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Abstract Background & objective Timely intervention for Acute coronary syndrome (ACS) could effectively reduce the mortality rate of ACS patients. This study aimed to investigate the clinical significance of miR-30c-5p for ACS and to provide a convenient biomarker for diagnosing of ACS. Methods Baseline information was collected from a total of 173 subjects (98 ACS subjects and 65 healthy subjects). The miR-30c-5p expression was evaluated by the Polymerase chain reaction (PCR). The predictive value of miR-30c-5p for ACS was assessed by Receiver Operating Characteristic (ROC) curve and multivariate logistic regression analysis. The relationship between miR-30c-5p expression and ACS severity was assessed by correlation analysis. Furthermore, the prognostic value of miR-30c-5p on Major Adverse Cardiovascular Events (MACE) occurrence was assessed by the Kaplan-Meier (K-M) curve to evaluate its prognostic significance. Results Downregulation of miR-30c-5p was observed in ACS subjects and its diagnostic value on ACS was confirmed by the ROC curve. MiR-30c-5p could also discriminate acute myocardial infarction (AMI) from unstable angina pectoris (UAP) subjects in ACS. The expression of miR-30c-5p was negatively correlated with the cardiac troponin I (cTnI) levels and the Gensini score. A lower miR-30c-5p expression was observed in ACS subjects who developed MACE (P = 0.020), and the K-M curve further confirmed the close correlation between miR-30c-5p expression and MACE occurrence in ACS. MiR-30c-5p was also identified as an independent prognostic factor for MACE in ACS. Conclusions Serum miR-30c-5p expression was correlated with the severity of ACS, and downregulated miR-30c-5p expression showed a diagnostic and prognostic value in ACS.

Repair of large bone defects remains to be clinically challenging, yet current bone repair strategies focus on optimizing the osteogenic capacity of bone grafts, while the role of osteoclasts in bone regeneration has been largely ignored. Herein, we designed a injectable self-curing bone grafting paste capable of regulating both anabolic/catabolic activities during bone healing by immobilizing the RANKL inhibitor denosumab on dermal-derived extracellular matrix (ECM) microfibres, which were then incorporated into an injectable paste via a hydration reaction between β-tricalcium phosphate (β-TCP), monocalcium phosphate monohydrate (MCPM) and calcium sulfate hemihydrate (CSH). The incorporation of ECM microfibres not only serves as a sustained-release denosumab carrier to inhibit osteoclastogenesis but also improves the mechanical properties of the resulting composite by increasing the interaction between the organic and inorganic phases. In vitro, calcium supply from the composite along with ECM enhanced osteogenic differentiation of BMSC while release of denosumab effectively inhibits osteoclast fusion and alleviate osteoclastic activity. In vivo, it was observed that CSH/CP@ECM-Deno significantly reduced the number of osteoclasts, slowed down the process of bone resorption, and accelerated collagen deposition to promote new bone generation. These results suggest that modulation of osteoclastogenesis by interfering with bone homeostasis may be an effective bone repair strategy.

Abstract Objectives To characterize the clinical features and to identify the predictors of recurrence of histiocytic necrotizing lymphadenitis (HNL) in Chinese children. Study design This study retrospectively analyzed the clinical characteristics, laboratory and pathological findings, and recurrence status of children diagnosed with HNL at a single center in China from January 2018 to May 2023. Logistic regression analysis was employed to identify predictors of HNL recurrence. Results 181 Chinese children with histopathologically confirmed HNL were enrolled (121 males and 60 females). The mean age was 9.3 ± 2.9 years. The most prominent clinical features were fever (98.9%) and cervical lymphadenopathy (98.3%). Aseptic meningitis was the most frequent complication (38.5%), while hemophagocytic lymphohistiocytosis and autoimmune disease were rare (1.7% and 1.2%, respectively). Recurrence occurred in 12.7% of patients. Erythrocyte sedimentation rate (> 30 mm/h) was the significant predictors of HNL recurrence, with odds ratios of 6.107, respectively. Conclusion Our study demonstrates that fever and cervical lymphadenopathy are the most frequent clinical manifestations of HNL in Chinese children, which often coexist with aseptic meningitis. HNL patients with risk factors require follow-up for recurrence.

