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Abstract This paper presents a novel bolt looseness detection method for power transmission towers based on vibration signal analysis. The proposed method utilizes pulse excitation to extract the vibration signal of the tower, which is then adaptively decomposed using the Variational Mode Decomposition of Spider Wasp optimizer (SWVMD). This overcomes limitations of traditional Variational Mode Decomposition methods by leveraging bio‐inspired optimization to improve signal decomposition. Simulated signals processed with different optimization methods verify the superiority of the SWO approach. Field tests on a 110‐kV transmission tower further demonstrate the effectiveness of the proposed SWVMD technique for analyzing on‐site vibration data. A new improved intrinsic multiscale sample entropy feature is also introduced for bolt state characterization. A Spider Wasp Support Vector Machine classifier is developed to realize accurate bolt loosening monitoring using the extracted features. Dynamic response tests under varying bolt conditions show that the method can identify early loosening and reduce tower damage risks compared to conventional techniques. This novel vibration‐based detection framework presents an innovative application of nature‐inspired computing for power infrastructure health monitoring.

Abstract The distribution of transmission lines is complex, influenced by terrain and topography, making their maintenance and management challenging. With the advancement of global mechanization, various types of construction machinery and large‐scale mechanical equipment are continually increasing. Accidental contact of construction machinery near transmission lines poses a serious threat to the stability of the power system. Traditional drones and inspection robots face difficulties in achieving real‐time monitoring of construction machinery and equipment around transmission lines, quickly identifying potential risks. Conventional image processing techniques and convolutional neural networks struggle with effective handling of small targets and densely packed detection tasks involving multiple targets. To address these challenges, this paper proposes an intelligent detection algorithm Swin Transformer attention efficient algorithm‐you only look once (YOLO) (STAE‐YOLO). It is based on the Swin Transformer global self‐attention mechanism, cross‐channel fusion attention mechanism, enhanced small object detection framework, and a focused and efficient regression loss function. The experimental results show that the STAE‐YOLO algorithm model improved by 6.3% in mean average precision, 3.7% in precision, and 3.1% in recall compared to the baseline model. Meanwhile, deploying the window multi‐head self‐attention global self‐attention mechanism in the model can strengthen the global multi‐scale semantic information in detection.

Abstract Cancer remains a significant global health challenge, necessitating the exploration of novel and more precise therapeutic options beyond conventional treatments. In this regard, clustered regularly interspaced short palindromic repeats (CRISPR) systems have emerged as highly promising tools for clinical gene editing applications. The CRISPR family encompasses diverse CRISPR-associated (Cas) proteins that possess the ability to recognize specific target sequences. The initial CRISPR system consisted of the Cas9 protein and a single-guide RNA, which guide Cas9 to the desired target sequence, facilitating precise double-stranded cleavage. In addition to the traditional cis-cleavage activity, the more recently discovered Cas12 and Cas13 proteins exhibit trans-cleavage activity, which expands their potential applications in cancer diagnosis. In this review, we provide an overview of the functional characteristics of Cas9, Cas12, and Cas13. Furthermore, we highlight the latest advancements and applications of these CRISPR systems in cancer gene therapy and molecular diagnosis. We also emphasize the importance of understanding the strengths and limitations of each CRISPR system to maximize their clinical utility. By providing a comprehensive overview of the current state of CRISPR technology in cancer research, we aim to inspire further exploration and innovation in this rapidly evolving field. Graphical abstract

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

Abstract Structural damage identification technology based on modal parameters is an extremely effective monitoring method. Because of the structural characteristics of the conductor, with its small diameter and long span, it is not easy to obtain the modal parameters by manual excitation. Here, a method for structural health monitoring of the conductor under wind load excitation is proposed. Firstly, the natural frequency change law of the conductor after broken strand damage is obtained by analyzing the conductor modal parameters calculation formula and IEEE standards. Then, the vibration characteristics are calculated by vibration experiments and finite element methods. Finally, through the broken strand experiment, this paper demonstrates how to determine the damage degree of the conductor using the modal parameters. And it is applied to online monitoring in the field. The results show that this method is convenient, fast, and accurate.

