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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.

To explore the deep geological structure of the Dongyang area in Fujian, China, gravity data from the area and its surroundings were collected and processed. Additionally, a high-precision magnetic survey was conducted in the Zhongxian region of this area, with subsequent analysis of the magnetic anomalies. Through the integration of regional geological data, a comprehensive analysis was carried out on the characteristics of gravity–magnetic anomalies and deep geological structures in the Dongyang area. The study indicates that the primary portion of the Dongyang area lies southwest of the expansive circular volcanic structure spanning Dehua to Yongtai. Two significant residual gravity anomalies were identified within the region, interpreted as the Xiaoban-Shuangqishan and Dongyang-Lingtouping residual gravity-positive anomalies. In the Zhongxian region, the magnetic field exhibits complexity with notable amplitude variations. Positive anomalies predominate in the western and northern sectors, while localized positive anomalies are prominent in the eastern region. The central area portrays a circular and disordered mix of positive and negative anomalies. Particularly distinctive are the band-shaped and fan-shaped negative anomalies curving from northeast to southeast through the central region. Various positive and negative anomalies of varying strengths, gradients, and orientations overlay both positive and negative magnetic backgrounds in specific locales. Moreover, the Dongyang area showcases well-developed fault structures, primarily oriented in northeast and northwest directions. Leveraging the regional magnetic attributes in conjunction with regional geological data, 39 faults were deduced in the Zhongxian region of the Dongyang area, delineating three promising mineralization zones.

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.

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

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.

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.

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.

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.

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.

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.

Type synthesis of mechanical structure is of great significance to the realization of mechanism target function, systematization, and stability of mechanical device. The type synthesis method of multilinkage robot has been given high demands with increasing number of degrees of freedom and high flexibility in special occasions. In order to improve the workspace and flexibility of mechanism, this paper studies the existing type synthesis theory and proposes a type synthesis method of modular combination with virtual rotation centers. Firstly, modular units are built. Secondly, modular units are expanded according to the needed paths. In the end, the expanded modular units are combined to form the kinematic linkages. Based on the proposed method, the configuration design of the aerial working platform and the self-adaptive levelling platform is completed. The stabilities of two platforms are checked by modal analysis. The prototype products are manufactured, respectively, for further verifying validity of the method. The designed aerial working platform with virtual rotation centers can achieve 360° rotating large workspace, more compact mechanical structure, and short arrival time at the same height than the common scissor-type and mast-type aerial working platforms. The designed adaptive levelling platform is tested that ensures the levelling of the upper surface at different inclinations. The method can provide new idea for the mechanism configuration and expand the application scope of new mechanisms.

The trend of low breastfeeding rates increases the demand for infant milk formula (IMF) worldwide, but the use of IMF may be one of the causes of bacterial infections in infants. Complete sterility in the whole production line of IMF cannot be guaranteed; therefore, it is necessary to closely monitor the microbial content in the process. In the present study, an IMF powder production line based on the wet mixing process was sampled at 27 suspicious points in spring and summer to analyze the bacterial diversity by high-throughput sequencing. We found that 70 and 69 different bacterial phyla were present in spring and summer samples, respectively, with Proteobacteria and Firmicutes being the dominant phyla (>80% relative abundance). Moreover, 13 dominant genera each were present in spring (e. g., Pseudomonas and Lactococcus) and summer (e. g., Pseudomonas, Bacillus, and Streptococcus). Samples associated with workers showed higher bacterial species diversity (Shannon index) and richness (Chao1 index) in summer than in spring. The bacterial community composition showed high similarity between liquid milk after pasteurization and concentrated milk after evaporation. The potential bacterial pathogens were identified as Pseudomonas aeruginosa in spring and Acinetobacter baumannii in summer. Through retrospective analysis of the two opportunistic pathogens identified, it was found that the workshop environment was the potential contamination point in spring, whereas the auxiliary ingredients were the potential source of contamination in summer. The results highlight the effect of season on bacterial diversity associated with the production process of IMF and are useful in controlling the microbial quality and safety of infant dairy products.