In complex industrial scenarios, high-quality fault data of rotating machinery are scarce and costly to collect. Therefore, small sample fault diagnosis needs further research. To solve this problem, in this work is proposed a minimum variance auxiliary classifier generation adversarial network based on a multi-scale convolutional block attention mechanism. Firstly, the multi-scale convolutional block attention mechanism is designed to extract multi-scale information and perform weighted fusion to enhance the ability of the model to capture effective features. Secondly, the minimum variance term is designed to minimize the variance of sample distribution, so that the generated samples are distributed more evenly in the feature space, avoiding the problem of pattern collapse. Finally, the objective function is reconstructed by independent classification loss to improve the ability of model data generation. Experimental results on CWRU and gearbox datasets validate the effectiveness and reliability of the proposed method.

Research on single faults like tooth root cracks and bearing outer ring failures is well-established. However, existing studies often replace the actual crack propagation path with a simplified linear crack model, neglecting the variation in stress intensity factors. They do not account for the coupling effect between gear teeth when calculating gear mesh stiffness. In practice, gearbox failures are predominantly compound failures involving both gears and bearings. This paper simulates the actual crack propagation path based on Workbench, introduces a dual-tooth meshing zone method to address the tooth-to-tooth coupling effect, and establishes a dynamic model of a system with compound faults of tooth root cracks and bearing outer ring defects. The dynamic response of the system under different levels of deterioration is then investigated. The findings indicate that the crack propagation path exhibits a concave shape, with the curvature of the path increasing as the crack deteriorates. The time-varying mesh stiffness calculated by considering tooth-to-tooth coupling effects aligns more closely with the finite element results. By analyzing the evolution patterns of the impact components and sidebands during the deterioration process, the dynamic evolution trends of the coupling phenomenon between tooth root cracks and bearing outer ring faults are clarified.

A gearbox compound fault intelligent diagnosis method based on the period group sparse model is proposed for the problem that the fault features are coupled with each other and the fault components are superimposed on each other and difficult to be separated in the gearbox compound fault signal. Firstly, a binary sequence is constructed to embed the fault pulse period as a priori knowledge into the group sparse model to decouple and separate the composite fault signal while maintaining the amplitude and sparsity of the extracted features. Secondly, the wavelet packet energy features of the decoupled signals are extracted to improve the data quality while enhancing the characterization ability of the dictionary in the classification model. Finally, the wavelet packet energy features are imported into the sparse dictionary classification model, and the fault diagnosis is completed by outputting the fault categories using the self-driven characteristics of the data. The results show that the fault identification accuracy using the proposed method is 97%. In addition, the experimental validation under different states and working conditions with different rotational speeds allows the superiority and effectiveness of the algorithm in this paper to be tested and has the feasibility of a practical application in engineering.

Gearboxes operate in challenging environments, which leads to a heightened incidence of failures, and ambient noise further compromises the accuracy of fault diagnosis. To address this issue, we introduce a fault diagnosis method that employs singular value decomposition (SVD) and graph Fourier transform (GFT). Singular values, commonly employed in feature extraction and fault diagnosis, effectively encapsulate various fault states of mechanical equipment. However, prior methods neglect the inter-relationships among singular values, resulting in the loss of subtle fault information concealed within. To precisely and effectively extract subtle fault information from gear vibration signals, this study incorporates graph signal processing (GSP) technology. Following SVD of the original vibration signal, the method constructs a graph signal using singular values as inputs, enabling the capture of topological relationships among these values and the extraction of concealed fault information. Subsequently, the graph signal undergoes a transformation via GFT, facilitating the extraction of fault features from the graph spectral domain. Ultimately, by assessing the Mahalanobis distance between training and testing samples, distinct defect states are discerned and diagnosed. Experimental results on bearing and gear faults demonstrate that the proposed method exhibits enhanced robustness to noise, enabling accurate and effective diagnosis of gearbox faults in environments with substantial noise.

