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Distance metric learning has been a promising technology to improve the performance of algorithms related to distance metrics. The existing distance metric learning methods are either based on the class center or the nearest neighbor relationship. In this work, we propose a new distance metric learning method based on the class center and nearest neighbor relationship (DMLCN). Specifically, when centers of different classes overlap, DMLCN first splits each class into several clusters and uses one center to represent one cluster. Then, a distance metric is learned such that each example is close to the corresponding cluster center and the nearest neighbor relationship is kept for each receptive field. Therefore, while characterizing the local structure of data, the proposed method leads to intra-class compactness and inter-class dispersion simultaneously. Further, to better process complex data, we introduce multiple metrics into DMLCN (MMLCN) by learning a local metric for each center. Following that, a new classification decision rule is designed based on the proposed methods. Moreover, we develop an iterative algorithm to optimize the proposed methods. The convergence and complexity are analyzed theoretically. Experiments on different types of data sets including artificial data sets, benchmark data sets and noise data sets show the feasibility and effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Introduction: The objective of this meta-analysis was to systematically assess the association between periodontal disease (PD) and myocardial infarction (MI).Methods: We searched the EMBASE, PubMed, and Cochrane Library databases for eligible cohort studies from inception to August 31st, 2020 that reported the association between PD and MI. Data extraction was conducted after screening the literature. The risk of bias of the included studies was evaluated by using the Newcastle-Ottawa Scale (NOS). The combined OR value and 95% confidence interval (CI) were calculated by using STATA 11.0 software, and the source of any heterogeneity was determined by performing subgroup analysis and sensitivity analysis.Results: A total of 10 cohort studies involving 5,369,235 participants fulfilled the inclusion criteria. The meta-analysis showed an increased risk for MI in patients with PD [RR = 1.13, 95% CI (1.04, 1.21), P = 0.004], and this result was robust according to sensitivity analysis. Subgroup analyses indicated that the results were affected by sex, effect value, study quality, survey form and investigation type.Discussion: This meta-analysis suggests that PD is modestly associated with MI, especially in women, which is similar to the conclusions of earlier meta-analyses of case-control or observational studies. PD may be an untraditional risk factor for cardiovascular diseases, including MI; thus, maintaining periodontal health may be an effective measure to prevent MI. More cohort studies are still needed to further explore the relationship between the incidence of MI and PD. [ABSTRACT FROM AUTHOR]

The morphology of soil particles is crucial in determining its granular characteristics and assembly responses. However, how to introduce accurate and various morphologies of realistic particles in modeling can be challenging, as it often requires time-consuming and costly X-ray Computed Tomography (XRCT). This has led to two prevalent problems in modeling: morphological reconstruction and generation. For reconstruction, we develop a geometric-based Metaball-Imaging algorithm. This algorithm is capable of accurately reconstructing the complex morphologies of realistic particles, including those with concave voids, which cannot be easily represented using other shape descriptors such as the spherical harmonic function. It employs a two-step approach, capturing the main contour of the particles using a series of non-overlapping spheres and then refining surface-texture details through gradient search. Four types of soil particles, hundreds of samples, are applied for evaluations. The result shows good matches on key morphological indicators (i.e., volume, surface area, sphericity, circularity, corey-shape factor, nominal diameter and surface-equivalent-sphere diameter), confirming its reconstruction precision. For generation, we propose the Metaball Variational Autoencoder. Assisted by deep neural networks, this method can generate new 3D particles in Metaball form, while retaining coessential morphological features with parental particles. Additionally, this method allows for control over the generated shapes through an arithmetic pattern, enabling the generation of particles with specific shapes. Two sets of XRCT images different in sample number and geometric features are chosen as parental data. On each training set, one thousand particles are generated for validations. The generation fidelity is demonstrated through comparisons of morphologies and shape-feature distributions between generated and parental particles. Examples are also provided to demonstrate controllability on the generated shapes. With the Metaball-based simulation framework previously proposed by the authors, these methods can incorporate the real shape of particles into simulations, making it possible to simulate a large number of soil particles with varying shapes and behaviors. Together, they have the potential to provide valuable insights into the properties and behavior of actual soil particles. [ABSTRACT FROM AUTHOR]