Abstract Background Even with the augmentative application of anal-preservation surgery in low rectal cancer, the role and indications of laparoscopic intersphincteric resection (Lap ISR) are still under debate, especially for T3 or node-positive (T3N0M0, T1–3N+M0) cancer, mainly due to the oncological safety and functional outcomes. INTRABEAM (Carl Zeiss, Germany) intraoperative radiotherapy (IORT) using low-energy X-rays features in accurate irradiation, less exposure, and reduced complications. Taking advantages of Lap ISR and INTRABEAM IORT, this innovative approach aims to increase the probability of the anal preservation with acceptable postoperative outcomes. Materials and methods From December 2015 to August 2019, we retrospectively analyzed the short-term outcomes of 12 patients evaluated preoperatively with T3 or node-positive (T3N0M0, T1–3N+M0) primary locally advanced low rectal cancer. They all had received Lap ISR and INTRABEAM IORT with a dose of 16–18 Gy applied by an applicator through the anus (natural orifice). Then, with no pre- or postoperative radiotherapy given, the patients were suggested to receive 6–8 cycles of the XELOX chemotherapy regimen (oxaliplatin, 130 mg/m2 and capecitabine, 1000 mg/m2). Results All patients achieved R0 resection. The median radiation time was 27 min and 15 s, and the mean radiative dose was 17.3 Gy (range 16–18 Gy). The median follow-up time was 18.5 months (range 3–45 months). Two patients experienced local recurrence. Two male patients experienced anastomotic stenosis. Furthermore, one of them experienced perianal abscess and the other one experienced pulmonary metastasis after refusing to receive chemotherapy. One female patient with internal anal sphincter invasion experienced distant metastases to the liver and gluteus maximus muscle 35 months after IORT. No acute radiation injuries or symptoms were observed. Although they experienced a reduction in anal function, every patient was satisfied with the postoperative outcomes. Conclusions For patients evaluated preoperatively with T3 or node-positive (T3N0M0, T1–3N+M0) primary locally advanced low rectal cancer, Lap ISR with INTRABEAM IORT may be a safe and feasible approach for anal preservation without compromising oncological outcomes.

Fault diagnosis and health condition monitoring have always been critical issues in the engineering research community [...]

Advanced sensing, fault diagnosis, and structural health management are important parts of the maintenance strategy of modern industries [...]

When considering the transition probability matrix of ordinal patterns, transition permutation entropy (TPE) can effectively extract fault features by quantifying the irregularity and complexity of signals. However, TPE can only characterize the complexity of the vibration signals at a single scale. Therefore, a multiscale transition permutation entropy (MTPE) technique has been proposed. However, the original multiscale method still has some inherent defects in the coarse-grained process, such as considerably shortening the length of time series at large scale, which leads to a low entropy evaluation accuracy. In order to solve these problems, a composite multiscale transition permutation entropy (CMTPE) method was proposed in order to improve the incomplete coarse-grained analysis of MTPE by avoiding the loss of some key information in the original fault signals, and to improve the performance of feature extraction, robustness to noise, and accuracy of entropy estimation. A fault diagnosis strategy based on CMTPE and an extreme learning machine (ELM) was proposed. Both simulation and experimental signals verified the advantages of the proposed CMTPE method. The results show that, compared with other comparison strategies, this strategy has better robustness, and can carry out feature recognition and bearing fault diagnosis more accurately and with improved stability.