CuCr alloys are the main contact materials used in medium-voltage and high-current vacuum interrupters. In this paper, the effect of large plastic deformation on microstructure and the transverse/longitudinal thermal conductivity of CuCr30 contact material were studied. The results show that the morphology of Cr particles in CuCr30 changes from dendritic to oriented fibrous after large plastic deformation, and the longitudinal thermal conductivity of CuCr30 contact material is about 1.5 times higher than its transverse thermal conductivity. During the electrical contacting process, the mass loss of the extruded CuCr30 contact material is lower than that of as-cast CuCr30 contact material, which is directly related to the significantly higher longitudinal thermal conductivity of the extruded CuCr30 contact material. The enhancement of longitudinal thermal conductivity is beneficial to reduce the mass loss of the contact material and improve the arc erosion resistance of the contact material. The mass loss of the cathode with higher longitudinal thermal conductivity is significantly lower than that of the anode contact in extruded CuCr30 contact material.

Carbon quantum dots are a novel form of carbon material. They offer numerous benefits including particle size adjustability, light resistance, ease of functionalization, low toxicity, excellent biocompatibility, and high-water solubility, as well as their easy accessibility of raw materials. Carbon quantum dots have been widely used in various fields. The preparation methods employed are predominantly top-down methods such as arc discharge, laser ablation, electrochemical and chemical oxidation, as well as bottom-up methods such as templates, microwave, and hydrothermal techniques. This article provides an overview of the properties, preparation methods, raw materials for preparation, and the heteroatom doping of carbon quantum dots, and it summarizes the applications in related fields, such as optoelectronics, bioimaging, drug delivery, cancer therapy, sensors, and environmental remediation. Finally, currently encountered issues of carbon quantum dots are presented. The latest research progress in synthesis and application, as well as the challenges outlined in this review, can help and encourage future research on carbon quantum dots.

Reliable tracking of the plume by the robot is the key to achieving plume source localization. To address the problem of low success rate and long search time of robot source location due to the unavailability of reliable information on gas diffusion flow direction and flow velocity in an indoor weak wind environment, a hybrid strategy is proposed to improve the grey wolf optimization algorithm for robot plume tracking and location. The plume is modeled using Computational Fluid Dynamics (CFD) in a two-dimensional indoor weak wind environment, and the plume concentration value is used as the individual adaptation degree of the algorithm. Without carrying plume velocity and flow direction sensors, the source-finding robot simulates the grey wolf population’s social mechanism and hunting behavior to update its position. The improved Grey Wolf Optimization algorithm is compared with the traditional Grey Wolf Optimization (GWO), Particle Swarm Algorithm (PSO), and Genetic Algorithm (GA) in simulation experiments. The simulation experiments show that the average number of iterations of the improved GWO is 7, 108, and 118 times shorter than the four source finding algorithms of GWO, PSO, and GA. The average planning path is reduced by 0.91 meters, 2.35meters, and 2.90 meters. 3s, 10.3s, and 9.3s reduce the average running time. The average positioning success rate is improved by 30%, 32%, and 40%. The applicability and Stability of the improved GWO algorithm in solving the plume tracking and localization problem in an indoor weak wind environment are verified.

Cu–Al2O3 composites are widely used in electrical contact fields because of their excellent electrical conductivity, thermal conductivity and arc erosion resistance. (WC + SiCw)/Cu–Al2O3 composites with better properties were prepared by powder metallurgy combined with an internal oxidation process that introduced micron-sized WC particles and SiCw into the Cu–Al2O3 composite. The synergistic strengthening mechanism effect of nano-Al2O3 particles, micron-sized WC particles and SiCw in copper matrix composites was analyzed. The results show that there is a semi-coherent interface between the nano-Al2O3 particles and the copper matrix. The ultimate tensile strength of the (1WC + 2SiCw)/Cu–Al2O3 composite is 531.6 MPa, which is 12.7% and 4.5% higher than that of the Cu–Al2O3 composite and the SiCw/Cu–Al2O3 composite, respectively. According to theoretical calculations and an analysis of the strengthening mechanism, the contributions of the Orowan strengthening and load transfer strengthening mechanism in the (1WC + 2SiCw)/Cu–Al2O3 composite are 27.3% and 16.6% respectively. The synergistic strengthening effect of particles and whiskers contributed 6.0% to the tensile strength of the (1WC + 2SiCw)/Cu–Al2O3 composite.