Device-to-Device (D2D) communication is one of the promising technologies in 5G wireless communication system. The concept of D2D communication is to allow the direct communications between two devices reusing the cellular spectrum. Considering the large scale D2D networks, where multiple D2D users can download common files (called content below) from one or multiple other users directly by reusing the same spectrum with cellular users (CU). In this paper, we propose the peer-to-peer (P2P) based resource allocation for D2D networks in which resource includes spectrum and content. We aim at maximizing the sum download rate based on jointly effective spectrum and content allocation. However, this problem is NP-hard. Thus, we firstly develop a distance constrained coalitional game to solve the optimal resource allocation problem. We formulate the optimal spectrum and content allocation as an utility maximization problem and distribute the utility based on each player’s successful transmission probability (STP) which is derived by the tool of stochastic geometry. By theoretical analysis, we derive the proper constrained distance, prove that the proposed algorithm is Nash-stable and get the improvement of the convergence rate. By extensive simulation, we demonstrate the effectiveness of our proposed algorithm which obtains the best sum download rate comparing with other schemes and it is close to the optimal solution. [ABSTRACT FROM AUTHOR]

Hyperglycemia remission by metabolic surgery is implicated in the resolution of low-grade inflammation in type 2 diabetes mellitus (T2DM). However, whether this beneficial effect of metabolic surgery is related to improving monocyte inflammatory response remains undefined. This investigation is addressed to evaluate this relationship. For this purpose, T2DM rats were subjected to Roux-en-Y gastric bypass (RYGB) and/or monocyte depletion or splenic sympathetic denervation. Fasting blood glucose (FBG), plasma tumor necrosis factor-alpha (TNF-α) and interleukin-1 beta (IL-1β) were measured, and monocyte inflammatory response was assessed in vitro. The data showed that RYGB significantly reduced lipopolysaccharide (LPS)-induced release of TNF-α and IL-1β from peripheral monocytes while alleviating hyperglycemia and reducing plasma TNF-α and IL-1β levels. Hyperglycemia resulting from monocyte depletion by injection of clodronate liposomes resolved one week earlier than vehicle control after RYGB. Splenic denervation abrogated the glucose-lowering effect and decreased LPS-stimulated TNF-α and IL-1β release from monocytes following RYGB. Overall, our results reveal that a marked reduction of monocyte inflammatory response after RYGB contributes to hyperglycemia remission in T2DM rats. The beneficial effect of RYGB is mediated through vagal-spleen axis anti-inflammatory activity. [Display omitted] • RYGB improves inflammatory response of peripheral monocytes in T2DM rats. • Monocyte depletion accelerated hyperglycemia remission following RYGB. • Splenic denervation abrogated improvement of monocyte inflammatory response. • Splenic denervation abolished glucose-lowering effect by RYGB. • Hyperglycemia remission after RYGB depends on monocyte inflammatory response. [ABSTRACT FROM AUTHOR]

• A hybrid test is used to conduct experiment of large-span spatial structures & glass façade coupling system. • The glass façade can reduce the acceleration response of mid-span upper chord node of main structure. • The DIC method shows the deformation and failure mode of the glass panels. • Seismic performance evaluation indexes of point-supported glass facade are determined. Point-supported glass façade (PSGF) is one of the most widely used types of non-structural components. Its earthquake damage may not only cause economic loss, but also interruption of building use and even casualties. This paper aims at investigating the seismic response of the large-span spatial structure and PSGF coupling system through the substructure hybrid test. The substructure pseudo-dynamic (PSD) test method is used, that is, the loading rate of the test is slow as that of the quasi-static tests. The finding is that when the glass panel begins to fail, the cracks occur and instantly spread to the whole glass panel along the diagonal direction due to the stress concentration at a bolt hole of double-layer laminated glass, accompanied by looseness of several point-fixed fitting screws. The drift angle of one glass panel is about 50.0%–81.9% of that of glass façade δ in the critical and failure state, indicating that the supporting system can reduce the in-plane deformation of the glass panel. And the glass façade can reduce the acceleration responses of the mid-span upper chord nodes of the large-span spatial structure by 4.74%–13.98%. Finally, the seismic performance evaluation indexes of PSGF are determined based on the drift angle δ and percentage of damaged glass panels for the slight, moderate, and extensive limit states. It will provide an example and reference for the seismic performance evaluation of the PSGF. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a certain type of cars side impact performance is discussed. The purpose is to express the relationship of the side impact response and safety. First, A certain cars Finite Element Model (FEM) is established which is based on a logical method to solve the problem during the modeling process; Then, analysis the evaluation indexes of side impact safety, traces the designing and manufacturing process, got the side impact feature and point test data of this type of car, and verified the practicability of simulation result in the car developing process. Finally, express the connection between the side impact response and safety of this car, aiming at discussing the effective method of whole car side impact mode and response evaluation. [Copyright &y& Elsevier]