More and more studies have shown that the intestinal microbiota is the main factor in the pathogenesis of type 1 diabetes mellitus (T1DM). Beta cell expansion factor A (BefA) is a protein expressed by intestinal microorganisms. It has been proven to promote the proliferation of β-cells and has broad application prospects. However, as an intestinal protein, there have not been studies and reports on its application in diabetes and its mechanism of action. In this study, a T1DM model induced by multiple low-dose STZ (MLD-STZ) injections was established, and BefA protein was administered to explore its therapeutic effect in T1DM and the potential mechanism of intestinal microbiota. BefA protein significantly reduced the blood glucose, maintained the body weight, and improved the glucose tolerance of the mice. At the same time, the BefA protein significantly increased the expression of ZO-1, Occludin, and significantly reduced the expression of TLR-4, Myd88, and p-p65/p65. BefA protein significantly reduced the relative expression of pro-inflammatory cytokines IL-1β, IL-6 and TNF-α. In addition, our high-throughput sequencing shows for the first time that the good hypoglycemic effect of BefA protein is strongly related to the increase in the abundance of the beneficial gut bacteria Lactobacillus, Bifidobacterium and Oscillospria and the decrease in the abundance of the opportunistic pathogen Acinetobacter. Our group used animal models to verify the hypoglycemic effect of BefA protein, and first explored the potential mechanism of intestinal microbiota in BefA protein treatment.

Increasing and improving the critical transition temperature (TC), current density (JC) and the Meissner effect (HC) of conventional superconductors are the most important problems in superconductivity research, but progress has been slow for many years. In this study, by introducing the p-n junction nanostructured electroluminescent inhomogeneous phase with a red wavelength to realize energy injection, we found the improved property of smart meta-superconductors MgB2, the critical transition temperature TC increases by 0.8 K, the current density JC increases by 37%, and the diamagnetism of the Meissner effect HC also significantly improved, compared with pure MgB2. Compared with the previous yttrium oxide inhomogeneous phase, the p-n junction has a higher luminescence intensity, a longer stable life and simpler external field requirements. The coupling between superconducting electrons and surface plasmon polaritons may be explained by this phenomenon. The realization of smart meta-superconductor by the electroluminescent inhomogeneous phase provides a new way to improve the performance of superconductors.

TNNT2 mutation is associated with a range of cardiac diseases, including dilated cardiomyopathy (DCM). However, the mechanisms underlying the development of DCM and heart failure remain incompletely understood. In the present study, we found the expression of cardiac XIN protein was reduced in TNNT2-ΔK210 hESCs-derived cardiomyocytes and mouse heart tissues. We further investigated whether XIN protects against TNNT2 mutation-induced DCM. Overexpression of the repeat-containing isoform XINB decreased the percentage of myofilaments disorganization and increased cell contractility of TNNT2-ΔK210 cardiomyocytes. Moreover, overexpression of XINB by heart-specific delivery via AAV9 ameliorates DCM remodeling caused by TNNT2-ΔK210 mutation in mice, revealed by partially reversed cardiac dilation, systolic dysfunction and heart fibrosis. These results suggest that deficiency of XIN may play a critical role in the development of DCM. Consequently, our findings may provide a new mechanistic insight and represent a therapeutic target for the treatment of idiopathic DCM.

In China, under the planning-market double-track mechanism implemented on the generation side of electricity, unreasonable market-oriented power generation proportion may lead to unnecessary vicious competition and market price changes, and it is against the will of power exchange (PX). Given this background, in this study, a bi-level model for planning-market electricity allocation that considers the bidding game of generation companies is proposed for a smooth transition of power system reform. In the upper level of the model, the proportion of planned electricity is optimized by PX to minimize the average social electricity purchase price. In the lower level of the model, considering the impact of market power on the bidding strategy of generation companies, the bidding strategy of generation companies set as price makers is proposed using the residual demand curve analysis method, while the price takers adopt the lowest bidding strategy. Simulations based on data from a provincial electricity market in China illustrate that the proposed model can effectively reflect the impact of market-oriented electricity proportion on market power and market-clearing price, thus providing a quantitative basis for PX to determine the proportion of market-oriented electricity in total electricity consumption.