Vascular endothelial dysfunction (VED) is linked with the pathogenesis of obstructive sleep apnea (OSA) comorbidities, such as cardiovascular disease. Astragaloside IV (As-IV) has exhibited significant improvement for endothelial dysfunction. Nonetheless, the protective mechanism is not clear. Therefore, the present study investigated the potential mechanism of As-IV on VED. Calpain-1 knockout and wild-type C57BL/6 mice exposed to chronic intermittent hypoxia (CIH) were established and treated with As-IV (40, 80 mg/kg) for 4 weeks. Human coronary artery endothelial cells (HCAECs) subjected to CIH exposure were pretreated with As-IV, MDL-28170 (calpain-1 inhibitor) and SRT1720 (SIRT1 activator) for 48 h in vitro. The endothelial function, inflammation, oxidative stress and mitochondrial function were measured to evaluate VED. Our data revealed that As-IV treatment ameliorated CIH-induced endothelial-dependent vasomotion and augmented nitric oxide (NO) production. As-IV administration suppressed the secretion of inflammation, oxidative stress and mitochondrial dysfunction. As-IV treatment reduced the expression of calpain-1 and restored the downregulated expression of SIRT1 and Thr172 AMPK and Ser1177 eNOS phosphorylation. The effects of calpain-1 knockout and SRT1720 were similar to the effect of As-IV on VED. These findings demonstrated that As-IV ameliorated VED induced by chronic intermittent hypoxia via the calpain-1/SIRT1/AMPK signaling pathway.

Gearbox fault diagnosis is vital to ensure the efficient operation of rotating machinery, and most gearbox faults in industrial production occur in the form of compound faults. To realize the diagnosis of compound faults in gearboxes at different speeds, an “end-to-end” intelligent diagnosis method based on an efficient channel attention capsule network (ECA-CN) is proposed. First, the process uses a deep convolutional neural network to extract fault features from the collected raw vibration signals, embeds the efficient channel attention module to filter important fault features, uses the capsule network to vectorize the feature space information and, finally, calculates the correlation between different levels of capsules using a dynamic routing algorithm to achieve accurate gearbox compound fault diagnosis. The effectiveness of the proposed ECA-CN fault diagnosis method is verified using the composite fault dataset of the 2009 PHM Challenge gearbox, with an average accuracy of 99.63 ± 0.22%. In the comparison experiments using the traditional fault diagnosis method, the average accuracy of the ECA-CN method improved by 4.62%, and the standard deviation was reduced by 0.58%. The experimental results show that the ECA-CN has a more competitive diagnostic performance than traditional shallow machine learning models and CNNs.

Gearboxes are utilized in practically all complicated machinery equipment because they have great transmission accuracy and load capacities, so their failure frequently results in significant financial losses. The classification of high-dimensional data remains a difficult topic despite the fact that numerous data-driven intelligent diagnosis approaches have been suggested and employed for compound fault diagnosis in recent years with successful outcomes. In order to achieve the best diagnostic performance as the ultimate objective, a feature selection and fault decoupling framework is proposed in this paper. That is based on multi-label K-nearest neighbors (ML-kNN) as classifiers and can automatically determine the optimal subset from the original high-dimensional feature set. The proposed feature selection method is a hybrid framework that can be divided into three stages. The Fisher score, information gain, and Pearson’s correlation coefficient are three filter models that are used in the first stage to pre-rank candidate features. In the second stage, a weighting scheme based on the weighted average method is proposed to fuse the pre-ranking results obtained in the first stage and optimize the weights using a genetic algorithm to re-rank the features. The optimal subset is automatically and iteratively found in the third stage using three heuristic strategies, including binary search, sequential forward search, and sequential backward search. The method takes into account the consideration of feature irrelevance, redundancy and inter-feature interaction in the selection process, and the selected optimal subsets have better diagnostic performance. In two gearbox compound fault datasets, ML-kNN performs exceptionally well using the optimal subset with subset accuracy of 96.22% and 100%. The experimental findings demonstrate the effectiveness of the proposed method in predicting various labels for compound fault samples to identify and decouple compound faults. The proposed method performs better in terms of classification accuracy and optimal subset dimensionality when compared to other existing methods.

