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Abstract Adjacent vertebral fracture (AVF) is a serious complication of percutaneous vertebroplasty (PVP) or kyphoplasty (PKP) for osteoporotic vertebral compression fracture (OVCF). This study aimed to explore the incidence and risk factors of AVF following PVP or PKP in postmenopausal women. The incidence of AVF was determined by spinal radiographic examinations. The potential risk factors of AVF were identified by univariate analysis, followed by multivariate logistic regression analyses to determine the independent risk factors. In total, 674 postmenopausal women who were treated with PVP or PKP from December 2019 to February 2022 were enrolled in the study. Among them, 58 (8.61%) women experienced an AVF following PVP or PKP. After adjusting for confounding factors, BMI (OR [95% CI] 0.863 [0.781–0.952]; p = 0.003), previous history of OVCF (OR [95% CI] 1.931 [1.044–3.571]; p = 0.036), and Hounsfield unit (HU) value (OR [95% CI] 0.979 [0.967–0.990]; p

Abstract The Dongba manuscripts are a unique primitive pictographic writing system that originated among the Naxi people of Lijiang, China, boasting over a thousand years of history. The uniqueness of the Dongba manuscripts stems from their pronounced pictorial and ideographic characteristics. However, the digital preservation and inheritance of Dongba manu manuscripts face multiple challenges, including extracting its rich semantic information, recognizing individual characters, retrieving Dongba manuscripts, and automatically interpreting the meanings of Dongba manuscripts. Developing efficient Dongba character detection technology has become a key research focus, wherein establishing a standardized Dongba detection dataset is crucial for training and evaluating techniques. In this study, we have created a comprehensive Dongba manuscripts detection dataset covering various commonly used Dongba characters and vocabularies. Additionally, we propose a model named STEF. Firstly, the Swin Transformer extracts the complex structures and diverse shapes of Dongba manuscripts’ features. Then, by introducing a Feature Pyramid Enhancement Module, features of different sizes are cascaded to preserve multi-scale information. Subsequently, all features are fused in a FUSION module, resulting in features of various Dongba manuscript styles. Each pixel’s binarisation threshold is dynamically adjusted through a differentiable binarisation operation, accurately distinguishing between foreground Dongba manuscripts and background. Lastly, deformable convolution is introduced, allowing the model to dynamically adjust the convolution kernel’s size and shape based on the Dongba manuscripts’ size, thereby better capturing the detailed information of Dongba characters of different sizes. Experimental results show that STEF achieves a recall rate of 88.88%, a precision rate of 88.65%, and an F-measure of 88.76%, outperforming other text detection algorithms. Visualization experiments demonstrate that STEF performs well in detecting Dongba manuscripts of various sizes, shapes, and styles, especially in blurred handwriting and complex backgrounds.

In the urban environment, the transient electromagnetic method often suffers from severe electromagnetic interference because it employs coils to perceive the electromagnetic field. To address this issue, the DC method employs electrodes to shield the electromagnetic noise in the urban environment. In this paper, a new geophysical resistivity imaging sensing approach based on a DC inversion model is proposed to explore and identify the path of underground water to solve the water disaster problem. A real-world case study is carried out in Xinjiang Region. In order to investigate the underground water conducted path, an electrode network is designed and installed for the buildings of interest to collect the DC electrical current and DC electrical potential data. Then, the proposed method establishes a DC resistivity inversion model based on the forward theory and finite-volume technique to extract the geological information from the collected data and visualize the underground water conducted path. Different from existing popular numerical techniques, such as the finite element and finite difference, the finite-volume technique has clear theoretical explanations to make the obtained geological images understandable. As a result, the underground water path can be directly observed from the geological images calculated by the proposed approach to successfully identify and locate the underground water disaster in real-world applications.