Superionic conductors, which exhibit liquid-like phonon transport but crystal-like carrier transport, have attracted great attention and broad research interest in the thermoelectric community. Ag2Te is a superionic conductor; however, its small band gap and large Ag vacancy formation energy impede its application as a prominent p-type thermoelectric material. In this work, synergistic optimization of the thermoelectric performance of Ag2Te through Cu substitution is realized through a combination of experimental and theoretical efforts. For the electrical transport, Cu substitution systematically increases the band gap of Ag2Te and reduces the cation vacancy formation energy. These two beneficial effects simultaneously increase the electrical conductivity and suppress the bipolar effect, thereby greatly enhancing the p-type electrical transport properties of Ag2Te. For the thermal transport, alloying Cu2Te with Ag2Te significantly reduces the thermal conductivity through not only point defect scattering but also softening of the interatomic interactions. The latter is attributed to the relatively small Cu atoms vibrating in the oversized 8c sites. This two-fold optimization results in maximum thermoelectric figure of merit zT values of over 1.3 at 773 K for both Ag1.2Cu0.8Te and AgCuTe, demonstrating the great potential of Ag2−xCuxTe as a promising p-type thermoelectric material system. Keywords: Ag2Te, Band gap, Carrier concentration, Thermoelectric material

A novel hybrid fault diagnosis method based on ensemble empirical mode decomposition and weighted adaptive multi-scale morphological analysis (WAMMA) is proposed to detect the early damage of gearboxes. In this method, we propose a characteristic frequency ratio (CFR) method to determine the weighted coefficient for each scale of AMMA. First, multiple scales are obtained using the AMMA method. Second, the weighted coefficient of each scale in the AMMA method is calculated using the CFR. Third, the final results can be obtained by multiplying the weighted coefficients and filtering results with all scales. Since the performance of each scale of AMMA is evaluated using the CFR, the demodulation ability can be effectively improved. However, the WAMMA is easily disturbed by heavy noise when extracting early fault feature directly. A method combined EEMD with the WAMMA is proposed. The effectiveness of the proposed method has been verified using two experimental vibration signals of gearboxes. The results demonstrate that the proposed method has a superior performance in the extraction of weak fault characteristics of gearboxes in comparison with the WAMMA and EEMD-AMMA methods.

Data-driven fault diagnosis has been a hot topic in recent years with the development of machine learning techniques. However, the prerequisite that the training data and the test data should follow an identical distribution prevents the conventional data-driven diagnosis methods from being applied to the engineering diagnosis problems. To tackle this dilemma, cross-domain fault diagnosis using knowledge transfer strategy is becoming popular in the past five years. The diagnosis methods based on transfer learning aim to build models that can perform well on target tasks by leveraging knowledge from semantic related but distribution different source domains. This paper for the first time summarizes the state-of-art cross-domain fault diagnosis research works. The literatures are introduced from three different viewpoints: research motivations, cross-domain strategies, and application objects. In addition, the corresponding open-source fault datasets and several future directions are also presented. The survey provides readers a framework for better understanding and identifying the research status, challenges and future directions of cross-domain fault diagnosis.

Most cross-domain intelligent diagnosis approaches presume that the health states in training datasets are consistent with those in testing. However, it is usually difficult and expensive to collect samples under all failure states during the training stage in actual engineering; this causes the training dataset to be incomplete. These existing methods may not be favorably implemented with an incomplete training dataset. To address this problem, a novel deep-learning-based model called partial transfer ensemble learning framework (PT-ELF) is proposed in this paper. The major procedures of this study consist of three steps. First, the missing health states in the training dataset are supplemented by another dataset. Second, since the training dataset is drawn from two different distributions, a partial transfer mechanism is explored to train a weak global classifier and two partial domain adaptation classifiers. Third, a particular ensemble strategy combines these classifiers with different classification ranges and capabilities to obtain the final diagnosis result. Two case studies are used to validate our method. Results indicate that our method can provide robust diagnosis results based on an incomplete source domain under variable working conditions.