Gas turbines are widely used as important equipment for electricity generation on islands and offshore platforms. During the operation of a gas turbine, the flame shape in the combustion chamber undergoes variations in response to changes in parameters such as gas turbine load, fuel distribution, and burner structure. These alterations in flame shape exert influence on combustion instability, emissions, and load characteristics. This study explores the variations in flame transition, emissions, and operating parameters among three distinct center stage structures: namely, the non-premix center stage (NPCS), premix center stage (PCS), and enhanced premix center stage (PCSE). The investigation is conducted using a heavy-duty gas turbine hybrid burner on a full temperature, full pressure, and full-size single burner experimental bench. Simultaneously, a multi-parameter numerical simulation regarding the influence of the central fuel split on flame shape analysis was conducted using the PCS burner under the design point for a more in-depth understanding of the mechanisms and for influencing factors associated with flame transition. The findings indicate that variations in flame transition loads among different central stage structures: for the NPCS burner, the transition occurs between 45% and 50% load; for the PCS burners, it takes place between 60% and 65% load; for the PCSE burners, it shifts between 55% and 60% load. Additionally, a reduction in NOx emissions is observed during the flame transition process. Furthermore, it was found that decreasing the central stage fuel results in a decline in flame angle for the same burner structure. As the central stage fuel diminishes to a specific value, the flame shape undergoes a sudden change. Further reduction in central stage fuel does not significantly affect the flame shape and temperature distribution.

In this study, large eddy simulation (LES) was used to investigate the dynamic characteristics of diffusion flames in a swirl combustion chamber at an oxygen content of 11–23 wt% and temperature of 770 K. The proper orthogonal decomposition (POD) method was employed to obtain flame dynamic modes. The results indicate that oxygen content has a significant impact on the downstream flow and flame combustion characteristics of the swirl combustion chamber. With oxygen content increasing, the size of the recirculation zone is reduced, and the flame field fluctuations are intensified. The pressure and heat release fluctuations under different oxygen contents were analyzed using frequency spectrum analysis. Finally, the flame modes were analyzed using the POD method, and it was found that the coherent structures are asymmetric relative to the local coordinate system. At an oxygen content of 11 wt%, they exhibit larger coherent structures, while at an oxygen content of 23 wt%, they exhibit numerous turbulent structures.

Insulator defect detection of transmission line insulators is an important task for unmanned aerial vehicle (UAV) inspection, which is of immense importance in ensuring the stable operation of transmission lines. Transmission line insulators exist in complex weather scenarios, with small and inconsistent shapes. These insulators under various weather conditions could result in low-quality images captured, limited data numbers, and imbalanced sample problems. Traditional detection methods often struggle to accurately identify defect information, resulting in missed or false detections in real-world scenarios. In this paper, we propose a weather domain synthesis network for extracting cross-modality discriminative information on multi-domain insulator defect detection and classification tasks. Firstly, we design a novel weather domain synthesis (WDSt) module to convert various weather-conditioned insulator images to the uniform weather domain to decrease the existing domain gap. To further improve the detection performance, we leverage the attention mechanism to construct the Cross-modality Information Attention YOLO (CIA-YOLO) model to improve the detection capability for insulator defects. Here, we fuse both shallow and deep feature maps by adding the extra object detection layer, increasing the accuracy for detecting small targets. The experimental results prove the proposed Cross-modality Information Attention YOLO with the weather domain synthesis algorithm can achieve superior performance in multi-domain insulator datasets (MD-Insulator). Moreover, the proposed algorithm also gives a new perspective for decreasing the multi-domain insulator modality gap with weather-domain transfer, which can inspire more researchers to focus on the field.

Regular inspection of the insulator operating status is essential to ensure the safe and stable operation of the power system. Unmanned aerial vehicle (UAV) inspection has played an important role in transmission line inspection, replacing former manual inspection. With the development of deep learning technologies, deep learning-based insulator defect detection methods have drawn more and more attention and gained great improvement. However, former insulator defect detection methods mostly focus on designing complex refined network architecture, which will increase inference complexity in real applications. In this paper, we propose a novel efficient cross-modality insulator augmentation algorithm for multi-domain insulator defect detection to mimic real complex scenarios. It also alleviates the overfitting problem without adding the inference resources. The high-resolution insulator cross-modality translation (HICT) module is designed to generate multi-modality insulator images with rich texture information to eliminate the adverse effects of existing modality discrepancy. We propose the multi-domain insulator multi-scale spatial augmentation (MMA) module to simultaneously augment multi-domain insulator images with different spatial scales and leverage these fused images and location information to help the target model locate defects with various scales more accurately. Experimental results prove that the proposed cross-modality insulator augmentation algorithm can achieve superior performance in public UPID and SFID insulator defect datasets. Moreover, the proposed algorithm also gives a new perspective for improving insulator defect detection precision without adding inference resources, which is of great significance for advancing the detection of transmission lines.