Abstract In order to improve the intelligence of the medical system, this paper designs and implements a secure medical big data ecosystem on top of the Hadoop big data platform. It is designed against the background of the increasingly serious trend of the current security medical big data ecosystem. In order to improve the efficiency of traditional medical rehabilitation activities and enable patients to maximize their understanding of their treatment status, this paper designs a personalized health information system that allows patient users to understand their treatment and rehabilitation status anytime and anywhere, and all medical health data distributed in different independent medical institutions to ensure that these data are stored independently. As a distributed accounting technology for multi-party maintenance and backup information security, blockchain is a good breakthrough point for innovation in medical data sharing. In this paper, the system realizes the personal health data centre on the Hadoop big data platform, and the original distributed data are stored and analyzed centrally through the data synchronization module and the independent data acquisition system. Utilizing the advantages of the Hadoop big data platform, the personalized health information system for stroke has designed to provide personalized health management services for patients and facilitate the management of patients by medical staff.

Aiming at the problem that the vibration energy of shaft crack early weak fault is small and the feature extraction was difficult, the fault diagnosis method of cuckoo search, maximum correlation kurtosis deconvolution and variational mode decomposition (CS-MCKD-VMD) was proposed to diagnose the early weak fault of shaft cracks. Firstly, considering the periodic characteristics of fault signal, MCKD was used to highlight the fault signal, and the kurtosis correlation of deconvolution signal processed by MCKD were enhanced. Then, VMD could be used to decompose the harmonic signal, and VMD was used to process the deconvolution signal to generate some intrinsic mode functions (IMF), and the correlation coefficient method was used to calculate the components with the maximum correlation. At the same time, in order to improve the correlation of parameters in MCKD and VMD, CS was used to optimize the parameters to reduce the error of human selection. Thirdly, the optimal component was calculated by the fast spectral kurtosis (FSK), and the envelope spectrum was obtained by filtering the optimal component with the parameters obtained. Finally, the simulation and experimental results show that the transfer frequency and its 2 and 3 times frequency were prominent in the envelope spectrum, which was consistent with the analysis of shaft crack fault mechanism, Therefore, it was proved that the CS-MCKD-VMD method can effectively extract the early weak fault features of shaft cracks.

Gearboxes are one of the most widely used speed and power transfer elements in rotating machinery. Highly accurate compound fault diagnosis of gearboxes is of great significance for the safe and reliable operation of rotating machinery systems. However, traditional compound fault diagnosis methods treat compound faults as an independent fault mode in the diagnosis process and cannot decouple them into multiple single faults. To address this problem, this paper proposes a gearbox compound fault diagnosis method. First, a multiscale convolutional neural network (MSCNN) is used as a feature learning model, which can effectively mine the compound fault information from vibration signals. Then, an improved hybrid attention module, named the channel–space attention module (CSAM), is proposed. It is embedded into the MSCNN to assign weights to multiscale features for enhancing the feature differentiation processing ability of the MSCNN. The new neural network is named CSAM-MSCNN. Finally, a multilabel classifier is used to output single or multiple labels for recognizing single or compound faults. The effectiveness of the method was verified with two gearbox datasets. The results show that the method possesses higher accuracy and stability than other models for gearbox compound fault diagnosis.

This paper established the system dynamics model for two kinds of tooth cracks of different depths of the sun gear and inner gear ring to study the influence mechanism of crack failure on the tooth root strain of the planetary gear transmission system. Combined with the finite element model of the inner gear ring, the tooth root strain of the ring was solved. Experiments verified the correctness of the solution method. The root strain under the crack fault of the sun gear and the tooth crack fault of the inner gear ring is analyzed, and the following conclusions are drawn: Periodic fault impact occurs in the strain signal of the tooth root of the inner gear ring during the crack fault of the sun gear root. The fault can be extracted by the fast spectral kurtosis method (FSK), and the fault components are used to determine whether the sun gear cracks. The Lempel–Ziv index showed a tendency to increase gradually during the process of solar wheel crack deepening, which could be used as the damage index of crack depth. The results can provide a basis and reference for fault diagnosis.