• A hybrid model is introduced to simulate scalar transport in fluid and particle flows. • Strict scalar mass conservation is achieved by using Random Walk Method. • A moving boundary condition is introduced for Random Walk Method. • Numerical dispersion is totally removed from the proposed model. Scalar transport in fluid-particle systems can be found in many natural processes and engineering applications. Modeling these systems can be challenging due to complex moving boundaries involved, such as fluid-particle, solute-particle interfaces. One often overlooked issue is: the scalar mass within the fluid domain is not strictly conserved even the no-flux boundary condition is applied in many grid-based methods. Here we introduce a hybrid model for simulating scalar transport in particulate flows with strict scalar mass conservation. It is achieved by adapting the Random Walk Method (RWM) to simulate the scalar transport and coupled with the Discrete Element Lattice Boltzmann Method (DE-LBM) for fluid-particle interactions. A moving boundary condition is proposed to prevent the scalar from penetrating solid boundaries and special treatments are presented in detail. The proposed model is validated by a series of benchmarks and experiments, then the mass conservation for both RWM and grid-based methods are checked. Finally, the model is applied to simulate scalar (solute) dispersion with particles in an oscillator which demonstrate its capability on simulating fluid-particle-solute systems. Furthermore, it is straightforward to extend the proposed model for non-spherical particles and reactive boundary conditions. [ABSTRACT FROM AUTHOR]

Multi-metric learning plays a significant role in improving the generalization of algorithms related to distance metrics since using a single metric is sometimes insufficient to handle complex data. Metric learning can adjust automatically the distance between samples to make the intra-class samples compact while making the inter-class distance as far as possible. To implement this intention better,in this work, we propose a novel multi-metric learning framework based on the pair constraints instead of triple constraints to reduce computational burden. To solve effectively the problem, we first propose a multi-birth metric learning model (termed MBML), where for each class sample, the global metric and a local metric are jointly trained. Both global and local structural information are adapted to better depict sample information. Then two alternating iterative algorithms are developed to optimize the MBML. The convergence of the proposed algorithm and complexity are analyzed theoretically. Moreover, a fast diagonal multi-metric learning method is proposed based on binary constraints, and problem can be reformulated a linear programming, with fast training speed, low the computational burden and the global optimal solutions. Numerical experiments are carried out on different scales and different types of datasets including an artificial data, benchmark datasets and an image database from binary class and multi-class problems. Experiment results confirm the feasibility and effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

• Cross-dimension analyses with theories, simulations and experiments are conducted to investigate the evolution of discharge behaviours at the XLPE-SiR interface of cable joints under moisture conditions. • The optical and electrical characteristics of the entire discharge evolution at the XLPE-SiR interface under moisture conditions are captured experimentally. • The mechanism of arcs' generation and extinction at the XLPE-SiR interface under moisture conditions is demonstrated. The discharge behaviors at the XLPE-SiR interface of 10 kV cable joints under moisture conditions seriously affect the reliability of the distribution network. However, there have been few studies on the development and characteristics of interfacial discharge behaviors. In this paper, an investigation was conducted to replicate the discharge behaviors at the XLPE-SiR interface of 10 kV cable joints under moisture conditions. Both optical and electrical characteristics were measured during the process. The results indicate that water initially triggers partial discharges at the XLPE-SiR interface, gradually evolving into arc discharges. The early discharge has two effects: on one hand, it causes pyrolysis of the insulating material, forming conductive traces; on the other hand, the heat generated by the discharge leads to the evaporation of water, effectively preventing the continuation of discharges at the interface. Subsequently, the discharge behaviors at the XLPE-SiR interface were further investigated in the presence of electric traces. The results indicate that when there are electric traces at the XLPE-SiR interface, moisture can rapidly lead to the interface breakdown. The findings contribute to the establishment of effective fault identification strategies for 10 kV cable joints, enhancing overall system reliability. [ABSTRACT FROM AUTHOR]

