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Aiming at the problem that frequently train traction starting causes wheel wear and fatigue damage, on the basis of the constant traction force, the influence of the speed at the end of the constant power section of different traction characteristic curves on wheel wear and fatigue was studied. SIMPACK and MATLAB software were used for co-simulation to establish the dynamic model of urban rail trains(URTs) under different traction characteristics and the wheel wear model based on Archard. The influence of the speed at the end of the constant power section of the traction characteristic curve on the wheel-rail creepage, wheel wear depth, wear area and fatigue index of URTs under different starting torque of traction motor was analyzed. The results show that during the train traction starting process, the larger the starting torque of the motor, the greater the wheel-rail creepage, and the wheel wear depth and wear area, while the smaller the fatigue index; For agiven starting torque of the motor, with the increase of the speed at the end of the constant power section of the train traction characteristic curve, the wheel-rail creepage, and the wheel wear depth and wear area also increase, while the fatigue index decreases.When the starting torque of the motor increases from 800 N·m to 1 400 N·m, the maximum growth rates of wheel wear depth and wear area are 6.9% and 12.6%, respectively, while the maximum decline rate of fatigue index is 7.2%; When the speed at the end of the constant power section increases from 50 km/h to 80 km/h, the maximum growth rates of wheel wear depth and wear area are 4.4% and 4.3%,respectively, and the maximum decline rate of fatigue index is 1.9%.The effects of starting torque and the speed at the end of the constant power section on wheel wear and fatigue are obtained, which provides reference for the design of vehicle traction characteristics, motor control and wheel operation and maintenance.

Objective: This study evaluated the safety and efficacy of an anti-epidermal growth factor receptor (EGFR) antibody (SCT200) and an anti-programmed cell death 1 (PD-1) antibody (SCT-I10A) as third-line or subsequent therapies in patients with rat sarcoma viral oncogene (RAS)/v-raf murine sarcoma viral oncogene homolog B (BRAF) wild-type (wt) metastatic colorectal cancer (mCRC). Methods: We conducted a multicenter, open-label, phase Ib clinical trial. Patients with histologically confirmed RAS/BRAF wt mCRC with more than two lines of treatment were enrolled and treated with SCT-I10A and SCT200. The primary endpoints were the objective response rate (ORR) and safety. The secondary endpoints included disease control rate (DCR), progression-free survival (PFS), and overall survival (OS). Results: Twenty-one patients were enrolled in the study through January 28, 2023. The ORR was 28.57% and the DCR was 85.71% (18/21). The median PFS and OS were 4.14 and 12.84 months, respectively. The treatment-related adverse events (TRAEs) were tolerable. Moreover, compared with the monotherapy cohort from our previous phase I study evaluating SCT200 for RAS/BRAF wt mCRC in a third-line setting, no significant improvements in PFS and OS were observed in the combination group. Conclusions: SCT200 combined with SCT-I10A demonstrated promising efficacy in previously treated RAS/BRAF wt mCRC patients with an acceptable safety profile. Further head-to-head studies with larger sample sizes are needed to validate whether the efficacy and safety of combined anti-EGFR and anti-PD-1 therapy are superior to anti-EGFR monotherapy in the third-line setting. (Registration No. NCT04229537).

The widespread deployment of electric buses (EBs) offers substantial flexibility resources to support the service restoration and enhance the resilience of distribution system (DS). Therefore, this paper presents a comprehensive and collaborative service restoration strategy for resilient DS, considering the support of EBs. First, the restoration flexibility model for EBs is derived, which quantifies the restoration capability of EBs by dividing effective EB clusters. This model is integrated into the DS restoration process. Then, a two-step collaborative restoration method of DS is proposed with the aim of minimizing the load curtailment cost. The first step addresses the restoration planning problem to determine the dispatch decisions for EB clusters. Based on above planning solutions, the second step focuses on the real-time restoration problem of DS, obtaining optimal restoration results through coordinating EB clusters and other local resources. In this stage, the impact of natural disasters on the EB cluster dispatch in transportation system is considered, and a rolling optimization method is employed to deal with uncertainties from renewables, load, and EB cluster dispatch time. Finally, numerical simulations are conducted using the modified IEEE 33-bus distribution test system and a 29-node transportation system to verify the effectiveness of the proposed restoration method.

