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Abstract Metastasis of hepatoblastoma (HB) is a key factor that impairs the prognosis and treatment of children. The suppressor of cytokine signaling 2 (SOCS2) is a classical negative feedback protein that regulates cytokine signal transduction and has been known to be downregulated in several tumor, but the molecular mechanisms of its involvement in HB metastasis are unknown. We found that SOCS2 was a gene down-regulated in hepatoblastoma and associated with HB metastasis through bioinformatics. The qRT-PCR, Western blot and IHC showed that SOCS2 was significantly lower in HB tissues. Clinicopathological correlation analysis revealed that low expression of SOCS2 was significantly correlated with tumor metastasis (P = 0.046) and vascular invasion (P = 0.028), associated with poor prognosis. Overexpression of SOCS2 inhibited the migration and invasion of hepatoblastoma cells, while knockdown of SOCS2 expression promoted these malignant phenotypes. In vivo studies revealed overexpression of SOCS2 inhibited the formation of lung metastasis. Up-regulation of SOCS2 in HB cell inhibited EMT and JAK2/STAT5. Conversely, down-regulation of SOCS2 promoted EMT and JAK2/STAT5. The addition of the JAK2 inhibitor Fedratinib partially reversed the effects of si-SOCS2 on HB cells. SOCS2 may inhibit the migration and invasion of HB cells by inhibiting the JAK2/STAT5 signaling pathway. These results may provide guiding significance for the clinical treatment of HB.

An analysis of the spatiotemporal evolution of ecological quality and its driving factors in the Lanxi urban agglomeration is important for ensuring environmental protection and high-quality, sustainable development in the region. We used moderate resolution imaging spectroradiometer (MODIS) remote sensing images to construct a remote sensing ecological index (RSEI) using principal component analysis (PCA) to reflect the ecological quality of the Lanxi urban agglomeration. The spatial and temporal characteristics and future changes in the RSEI of the study area from 2000 to 2020 were explored using the Sen and Mann–Kendall test and Hurst index, and the effects of natural and human factors on spatial variation in the RSEI of the study area were analyzed using Geodetector and geographically weighted regression (GWR) models. There were three main results. (1) In the past 20 years, the annual average RSEI value in the Lanxi urban agglomeration has been increasing at a rate of 0.0057/a, and areas of unsatisfactory ecological environmental quality have been reduced. (2) The Hurst index indicates that the majority of the area (47.54 % of the study area) will probably experience a degradation trend in the future, with 19.98 % of the area improving and random changes occurring in 9.45 % of the study area. (3) Vegetation type, soil type, and precipitation were the main reasons for the spatial differentiation in RSEI, land use type was the main human influence, and the influence of socioeconomic factors such as population density and gross domestic product (GDP) increased significantly. Vegetation, soil, and land use types were positively correlated with RSEI. The research results are of great significance for promoting the construction of an ecological civilization and coordinating a balance between social development and ecological environmental protection.

Objectives: From March to June 2021, the reported number of clinically diagnosed endemic typhus in Anhui and Hubei provinces of China nearly increased four-fold compared with the monthly average numbers in last 5 years. An etiological and epidemiological investigation was initiated. Methods: The clinical specimens from the reported patients and the potential vector ticks were collected for molecular and serological detection, as well as cell culturing assay to identify the potential pathogen. Results: Polymerase chain reaction and sequence analysis of rrs and groEL showed that the pathogen from these patients was Ehrlichia sp., isolated from Haemaphysalis longicornis attached to these patients. The phylogenetic analysis based on 39 Ehrlichia genomes suggested that it should be taxonomically classified as a novel species, tentatively named “Candidatus Ehrlichia erythraense”. A total of 19 of 106 cases were confirmed as Candidatus Ehrlichia erythraense infections by polymerase chain reaction, sequencing, and/or serological tests. The most frequent symptoms were fever (100%), rashes (100%), asthenia (100%), anorexia (100%), and myalgia (79%). Conclusion: The occurrence of the disease presenting with fever and rashes in Anhui and Hubei provinces was caused by a novel species of the genus Ehrlichia; physicians need to be aware of this newly-discovered pathogen to ensure appropriate testing, treatment, and regional surveillance.

Competition among trees for limited resources (e.g., sunlight, water, and nutrients) impacts their growth differently. Crown plays a crucial role in resource access for trees. However, uncertainties persist regarding crown size differences, tree growth, and tree competition levels between coniferous and broadleaf trees in mixed-age forests. We conducted a study on 3008 live trees across 28 plots in Hunan Province to measure individual crown size and tree growth increment. Introducing a new metric, the Crown Size Competition Index (CSCI), we assessed competition pressure among coniferous and broadleaf species in mixed-age forests. We examined the correlation between competition indices and tree growth increment while also investigating the influence of neighboring species diversity on tree competition. Our results revealed a significant negative correlation between the tree growth of Cunninghamia lanceolata (Lamb.) Hook.(CL) and Phoebe bournei (Hemsl.) Yang (PB) and the competition index. Increased competitive pressure on the focal tree corresponded to a decline in the growth of focal tree volume, with a more pronounced impact observed in PB compared to CL. The diversity of neighboring species contributed to variations in competition for the focal tree, with broadleaved species (PB) exerting a greater influence on the focal tree than coniferous species (CL). These findings underscore the competitive potential of PB in mature coniferous stands and advocate for the restoration of stratified mixes in CL broadleaf forests. Furthermore, our findings support the management strategies for the valuable tree species PB.

Abstract Objective This study aimed to explore lymphocyte subset determinations as an aid to understanding the pathophysiology of infectious mononucleosis (IM), pneumonia due to mycoplasma infection (P-MI) and Henoch–Schönlein purpura in children. Methods The peripheral blood lymphocyte subsets of 45 children with IM, 20 children with P-MI, and 31 children with Henoch–Schönlein purpura (HSP), who were treated in the pediatrics department of our hospital from April 2019 to February 2020, were determined by flow cytometry, and the number and percentage of lymphocyte subsets with CD3+, CD3 + CD4+, CD3 + CD8+, CD3 + CD4+/CD3 + CD8+, CD3–CD16 + CD56+, and CD3–CD19 + cells were observed, and the results were compared and analyzed. Results (1) The percentages of CD3+, CD3 + CD8 + lymphocyte subsets in children in IM group were significantly higher than those in children with P-MI and HSP, and the percentages of CD3-CD19 + lymphocyte subsets in children in IM group were significantly lower than those in children with P-MI and HSP. The percentages of CD3 + CD4 + lymphocyte subsets in children in the three groups were the lowest in children with IM, and the highest in children with P-MI.The differences in the percentages of CD3+, CD3 + CD4+, CD + CD8+, and CD3-CD19 + lymphocyte subsets among the IM, P-MI, and HSP groups were statistically significant (P 0.05). (3) The difference in the percentage of CD3–CD16 + CD56 + lymphocyte subsets among the three groups was not statistically significant (P > 0.05). Conclusion The determination of peripheral blood lymphocyte subsets is of significance for understanding the pathophysiology of IM, mycoplasma pneumonia, and HSP in children.