This study aimed to examine the mechanism of occipital condyle fractures (OCFs), their clinical symptoms, computer tomography (CT) scan findings, treatment options, and classification. A retrospective analysis was conducted on 43 patients with OCFs who were admitted to our neurosurgery center between 2017 and 2023. The investigation covered their clinical symptoms, CT scan results, and treatment outcomes. It was found that 25.6% of the patients suffered from severe craniocerebral injuries with Glasgow Coma Scale (GCS) scores of 3–8 points, 9.3% had moderate injuries with GCS scores of 9–12 points, and 65.1% exhibited mild injuries with GCS scores of 13–15 points. Of these patients, 90.7% showed improvement upon discharge, 4.7% succumbed to their injuries, and another 4.7% developed paraplegia. Symptoms indicative of OCF in individuals with CCJ injuries included neck pain, swelling, cranial nerve palsy, and posterior pharyngeal wall swelling. Frequently observed complications in OCF patients included cerebral contusion, occipital bone fractures, and skull base fractures. Employing thin-layer CT scans of the CCJ area, along with sagittal and coronal CT reconstructions, is essential for identifying OCFs. The fractures were classified into 3 types based on the Anderson-Montesano classification, which, when modified, provides enhanced treatment guidance. OCFs are predominantly present in cases of high-energy trauma, with high-resolution thin-layer CT scans serving as the preferred diagnostic method. The application of the modified Anderson-Montesano classification, distinguishing between stable and unstable fractures, facilitates the determination of suitable treatment strategies. Stable OCFs can be managed using a rigid neck brace, while unstable OCFs may require Halo-vest frame fixation or surgical intervention. [ABSTRACT FROM AUTHOR]

Online reinforcement learning (RL) methods for autonomous underwater vehicles (AUV) are time-consuming and unsafe due to the need for real-world interaction. Offline RL methods can improve efficiency and safety by training with dynamic models, but an accurate model for AUV is difficult to obtain due to its highly nonlinear dynamics. These limit the application of RL methods in AUV control. To solve this issue, we propose physics-informed model-based conservative offline policy optimization (PICOPO). It offers the advantages of small dataset, strong generalizability and high safety by combining the physics-informed dynamic modelling method and the offline RL technique. First, the PICOPO constructs a physics-informed model based on a small offline dataset to serve as the digital twins (DT) of the actual AUV. This DT can forecast the long-term motion states of AUV with high-precision. The RL-based controller is then trained offline within this DT, eliminating the need for real-world interaction and allowing direct deployment to the AUV without fine-tuning. In this paper, simulations and field tests are carried out to evaluate the proposed method. Our results demonstrate that PICOPO achieves accurate motion control with just 2000 samples and enables zero-shot sim-to-real transfer, showcasing strong generalizability across various motion control tasks. • A physics-informed dynamic modelling method of AUV is proposed based on PINN. The physics-informed model is constructed using the small offline dataset to serve as the DT of the actual AUV. This DT can forecast the long-term motion states of AUV with high-precision. • The physics-informed model based conservative offline policy optimization method is adopted to develop a novel RL-based motion controller. This controller is trained offline in the physics-informed model of AUV, without any real-world interaction. Furthermore, it achieves zero-shot sim-to-real transfer and exhibits excellent control performance with strong generalizability. • A general motion controller architecture is introduced and brings outstanding control performance across various AUV motion control tasks. [ABSTRACT FROM AUTHOR]