Different types of surface defects will occur during the production of strip steel. To ensure production quality, it is essential to classify these defects. Our research indicates that two main problems exist in the existing strip steel surface defect classification methods: (1) they cannot solve the problem of unbalanced data using few-shot in reality, (2) they cannot meet the requirement of online real-time classification. To solve the aforementioned problems, a relational knowledge distillation self-adaptive residual shrinkage network (RKD-SARSN) is presented in this work. First, the data enhancement strategy of Cycle GAN defective sample migration is designed. Second, the self-adaptive residual shrinkage network (SARSN) is intended as the backbone network for feature extraction. An adaptive loss function based on accuracy and geometric mean (Gmean) is proposed to solve the problem of unbalanced samples. Finally, a relational knowledge distillation model (RKD) is proposed, and the functions of GUI operation interface encapsulation are designed by combining image processing technology. SARSN is used as a teacher model, its generalization performance is transferred to the lightweight network ResNet34, and it is conveniently deployed as a student model. The results show that the proposed method can improve the deployment efficiency of the model and ensure the real-time performance of the classification algorithms. It is superior to other mainstream algorithms for fine-grained images with unbalanced data classification.

Abstract Transmission lines are affected by aeolian vibration, sandstorms and acid rain, which cause structural damage and eventually lead to line breaking accidents. In this paper, the structural health monitoring technology of conductors is researched, and a structural monitoring system for conductors is designed. First, based on the structure of the ACSR (Aluminum Conductor Steel Reinforced) conductor and the dynamic equation, the relation between the natural frequencies and the structure of the conductor is obtained. Then, an online health monitoring system of the conductor structure is proposed, and key issues, such as the power supply solution of sensors, data preprocessing technology, multiple‐sensor synchronization method, are analyzed in detail. Finally, the structural health monitoring of overhead transmission lines designed in this paper is tested on 110‐kV experimental transmission lines. The experimental results show that from the system proposed the operation parameters of the conductors can be well obtained.

Abstract Optogenetic systems and tetracycline‐induced expression systems have been used in biological research. In gene circuits, light and tetracycline are often used to control the activation or inhibition of gene expression. In our study, we used a blue light induction system and tetracycline induction system to construct an “OR” logic gate, which can specifically induce the expression of p53 protein or E‐cadherin in the case of blue light or tetracycline, thus inhibiting the growth of cutaneous squamous cell carcinoma cells. Cutaneous squamous cell carcinoma is a kind of skin surface tumour. The activation of two tumour suppressor genes has a synergistic inhibitory effect on cutaneous squamous cell carcinoma, and blue light and tetracycline also provide a flexible means for gene regulation.

Ever since the gene editing function was discovered in the CRISPR-Cas9 system, numerous applications and utilities were investigated in order to apply this technique to medical use. However, the clinical practice was limited by unsatisfactory efficiency and unacceptable off-target editing. Modifications from different aspects of the Cas9 protein and gRNAs were published that aimed to improve its function in one way or another. Under the inspiration of Jacob L. Litke and Samie R. Jaffrey, we propose a novel gRNA design that could achieve rapid circular gRNA assembly inside the cells. This circular design consists of the gRNA of interested flanked by Twister ribozymes. The function of this circular gRNA was proved in vitro in both CRISPR-dCas9 and CRISPR-Cas9 systems. It presented a remarkable reduction in the off-target rate in accompany with reduced efficiency. With future improvement in its efficiency, this tool broadens our understanding and possibility of the CRISPR application.

