technology for industrial NDT applications. To analyze of THz wave transmission characteristics within porcelain insulator and obtain large quantity of testing data with repeatable measurement condition, this paper reports an experimental approach using Kaolin, in powdery form, to emulate the aging behavior of porcelain, and a THz sensor to detect such imperceptible aging process where micrometer-scale cracks slowly develop inside of porcelain insulators. To support the data acquisition from distance, a LORA communication module was integrated in the sensor for using under high-voltage environment. In addition, a dedicated user-interface software has been developed to receive the wireless data as well as conducting data analysis, crack recognition and graphic display. The experiment successfully demonstrated a very feasible solution for online monitoring of the aging process of porcelain insulators by using compact THz sensor buried in the insulator.

The stability and high quality of electricity are the basic factors which ensure that the residents and enterprises lead a happy and productive life. Therefore, in order to meet the requirements of residents’ life and enterprise production, it is necessary to improve the efficiency and accuracy of power grid fault diagnosis. In this paper, the knowledge graph is integrated into the power grid fault diagnosis, and the fault diagnosis system of the knowledge graph is constructed to realise the fault diagnosis of the power grid. The article first completes the knowledge graph construction through knowledge extraction, knowledge fusion and knowledge processing; then, it completes the construction of the fault scheduling knowledge graph through power grid equipment fault records, entity attribute extraction, coreference resolution, relation extraction, relation screening and data integration; finally, combined with the fault information knowledge analysis technology, it builds a power grid fault diagnosis system using a knowledge graph. Experiments show that using the system to diagnose the fault quantity, fault location and fault analysis information of the pilot power grid not only has ideal efficiency but also has high accuracy.

Mineral-based oil is now often used for transformer oil. Unfortunately, this kind of oil is harmful and unfriendly to the environment, necessitating the use of vegetable-based products that are both ecologically benign and biodegradable. With its properties, crude palm oil (CPO) might be a possibility for a vegetable-based insulator. Concerning it, the goal of this study was to create a method for converting CPO into an insulator that fulfilled IEC 62770 requirements. The researchers created a CPO-based insulator using degumming, filtration, distillation, and a vacuum drying technique in their work. At vacuum pressures ranging from 15% to 65% vacuum and temperatures ranging from 150 °C to 200 °C, 0.05% phosphoric acid, 5% bleaching earth, and 10% zeolite were used in the degumming process. The best measurement results showed that the values of breakdown voltage and kinematic viscosity of 60 kV and 12.7 cSt, respectively, met the requirements. The moisture content was higher than the required value, while the acid content was still relatively high, namely 1.4 mg KOH/g, thus requiring further treatment. After treatment using 2% KOH, the acidity became 0.01 mg KOH/g, and the moisture content was 176 ppm thus meeting the requirement. The color of the insulator produced was light yellow, bright, and clear. The best treatment parameter combination found was a pressure of 51.9% vacuum with a temperature of 168.7 °C. This research showed that the vacuum pressure had a significant effect on moisture, kinematic viscosity, and acidity, while temperature had a significant effect on breakdown voltage and acidity.

Drosophila CP190 and CP60 are transcription factors that are associated with centrosomes during mitosis. CP190 is an essential transcription factor and preferentially binds to housekeeping gene promoters and insulators through interactions with architectural proteins, including Su(Hw) and dCTCF. CP60 belongs to a family of transcription factors that contain the N-terminal MADF domain and the C-terminal BESS domain, which is characterized by the ability to homodimerize. In this study, we show that the conserved CP60 region adjacent to MADF is responsible for interacting with CP190. In contrast to the well-characterized MADF-BESS transcriptional activator Adf-1, CP60 is recruited to most chromatin sites through its interaction with CP190, and the MADF domain is likely involved in protein–protein interactions but not in DNA binding. The deletion of the Map60 gene showed that CP60 is not an essential protein, despite the strong and ubiquitous expression of CP60 at all stages of Drosophila development. Although CP60 is a stable component of the Su(Hw) insulator complex, the inactivation of CP60 does not affect the enhancer-blocking activity of the Su(Hw)-dependent gypsy insulator. Overall, our results indicate that CP60 has an important but redundant function in transcriptional regulation as a partner of the CP190 protein. [ABSTRACT FROM AUTHOR]