The smart meta-superconductor MgB2 and Bi(Pb)SrCaCuO increase the superconducting transition temperature (TC), but the changes in the transport critical current density (JC) and Meissner effect are still unknown. Here, we investigated the JC and Meissner effect of smart meta-superconductor MgB2 and Bi(Pb)SrCaCuO. The use of the standard four-probe method shows that Y2O3:Eu3+ and Y2O3:Eu3++Ag inhomogeneous phase significantly increase the JC, and JC decreases to a minimum value at a higher temperature. The Meissner effect was measured by direct current magnetization. The doping of Y2O3:Eu3+ and Y2O3:Eu3++Ag luminescent inhomogeneous phase causes a Meissner effect of MgB2 and Bi(Pb)SrCaCuO at a higher temperature, while the non-luminescent dopant reduces the temperature at which samples have Meissner effect. The introduction of luminescent inhomogeneous phase in conventional MgB2 and copper oxide high-temperature Bi(Pb)SrCaCuO superconductor increases the TC and JC, and Meissner effect is exerted at higher temperature. Therefore, smart meta-superconductivity is suitable for conventional and copper oxide high-temperature superconductors.

Rotating machinery is part and parcel of modern industrial applications [...]

The vibration signal of gearboxes contains abundant fault information, which can be used for condition monitoring. However, vibration signal is ineffective for some non-structural failures. In order to resolve this dilemma, infrared thermal images are introduced to combine with vibration signals via fusion domain-adaptation convolutional neural network (FDACNN), which can diagnose both structural and non-structural failures under various working conditions. First, the measured raw signals are converted into frequency and squared envelope spectrum to characterize the health states of the gearbox. Second, the sequences of the frequency and squared envelope spectrum are arranged into two-dimensional format, which are combined with infrared thermal images to form fusion data. Finally, the adversarial network is introduced to realize the state recognition of structural and non-structural faults in the unlabeled target domain. An experiment of gearbox test rigs was used for effectiveness validation by measuring both vibration and infrared thermal images. The results suggest that the proposed FDACNN method performs best in cross-domain fault diagnosis of gearboxes via multi-source heterogeneous data compared with the other four methods.

A single-copy specific primer was designed based on beef and duck samples and through drop digital polymerase chain reaction (ddPCR) for the quantitative analysis. Results revealed that the primers had no specific amplification with sheep, chicken, pork, or other species. Both the relationships between meat weight and DNA weight and between DNA weight and DNA copy number (C) were nearly linear within the dynamic range. To calculate the original meat weight from the DNA copy number, the DNA weight was used as the intermediate value to establish the following formulae: Mbeef = 0.058C − 1.86; Mduck = 0.0268C − 7.78. To achieve a good quantitative analysis, all species used in the experiment were made of lean meat. The accuracy of the method was verified by artificial adulteration of different proportions. Testing of the commercial samples indicated that adulteration is present in the market. The established digital PCR method provided an effective tool for monitoring the adulterated meat products and reducing the adulteration in the market.