To analyze the vibration characteristic differences of a gear transmission system with gear and shaft cracks, an improved computational method for deriving the shaft stiffness matrix with breathing cracks is developed. Also, the three-dimensional contact model of spur gear with a crack is established via the finite element method (FEM), and its meshing stiffness is calculated. Simultaneously, considering bearing stiffness and shaft flexibility, the finite element dynamic models of two-stage gear transmission system with gear and shaft cracks are established. Based on this, different source fault vibration responses are compared and the influencing factors are explored. In addition, a novel signal processing method based on the particle swarm optimization, maximum correlated kurtosis deconvolution, variational, mode decomposition and fast spectral kurtosis (PSO-MCKD-VMD-FSK) is utilized to extract fault characteristics for the signal-to-noise ratio and uneven energy distribution problems. Results show that a system with gear cracks mainly presents periodic impact in the time domain, while in the frequency domain it impacts rotation frequency modulation near the meshing frequency and its multiple frequencies. However, the shaft crack breathing effect meant that the time domain mainly presents “simple harmonic” modulation, and the rotation frequency and its faulty shaft multiplication occurs in the low-frequency region of the frequency domain. The PSO-MCKD-VMD-FSK method extracts fault features in a strong noise environment and has good robustness. Results identify different source faults and provide a theoretical basis.

In this work, an improved computational method for the prediction of the vibration and noise of a gearbox that considers the flexibility of the shaft is developed. Based on the finite element method (FEM), a coupled dynamic model of a spur gear-shaft-bearing system is established, and the time-varying mesh stiffness (TVMS), the time-varying bearing stiffness (TVBS), and the flexibility of the shaft are considered. The Newmark integration method (NIM) is utilized to obtain the dynamic load of the bearing. Furthermore, the proposed model is validated by experiments. The bearing load is then considered to be the excitation of the housing, and the radiated noise is calculated via the finite element method/boundary element method (FEM/BEM). The effects of the shaft flexibility on the bearing response and radiated noise are discussed based on the proposed method. The results demonstrate that, when the shaft flexibility is considered, the system undergoes the bending vibration of the shaft, and the vibration amplitude and excitation frequency components of the bearing load decrease significantly. Additionally, the main resonance mode of the gearbox is changed, and the radiated noise is enhanced. The effects of the input speed and shaft stiffness on the bearing response and radiated noise are also investigated. The results provide a theoretical basis for the further development of the vibration and noise reduction of gearboxes.

The technique for target detection based on a convolutional neural network has been widely implemented in the industry. However, the detection accuracy of X-ray images in security screening scenarios still requires improvement. This paper proposes a coupled multi-scale feature extraction and multi-scale attention architecture. We integrate this architecture into the Single Shot MultiBox Detector (SSD) algorithm and find that it can significantly improve the effectiveness of target detection. Firstly, ResNet is used as the backbone network to replace the original VGG network to improve the feature extraction capability of the convolutional neural network for images. Secondly, a multi-scale feature extraction (MSE) structure is designed to enrich the information contained in the multi-stage prediction feature layer. Finally, the multi-scale attention architecture (MSA) is fused onto the prediction feature layer to eliminate the redundant features’ interference and extract effective contextual information. In addition, a combination of Adaptive-NMS and Soft-NMS is used to output the final prediction anchor boxes when performing non-maximum suppression. The results of the experiments show that the improved method improves the mean average precision (mAP) value by 7.4% compared to the original approach. New modules make detection much more accurate while keeping the detection speed the same.

Wind turbines usually operate in harsh environments. The gearbox, the key component of the transmission chain in wind turbines, can easily be affected by multiple factors during the operation process and develop compound faults. Different types of faults can occur, coupled with each other and staggered interference. Thus, a challenge is to extract the fault characteristics from the composite fault signal to improve the reliability and the accuracy of compound fault diagnosis. To address the above problems, we propose a compound fault diagnosis method for wind turbine gearboxes based on multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and parallel parameter optimized resonant sparse decomposition (RSSD). Firstly, the MOMEDA is applied to the preprocess, setting the deconvolution period with different fault frequency types to eliminate the interference of the transmission path and environmental noise, while decoupling and separating the different types of single faults. Then, the RSSD method with parallel parameter optimization is applied for decomposing the preprocessed signal to obtain the low resonance components, further suppressing the interference components and enhancing the periodic fault characteristics. Finally, envelope demodulation of the enhanced signal is applied to extract the fault features and identify the different fault types. The effectiveness of the proposed method was verified using the actual data from the wind turbine gearbox. In addition, a comparison with some existing methods demonstrates the superiority of this method for decoupling composite fault characteristics.