Calpain is a conserved cysteine protease readily expressed in several mammalian tissues, which is usually activated by Ca2+ and with maximum activity at neutral pH. The activity of calpain is tightly regulated because its aberrant activation will nonspecifically cleave various proteins in cells. Abnormally elevation of Ca2+ promotes the abnormal activation of calpain during myocardial ischemia-reperfusion, resulting in myocardial injury and cardiac dysfunction. In this paper, we mainly reviewed the effects of calpain in various programmed cell death (such as apoptosis, mitochondrial-mediated necrosis, autophagy-dependent cell death, and parthanatos) in myocardial ischemia-reperfusion. In addition, we also discussed the abnormal activation of calpain during myocardial ischemia-reperfusion, the effect of calpain on myocardial repair, and the possible future research directions of calpain.

The spatial structure of forest stands significantly influences inter-tree competition and the overall stability of the stand’s ecological dynamics, and a profound understanding of forest stand spatial structure is essential for both effective forest management and ecological research. Previous studies on spatial structure have primarily employed single variables, offering only one-dimensional structural insights and lacking the capacity to interpret multidimensional information. In light of these limitations, our study introduces a novel approach founded on a six-variable distribution, aimed at conducting a comprehensive analysis and interpretation of the spatial attributes of forest stands. Diverging from conventional univariate or bivariate methods, the hexi-variate approach simultaneously considers six variables, facilitating a more intricate exploration of the intricate interrelationships within forest ecosystems from six distinct dimensions. We conducted an in-depth analysis of the spatial structural attributes within the forest stand, encompassing factors such as species diversity, size variation, spatial distribution patterns, openness, vertical stratification, and stand competition. To capture a comprehensive view of the trees’ spatial information, we employed the hexadecimal distribution method, effectively quantifying their characteristics across six dimensions. Our study unveiled a significant correlation between spatial structure and stand growth, establishing a connection by integrating the spatial structure with key structural features relevant to tree size. The outcomes of this study shed light on the effectiveness and superiority of the six-element distribution method when it comes to the analysis of forest structural characteristics. Our approach offers valuable insights into the optimization of forest management strategies, encompassing selective harvesting and biodiversity conservation, thereby establishing a solid footing for sustainable forest management practices.

In order to solve the problem of the insufficient range of the traditional fast mirror (FSM) angle measurement system in practical applications, a 2D large-angle FSM photoelectric angle measurement system based on the principle of diffuse reflection is proposed. A mathematical model of the angle measurement system is established by combining the physical properties of the diffuse reflecting plate, such as the rotation angle, rotation center, rotation radius, reflection coefficient and the radius of the diffuse reflecting surface. This paper proposes a method that optimizes the degree of nonlinearity based on this mathematical model. The system is designed and tested. The experimental results show that changing the diffuse reflection surface area can improve the nonlinearity of the angle measurement system effectively. When the radius of the diffuse reflection surface is 3.3 mm, the range is ±20°, the non-linearity is 0.74%, and the resolution can reach up to 2.3″. The system’s body is simple and compact. It is also capable of measuring a wider range of angles while linearity is guaranteed.

The disposal of glass fiber-reinforced plastic (GFRP) waste has become an urgent issue in both the engineering and environmental fields. In this study, the feasibility of reusing mechanically recycled GFRP in concrete was evaluated. Secondary screening of the recycled material was conducted to obtain different types of products, and the recycled GFRP (rGFRP) was characterized. Subsequently, the effect of rGFRP on concrete performance was evaluated using different dosages (0%, 1%, 3%, 5%) of rGFRP powder and rGFRP cluster (with different sizes and fiber contents) to replace fine aggregate in concrete preparation. The experimental results indicated that the addition of rGFRP powder has no significant impact on the mechanical properties of concrete, while the addition of a small amount of rGFRP cluster slightly improves the compressive strength and splitting tensile strength of concrete. Additionally, the short fibers in rGFRP improve the failure mode of concrete, and increased fiber content and longer fiber length demonstrate a more pronounced reinforcing effect. The challenges and potential directions for future research in the realm of reusing rGFRP in concrete are discussed at the end. A systematic process for reusing GFRP waste in concrete is proposed to address the primary challenges and provide guidance for its practical engineering application.

Real-time detection of satellite remote sensing images is one of the key technologies in the field of remote sensing, which requires not only high-efficiency algorithms, but also low-power and high-performance hardware deployment platforms. At present, the image processing hardware acceleration platform mainly uses an image processing unit (GPU), but the GPU has the problem of large power consumption, and it is difficult to apply to micro-nano satellites and other devices with limited volume, weight, computing power, and power consumption. At the same time, the deep learning algorithm model has the problem of too many parameters, and it is difficult to directly deploy it on embedded devices. In order to solve the above problems, we propose a YOLOv4-MobileNetv3 field programmable gate array (FPGA) deployment scheme based on channel layer pruning. Experiments show that the acceleration strategy proposed by us can reduce the number of model parameters by 91.11%, and on the aerial remote sensing dataset DIOR, the average accuracy of the design scheme in this paper reaches 82.61%, the FPS reaches 48.14, and the average power consumption is 7.2 W, which is 317.88% FPS higher than the CPU and reduces the power consumption by 81.91%. Compared to the GPU, it reduces power consumption by 91.85% and improves FPS by 8.50%. Compared with CPUs and GPUs, our proposed lightweight algorithm model is more energy-efficient and more real-time, and is suitable for application in spaceborne remote sensing image processing systems.

The PBA construction method is suitable for urban subway construction in situations with dense traffic and many surrounding buildings and can effectively reduce surface disturbance and control surface subsidence. Based on field-measured data, this study creates numerical finite element models of 3D stratum-structure, studies the influence of each process conversion on the subsidence of the ground surface and the force of the station supporting structure during the construction of the PBA station, and clarifies the optimal construction parameters. The research results show that the consistency between the field monitoring data and the simulation data is more than 80%, which verifies the correctness of the model. It is concluded that the formation of the pile-beam-arch is the main part of the supporting structure and that digging the pilot tunnel and the formation of the beam arch are the control factors of surface settlement. Based on this, the best sequence of pilot tunnel excavation and arch buckle of the two-story two-span station is obtained, and the optimal pilot tunnel excavation scheme of first the middle and then the side, first the upper and then the lower, and the best arch buckle scheme of the synchronous arch buckle on both sides is determined. The results of the research can serve as a guideline when selection the construction parameters for similar PBA stations.