• Detailed disassembly of two faulty HV cable joints reveals the internal breakdown development. • Finite element analysis assesses how explosion sites affect faulty cable joint combustion. • Investigating the weld seam direction and joint combustion products' impact on combustion. • Proposing measures to mitigate losses and risks from HV cable joint explosions. High-voltage cable joints are critical components that connect cable sections, playing a vital role in cable circuits. However, their specific location and complex installation and maintenance requirements make them vulnerable points in cable circuits. In recent years, there has been a significant increase in breakdowns and explosions in high-voltage cable joints, and it is essential to examine the diverse outcomes resulting from these explosion incidents. This paper conducts an analysis of two incidents of breakdown and explosion in 220 kV integral prefabricated cable joints that occurred on the same cable circuit. Initially, the short-circuit breakdown processes of the two faulty cable joints are deduced through the disassembly inspection. Subsequently, fluid field simulations of the breakdown locations in the faulty joints are performed using finite element simulation software to analyze the impact of breakdown in different areas on the combustion situation of the faulty joints. The results indicate that for integral prefabricated cable joints with insulating flange, there are different discharge paths inside, leading to diverse failure outcomes. The faulty joint experiencing explosion at the insulating flange exhibit intense combustion due to strong air flow.The faulty joint experiencing explosion at the copper protective shell weld, there is limited airflow, and the non-combustible byproducts generated during combustion cover the surface of the integral prefabricated component, preventing further combustion of the cable joint. Lastly, this paper proposes corresponding improvement measures to minimize losses and hazards caused by cable joint explosions, providing references and recommendations for the production and construction of high-voltage cable joints. [ABSTRACT FROM AUTHOR]

Establishing a high-fidelity twin model and accurately analyzing instant mechanical behaviors are important ways to improve structural reliability. This study proposes a digital twin - based model updating method for mechanical behaviors analysis of cable truss structure. A mechanical behaviors analysis architecture integrating structural entity in physical space, structural twin model and model dynamic updating algorithms is developed. A computational method suitable for model updating of cable truss structures is developed by integrating the finite element model with the programming language. The twin modelling approach is developed by using the test structure as a comparative target for parameter correction. Relying on the twin model, the structural mechanical behaviors analysis is carried out. The effectiveness of the analysis method is verified by taking the cable truss structure as an example. The objective function and optimization parameters required for model updating are constructed to carry out mechanical behaviors analysis and damage identification of the structure. The results show that the updated twin model can accurately match the measured data of the mechanical behaviors of the structure. For the static and dynamic response of the structure, the simulation accuracy of the twin model is above 94%. For structural damage identification, the type, location and extent of damage can be efficiently obtained by comparing the parameters before and after the update. • A digital twin-based mechanical behaviors analysis architecture integrating structural entities, twin model and model dynamic updating algorithms is developed. • For the structural twin modelling, a method of modifying the initial model from both geometric and physical levels is proposed. • The model updating algorithm and calculation program are systematically established, and the evaluation index of model fidelity is given. • The static test, cable breaking test and damage identification test are carried out to verify the effectiveness of model update method. [ABSTRACT FROM AUTHOR]

In the early stages of diabetic kidney disease (DKD), the pathogenesis involves abnormal angiogenesis in the glomerulus. Hirudin, as a natural specific inhibitor of thrombin, has been shown in previous studies to inhibit the migration of various tumor endothelial cells and abnormal angiogenesis. However, its role in DKD remains unclear. The effects of hirudin in DKD were studied using spontaneous type 2 diabetic db/db mice (which develop kidney damage at 8 weeks). Network pharmacology was utilized to identify relevant targets. An in vitro high glucose model was established using mouse glomerular endothelial cells (MGECs) to investigate the effects of hirudin on the migration and angiogenic capacity of MGECs. Hirudin can ameliorate kidney damage in db/db mice. Network pharmacology suggests its potential association with the VEGFA/VEGFR2 pathway. Western blot and immunohistochemistry demonstrated elevated protein expression levels of VEGFA, VEGFR2, AQP1, and CD31 in db/db mice, while hirudin treatment reduced their expression. In the MGECs high glucose model, hirudin may reverse the enhanced migration and angiogenic capacity of MGECs in a high glucose environment by altering the expression of VEGFA, VEGFR2, AQP1, and CD31. Moreover, the drug effect gradually increases with higher concentrations of hirudin. This study suggests that hirudin can improve early-stage diabetic kidney disease kidney damage by inhibiting the migration and angiogenesis of glomerular endothelial cells, thereby further expanding the application scope of hirudin. Additionally, the study found increased expression of AQP1 in DKD, providing a new perspective for further research on the potential pathogenesis of DKD. [Display omitted] • Early diabetic kidney disease involves abnormal angiogenesis. • Hirudin alleviated early kidney damage in db/db mice. • Network pharmacology was used to study the effective pharmacological mechanisms of hirudin in the treatment of diabetic kidney disease. • Hirudin improves diabetic kidney disease by inhibiting glomerular endothelial cell migration and angiogenesis through the VEGF/VEGFR2 pathway. [ABSTRACT FROM AUTHOR]