Further processing and the added value of potatoes are limited by irregular potatoes. An ellipse-fitting-based Hausdorff distance and intersection over union (IoU) method for identifying irregular potatoes is proposed to solve the problem. First, the acquired potato image is resized, translated, segmented, and filtered to obtain the potato contour information. Secondly, a least-squares fitting method fits the extracted contour to an ellipse. Then, the similarity between the irregular potato contour and the fitted ellipse is characterized using the perimeter ratio, area ratio, Hausdorff distance, and IoU. Next, the characterization ability of the four features is analyzed, and an identification standard of irregular potatoes is established. Finally, we discuss the algorithm’s shortcomings in this paper and draw the advantages of the algorithm by comparison. The experimental results showed that the characterization ability of perimeter ratio and area ratio was inferior to that of Hausdorff distance and IoU, and using Hausdorff distance and IoU as feature parameters can effectively identify irregular potatoes. Using Hausdorff distance separately as a feature parameter, the algorithm achieved excellent performance, with precision, recall, and F1 scores reaching 0.9423, 0.98, and 0.9608, respectively. Using IoU separately as a feature parameter, the algorithm achieved a higher overall recognition rate, with precision, recall, and F1 scores of 1, 0.96, and 0.9796, respectively. Compared with existing studies, the proposed algorithm identifies irregular potatoes using only one feature, avoiding the complexity of high-dimensional features and significantly reducing the computing effort. Moreover, simple threshold segmentation does not require data training and saves algorithm execution time.

The reliability and safety of rotating equipment depend on the performance of bearings. For complex systems with high reliability and safety needs, effectively predicting the fault data in the use stage has important guiding significance for reasonably formulating reliability plans and carrying out reliability maintenance activities. Many methods have been used to solve the problem of reliability prediction. Due to its convenience and efficiency, the data-driven method is increasingly widely used in practical reliability prediction. In order to ensure the reliability of bearing operation, the main objective of the present study is to establish a novel model based on the optimized maximum correlation kurtosis deconvolution (MCKD) and long short-term memory (LSTM) recurrent neural network to realize early bearing fault warnings by predicting bearing fault time series. The proposed model is based on the lifecycle vibration signal of the bearing. In the first step, the cuckoo search (CS) is utilized to optimize the parameter filter length and deconvolution period of MCKD, considering the influence of periodic bearing time series, and to improve the fault impact component of the optimized MCKD deconvolution time series. Then the LSTM learning rate is selected according to the deconvolution time series. Finally, the dataset obtained through various preprocessing approaches is used to train and predict the LSTM model. The analyses performed using the XJTU-SY bearing dataset demonstrate that the prediction results are in good consistency with real fault data, and the average prediction accuracy of the optimized MCKD–LSTM model is 26% higher than that of the original time series.

Background/Aims: Growing evidence has shown that miR-330-3p is closely related to the biological behavior of cancer, including proliferation, metastasis, and prognosis. However, there have been no reports on miR-330-3p expression and function in osteosarcoma. Methods: Expression of miR-330-3p in osteosarcoma tissues and cell lines was examined by quantitative PCR. Effects of miR-330-3p on osteosarcoma cell proliferation were investigated in vitro with the Cell Counting Kit-8 colorimetric assay. Targets of miR-330-3p were identified by dual-luciferase reporter assay. Results: The results showed that expression of miR-330 decreased in osteosarcoma tissues and cell lines. Prognosis of patients with high miR-330-3p expression was much better than that of those with low expression (P=0.001), and multivariate analysis suggested that miR-330-3p is an independent prognostic factor for osteosarcoma. In addition, miR-330-3p overexpression significantly inhibited the growth of MG-63 and U2OS osteosarcoma cells. Dual-luciferase reporter assay demonstrated that Bmi-1 was a direct target gene of miR-330-3p, and in a recovery experiment, miR-330-3p suppressed osteosarcoma cell proliferation by directly targeting Bmi-1. Conclusion: Our results suggest that miR-330-3p acts as a tumor suppressor by regulating Bmi-1 expression in osteosarcoma. Thus, miR-330-3p may represent a novel therapeutic target for the treatment of osteosarcoma.