BackgroundA substantial heterogeneity exists in patients with upper gastrointestinal submucosal tumors (SMTs). This study aimed to identify predictors of long procedure time (≥60 min), occurrence of procedure-related complications, and long hospital stay (≥6 days) in patients with SMTs undergoing submucosal tunnel endoscopic resection (STER) and stratify risk based on the predictors.MethodsSixty-six consecutive patients with upper gastrointestinal SMTs undergoing STER between January 2013 and December 2018 were retrospectively included. Binary logistic regression models were developed to identify predictors of outcomes. Receiver operating characteristic (ROC) curves were constructed to evaluate the discrimination of tumor size.ResultsComplete resection and en bloc resection of tumor were achieved in 66 (100%) and 64 patients (97%), respectively. Twenty-seven patients (41%) had a long procedure time, 10 (15%) developed STER-related complications, and 17 (26%) had a long hospital stay. On multivariable analysis, tumor size was an independent predictor of long procedure time (OR 1.37, 95% CI 1.13–1.67; p = 0.001), occurrence of complications (OR 1.06, 95% CI 1.01–1.10; p = 0.012), and long hospital stay (OR 1.05, 95% CI 1.01–1.09; p = 0.035). ROC curves identified a tumor of size 25 mm as the best cutoff; those who had a tumor above this value had a 76-fold risk of long procedure time, 8.56-fold risk of occurrence of complications, and 6.35-fold risk of long hospital stay.ConclusionPatients with a tumor size ≥25 mm had longer procedure time, higher risk of STER-related complications, and longer hospital stay; therefore, they should be classified as a high-risk group.

Background & Aims: Among individuals with Child-Pugh B cirrhosis and acute variceal bleeding (AVB), the Baveno VII workshop recommended pre-emptive TIPS in those with a Child-Pugh score of 8-9 and active bleeding at initial endoscopy (Child B8-9 + AB criteria). Nevertheless, whether this criterion is superior to the CLIF-Consortium acute decompensation score (CLIF-C ADs) remains unclear. Methods: Data on 1,021 consecutive individuals with Child-Pugh B cirrhosis and AVB from 13 university hospitals in China who were treated with pre-emptive TIPS (n = 297) or drug plus endoscopic treatment (n = 724) between 2010 to 2019 were retrospectively analysed. A competing risk regression model was used to compare the outcomes between the two groups after adjusting for confounders. The concordance-statistic for benefit (c-for-benefit) was used to evaluate a models’ ability to predict treatment benefit (risk difference between treatment groups). Results: Pre-emptive TIPS was associated with reduced mortality compared to drug plus endoscopic treatment (adjusted hazard ratio 0.62, 95% CI 0.44 to 0.88). A higher baseline CLIF-C AD score was associated with greater survival benefit (i.e., larger absolute mortality risk reduction). After adjusting for confounders, a survival benefit was observed in individuals with CLIF-C ADs ≥48 or Child-Pugh B8-9 with active bleeding, but not in those with CILF-C ADs

The accuracy of current deep learning algorithms has certainly increased. However, deploying deep learning networks on edge devices with limited resources is challenging due to their inherent depth and high parameter count. Here, we proposed an improved YOLO model based on an attention mechanism and receptive field (RFA-YOLO) model, applying the MobileNeXt network as the backbone to reduce parameters and complexity, adopting the Receptive Field Block (RFB) and Efficient Channel Attention (ECA) modules to improve the detection accuracy of multi-scale and small objects. Meanwhile, an FPGA-based model deployment solution was proposed to implement parallel acceleration and low-power deployment of the detection algorithm model, which achieved real-time object detection for optical remote sensing images. We implement the proposed DPU and Vitis AI-based object detection algorithms with FPGA deployment to achieve low power consumption and real-time performance requirements. Experimental results on DIOR dataset demonstrate the effectiveness and superiority of our RFA-YOLO model for object detection algorithms. Moreover, to evaluate the performance of the proposed hardware implementation, it was implemented on a Xilinx ZCU104 board. Results of the experiments for hardware and software simulation show that our DPU-based hardware implementation are more power efficient than central processing units (CPUs) and graphics processing units (GPUs), and have the potential to be applied to onboard processing systems with limited resources and power consumption.

A complementary ensemble adaptive local iterative filtering (CEALIF) and enhanced maximum correlation kurtosis deconvolution (EMCKD) approach is proposed for weak fault signals in wind turbine bearings, which are easily concealed by strong background noise and susceptible to intermittent interference. The adaptive local iterative filtering (ALIF), as a novel nonstationary signal processing technique, can perform adaptive filtering based on the signal itself characteristics. However, its mode mixing is an annoying problem. To relieve this problem, the noise-assisted CEALIF-based filtering is proposed. Nonetheless, the ambient noise present in the original signal is retained in the component of interest. Maximum correlation kurtosis deconvolution (MCKD) is an effective tool for enhancing periodic pulses. However, its deconvolution parameters need to be set manually, and are more demanding when the bearing failure is weak. To address this circumstance, this paper introduces the particle swarm algorithm (PSO) to solve the optimal deconvolution parameters and proposes EMCKD. Firstly, by employing CEALIF, the original signal is adaptively filtered into a sequence of IMFs. Then, the IMF that best characterizes the fault information is selected based on the weighted kurtosis index (WKI). Finally, the shock components of the selected IMF are enhanced to extract the periodic shock based on EMCKD. The proposed approach can accurately extract fault characteristics by analyzing the whole life cycle signals of bearings and fault signals of a 1.5 MW direct-drive wind turbine within strong background noise. Further, the proposed approach is implemented for the compound fault extraction of bearings, and the compound fault information of the inner race as well as the outer race of the bearings is successfully extracted.

ObjectiveWe aimed to construct and validate a predictive model for the risk of hypocalcemia following parathyroidectomy (PTX) for the treatment of secondary(renal) hyperparathyroidism (SHPT).MethodsInformation regarding patients with SHPT who underwent PTX between January 2019 and April 2022 was collected retrospectively. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for hypocalcemia following PTX and to construct predictive models. The areas under the receiver operating characteristic curve (AUC), the calibration curve, and the clinical decision curve (decision curve analysis, DCA) were used to assess the discrimination, calibration, and level of clinical benefit obtained using the predictive models.ResultsWe studied 238 patients who were randomly allocated in a 7:3 ratio to a training group (n=166) and a test group (n=72). Univariate and multivariate logistic regression analyses were performed, in which three variables (the circulating parathyroid hormone (PTH) and Ca concentrations, and alkaline phosphatase (ALP) activity) were interrogated for possible roles as independent risk factors for hypocalcemia in patients with SHPT who undergo PTX, and used to construct predictive models. The AUCs for the constructed models were high for both the training (0.903) and test (0.948) groups. The calibration curve showed good agreement between the incidence of postoperative hypocalcemia estimated using the predictive model and the actual incidence. The DCA curve indicated that the predictive model performed well.ConclusionA predictive model constructed using a combination of preoperative PTH, Ca, and ALP may represent a useful means of identifying patients with SHPT at high risk of developing hypocalcemia following PTX in clinical practice.