Abstract It is an increasingly mature application solution that triboelectric nanogenerator (TENG) supplies power to electronic devices through its power management system (PMS). However, the previous PMS is able to manage a limited voltage magnitude and the energy storage elements are limited to capacitors. This work proposes an ultrahigh voltage PMS (UV‐PMS) to realize the charging of commercial lithium cells (LCs) by TENG. The design of UV‐PMS enables energy management of TENGs with ultrahigh open‐circuit voltages up to 3500 V and boosts the peak charging current from 30.9 µA to 2.77 mA, an increase of 89.64 times. With the introduction of UV‐PMS, the effective charging capacity of LC charged by a TENG at a working frequency of 1.5 Hz for 1 h comes to 429.7 µAh, making a 75.3 times enhancement compared to charging by TENG directly. The maximum charging power comes to 1.56 mW. The energy storage efficiency is above 97% and the overall charge efficiency can be maintained at 81.2%. This work provides a reliable strategy for TENG to store energy in LC, and has promising applications in energy storage, LC's life, and self‐powered systems.

This research addresses the limitations of existing autonomous vehicle path planning algorithms, notably their slow processing speeds and suboptimal route efficiency. We introduce an innovative path planning algorithm that synergizes the A* algorithm with the Rapidly-exploring Random Tree (RRT) approach. This hybrid model significantly enhances route timeliness and reliability, particularly in obstacle avoidance scenarios for driverless vehicles. Our methodology employs a 'two-level map' approach, where a lower-resolution grid map is derived from a high-resolution map. Utilizing the A* algorithm on this framework, we ascertain a preliminary 'coarse path' for the navigation target. The RRT algorithm, modified to reduce the traditional redundancy associated with random uniform sampling, is then applied for probabilistic sampling within this defined area. ovel aspect of our approach is the simultaneous generation of two trees, originating from both the start and end points, guided by a target-biased strategy and dual-direction theory. This method probabilistically expands towards the node of the opposite tree, thereby enhancing both the generation speed and trajectory viability. Further refinements are made through a pruning process, optimizing the path, and employing Bezier curves for smoothing, ensuring compliance with the dynamic constraints of Ackerman chassis vehicles. Comparative analysis in complex environments demonstrates the superiority of our proposed algorithm. It outperforms traditional methods with a 400 % increase in planning speed relative to the RRT-Connect algorithm, and a 30 % reduction in average path length. Additionally, the mean curvature of routes generated by our algorithm is 19 % lower than traditional routes, underscoring significant advancements in both the timeliness and viability of the planned routes.

To enhance the utilization of clean energy sources, such as wind power and photovoltaic within virtual power plants, and mitigate carbon emissions, this paper proposes a virtual power plant participation in the electricity carbon joint market optimization operation model based on ensemble empirical mode decomposition–improved bat algorithm (IBA)–Markov chain new energy output prediction. First, complementary set empirical mode decomposition is performed on historical data to construct a Markov chain based wind power and photovoltaic prediction model optimized by IBA. Second, this prediction model is used to predict the daily generation power of wind power and photovoltaic power. Finally, with the optimization goals of maximizing the benefits and minimizing the carbon costs of virtual power plants, a virtual power plant system participating in the electricity carbon joint market model based on wind power and photovoltaic output prediction results is constructed. At the same time, demand response factors are introduced and solved using the NSGA-II algorithm. Taking a certain park as an example for simulation analysis, the research results show that the combined effect of carbon market and demand response can achieve 99.82% of new energy consumption in virtual power plants without significantly reducing profits, basically achieving complete new energy consumption, demonstrating the effectiveness of the proposed model in this paper.