The series of physical processes contained in the corona layer is the source of space ions formed by the corona discharge of the high-voltage AC line. However, since these physical processes are difficult to calculate accurately and the corona layer thickness is usually small, the corona layer is ignored in the published literature on the corona discharge of the AC line, and the ions are considered to be emitted directly from the conductor surface. In this paper, the distribution area of the charged particles is divided into the corona plasma region and the unipolar drift region. A mathematical physical model of the photoionization and photoemission, which sustain self-sustaining discharge in the positive and negative half-cycle of corona plasma region of the AC conductor in dry air, are established. The influence of the ions generated in the previous cycle is also considered in the numerical simulation process. Finally, a complete microscopic physical model describing the corona discharge of the AC conductor under dry air is established. The validity of the physical model proposed in this paper is demonstrated by comparison with experimental results reported in the literature. Through the numerical simulation of the ion flow field of positive and negative half-cycle and the corona current, the physical process of the corona discharge around the AC line is better understood and analysed. Further positive and negative half-cycle charged particle motion and corona plasma regions were compared and analyzed. This work provides a brand new mathematical physics method for understanding and applying AC corona discharge.

Transmission line fittings have been exposed to complex environments for a long time. Due to the interference of haze and other environmental factors, it is often difficult for the camera to obtain high quality on-site images, and the traditional image processing technology and convolution neural networks find it difficult to effectively deal with the dense detection task of small targets with occlusion interference. Therefore, an image processing method based on an improved dark channel defogging algorithm, the fusion channel spatial attention mechanism, Vision Transformer, and the GhostNet model compression method is proposed in this paper. Based on the global receptive field of the saliency region capture and enhancement model, a small target detection network Double-attention YOLO for complex environments is constructed. The experimental results show that embedding a multi-head self-attention component into a convolutional neural network can help the model to better interpret the multi-scale global semantic information of images. In this way, the model learns more easily the distinguishable features in the image representation. Embedding an attention mechanism module can make the neural network pay more attention to the salient region of image. Dual attention fusion can balance the global and local characteristics of the model, to improve the performance of model detection.

N6-methyladenosine (m6A) RNA modification is the most common internal mRNA modification in mammals and has been reported to play a key role in gene expression regulation. In this study, we detected a high level of m6A methylation of the PLOD2 3′-untranslated regions (3′UTR) in renal cell carcinoma (RCC). Furthermore, we found that the high expression level of PLOD2 was a prognostic indicator for patients with RCC. A dm6ACRISPR demethylation system was performed to accurately and specifically demethylate 3′UTR of PLOD2 and caused an inactivation of PLOD2 expression. Furthermore, we also performed many in vitro experiments to confirm that PLOD2 exerted tumor promoter effects by promoting tumor proliferation and migration. In conclusion, PLOD2 mRNA demethylated by dCas13b-ALKBH5 might provide a new light on the treatment for RCC.

Background: Renal cell carcinoma (RCC) accounts for more than 90% of cancers in the kidney. RCC is often asymptomatic, as a result people with RCC generally have advanced disease by the time it is discovered and has a poor prognosis compared to other cancers. Therefore, it is necessary to explore its pathogenesis and identify some reliable prognostic biomarker of RCC. miRNAs are emerging as important players in the development and progression of RCC. miR-31-5p has been reported to act as a tumor suppressor in hepatocellular carcinoma (HCC). The aim of this study is to determine the detailed molecular mechanism of miR-31-5p in the progression of RCC and to investigate its potential clinical value.Methods: In this study, RT-qPCR, EdU assay, CCK-8 assay, wound scratch assay, transwell assay, flow cytometry assay and cell cycle assay were performed to detect miR-31-5p expression and its functions in RCC. Moreover, 42 formalin-fixed paraffin-embedded (FFPE) RCC samples were used to analyze the relationship between miR-31-5p expression and patients’ overall survival. Finally, luciferase reporter assay, RT-qPCR assay and western blot were used to explore the association between miR-31-5p and its potential targets.Results: miR-31-5p was significantly down-regulated in RCC tissues and RCC cell lines compared with paired adjacent normal tissues and normal cell lines. miR-31-5p downregulation was associated with poor prognosis in RCC patients. Overexpression of miR-31-5p inhibited RCC cell proliferation, migration and invasion and cell cycle. Conversely, down-regulation of miR-31-5p promoted cell proliferation, migration and invasion. Furthermore, cyclin-dependent kinasec1 (CDK1), a key player in cell cycle regulation, was identified as a functional target of miR-31-5p.Conclusions: Our results suggest that miR-31-5p serves as a tumor suppressor in RCC and is expected to be a molecular biomarker for poor prognosis of RCC.