BackgroundCavernous transformation of the portal vein (CTPV) causes portal hypertension in children. Among Meso-Rex treatments, it is unclear whether the Meso-Rex bypass shunt (MRB) or the Meso-Rex transposition shunt (MRT) offers lower postoperative morbidity. Our objective was to evaluate postoperative outcomes, comparing MRB and MRT for children with CTPV.MethodsA retrospective study was conducted on children undergoing Meso-Rex for CTPV from January 2010 to December 2020. The primary outcome was shunt complications, including shunt stenosis and thrombus. The secondary outcome was re-operation.ResultsOf the 43 patients included, 21 underwent MRT and 22 underwent MRB. MRT was associated with a higher rate of shunt complications when compared to MRB (23.8 vs. 9.1%, p = 0.191). The patients exhibited a higher rate of re-operation under the MRT than under the MRB (19 vs. 4.5%, p = 0.138). The operative time in the MRT group was significantly shorter than in the MRB group. Compared to MRT, the reduction in the length and thickness of the spleen was significantly greater in the MRB group. The increases in platelets were significantly higher in the MRB group than in the MRT group. The postoperative shunt velocity of MRB was notably faster than MRT. There was no significant difference in postoperative portal pressure between the two groups (p > 0.05).ConclusionBoth MRB and MRT result in acceptable postoperative outcomes, but MRT is associated with higher post-shunt complications, which often increase the re-operation rate. This study suggests that MRB may offer advantages for children with CTPV.

IntroductionSecondary hyperparathyroidism (SHPT) is a common complication in hemodialysis patients with chronic renal failure uremia. For severe SHPT, parathyroidectomy is effective. Owing to the variability in parathyroid anatomy, surgical parathyroidectomy can be complex and many patients experience recurrent SHPT, which may require repeated surgery. These cases pose significant challenges to surgeons.Case descriptionAn elderly woman with recurrent severe SHPT was admitted to our hospital. Preoperative methoxyisobutylisonitrile (MIBI) examination found a large ectopic parathyroid gland in the superior mediastinum, and she underwent reoperative parathyroidectomy. A large parathyroid gland in the right anterior mediastinum and another parathyroid gland in the left lingual lobe of the thymus were removed. The patient had postoperative hypocalcemia that was successfully corrected with calcium supplementation via femoral vein catheterization. During the 1-year postoperative follow-up, the patient's iPTH was well controlled and her blood calcium was within the normal range.ConclusionWe report a case of parathyroidectomy to remove multifocal ectopic hyperplastic parathyroid tissue in the mediastinum. Preoperative MIBI accurately detected the lesions. Calcium supplementation via femoral vein catheterization successfully corrected postoperative hypocalcemia. Postoperative follow-up for 1 year indicated that the surgery was successful.

We investigate a backward problem of the time-space fractional symmetric diffusion equation with a source term, wherein the negative Laplace operator −Δ contained in the main equation belongs to the category of uniformly symmetric elliptic operators. The problem is ill-posed because the solution does not depend continuously on the measured data. In this paper, the existence and uniqueness of the solution and the conditional stability for the inverse problem are given and proven. Based on the least squares technique, we construct a Galerkin regularization method to overcome the ill-posedness of the considered problem. Under a priori and a posteriori selection rules for the regularization parameter, the Hölder-type convergence results of optimal order for the proposed method are derived. Meanwhile, we verify the regularized effect of our method by carrying out some numerical experiments where the initial value function is a smooth function or a non-smooth one. Numerical results show that this method works well in dealing with the backward problem of the time-space fractional parabolic equation.

Background & Aims: Hepatic encephalopathy (HE) is a major complication after transjugular intrahepatic portosystemic shunt (TIPS) and is primarily influenced by the gut microbiota. We aimed to evaluate alterations in the microbiota after TIPS and the association between such alterations and HE. Methods: We conducted a prospective longitudinal study of 106 patients with cirrhosis receiving TIPS. Faecal samples were collected before and after TIPS, and the gut microbiota was analysed by 16S ribosomal RNA sequencing. Results: Among all patients, 33 developed HE (HE+ group) within 6 months after TIPS and 73 did not (HE- group), and 18 died during follow-up. After TIPS, the autochthonous taxa increased, whereas the potential pathogenic taxa decreased in the HE- group, and the autochthonous taxon Lachnospiraceae decreased in the HE+ group. Furthermore, synergism among harmful bacteria was observed in all patients, which was weakened in the HE- group (p

Depression is common among patients who have chronic myocardial infarction (CMI). Despite their frequency, depression and CMI are bidirectional related conditions, each is a risk for the other, and they often co-exist, suggesting shared or interacting pathomechanisms. Accumulating data revealed the effects of gut microbiota in terms of regulating depression via the gut–brain axis. Thus, we investigated the role of gut microbial dysbiosis in CMI-induced depression-like behavior. Hierarchical cluster analysis of sucrose preference test (SPT) results was adopted to classify the CMI rats into depression-like behavior (CMI + Dep) or non-depression-like behavior (CMI + Non-Dep) phenotypes. First, 16S ribosomal RNA sequencing analysis showed both β-diversity and relative abundance of several gut bacteria significantly differed between the CMI + Dep and CMI + Non-Dep rats. Next, transplantation of fecal microbiota from CMI + Dep rats visibly altered the relative abundance of gut microbiota and also induced depression-like behavior in the antibiotics-treated pseudo-germ-free rats. In conclusion, these findings suggested that dysbiosis in gut microbial composition contributed to the onset of CMI-induced depression-like behavior and that exogenous regulation of gut microbiota composition could be a potential therapeutic strategy for CMI and related depression-like behavior.

Introduction: Lenvatinib is the first-line treatment for advanced hepatocellular carcinoma (HCC). We aimed to compare the clinical outcomes of lenvatinib plus drug-eluting beads transarterial chemoembolization (DEB-TACE) versus lenvatinib alone in real-world practice. Methods: This retrospective analysis included 142 consecutive patients who received lenvatinib plus DEB-TACE and 69 patients who received lenvatinib alone as first-line treatment from 15 Chinese academic centers from Nov 2018 to Nov 2019. Overall survival (OS), progression-free survival (PFS), objective response rate (ORR) evaluated by mRECIST criteria, and safety profiles were compared between the two groups. Results: The median OS and PFS were significantly longer in the combined therapy group than in the monotherapy group in whole cohort (median OS, 15.9 vs 8.6 months, p=0.0022; median PFS, 8.6 vs 4.4 months, p

Object detection plays an important role in computer vision. It has a variety of applications, including security detection, vehicle recognition, and service robots. With the continuous improvement of public databases and the development of deep learning, object detection has witnessed significant breakthroughs. However, the object detection of sweeping robots during operations should consider various factors, including the camera angle, indoor scenery, and identification of object category. To the best of our knowledge, no corresponding database on these conditions has been developed. In this study, we review the development of object detection based on deep learning in computer vision. Then, we propose a large-scale publicly available benchmark dataset called object detection for sweeping robots in home scenes (ODSR-IHS). The dataset has 6,000 images and 16,409 instances of 14 object categories. Finally, we evaluate several state-of-the-art methods on the ODSR-IHS dataset and transplant them to the hardware to establish a benchmark dataset for object recognition research on sweeping robots.