With the development of precision sensing instruments and data storage devices, the fusion of multi-sensor data in gearbox fault diagnosis has attracted much attention. However, existing methods have difficulty in capturing the local temporal dependencies of multi-sensor monitoring information, and the inescapable noise severely decreases the accuracy of multi-sensor information fusion diagnosis. To address these issues, this paper proposes a fault diagnosis method based on dynamic graph convolutional neural networks and hard threshold denoising. Firstly, considering that the relationships between monitoring data from different sensors change over time, a dynamic graph structure is adopted to model the temporal dependencies of multi-sensor data, and, further, a graph convolutional neural network is constructed to achieve the interaction and feature extraction of temporal information from multi-sensor data. Secondly, to avoid the influence of noise in practical engineering, a hard threshold denoising strategy is designed, and a learnable hard threshold denoising layer is embedded into the graph neural network. Experimental fault datasets from two typical gearbox fault test benches under environmental noise are used to verify the effectiveness of the proposed method in gearbox fault diagnosis. The experimental results show that the proposed DDGCN method achieves an average diagnostic accuracy of up to 99.7% under different levels of environmental noise, demonstrating good noise resistance.

The automatic segmentation of cardiac computed tomography (CT) and magnetic resonance imaging (MRI) plays a pivotal role in the prevention and treatment of cardiovascular diseases. In this study, we propose an efficient network based on the multi-scale, multi-head self-attention (MSMHSA) mechanism. The incorporation of this mechanism enables us to achieve larger receptive fields, facilitating the accurate segmentation of whole heart structures in both CT and MRI images. Within this network, features extracted from the shallow feature extraction network undergo a MHSA mechanism that closely aligns with human vision, resulting in the extraction of contextual semantic information more comprehensively and accurately. To improve the precision of cardiac substructure segmentation across varying sizes, our proposed method introduces three MHSA networks at distinct scales. This approach allows for fine-tuning the accuracy of micro-object segmentation by adapting the size of the segmented images. The efficacy of our method is rigorously validated on the Multi-Modality Whole Heart Segmentation (MM-WHS) Challenge 2017 dataset, demonstrating competitive results and the accurate segmentation of seven cardiac substructures in both cardiac CT and MRI images. Through comparative experiments with advanced transformer-based models, our study provides compelling evidence that despite the remarkable achievements of transformer-based models, the fusion of CNN models and self-attention remains a simple yet highly effective approach for dual-modality whole heart segmentation.

Summary: The role of BACH1 in the process of vascular smooth muscle cell (VSMC) differentiation from human embryonic stem cells (hESCs) remains unknown. Here, we find that the loss of BACH1 in hESCs attenuates the expression of VSMC marker genes, whereas overexpression of BACH1 after mesoderm induction increases the expression of VSMC markers during in vitro hESC-VSMC differentiation. Mechanistically, BACH1 binds directly to coactivator-associated arginine methyltransferase 1 (CARM1) during in vitro hESC-VSMC differentiation, and this interaction is mediated by the BACH1 bZIP domain. BACH1 recruits CARM1 to VSMC marker gene promoters and promotes VSMC marker expression by increasing H3R17me2 modification, thus facilitating in vitro VSMC differentiation from hESCs after the mesoderm induction. The increased expression of VSMC marker genes by BACH1 overexpression is partially abolished by inhibition of CARM1 or the H3R17me2 inhibitor TBBD in hESC-derived cells. These findings highlight the critical role of BACH1 in hESC differentiation into VSMCs by CARM1-mediated methylation of H3R17.

Field emission is an important work mode for electron sources, and carbon nanotubes (CNTs) have been extensively studied for their good emission properties. It is well known that the parameters of the grid deeply influence the field emission performance of CNTs, a relationship that requires further elucidation. Therefore, in this study, the relationship between the grid aperture ratio and electron transmittance was studied through simulations and experiments. This study’s results indicate that the electron transmittance improved as the grid aperture ratio increased. Meanwhile, electron beam spot simulations and imaging experiments indicate that an increased grid aperture ratio will expand the cathode electron divergence, leading to a larger electron beam spot size. These results demonstrate that there is a trade-off in maintaining the grid aperture ratio between high electron transmittance and relatively small electron beam spot size, and the optimum grid aperture ratio is between 75% and 85%. These results will provide a reference for the design and optimization of X-ray tubes and other electron sources.