The aluminum strands break and surface damage will gradually develop into irreversible destruction to the conductors and the whole transmission lines, and even lead to serious power outages. In order to find out the potential strands break and damage faults and prevent its further deterioration, a recognition method of conductor break and surface defects in transmission lines' unmanned aerial vehicle (UAV) inspection is presented in this paper. First, a conductor image is obtained by the UAV image acquisition system, and then, the conductor region is extracted by the adaptive threshold segmentation after the enhancement processing by the gray variance normalization method (GVN). Second, the conductor break is detected by the square wave transformation (SWT) of its grayscale distribution curves, which is simple and effective. Meanwhile, the conductor surface defects are identified by the projection algorithm of the GVN image of the conductor region. Finally, calculating the number of broken strands and filtering the suspect defects, the final fault diagnosis results can be obtained. We analyze the performance of the technology by a series of experiments, and the results demonstrate that the proposed method can measure the conductor break and surface defects faults with the average accuracy of 90.45% and 92.05%, respectively. The health condition of the conductor can be monitored based on the method presented in this paper, by which the safe operation of transmission lines can be guaranteed.

Hydrophobicity of the composite insulators is a parameter which is closely related to the insulation properties. In order to determine whether the insulator in the transmission line needs to be withdrawn, it is necessary to objectively and accurately determine the hydrophobicity grade of the composite insulator. The study on hydrophobicity detection of composite insulators of transmission lines by image analysis is proposed in this paper, by which the hydrophobicity of composite insulators can be measured timely. The grayscale, median filtering, and histogram equalization of the hydrophobic images are fulfilled sequentially, and the water drops (or water marks) region is segmented by binary large object analysis and four parameters related to water repellency are extracted. In addition, the improved support vector machine algorithm is adopted to calculate the hydrophobicity of composite insulators. The results show that seven typical hydrophobic levels of insulators can be recognized by the proposed hydrophobicity detection method whose accuracy is more than 91%.

Finding out the wind deviation fault of the insulators of transmission lines in time can not only help people better complete the wind bias warning and prevention work, but also reduce the economic loss and the maintenance difficulty. A technology of Actual Wind Deviation Monitoring for Suspension Insulator Strings Based on Improved Edge Detection is proposed in this paper. It can calculate the actual wind deviation angle and distance of the insulator through image processing technology and camera calibration. Firstly, the video monitoring device is installed on the tower to collect the field insulator images, and the R + G improved grayscale processing and median filtering are carried out. Secondly, two-dimensional Otsu threshold segmentation is applied to the pre-processed image, and the insulator string target is obtained by combining morphological filtering and connection domain extraction. Then, the improved Kirsch operator is used to obtain the complete single-pixel wide edge of the insulator string. Searching the left and right boundary points of each insulator separately and the center points of each insulator can be calculated. Finally, these center points are transformed into world coordinates by camera calibration and fitted by least square method (LSM). The deviation angle θ of the insulator string can be obtained according to the slope of the fitting line, and the deviation distance d according to the length of the insulator string. When θ is greater than 7.5° and d is greater than 300mm, an alarm is issued to alert the staff. We analyze the performance of the technology by a series of experiments, the maximum error between the proposed method and the manual method is 8.72% and the minimum is 1.01%. Moreover, by analyzing 200 field insulator images, the identification accuracy are 93.75% and 91.36% respectively, which shows that the proposed method is effective and practical. It provides a new idea for the wind deviation monitoring of suspension insulator strings.

Icicle will cause serious distortion of insulator electric field, weaken its voltage strength, and easily cause insulator flashover. Previous studies have shown that the longer the length of icicles on insulator, the greater the possibility of electric flashover. Then, it is of great theoretical significance and practical value to study the image identification technology of icicle length of iced insulators. Firstly, the growth mechanism and structural characteristics of insulator icicles are discussed in this paper, and the image acquisition method of iced insulator is discussed. Secondly, the methods of saliency detection and color feature analysis are used to extract the region of icicle and insulator sheds respectively, then, the icicle structure on insulator is obtained by the difference calculation of above two images. Meanwhile, the pixel curve of the iced insulator image is processed by Fourier transform, and the reference size of insulators icicle is obtained by calculating the length of the shed spacing. Finally, a practical case about iced insulators in substations is analyzed in detail, which proves the effectiveness of the method proposed in this paper.