A fault diagnosis model for rolling bearing based on complementary ensemble adaptive local iterative filtering (CEALIF), Laplacian score (LS) feature selection, and genetic algorithm-based backpropagation neural network (GA-BPNN) is proposed in this article. When the rolling bearing fails, the field-measured waveforms usually shows strong nonlinearity and non-stationary characteristics. Adaptive Local Iterative Filtering (ALIF) is an alternative novel approach to empirical mode decomposition to decompose complex signals into multiple intrinsic mode functions (IMFs), but modal aliasing will occur in actual processing. Aiming at the phenomenon of modal aliasing, a noise-assisted analysis methodology, namely complementary ensemble adaptive local iterative filtering, which could overcome the modal aliasing problem of ALIF. This article applies it to the pre-processing of rolling bearing time series. Then, the time domain (TD) statistical features of the IMFs, their Fourier frequency domain (FD) features, and the time frequency domain (TFD) energy features are extracted to capture the fault information. Meanwhile, to avoid feature redundancy and enhance the diagnostic performance, the LS is adopted to rank the features to improve the fault characteristics. Subsequently, the optimized feature vectors are entered into the GA-BPNN to automatically achieve the fault type recognition. The experimental data analysis results of rolling bearings indicate that the model can effectively diagnose the degree and type of failure.

Glass Fiber Reinforced Plastic (GFRP) pipes are widely used as polymer-based composite pipes in various engineering fields where the temperature influences their performance. This paper investigated the circumferential bending properties of GFRP pipes with different continuous fiber contents at 30 °C, 50 °C and 70 °C. GFRP pipes are classified into three types according to their component content: type I, type II and type III. The results show that the bending performance of GFRP pipes tends to decrease with increasing temperature, with the retention of circumferential stiffness being 80–85% and the retention of bending strength and damage displacement being about 25–40% from 30 °C to 70 °C. The rate of decay of ring stiffness, bending strength and damage displacement is significantly higher from 30 °C to 50 °C than from 50 °C to 70 °C. Both temperature and continuous fiber content greatly influenced the damage pattern. At 30 °C, delamination damage occurred at the top and bottom of the Type I GFRP pipe before fracture damage happened at the left and right ends and fracture damage occurred at both the left and right ends of Type II and Type III GFRP pipes. Delamination damage happened at the upper and lower ends of the GFRP pipes at 50 °C and 70 °C. In addition, the paper analyses the mechanisms of the associated effects.

Starch is a major component of crop grains, and its content affects food quality and taste. Tartary buckwheat is a traditional pseudo-cereal used in food as well as medicine. Starch content, granule morphology, and physicochemical properties have been extensively studied in Tartary buckwheat. However, the complex regulatory network related to its starch biosynthesis needs to be elucidated. Here, we performed RNA-seq analyses using seven Tartary buckwheat varieties differing in starch content and combined the RNA-seq data with starch content by weighted correlation network analysis (WGCNA). As a result, 10,873 differentially expressed genes (DEGs) were identified and were functionally clustered to six hierarchical clusters. Fifteen starch biosynthesis genes had higher expression level in seeds. Four trait-specific modules and 3131 hub genes were identified by WGCNA, with the lightcyan and brown modules positively correlated with starch-related traits. Furthermore, two potential gene regulatory networks were proposed, including the co-expression of FtNAC70, FtPUL, and FtGBSS1-3 in the lightcyan module and FtbHLH5, C3H, FtBE2, FtISA3, FtSS3-5, and FtSS1 in the brown. All the above genes were preferentially expressed in seeds, further suggesting their role in seed starch biosynthesis. These results provide crucial guidance for further research on starch biosynthesis and its regulatory network in Tartary buckwheat.

Purpose. To determine prognostic factors for patients with uveal melanoma without metastases and to construct nomograms to predict their 3- and 5-year overall survival (OS) and cancer-specific survival (CSS). Methods. We included 4119 patients who were registered from 2004 to 2015 in the Surveillance, Epidemiology, and End Results database. The median follow-up time was 5.8 years. Independent risk factors affecting OS and CSS were identified with univariate and multivariate Cox regression analyses and used to construct nomograms. Internal and external validation were carried out by using the bootstrap method to calculate the concordance indices (C-indices) and plot the calibration curves. Results. Age, primary site, histological type, T-stage, and treatment were independent risk factors for OS and CSS; marital status and sequence number were factors only for OS. The C-indices for internal validation of OS and CSS were 0.713 (95% CI, 0.697–0.729) and 0.708 (95% CI, 0.688–0.728), respectively, and for external validation they were 0.729 (95% CI, 0.705–0.753) and 0.731 (95% CI, 0.700–0.762), respectively. The calibration curves also revealed good agreement between the predicted and actual survival rates. Conclusions. We constructed nomograms to predict the 3- and 5-year OS and CSS of patients with uveal melanoma without metastases. Our nomograms may improve prognostication and assist with the development of individualized treatment strategies.

We, the Editors and Publisher of the journal Artificial Cells, Nanomedicine, and Biotechnology, have retracted the following article:Wencui Wan, Weiwei Wan, Yang Long, Qiuming Li, Xuemin Jin, Guangming Wan, Fengyan Zhang & Yong Lv. (2020). Physcion 8-O-β-glucopyranoside exerts protective roles in high glucose-induced diabetic retinopathy via regulating lncRNA NORAD/miR-125/STAT3 signalling. Artificial Cells, Nanomedicine, and Biotechnology, 48(1), 463–472, DOI: 10.1080/21691401.2019.1709861Since publication, concerns have been raised about the integrity of the data in the article. When approached for an explanation, the authors checked their data and confirmed there are fundamental errors associated with the figures presented. In addition, the corresponding author of the article, Qiuming, has informed us they were not aware of the submission made under their name. The authors have agreed to the retraction of this article, and we have also informed the institution about the authorship issues related to this article.We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions.The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as ‘Retracted’.

Tensor decomposition technique has been widely used in multichannel signals processing for its distinct superiority. The early fault feature signal of bearing is weak and easily inundated by ambient noise. In addition, the interference signals generated by other mechanical components also have a serious impact on the result of fault diagnosis. Aiming at the above issues, and built on the methods of generalized non-convex tensor robust principal component analysis (GNCTRPCA) and tensor singular value kurtosis (TSVK), this paper introduces a new fault diagnosis technique for multichannel bearing signals. First, the attractor tensor is formed by reconstructing the acquired multichannel signals in phase space. Second, based on tensor singular value decomposition (TSVD), the tensor robust principal component analysis (TRPCA) can provide a favorable noise reduction performance. However, TRPCA usually lowers the amplitude of useful singular value tubes (SVTs). To tackle this problem, the GNCTRPCA method is proposed to avoid the amplitude reduction. Third, a new TSVK method is introduced to determine the reconstructed order of SVTs, so as to extract the multichannel fault feature signals. Finally, the bearing fault type can be identified by comparing the peak frequencies of the extracted signals with the theoretical fault-related frequencies. Simulation analysis and experiment studies verify the principle and effectiveness of the proposed technique.