Objective To investigate the effect of transforming growth factor β1 (TGF-β1) on the proliferation and migration ability of bladder cancer T24 cells and its mechanism. Methods T24 cells were treated with TGF-β1 at concentrations of 0, 1, 5, 10, 20 and 40 μg/L for 24 h. The proliferation activity of T24 cells was determined by methyl thiazolyl tetrazolium assay; their migration ability was detected by cell scratching method; the relative expression of programmed death ligand-1 (PD-L1) protein in T24 cells was detected by Western blot. T24 cells were treated with TGF-β1 at a concentration of 5 μg/L for 0, 0.5, 1, 2, 6 and 24 h. The relative expression of PD-L1 protein as well as phosphorylated serine-threonine protein kinase (p-AKT) and phosphorylated extracellular regulated kinase (p-ERK) proteins in T24 cells were detected by Western blot. T24 cells were divided into groups A to G. Group A was cultured with normal medium; group B was cultured with the medium containing TGF-β1 at a final concentration of 5 μg/L; group C was cultured with the medium containing TGF-β1 and SB431542 at a final concentration of 5 μg/L. The relative expression of PD-L1 in groups A to C was measured by Western blot. Group D was cultured with normal medium; group E was cultured with the medium containing TGF-β1 at a final concentration of 5 μg/L; group F was cultured with the medium containing TGF-β1 and AKT inhibitor MK2206 at a final concentration of 5 μg/L; group G was cultured with the medium containing TGF-β1 and ERK inhibitor U0126 at a final concentration of 5 μg/L. All T24 cells were treated for 24 h. The relative expression of p-AKT, p-ERK, and PD-L1 proteins in groups D to G was detected by Western blot. Results After T24 cells were treated with TGF-β1 at concentrations of 0, 1, 5, 10, 20 and 40 μg/L for 24 h, the proliferation ability of T24 cells was significantly enhanced compared with that of T24 cells treated with TGF-β1 at 0 μg/L (t=3.59-12.18,P0.05); the PD-L1 protein expression was significantly higher in T24 cells treated with TGF-β1 at 5 μg/L than at other concentrations (t=3.22-5.64,P

The cart–inverted pendulum system (CIPS) is a typical example of underactuated mechanical systems. For the CIPS with friction and disturbances, a gain-scheduled model predictive control method is proposed to achieve the upright stabilization objective of the single inverted pendulum (SIP) while controlling the cart to reach a desired new position. To this end, first, a dynamic equation of the CIPS with friction and disturbances is formulated based on the Newton–Euler equation. On the basis of the dynamic equation of the CIPS, its motion characteristics and control process are analyzed. Next, the given dynamic equation of the CIPS is linearized to obtain a series of linearized models at seven different pendulum angles. Then, seven model predictive controllers (MPCs) are designed based on the above-linearized models, respectively. Introducing the idea of the gain-schedule, a gain-scheduled MPC (GSMPC) is designed to switch one of these seven MPCs to realize the control objective of the CIPS, according to the actual pendulum angle of the SIP during the control process. Finally, multi-group simulations that consider the friction and disturbances of the CIPS are implemented to demonstrate the effectiveness of the proposed gain-scheduled model predictive control method.

Power market evaluation can reflect the problems in market construction and operation, which is of great significance to the healthy development of power market and the improvement of market rules. Nowadays, several countries are still in the progress of power market liberalization, and an effective power market evaluation system has not been established. In this paper, a hierarchical evaluation system of power market is established according to the construction situation of the developing power markets, which includes market competition level, market operation level, market development level, market opening level and market efficiency level. Then, the Delphi method and the entropy method are used to comprehensively weight the index based on the multiple attribute decision-making theory, and radar charts are used to evaluate the power market. By using the proposed evaluation index and method, the operation condition of a provincial power market in China is analyzed.