High-frequency oscillation (HFO) of grid-connected wind power generation systems (WPGS) is one of the most critical issues in recent years that threaten the safe access of WPGS to the grid. Ensuring the WPGS can damp HFO is becoming more and more vital for the development of wind power. The HFO phenomenon of wind turbines under different scenarios usually has different mechanisms. Hence, engineers need to acquire the working mechanisms of the different HFO damping technologies and select the appropriate one to ensure the effective implementation of oscillation damping in practical engineering. This paper introduces the general assumptions of WPGS when analyzing HFO, systematically summarizes the reasons for the occurrence of HFO in different scenarios, deeply analyses the key points and difficulties of HFO damping under different scenarios, and then compares the technical performances of various types of HFO suppression methods to provide adequate references for engineers in the application of technology. Finally, this paper discusses possible future research difficulties in the problem of HFO, as well as the possible future trends in the demand for HFO damping.

High-voltage direct current (HVDC) transmission is a crucial way to solve the reverse distribution of clean energy and loads. The line commutated converter-based HVDC (LCC-HVDC) has become a vital structure for HVDC due to its high technological maturity and economic advantages. During the DC fault of LCC-HVDC, such as commutation failure, the reactive power regulation of the AC grid always lags the DC control process, causing overvoltage in the AC sending grid, which brings off-grid risk to the wind power generation based on power electronic devices. Nevertheless, considering that wind turbine generators have fast and flexible reactive power control capability, optimizing the reactive power control of wind turbines to participate in the transient overvoltage suppression of the sending grid not only improves the operational safety at the equipment level but also enhances the voltage stability of the system. This paper firstly analyses the impact of wind turbine's reactive power on AC transient overvoltage. Then, it proposes an improved voltage-reactive power control strategy, which contains a reactive power control delay compensation and a power command optimization based on the voltage time series prediction. The delay compensation is used to reduce the contribution of the untimely reactive power of wind turbines on transient overvoltage, and the power command optimization enables wind turbines to have the ability to regulate transient overvoltage, leading to the variation of AC voltage, thus suppressing the transient overvoltage. Finally, the effectiveness and feasibility of the proposed method are verified in a ±800kV/5000MW LCC-HVDC sending grid model based on MATLAB/Simulink.

Spinal cord ischemic-reperfusion injury (SCIRI) could occurs during surgical procedures without detection, presenting a complex course and an unfavorable prognosis. This may lead to postoperative sensory or motor dysfunction in areas innervated by the spinal cord, and in some cases, permanent paralysis. Timely detection of SCIRI and immediate waring can help surgeons implement remedial intervention to prevent irreversible spinal cord injury. Therefore, it is crucial to develop a precise and effective method for early detection of SCIRI. This study utilized rat models to simulate intraoperative SCIRI and employed somatosensory evoked potentials (SEP) for continuous monitoring during surgery. In this study, SEP signal changes were examined in six groups with varying severities of SCIRI and one normal control group. SEP signal changes were examined during operations in different groups and correlated with postoperative behavioral and histopathological data. The result demonstrated specific changes in SEP signals during SCIRI, termed as time-varying characteristics, which are associated with the duration of ischemia and subsequent reperfusion. Time-varying characteristics in SEP could potentially serve as a new biomarker for the intraoperative detection of SCIRI. This finding is significant for clinical surgeons to identify and guide early intervention of SCIRI timely. Additionally, this measurement is easily translatable to clinical application.