Featured Application: The results of this paper can be instrumental in establishing insulation material management standards for determining the stable operation condition of XLPE cables in the future. Cross-linked polyethylene (XLPE) cable is a representative power transmission cable. XLPE has excellent mechanical properties, chemical and heat resistance, and insulation. However, XLPE insulation deteriorates during operation due to electrical, mechanical, and thermal stresses. Among these, thermal stress is a major factor and reduces insulation properties due to a change in molecular structure. Therefore, XLPE characteristic evaluation by heat exposure is essential for power cable condition evaluation. Herein, deteriorated XLPE samples were characterized by tensile strength, X-ray diffraction, Fourier-transform infrared spectroscopy, differential scanning calorimetry, and ultraviolet-visible spectroscopy after exposure to various temperatures and durations. Comparing the tensile strength with other analysis results yielded correlations. Each characteristic showed a linear relationship. The correlation between tensile strength and carbonyl index was the strongest, and the coefficient of determination, R2, was 0.9299. Therefore, these results will provide important information on chemical properties when establishing operational management standards for XLPE insulators in the future. [ABSTRACT FROM AUTHOR]

Microwave energy heating is one of the methods to improve product quality, faster processing, eco-friendliness, and cost and energy savings. The unique heating ability leads to explore this heat treatment method by exploiting its process parameters to improve its effectiveness. This research aimed to predict the effect of microwave heat treatment on aluminium alloy 6063-T6 sheets using fuzzy logic. Microwave heat-treatment trials are designed using the Design of Experiment (DOE) method. The input parameters are heating time, susceptor, and insulator. The non-heated and heated aluminium 'specimen's mechanical properties have been tested using a hardness and tensile testing machine. The experimental results are used to develop a Mamdani fuzzy logic model system. The results indicate that the mechanical properties in terms of tensile Load, and hardness of the specimen have improved after being microwave heat-treated for a short time. The susceptor material and insulator can assist in the microwave processing of materials. The percentage difference between the experimental and simulation values are 0.27 and 6.31%, respectively, for tensile Load and hardness. The experimental and predicted results are still compatible with a small percentage of errors. The fuzzy model can be used to predict the parameters.

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.

BackgroundSelf-powered neutron detectors (SPNDs) are critical devices in the monitoring and protection systems of nuclear reactors, and their signal current directly reflects the value and distribution of the core power. Insulators play an essential role in the design of SPNDs and are the main factor affecting the calculation accuracy of the signal current.PurposeThis study aims to improve the accuracy of the calculation method of the SPND signal current, ensuring that the measured currents accurately reflect the reactor conditions and meet the highest industrial standards.MethodsFirstly, the signal generation mechanism of the SPND was thoroughly discussed, and three independent calculation methods of the current based on the inherent physical characteristics of the space electric field of an insulator were proposed. Then, high-fidelity simulations of the SPND were performed using the Monte Carlo code, and the three methods were validated based on the simulation results. In addition to the current caused by the neutrons, the current caused by the photons inside the reactor was quantitatively analyzed. Meanwhile, extensive radiation experiments on the various reactors have been performed to verify these three current calculation methods.ResultsThe difference between the results obtained by using the three methods is less than 1%, demonstrating a considerable accuracy. In addition, the current of the rhodium SPND is primarily owing to the neutrons, whereas the photon-induced current is generally less than 5%. Experimental verification results on the several operating reactors show that the difference between the theoretical and experimental results is less than 3%, which also proves its effectiveness and accuracy.ConclusionsThis method has been applied to the large Chinese Gen-III advanced pressurized water reactor (HPR1000) and is universal. It can be used for the signal analysis of different types of SPNDs, as well as for providing valuable references for core monitoring systems in other reactors, such as the Gen-IV fast reactor as well as future fusion reactors.

IntroductionGene 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.MethodsTo 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.ResultsWe 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.DiscussionGiven 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.