Airborne geophysical surveys serve as an effective tool for litho-structural mapping, providing extensive and high-resolution underground information. They offer vital data support for the interpretation and analysis of lithologies and structures, complementing surface geological mapping. In the study area of the Nanpanjiang-Youjiang metallogenic belt in southeast China, we obtained high-resolution aeromagnetic and gamma-ray spectrometry data covering an area of approximately 27,000 km2, which facilitated the conduct of litho-structural mapping. The total magnetic intensity, reduction to the pole, and directional derivative maps generated from the aeromagnetic data, efficiently identified concealed rocks and faults. Additionally, the total count, potassium, thorium to potassium ratio, and ternary maps generated from the airborne gamma-ray spectrometry data demonstrated advantages in distinguishing carbonate rocks from clastic rocks. They also provided more comprehensive geological information, refining the structural strike and location interpreted by the aeromagnetic data. The litho-structural map produced in this study significantly contributes to our understanding of the structures in the Nanpanjiang area and offers valuable guidance for successful mineral exploration endeavors.

Downward continuation (DC) of the gravity potential field is an important approach used to understand and interpret the density structure and boundary of anomalous bodies. It is widely used to delineate and highlight local and shallow anomalous sources. However, it is well known that direct DC transformation in the frequency domain is unstable and easily affected by high-frequency noise. Recent deep learning applications have led to the development of image recognition and resolution enhancement using the convolutional neural network technique. A similar deep learning architecture is also suitable for training a model for the DC problem. In this study, to solve the problems in existing DC methods, we constructed a dedicated model called DC-Net for the DC problem. We fully trained the DC-Net model on 38,400 pairs of gravity anomaly data at different altitudes using a convolutional neural network. We conducted several experiments and implemented a real-world example. The results demonstrate the following. First, several validation data subset and test data prediction results indicate that the DC-Net model was sufficiently trained. Moreover, it performed better than the traditional strategy in refining the upscaling of low-resolution images. Second, we performed tests on test datasets with changing levels of noise and demonstrated that the DC-Net model is noise-resistant and robust. Finally, we used the proposed model in a real-world example, which demonstrates that the DC-Net model is suitable for solving the DC problem and delineating the detailed gravity anomaly feature near the field source. For real data processing, noise in the gravity anomaly should be reduced in advance. Additionally, we recommend noise quantification of the gravity anomaly before network training.

Residual Bouguer gravity anomaly inversion can be used to imaging for local density structures or to interpret near-surface anomalous mass distribution. The reasonable prior information is the crucial recipe for obtaining a realistic geological inversion result, especially for the ill-posed geophysical inversion problem. The conventional strategies introduce the prior constraints or joint multidisciplinary information in object function as regularization, and then use some optimization algorithm to minimize the object function. This process is called model-driven approach and is usually time-consuming. In recent years, the rapid development of machine learning technology has provided new solutions for solving geophysical inversion problems. Machine learning methods can reduce the dependence on prior information in the inversion process through setting special training datasets, and the time consumption of an inversion process executed by the trained model can be shortened by several orders of magnitude, which is conducive to fast inversion for the same type of application scenarios. In this study, we were inspired by the U-net model and develops the GV-Net (Gravity voxels inversion network) model using the convolutional neural network for the inversion of residual gravity anomalies. We first discussed the effects of different loss functions on the convergence speed of model training and prediction accuracy. Then, we analyzed the robustness of our model by changing noise levels of the datasets. At last, we employed this model in a real scenario. The results have demonstrated that the GV-Net model has the ability to deal with specific inverse problems by predefined training datasets.