This paper investigates distributed adaptive bipartite consensus for second-order multi-agent systems (MASs) with bounded disturbances under a signed graph. Based on adaptive variable structure control strategies, an adaptive bipartite consensus protocol is proposed, which guarantees that the bipartite consensus can be realized if the signed graph is structurally balanced and connected. Furthermore, the external disturbances are attenuated effectively by the variable structure control. And the obtained results are applied to adaptive consensus of disturbed second-order MASs under cooperative networks. Finally, a numerical simulation is presented to demonstrate the theoretical finding.

Random lasers (RLs) are a kind of coherent light source with optical feedback based on disorder-induced multiple scattering effects instead of a specific cavity. The unique feedback mechanism makes RLs different from conventional lasers. They have the advantages of small volume, flexible shape, omnidirectional emission, etc., and have broad application prospects in the fields of laser illumination, speckle-free imaging, display, and sensing. Colloidal metal-halide perovskite nanomaterials are a hot research field in light sources. They have been considered as desired gain media owing to their superior properties, such as high photoluminescence, tunable emission wavelengths, and easy fabrication processes. In this review, we summarize the research progress of RLs based on perovskite nanomaterials. We first present the evolution of the RLs based on the perovskite quantum dots (QDs) and perovskite films. The fabrication process of perovskite nano-/microstructures and lasers is discussed in detail. After that, the frontier applications of perovskite RLs are discussed. Finally, the challenges are discussed, and the prospects for further development are proposed.

To meet the demand of the breeding and processing industry of Golden Tartary buckwheat, quantitative identification models were established to test the content of leucine (Leu) and tyrosine (Tyr) in Golden Tartary buckwheat leaves by near-infrared reflectance spectroscopy (NIRS) with quantitative partial least squares (PLS). Leu’s modeling results were as follows: first derivative (11) pretreatment, the wavenumber range of 4000–9000 cm−1 was appropriate for modeling (calibration sets: validation set = 6:1), the mean coefficient of determination (R2), standard error of calibration (SEC), and relative standard deviation (RSD) for the calibration set were 0.9229, 0.45, and 3.45%, respectively; for the validation set, the mean R2, SEC, and RSD were 0.9502, 0.47, and 3.65%, respectively. Tyr modeling results were as follows: first derivative (11) pretreatment, the wavenumber range of 4000–10,000 cm−1 was suitable for modeling (calibration sets: validation set = 4:1), the R2, SEC, and RSD for the calibration set was 0.9016, 0.15, and 5.72%, respectively; for the validation set, the mean R2, SEC, and RSD were 0.9012, 0.15, and 5.53%, respectively. It was proved that the Leu and Tyr content of Golden Tartary buckwheat could be quantified using the model structured by near infrared spectroscopy combined with the partial least squares method.

Tool wear condition monitoring during the machining process is one of the most important considerations in precision manufacturing. Cutting force is one of the signals that has been widely used for tool wear condition monitoring, which contains the dynamical information of tool wear conditions. This paper proposes a novel multivariate cutting force-based tool wear monitoring method using one-dimensional convolutional neural network (1D CNN). Firstly, multivariate variational mode decomposition (MVMD) is used to process the multivariate cutting force signals. The multivariate band-limited intrinsic mode functions (BLIMFs) are obtained, which contain a large number of nonlinear and nonstationary tool wear characteristics. Afterwards, the proposed modified multiscale permutation entropy (MMPE) is used to measure the complexity of multivariate BLIMFs. The entropy values on multiple scales are calculated as condition indicators in tool wear condition monitoring. Finally, the one-dimensional feature vectors are constructed and employed as the input of 1D CNN to achieve accurate and stable tool wear condition monitoring. The results of the research in this paper demonstrate that the proposed approach has broad prospects in tool wear condition monitoring.

A choledochal cyst (CC) is a common congenital biliary disease in children, yet the underlying molecular bases for the cystic and fusiform clinical subtypes are unknown. RNA sequencing (RNA-seq) has been performed on 22 high-quality CC samples, including 12 cystic CC and 10 fusiform CC samples, to search for molecular features. Weighted gene co-expression network analysis (WGCNA) was performed to identify key modules associated with clinical subtypes. Bioinformatic analyses were conducted to elucidate potential mechanisms. Then, we constructed protein–protein interaction (PPI) networks to identify candidate hub genes related to CC. Finally, we used the support vector machine (SVM) to eliminate redundant features and screen out the hub genes. The selected gene expression was determined in CC patients through quantitative real-time polymerase chain reaction (PCR). A total of 6,463 genes were found to be aberrantly expressed between cystic CC and fusiform CC. Twelve co-expression modules that correlated with clinical subtypes of CC were identified and assigned representative colors. Among the 12 modules, the blue module was considered the key module. Two functionally distinct sets of dysregulated genes have been identified in two major subtypes, metabolism-related genes in cystic CC and immune-related genes in fusiform CC. A total of 20 candidate hub genes that were correlated with clinical subtypes were found in the blue module. In addition, we found ERBB2 and WNT11 that have not been studied in CC and verified their differential expression in CC through quantitative real-time PCR experiments. For the first time, we have described the transcriptome characteristics of CC. These results suggest that cystic CC and fusiform CC have different molecular mechanisms. The bi-omics-identified novel candidate genes and pathways might be helpful for personalized treatment and are of great clinical significance for CC.

Background and Aim: The impact of liver function test (LFTs) abnormality on adverse clinical outcomes in coronavirus disease 2019 (COVID-19) patients remains controversial. The aim of this study was to assess the impact of abnormal LFTs on clinical outcomes in a large cohort of hospitalized patients with COVID-19.Methods: We retrospectively collected data on 2,912 consecutive patients with COVID-19 who were admitted to a makeshift hospital in China between 5 February and 23 March 2020. The association between LFTs abnormalities (baseline and peak values) and clinical outcomes was measured by using Cox regression models.Results: On admission 1,414 patients (48.6%) had abnormal LFTs, with alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL), alkaline phosphatase (ALP), and gamma-glutamyltransferase (GGT) elevation in 662 (22.7%), 221 (7.6%), 52 (1.8%), 135 (4.6%), and 536 (18.5%) patients, respectively, and hypoalbuminemia in 737 (25.3%) patients. During a median 13 (IQR: 8–19) days of hospitalization, 61 patients (2.1%) died, 106 patients (3.6%) admitted to intensive care unit (ICU), and 75 patients (2.6%) required mechanical ventilation. After adjustment for confounders, baseline abnormal LFTs were independently associated with increased risks of mortality (adjusted HR 3.66, 95%CI 1.64–8.19, p = 0.002), ICU admission (adjusted HR 3.12 95%CI 1.86–5.23, p < 0.001), and mechanical ventilation (adjusted HR 3.00, 95%CI 1.63–5.52, p < 0.001), which was homogeneous across the severity of COVID-19 infection. Among the parameters of LTFs, the associations with the outcomes were more pronounced for AST and albumin abnormality. In contrast, ALT elevation was not significantly associated with those outcomes. Similar results were observed for peak values of LFTs during hospitalization.Conclusions: Abnormality of AST, albumin, TBIL, ALP, and GGT but not ALT were independently associated with adverse outcomes.