• Failure reason of the bolt connecting the head-cover and stay ring is analyzed. • The experimental study and finite element analysis are carried out. • Chemical composition, metallographic structure and the impact property are qualified. • Tensile strength, plastic elongation strength and Brinell hardness are unqualified. • The stress concentration exceeds the yield limit of the bolt. This paper analyzed a typical failure case of the bolt which connect the head-cover and stay ring of the pumped storage unit by means of experimental study and numerical method. The non-destructive testing, chemical composition tests, metallographic inspection and mechanical properties test are performed for three different bolts, and the finite element analysis of the bolt used in the prototype pump-turbine is carried out to discuss the stress characteristics. Combined the experimental study with finite element analysis, the failure reason of the bolt is concluded as the tensile strength R m , the plastic elongation strength and the Brinell hardness of the tested bolts do not meet the requirements of standard, and the value of the stress concentration 694 MPa exceeds the yield limit 650 MPa of the bolt. The research in this paper is helpful for analyzing the fracture mechanism of the bolt, and then ensure the safe and stable operation of the unit. [ABSTRACT FROM AUTHOR]

Objective Post-operation of cervical decompression fusion and internal fixation (CDF) accelerated adjacent segment disc degeneration (ASD). It is not clear that whether instability of one single segmental accelerates the degeneration of adjacent segment disc. This study aims to explore the effect of cervical instability on the change of morphology and biochemistry in adjective segment (above) in the L5/6 cervical instability rabbit models. Methods Thirty-two mature New Zealand white rabbits (3000 ± 250 g) were randomly divided into two groups, control group (n = 8) and model group (n = 24). The animal models were established by destruction of partly annulus fibrosus and suction of nucleus pulposus. ASD was detected by X-ray after 4, 8 or 12 weeks surgery (8 model rabbits of each time). Animals were then euthanatized for cervical intervertebral disc tissue samples separation. Histomorphology, proteoglycan and collagen II of samples were detected. Results Histomorphology data showed that notochord cells were decreased in C4/5 cervical nucleus pulposus and were replaced by fibroblast-like cells; a small amount cartilage cells were emerged; intervertebral disc anulus fibrosus becomes rough, disorganized, hyaline degeneration and pigmentation, in which contained fibrocartilage cells and cracks between the inner and outer layers. Proteoglycan content of nucleus pulposus was significantly decreased. Meanwhile, type II collagen of nucleus pulposus and annulus was also apparently reduced. Conclusion Cervical instability can alter morphology and reduce the content of proteoglycan and collagen II in adjacent intervertebral disc, thereby contributes adjacent intervertebral disc degeneration. [ABSTRACT FROM AUTHOR]

• A transmission line model is established to analyze the overvoltage amplitude. • A structure model of cable joint is built to calculate the stress distribution. • The relationship between stress and physical crosslinked network is discussed. • The physical crosslinking state is determined by means of physicochemical experiments. • The reasons and process of cable joint breakdown are summarized. The silicone rubber is subjected to either the mechanical stress or the pulse voltage, making it is easier for initiation and development of electrical tree, which is the main reason for breakdown failure of cable joint. Studying the effect of electrical tree for breakdown failure of cable joint is the valid way to ensure the safety and stability of the cable system. In this paper, a breakdown failure case of 220 kV integral prefabricated silicone rubber cable joint with large expanding rate under closing overvoltage is discussed. Through dissection, it was found that there were electric trees inside the cable joint produced by factory C. Based on the failure characteristics, the mechanical stress model of the cable joint and the closing overvoltage model of the system were established to analyze the influencing factors of electric trees. Furthermore, some sample tests were carried out to discuss the relationship between material performance and electrical tree, including carbon black test, gel content test, thermogravimetric test, tensile test and volume resistivity. The results showed that the large circumferential stress would destroy the physical crosslinking of silicone rubber, leading to a decrease in tensile strength and volume resistivity. This was the main factor affecting the growth of electrical trees. Meanwhile the overvoltage and the poor material characteristics of cable joint would accelerate the growth rate of electrical trees. Eventually the breakdown failure of cable joint occurred when the electric trees running through both polarities. The analytical method and conclusions proposed in this paper can provide suggestions and guidance for the analysis of similar breakdown accidents in the future. [ABSTRACT FROM AUTHOR]