A new modeling method is proposed to simulate the dynamic response of a two-stage gear transmission system using the finite element method (FEM). The continuous system is divided into four modules: shaft-shaft element, shaft-gear element, shaft-bearing element, and gear-gear element. According to the FEM, the model is built with each element assembled. Meanwhile, the model considers the time-varying mesh stiffness (TVMS), bearing time-varying stiffness (BTVS), and the shaft flexibility. The Newmark integration method (NIM) is used to obtain the dynamic response of the spur gear system. Results show that vibration amplitude and the number of frequency components decrease after considering shaft flexibility through comparing the gear dynamic response under the condition of flexible shaft and rigid shaft. When the effect of bearing stiffness is considered, there will be a bearing passing frequency component in the frequency spectrum. In addition, the result shows that the simulation and experimental test of the frequency component are basically consistent. Furthermore, the theoretical model is validated against an experimental platform of the two-stage gear transmission system and the dynamic responses are compared under the condition of increasing speed. Additionally, the increase of shaft stiffness not only changes some of the dominant mode shapes (torsional mode shapes) but also makes the number of primary resonance speeds added. The method can be used to guide the design of gear systems.

Increasing penetration of electric vehicles (EVs) gives rise to the challenges in the secure operation of power systems. The EV charging loads should be distributed among charging stations in a fair and incentive-compatible manner while ensuring that power transmission and transformation facilities are not overloaded. This paper first proposes a charging right (or charging power ration) trading mechanism and model based on blockchain. Considering all kinds of random factors of charging station loads, we use Monte Carlo modeling to determine the charging demand of charging stations in the future. Based on the charging demand of charging stations, a charging station needs to submit the charging demand for a future period. The blockchain first distributes initial charging right in a just manner and ensures the security of facilities. Given that the charging urgency and elasticity differences vary by charging stations, all charging stations then proceed with double auction and peer-to-peer (P2P) transaction of charging right. Bids and offers are cleared via double auctions if bids are higher than offers. The remaining bids and offers are cleared via the P2P market. Then, this paper designs the charging right allocation and trading platform and smart contract based on the Ethernet blockchain to ensure the safety of the distribution network (DN) and the transparency and efficiency of charging right trading. Simulation results based on the Ethereum private blockchain show the fairness and efficiency of the proposed mechanism and the effectiveness of the method and the mechanism.

Ten case studies from seven major pastoral regions across six continents were studied in this paper by conceptualizing three factors (agro-ecosystem resilience, livelihood options, and institution capacity) as the axes of a three-dimensional vulnerability framework. This analysis highlights the vulnerability of agriculture-based livelihood systems to global changes and helps identify what institutions have the potential to mobilize effective relief in different pastoral regions. In terms of results, this vulnerability assessment shows that the vulnerability of pastoralism was very different in all the cases across the globe. As such, a further analysis, based on the pressure-state-response (PSR) model was undertaken to enhance our understanding of the ways that global changes put pressures on pastoral livelihoods worldwide. From this we conclude that climate change and climate variability are driving fragile pastoral ecosystems into more vulnerable conditions. Socioeconomic factors, such as changes in land tenure, agriculture, sedentarization, and institutions are fracturing large-scale pastoral ecosystems into spatially isolated systems. The implications of this analysis are that professionals, practitioners, and policy makers should jointly develop a coupled human and natural systems approach that focuses on enhancing the resilience of pastoral communities and their practices. This requires institutional developments to support asset building and good governance to enhance adaptive capability. In addition, pastoralists' adaptation strategies to global change need to be supported by public awareness and improved by institutional decisions at different scales and dimensions.