In the production and manufacturing industry, factors such as rolling equipment and processes may cause various defects on the surface of the steel plate, which greatly affect the performance and subsequent machining accuracy. Therefore, it is essential to identify defects in time and improve the quality of production. An intelligent detection system was constructed, and some improved algorithms such as dataset enhancement, annotation and lightweight convolution neural network are proposed in this paper. (1) Compared with the original YOLOV5 (You Only Look Once), the precision is 0.924, and the inference time is 29.8 ms, which is 13.8 ms faster than the original model. Additionally, the parameters and calculations are also far less than YOLOV5. (2) Ablation experiments were designed to verify the effectiveness of the proposed algorithms. The overall accuracy was improved by 0.062; meanwhile, the inference time was reduced by 21.7 ms. (3) Compared with other detection models, although RetinaNet has the highest accuracy, it takes the longest time. The overall performance of the proposed method is better than other methods. This research can better meet the requirements of the industry for precision and real-time performance. It can also provide ideas for industrial detection and lay the foundation for industrial automation.

Optogenetic systems have been increasingly investigated in the field of biomedicine. Previous studies had found the inhibitory effect of the light-inducible genetic circuits on cancer cell growth. In our study, we applied an AND logic gates to the light-inducible genetic circuits to inhibit the cancer cells more specifically. The circuit would only be activated in the presence of both the human telomerase reverse transcriptase (hTERT) and the human uroplakin II (hUPII) promoter. The activated logic gate led to the expression of the p53 or E-cadherin protein, which could inhibit the biological function of tumor cells. In addition, we split the dCas9 protein to reduce the size of the synthetic circuit compared to the full-length dCas9. This light-inducible system provides a potential therapeutic strategy for future bladder cancer.

Abstract The traditional treatment for cancer is lack of specificity and efficacy. Modular synthetic regulatory RNAs, such as inhibitive RNA (iRNA) and active RNA (aRNA), may overcome these limitations. Here, we synthesize a new iRNA to bind the upstream activating sequence (UAS) of a minimal promoter that drives expression of artificial miRNAs (amiRNAs) targeting MYC, which represses the binding interaction between UAS and GAL4 fusion protein (GAL4-VP64) in GAL4/UAS system. The aRNA driven by a tumor-specific mutant human telomerase reverse transcriptase (hTERT) promoter is created to interact with iRNA to expose UAS again in bladder cancer. Without the aRNA, mRNA and protein levels of MYC, cell growth, cell apoptosis and cell migration were no significance in two bladder cancer cell lines, T24 and 5637, and human foreskin fibroblast (HFF) cells. The aRNA significantly inhibited the expression of MYC in mRNA and protein levels, as well as the proliferation and migration of the cancer cells, but not in HFF cells. These results indicated that regulatory RNAs selectively controlled the expression of amiRNAs and ultimately suppress the progression of bladder cancer cells without affecting normal cells. Synthetic regulatory RNAs might be a selective therapeutic approach for bladder cancer.

In view of the settlement problem of transmission tower foundation, the vibration characteristics of transmission towers under wind force are measured experimentally. In this paper, the 110 kV cat head transmission tower of Xi’an Polytechnic University is measured and analyzed. Firstly, the acceleration sensor and meteorological sensor are installed on the tower to collect the vibration response and environment parameters of the tower in real time. Then, an experiment platform is built to simulate the tower settlement, and the vibration response of the tower after settlement is measured in time. Finally, the low-order modal frequencies of the transmission tower before and after settlement under wind force load are extracted by stochastic subspace identification (SSI), and the relationship between modal frequencies of different modes is analyzed via temperature correction. By comparison and analysis, it is obvious that the X-direction modal frequencies before and after settlement under natural wind load are changed, and the change rate increases with the increase of settlement displacement, which can be used as effective evidence for judging the settlement of transmission tower foundation.

The capacitive method is considered to be a suitable icing-detection technology, but the lack of fundamental parameters restricts the development of icing-detection sensors. In this paper, an artificial icing laboratory, a capacitive sensor, and some simulation conductors have been designed for obtaining the artificial icing samples. Subsequently, the same characteristic values of artificial icing have been measured by an LCR device, under a selected frequency. This research found that the value of the icing dielectric constant closely correlated with its density, internal sublayer, and the test temperature. Finally, a fitting formula has been presented for calculating the relative dielectric constant, which may provide some important reference value for the design of icing-detection sensors.