Fault diagnosis of rolling bearings can be a serious challenge, as rolling bearings often work under complex conditions and their vibration signals are typically nonlinear and nonstationary. This paper proposes a novel approach to diagnosing faults of rolling bearings based on variational mode decomposition (VMD) and genetic algorithm-optimized wavelet threshold denoising. First, VMD was used to decompose the vibration signals of faulty rolling bearings into a series of band-limited intrinsic mode functions (BLIMFs). During the decomposition, the parameters of VMD were selected by Kullback–Leibler (K–L) divergence. Then, the effective BLIMFs were determined by the analysis of their correlation coefficients and variance contributions. Finally, genetic algorithm-optimized wavelet threshold denoising was proposed to optimize the selection of important parameters, and the optimized threshold function used not only ensures the continuity of the threshold function but also avoids the fixed deviation of the soft threshold. The validity and superiority of the proposed approach were verified by theoretical calculations, numerical simulations and application studies. The results indicate that the proposed approach is promising in fault diagnosis of rotary machinery.

A novel output-only crack localization method is proposed for operating rotors based on an enhanced higher-order dynamic mode decomposition (HODMD), in which the nonlinear breathing crack-induced super-harmonic characteristic components from multiple vibration measurement points are simultaneously extracted to compose the corresponding super-harmonic transmissibility damage indexes. Firstly, the theoretical background of the HODMD is briefly reviewed. Secondly, the proposed crack localization method is dedicated which improving the HODMD for multivariate signals by casting the total least square method into standard HODMD and adaptively selecting the order parameter of Koopman approximation by optimizing the super-harmonic frequency vector. In addition, the super-harmonic characteristic components are evaluated and harnessed to derive the damage index based on super-harmonic transmissibility and fractal dimension. Finally, the proposed method is investigated and demonstrated by numerical simulations and experiments. Both numerical and experimental results show that the proposed method is powerful in realizing multi-crack localization for running rotors accurately and robustly in the case of no baseline information on intact rotors. Moreover, the interferences from commonly existing steps and misalignment can also be eliminated.

olling bearings are crucial components in mechanical, civil and aerospace engineering. The practical working conditions of rolling bearings are complex and tough, hence fault diagnosis of rolling bearings under varying operating conditions is very challenging. This paper proposes a robust fault diagnosis approach of rolling bearings using multivariate intrinsic multiscale entropy analysis and neural network under varying operating conditions. The proposed approach deals with multivariate signal collected from multi-sensor acquisition system to capture much dynamical characteristic information. Multivariate intrinsic multiscale entropy analysis consists of adaptive projection intrinsically transformed multivariate empirical mode decomposition with adaptive noise (APIT-MEMD-AN) and improved multivariate multiscale sample entropy (IMMSE) with smoothed coarse graining process. Intrinsic mode functions (IMFs) obtained by APIT-MEMD-AN depict dynamical properties of multivariate signals. IMMSE of certain orders IMFs are adopted as input values of back propagation (BP) neural network to achieve fault classification of rolling bearings. APIT-MEMD-AN and IMMSE endow the proposed approach with the underlying adaptivity and robustness, making the proposed approach a fully data driven and robust method. Theoretical derivations, numerical simulations and experimental results verify the effectiveness and superiority of the proposed approach. The research work demonstrates the proposed approach is promising in fault diagnosis of rotary machinery under varying operating conditions.

Bearings are widely used in mechanical equipment; nevertheless, potential dangers are also widespread, making bearing fault detection very important. For large equipment, the amount of collected signals tends to be huge, which challenges both signal transmission and storage. To solve this problem, compressed sensing (CS), based on specific dynamic modes of adaptive truncated rank dynamic mode decomposition (ADMD), is adopted to achieve the purpose of compressing and transmitting the signal, as well as extracting fault features. Firstly, this paper has proposed a new fitness function, which is called the synthetic envelope kurtosis characteristic energy difference ratio, and adopted the improved particle swarm optimization algorithm (IPSO) to select the best truncated rank adaptively. Then, the historical signal attempts to be decomposed into a series of temporal and spatial coherent modes, through ADMD, and those modes are filtered and cascaded into a highly self-adaptive dictionary, the DMD dictionary, which approximates the original signal with some error. Next, CS is employed to compress and reconstruct the signal, in order to reduce storage space and improve transmission efficiency. Finally, signals of high quality can be reconstructed through orthogonal matching pursuit (OMP) algorithm. Compared with traditional dictionaries, the DMD dictionary, based on the mode structure generated by ADMD decomposition, ass proposed in this paper, can better represent the original signal in the simulation signal and have good noise reduction performance. The correlation coefficient (CORR) between the reconstructed signal and noise signal is 0.8109, between the reconstructed signal and non-noise signal is 0.9278, and the root mean square error (RMSE) is 0.0659 and 0.0351, respectively. Compared with the traditional SVD and EMD denoising methods, ADMD-CS has better noise reduction performance. In this paper, the signal-to-noise ratio (SNR) is taken as the quantitative indicator of denoising performance. It is found that the SNR of simulation signal and experimental signal processed by ADMD-CS is higher than that of the traditional denoising methods, which is 0.3017 and 0.8407, respectively. The storage space of the signal is quite smaller than traditional methods, and the compression ratios (CR) of the simulation and experimental signals are 66.16% and 59.08%, respectively. In conclusion, ADMD-CS has a good application prospect in signal transmission, storage, and feature extraction.

Aberrant expression of microRNAs plays an important role in the pathogenesis and progression of retinoblastoma. MiR-25, a member of the miR-106b˜25 cluster, has been reported to be abnormally expressed in retinoblastoma, but the exact role of it remains unclear. In our study, we found that miR-25-3p was upregulated in retinoblastoma tissues and cell lines. Enforced expression of miR-25-3p in retinoblastoma cell line WERI-RB-1 increased cell growth, colony formation, anchorage-independent growth, cell migration and invasion in vitro and tumor xenograft growth in vivo. In contrast, inhibited miR-25-3p expression in retinoblastoma cell line Y79 suppressed cell growth, colony formation, anchorage-independent growth, cell migration and invasion. Through luciferase reporter assay, we found that phosphatase and tensin homolog (PTEN) was a direct target of miR-25-3p. This was verified by western blot that miR-25-3p overexpression suppressed PTEN and activated Akt signaling. In addition, miR-25-3p was found to promote epithelial-mesenchymal transition (EMT) of WERI-RB-1 cells through PTEN/Akt pathway. Western blot analysis revealed that miR-25-3p overexpression increased Vimentin and Snail expression, and suppressed E-cadherin expression, but this could be reversed by restoring PTEN. Moreover, LY294002 treatment or restoring PTEN expression abolished the effects of miR-25-3p on cell invasion, colony formation and anchorage-independent growth in vitro and tumor xenograft growth in vivo. Taken together, our results suggested that miR-25-3p promotes malignant transformation of retinoblastoma cells by suppressing PTEN.