Abstract Background In order to overcome the shortcomings of laparoscopic intersphincteric resection (Lap ISR), an alternative method of delivering intraoperative radiotherapy by Intrabeam X-rays radiotherapy system (XRS) is proposed in this paper. Intrabeam XRS is a device that uses low-energy X-rays source generated by a mobile controller unit, which is featured in accurate irradiation, reduced complications, and less exposure. The purpose of this study is to discuss the feasibility of Lap ISR with intra-operative radiotherapy using low-energy X-rays for locally advanced ultra-low rectal cancer in Asian woman. This novel proposed method will greatly increase the anus preserving probability and improved the quality of life. Methods A 53-year-old woman diagnosed with rectal adenocarcinoma had a strong desire to preserve the anal function and presented at the Jilin University Second Hospital, Jilin, China. The tumor’s size was 4 cm × 3 cm. It was located 2 cm from the anus merge and invaded the levator ani muscle. Preoperative clinical staging was T4N1M0 and could be reached R0 resection. After the consent form was signed by the patient, Lap ISR combined with the applicator put through the anus (natural orifice) to the tumor bed was performed and prophylactic ileostomy synchronized the anastomosis. Patient only received 1-cycle chemotherapy regimen of oxaliplatin with capecitabine postoperatively due to personal reasons. Pre- or postoperative radiotherapy was not given. Results After clinical follow-up, until now, there is not any sign of local recurrence. Anus function and short-term complications are acceptable. The short-term effect is satisfying and we look forward to further assess the long-term effect. Conclusion Laparoscopic intersphincteric resection with IORT using low-energy X-rays for the patients with late-stage ultra-low rectal cancer could provide an opportunity of preserving the anus function, and it is feasible for the selected patients. Trial registration Retrospectively registered; Trial registration: NCT03393234; Registered time: 05 January 2017.

Rotating machines have been widely used in industrial engineering. The fault diagnosis of rotating machines plays a vital important role to reduce the catastrophic failures and heavy economic loss. However, the measured vibration signal of rotating machinery often represents non-linear and non-stationary characteristics, resulting in difficulty in the fault feature extraction. As a statistical measure, entropy can quantify the complexity and detect dynamic change through taking into account the non-linear behavior of time series. Therefore, entropy can be served as a promising tool to extract the dynamic characteristics of rotating machines. Recently, many studies have applied entropy in fault diagnosis of rotating machinery. This paper aims to investigate the applications of entropy for the fault characteristics extraction of rotating machines. First, various entropy methods are briefly introduced. Its foundation, application, and some improvements are described and discussed. The review is divided into eight parts: Shannon entropy, Rényi entropy, approximate entropy, sample entropy, fuzzy entropy, permutation entropy, and other entropy methods. In each part, we will review the applications using the original entropy method and the improved entropy methods, respectively. In the end, a summary and some research prospects are given.

Despite rapid advances in the field of image denoising, heavy noise removal has remained an under-explored area. When the strength of noise becomes comparable to or even more dominating than that of signal, restoration of important structures from such heavily-contaminated images becomes more challenging. Existing model-based and learning-based image denoising techniques often cannot delivery satisfactory results, which call for new insights and algorithmic tools. In this paper, we propose to remove heavy noise with the help of similar images retrieved from datasets, which leads to a hybrid approach combining sparsity-based and learning-based methods. Specifically, the parametric sparse prior of underlying clean image is learned from the retrieved reference images and the input noisy image. Leveraging prior from the reference images, the estimated parametric model contains much more accurate information of image details. When compared against existing conventional methods, the proposed hybrid approach is more capable of restoring fine-detailed structures in the presence of heavy noise. Experimental results show that the proposed method dramatically outperforms current state-of-the-art image denoising methods both subjectively and objectively.

Rolling bearings' operation under variable speed conditions exhibits complex time-varying modulations and spectral structures, resulting in difficulty in the fault diagnosis. In order to effectively remove the influence of the rotational speed and extract the fault characteristics, this paper develops a fault diagnosis scheme based on the Vold-Kalman filter (VKF), refined composite multi-scale fuzzy entropy (RCMFE), and Laplacian score (LS). In the proposed method, the VKF is first adopted to remove the fault-unrelated components and give a clear representation of the fault symptoms. Second, the RCMFE is applied to extract fault features from the denoised vibration signal. Third, the LS approach is introduced to refine the fault features by sorting the scale factors. In the end, the selected features are fed into the logistic regression to automatically complete the fault pattern identifications. The proposed method is experimentally demonstrated to be able to recognize the localized defect on the inner race, outer race, and rolling element under variable speed conditions.