Transmission lines are affected by Aeolian vibration, which causes strands to break and eventually causes an entire line to break. In this paper, a method for monitoring strand breaking based on modal identification is proposed. First, the natural frequency variation of a conductor caused by strand breakage is analyzed, and a modal experiment of the LGJ-95/15 conductor is conducted. The measurement results show that the natural frequencies of the conductor decrease with an increasing number of broken strands. Next, a monitoring system incorporating a fiber Bragg grating (FBG)-based accelerometer is designed in detail. The FBG sensor is mounted on the conductor to measure the vibration signal. A wind speed sensor is used to measure the wind speed signal and is installed on the tower. An analyzer is also installed on the tower to calculate the natural frequencies, and the data are sent to the monitoring center via 3G. Finally, a monitoring system is tested on a 110 kV experimental transmission line, and the short-time Fourier transform (STFT) method and stochastic subspace identification (SSI) method are used to identify the natural frequencies of the conductor vibration. The experimental results show that SSI analysis provides a higher precision than does STFT and can extract the natural frequency under various wind speeds as an effective basis for discriminating between broken strands.

Conductor galloping may cause flashovers and even tower collapses. The available conductor galloping monitoring methods often employ acceleration sensors to measure the conductor translations without considering the conductor twist. In this paper, a new sensor for monitoring conductor galloping of transmission lines based on an inertial measurement unit and wireless communication is proposed. An inertial measurement unit is used for collecting the accelerations and angular rates of a conductor, which are further transformed into the corresponding geographic coordinate frame using a quaternion transformation to reconstruct the galloping of the conductor. Both the hardware design and the software design are described in details. The corresponding test platforms are established, and the experiments show the feasibility and accuracy of the proposed monitoring sensor. The field operation of the proposed sensor in a conductor spanning 734 m also shows its effectiveness.

Franchising has played a pivotal role in driving China's economic growth in recent years. With the prominent problem of franchise asymmetry, the protection of franchisees has emerged as a significant concern. This paper conducts a comparative study of franchisee protection practices under franchising regulation in China and certain jurisdictions such as the United States, Australia, and Malaysia. This paper aims to analyze three aspects of franchisee protection, including information disclosure, registration, liability, and relief for violations. To this end, the researchers adopted library data collection methods. Franchisingrelated laws, regulations, rules, and cases are the primary data sources, while journals, books, industry reports, and network resources are the secondary data sources. The results of this study show that although China's franchisee protection is generally in line with international practice, there is still room for further improvement. The authors proposed recommendations such as strictly implementing registration requirements, introducing a disclosure exemption system, specifying the exact duration of the cooling-off period, and diversifying dispute resolution. The contribution of this paper is to learn from the advanced experience of foreign franchisee protection and improve the current franchisee protection system in China. [ABSTRACT FROM AUTHOR]

The accumulation of snow and ice on PV modules can have a detrimental impact on power generation, leading to reduced efficiency for prolonged periods. Thus, it becomes imperative to develop an intelligent system capable of accurately assessing the extent of snowand ice coverage onPVmodules. To address this issue, the article proposes an innovative ice and snow recognition algorithm that effectively segments the ice and snow areas within the collected images. Furthermore, the algorithm incorporates an analysis of the morphological characteristics of ice and snow coverage on PVmodules, allowing for the establishment of a residual ice and snow recognition process. This process utilizes both the external ellipse method and the pixel statistical method to refine the identification process. The effectiveness of the proposed algorithm is validated through extensive testing with isolated and continuous snow area pictures. The results demonstrate the algorithm's accuracy and reliability in identifying and quantifying residual snow and ice on PV modules. In conclusion, this research presents a valuable method for accurately detecting and quantifying snow and ice coverage on PV modules. This breakthrough is of utmost significance for PV power plants, as it enables predictions of power generation efficiency and facilitates efficient PV maintenance during the challenging winter conditions characterized by snow and ice. By proactively managing snow and ice coverage, PV power plants can optimize energy production and minimize downtime, ensuring a sustainable and reliable renewable energy supply. [ABSTRACT FROM AUTHOR]