Hepatic hydrothorax (HH) is a pleural effusion that develops in a patient with cirrhosis and portal hypertension in the absence of cardiopulmonary disease. Although the development of HH remains incompletely understood, the most acceptable explanation is that the pleural effusion is a result of a direct passage of ascitic fluid into the pleural cavity through a defect in the diaphragm due to the raised abdominal pressure and the negative pressure within the pleural space. Patients with HH can be asymptomatic or present with pulmonary symptoms such as shortness of breath, cough, hypoxemia, or respiratory failure associated with large pleural effusions. The diagnosis is established clinically by finding a serous transudate after exclusion of cardiopulmonary disease and is confirmed by radionuclide imaging demonstrating communication between the peritoneal and pleural spaces when necessary. Spontaneous bacterial empyema is serious complication of HH, which manifest by increased pleural fluid neutrophils or a positive bacterial culture and will require antibiotic therapy. The mainstay of therapy of HH is sodium restriction and administration of diuretics. When medical therapy fails, the only definitive treatment is liver transplantation. Therapeutic thoracentesis, indwelling tunneled pleural catheters, transjugular intrahepatic portosystemic shunt and thoracoscopic repair of diaphragmatic defects with pleural sclerosis can provide symptomatic relief, but the morbidity and mortality is high in these extremely ill patients.

Dynamic mode decomposition (DMD) has certain advantages compared with the traditional fault signal diagnosis method. By exploiting the strength of DMD algorithm in signal processing, this paper proposes a joint fault diagnosis scheme to extract the spatial and temporal patterns and evaluate them for the complexity to diagnose the fault for one-dimensional mechanical signal. The multiscale method is adopted to decompose the reconstructed matrix of standard DMD modes into multiple scales with a given level parameter. Total least squares DMD algorithm is performed on each level to solve the noise sensitivity problem. Approximate entropy (ApEn) is performed on the grouped multiscale spatiotemporal modes that represent the dynamic characteristic information of the original signal. ApEn values are used as a fault recognizer to identify fault types. By applying the algorithm on three experimental mechanical vibration data, we verify the effectiveness of the proposed method. The result demonstrates that the proposed scheme can effectively recognize different fault forms as a fault diagnosis method.

Absorptivity of three kinds of surface morphology, i.e., V-type groove surface, sinusoidal surface, and random distribution, is investigated using a rigorous electromagnetic theory and a finite element method. Influences of surface contour parameters (span distance, intersection angle, and height) and light wave parameters (incident angle and wavelength) on absorptivity are numerically simulated and analyzed for the three kinds of surfaces, respectively. Absorbing spectra about three silicon wafers with different surface roughness are recorded, and the results are coincident with simulated results.

Magnetic rotor-bearing system has drawn great attention because of its several advantages compared to existent rotor-bearing system, and explicit Runge–Kutta method has achieved good results in solving dynamic equation. However, research on flexible rotor of magnetic bearing is relatively less. Moreover, explicit Runge–Kutta needs a smaller integral step to ensure the stability of the calculation. In this article, we propose a nonlinear dynamic analysis of flexible rotor of active magnetic bearing established by using the finite element method. The precise Runge–Kutta hybrid integration method and the largest Lyapunov exponent are used to analyze the chaos of the rotor system at the first- and second-order critical speed of the rotor. Experiment on chaos analysis has shown that compared with the explicit Runge–Kutta method, the precise Runge–Kutta hybrid integration method can improve the convergence step of calculation significantly while avoiding iterative solution and maintain high accuracy which is four times the increase of the integral step.

The health condition of the rolling bearing seriously affects the operation of the whole mechanical system. When the rolling bearing parts fail, the time series collected in the field generally shows strong nonlinearity and non-stationarity. To obtain the faulty characteristics of mechanical equipment accurately, a rolling bearing fault detection technique based on k-optimized adaptive local iterative filtering (ALIF), improved multiscale permutation entropy (improved MPE), and BP neural network was proposed. In the ALIF algorithm, a k-optimized ALIF method based on permutation entropy (PE) is presented to select the number of ALIF decomposition layers adaptively. The completely average coarse-graining method was proposed to excavate more hidden information. The performance analysis of the simulation signal shows that the improved MPE can more accurately dig out the depth information of the time series, and the entropy value obtained is more consistent and stable. In the research application, rolling bearing time series are decomposed by k-optimized ALIF to obtain a certain number of intrinsic mode functions (IMFs). Then the improved MPE value of effective IMF is calculated and input into backpropagation (BP) neural network as the feature vector for automatic fault identification. The comparative analysis of simulation signals shows that this method can extract fault information effectively. At the same time, the experimental part shows that this scheme not only effectively extracts the fault features, but also realizes the classification and identification of different fault modes and faults of different degrees, which has a certain application prospect in the research and application direction of rolling bearing fault identification.

Based on the all-atomic molecular dynamics simulation method, the tensile creep behavior of epoxy-based polymer was discussed. The physical and mechanical properties of the model were characterized, such as glass transition temperature and yield strength. The simulation results are very close to the previous simulation and experimental results, and the correctness of the model is verified. On this basis, the tensile creep behavior and free volume evolution of polymer epoxy resin at different temperatures and stress levels were studied. The model fully predicted the three classical stages of epoxy resin creep (the primary, secondary and tertiary) and the dependent behavior of epoxy resin creep on temperature and stress level at the molecular level, and the creep rate increases with the increase of temperature and stress level. It was found that with the progress of the creep process, the proportion of free volume increases gradually under high stress levels, indicating that the effect of creep behavior on the structure of epoxy resin is that the interaction between atoms becomes weaker and weaker by increasing the distance between atoms, which finally induces creep failure in the material.

Based on the piezoresistive effect, the piezoresistive constitutive relation of a carbon fiber mat under orthogonal strain was deduced. Considering the Poisson effect, the piezoresistive responses and measurement of the sensitivity factor of a polymer-matrix carbon fiber mat under bidirectional strain were studied by a two-times uniaxial tension loading method in different directions, which was pasted in the center area of a cruciform aluminum substrate. The relations between the resistance change rate and the orthogonal strains were established, the reasonability of which was confirmed by comparison with the experimental results. The results show that the longitudinal piezoresistive sensitivity factor C11 is 21.55, and the lateral piezoresistive sensitivity factor C12 is 24.15. Using these factors, the resistance change rate of another polymer-matrix carbon mat was predicted, which was made by the same technique, and the error between the predicted and the experimental results was 1.3% in the longitudinal direction and 6.1% in the lateral direction.