In the present work, microalloying is put forward to improve the microstructure and tensile properties of A356.2-0.15Fe (wt.%) alloy by the co-addition of trace Mn and Mg. A suitable Mn/Fe mass ratio of 0.5 is obtained for alloys with 0.15Fe. The yield strength, ultimate tensile strength, and elongation of the A356.2-0.15Fe alloy with an Mn/Fe ratio of 0.5 and containing 0.42 wt.% Mg is 179 MPa, 286 MPa, and 9.1%, respectively, which is acceptable for automotive wheel hub applications. Optical microscopy (OM), scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and electron-probe microanalyzer (EPMA) methods are used to characterize the microstructure of the alloys. The results indicate that Mn addition promotes the transformation of the acicular β-Al5FeSi phase to the block-shaped α-Al(Fe, Mn)Si phase. The ratio of length/width of the Fe-rich phase in the alloy is reduced by 78.8% with an Mn/Fe ratio of 0.5 and containing 0.35 wt.% Mg, compared with that of the alloy without Mn addition and containing 0.35 wt.% Mg. The addition of Mg reduces the secondary dendrite arm spacing (SDAS) from 26.1 μm to 20.9 μm. The volume fraction of the precipitated Mg2Si phase in the alloy containing 0.42 wt.% Mg increases by 60% compared with that in the alloy containing 0.35 wt.% Mg. The morphology transformation of the Fe-rich phase, the reduction of SDAS, and the increase in volume fraction of precipitated Mg2Si phase comprehensively contribute to the improvement of A356.2-0.15Fe alloy. The microstructure evolution mechanism and the effect of microstructure on tensile properties are analyzed and discussed.

In recent years, the development of airborne magnetic survey technology based on unmanned aerial vehicles (UAVs) has been rapidly advancing. The commonly used systems are the fixed-wing UAV-based, multi-rotors UAV-based, and unmanned helicopters-based magnetic survey systems. And, a type of hybrid UAV that uses a vertical takeoff and landing (VTOL) and fixed-wing cruise mode is increasingly being used to carry airborne magnetic survey systems. To meet the requirements of most UAVs for small-sized and lightweight payloads, a miniature magnetic survey system was developed and integrated into a hybrid fixed-wing UAV and formed an aeromagnetic survey system. And, a peripheral mineral exploration test was conducted in a known porphyry copper–gold deposit in southeastern China using the system. By processing the collected magnetic data with 3D inversion of susceptibilities, potential ore-bearing intrusive rocks were quickly identified and delineated, providing clues for peripheral mineral exploration in the mining area.

Determining mineral prospecting targets is crucial for mineral prediction and evaluation. In this study, an evaluation index system for solid mineral exploration and metallogenic target assessment was established using the Analytic Hierarchy Process (AHP) for the Naoniushan area (China). Furthermore, an integrated model combining geology–aerogeophysics–geochemistry was developed for copper, lead, zinc, silver, and other polymetallic deposits. The information content of each index in the model was reasonably assigned, and the mineral prospecting targets in the central and southern parts of the Daxinganling were recommended. By focusing on the copper polymetallic mineral prospecting target in the Naoniushan area, this paper demonstrates that the AHP method can comprehensively consider various influencing factors and their interactions, realize a reasonable division of the optimal mineral prospecting target, and reflect the key factors affecting the mineral prospecting target to a certain extent. Importantly, this approach reduces the influence of human subjective factors, and the optimization results are objective and scientifically grounded.

BackgroundCalcified lumbar disc herniation (CLDH) is considered to be a special type of lumbar disc herniation (LDH). Percutaneous endoscopic interlaminar discectomy (PEID), with safety and efficacy, has been proved to be a minimally invasive surgery for LDH. However, there are few studies on PEID in the treatment of CLDH at the L5-S1 level. This study aimed to analyze the clinical efficacy of PEID for L5-S1 CLDH.MethodsFrom August 2016 to April 2020, we retrospectively analyzed 28 consecutive patients (17 males and 11 females) with L5-S1 CLDH treated with PEID at our institution. All the patients were monitored for more than 1 year postoperatively. The demographic characteristics, surgical results, and clinical outcomes estimated by the visual analog scale (VAS) for leg pain, the Oswestry disability index (ODI), and the modified MacNab criteria were collected.ResultsAll patients successfully underwent PEID. The mean operative time and intraoperative blood loss were 65.36 ± 5.26 min and 13.21 ± 4.35 ml, respectively. The VAS for leg pain and ODI scores improved remarkably from 7.54 ± 0.96 to 1.50 ± 0.51 (P

At present, bone-based products are abundant, and the main sources are bovine bone and pig bone, but there are few studies on the development of deer bone as a bone repair material. Deer bone has important osteogenic effects in the theory of traditional Chinese medicine. It is rich in protein, ossein, and a variety of trace elements, with the effect of strengthening tendons and bones. Nanomaterials and their application in the repair of bone defects have become a research hotspot in bone tissue engineering. In this study, nano-deer bone meal (nBM), nano-calcined deer bone meal, and nano-demineralized bone matrix were successfully prepared. It was found that the Ca/P ratio in deer bone was significantly higher than that in cow bone and human bone tissue, and deer bone contained beneficial trace elements, such as potassium, iron, selenium, and zinc, which were not found in cow bone. The three kinds of deer bone powders prepared in this study had good biocompatibility and met the implantation standards of medical biomaterials. Cell function studies showed that compared with other bone powders, due to the presence of organic active ingredients and inorganic calcium and phosphate salts, nBM had excellent performance in the proliferation, adhesion, migration, and differentiation of bone marrow mesenchymal stem cells. These findings indicate that nBM can be used as a potential osteoinductive active nanomaterial to enhance bone tissue engineering scaffolds with certain application prospects.

In the economically developed Beijing–Tianjin–Hebei region, magnetotelluric data are susceptible to contamination from cultural noise, which can be caused, for example, by urban stray currents, high-speed railways, or high-voltage lines. The multiple remote references method is an effective tool that can be used to suppress interference and improve signal-to-noise ratios. Therefore, this paper first introduces the basic principles of multiple remote references and then takes high-speed railway noise as an example. The characteristics of the time domain and frequency domain of the high-speed railway noise signals are analyzed. Then, we use two remote reference stations (with a single remote reference and multiple remote references) to process the data interfering with the high-speed railway and compare the results. Finally, the multiple remote references method is used to process the data for the entire section. Coupled with the known geological and seismic data, the inversion results well-reflect the deep underground geological structure.

The orderly release of electricity plan is an important measure for China’s electricity market reform which also lead to excessive pressure for coal power units caused by the low planned electricity quantity. Allowing high-priced units to participate in the market can effectively alleviate the market pressure. However, the participation of high-priced units in electricity market will have an impact on the declaration behavior of other generators. In this context, an evolutionary game model of bidding for two groups of generators considering the market liberalizing degree (MLD) is proposed. Above all, the profit model of two kinds of generators is proposed under two bidding strategies considering the MLD. On this basis, an evolutionary game model of generators’ bidding is built to qualitatively analyze the market equilibrium solution which is affected by the MLD coefficient. Thus, the expected evolutionary stable equilibrium of bidding can be formed spontaneously in electricity market by adjusting the MLD coefficient. The case studies are performed for demonstrating the proposed evolutionary game model of generators’ bidding, and simulation results show that different MLD and the initial state of bidding behavior of generators will influence the final evolutionary stable equilibrium of bidding market, and it also shown that the government can make the electricity market develop towards​ the expected state by adjusting MLD and supervising the bidding behavior of generators.

A novel rotating machine fault diagnosis method based on multi-scale transfer fuzzy entropy (MTFE) and support vector machine (SVM) is proposed in this paper. Compared with traditional machine learning methods, our proposed method can identify various fault types of rotating machinery with different data distribution by learning the transfer knowledge from different distribution source domains. First, multi-scale fuzzy entropy (MFE) features of all samples are extracted as input. Second, build a transfer learning model to find a projection matrix to map the MFE features of the source and target domains into a common subspace, called MTFE features. In this process, the distribution structure of training data is maintained and the distribution difference between training data and test data is reduced. Finally, the SVM classifier identifies the fault type of the test data. Two cases including gearbox and rolling bearing are used for validation. Experimental results demonstrate that our proposed MTFE method performs best in recognizing various fault types comparing with other six methods.