The snow water equivalent (SWE) of a snowpack represents the amount of water it contains. SWE retrieval with passive microwave remote sensing techniques is notably affected by snow metamorphism. The effective snow grain size (effGS) described in the GlobSnow methodology can be retrieved over areas where prior snow depth data are available. However, this dependency on the availability of prior knowledge results in increased retrieval uncertainty in areas where such information is unavailable. In this work, we proposed a new method to predict the effGS using a random forest (RF) model. The results indicated that the geographical location, orographic effects, and seasonal characteristics are important for retrieving the effGS, and they are important components of the kriging interpolation of the daily effGS in the GlobSnow methodology, resulting in spatial and temporal autocorrelation phenomena. We assessed the performances of snow depth priors from either station measurements or gridded products in predicting effGS and found that the RF model is promising for effGS prediction even without snow depth priors. The application of spatially independent verification and 10-fold cross-validation (10-CV) techniques revealed that the effGS estimates generally agreed with GlobSnow reference data over Eurasia, with overall unbiased root mean square error (unRMSE) values of 0.15.0.18 mm, but high errors were observed over North America due to terrain variability and heterogeneous forest cover, with overall unRMSE values ranging from 0.22.0.31 mm. Compared to the GlobSnow methodology, the proposed method does not rely on ground-based prior information, and improved computational efficiency can be achieved.

In this paper, we studied a double-phase eigenvalue problem with large variable exponents. Let $ \lambda^{1}_{(p_{n}(\cdot), \, q_{n}(\cdot))} $ be the first eigenvalues and $ u_{n} $ be the first eigenfunctions, normalized by $ \|u_{n}\|_{\mathcal{H}_{n}} = 1 $. Under some assumptions on the variable exponents $ p_{n}(\cdot) $ and $ q_{n}(\cdot) $, we showed that $ \lambda^{1}_{(p_{n}(\cdot), \, q_{n}(\cdot))} $ converges to $ \Lambda_{\infty} $, $ u_{n} $ converges to $ u_{\infty} $ uniformly in the space $ C^{\alpha}(\Omega)\, (0 < \alpha < 1) $ and $ u_{\infty} $ is a nontrivial viscosity solution to a Dirichlet $ \infty $-Laplacian problem. Even in the case where the variable exponents reduce to the constant exponents, our work is the first one dealing with a double-phase eigenvalue problem with large exponents.

The morphological characteristics of a product serve as essential carriers for conveying design intentions. These characteristics directly affect users’ comprehension of the product’s functions and proper usage, which are critical to the safety of product utilization and the overall comfort of the user experience. Incorporating prior experience to predict users’ cognitive intentions regarding product form characteristics can provide valuable evaluation and decision-making references for design. This approach effectively reduces product development risks and contributes to enhancing user acceptance and experience. The study established intention discrimination indicators for form characteristics, covering six dimensions: functional orientation, behavioral intention, recognizability, cognitive load, attention distribution, and experiential feeling. Combining multidimensional scaling (MDS) and systematic clustering, samples were screened, and the morphological decomposition method was used to categorize and extract form characteristic categories and feature factors. The entropy weight method was applied to assign weights to the feature categories, and a feedforward neural network (FNN) was employed to construct a prediction model for cognitive intentions regarding product form characteristics. The model was tested using leave-one-out cross-validation, yielding a mean squared error (MSE) of 0.0089 and an R correlation coefficient of 0.9998, indicating high reliability. Finally, the feasibility and effectiveness of this method were validated through a case study on earthquake science experience facilities.

AbstractBackground It is possible that neonates with pneumonia also have unrecognized sepsis. Identifying sepsis in neonates with pneumonia may cause some trouble for clinicians. This study aimed to evaluate the clinical value of the procalcitonin-to-albumin ratio (PAR) in identifying sepsis in neonates with pneumonia.Methods We retrospectively included 912 neonates with pneumonia from January 2016 to July 2021. Clinical and laboratory data were collected from electronic medical records. Among neonates with pneumonia, 561 neonates were diagnosed with sepsis, according to the International Pediatric Sepsis Consensus. Neonates were divided into a sepsis group and a pneumonia group. A multivariate logistic regression analysis was used to evaluate whether PAR was a potential independent indicator for identifying sepsis in neonates with pneumonia. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic value of PAR in sepsis.Results Neonates with sepsis have a higher PAR (p

Abstract High-risk neuroblastoma (HR-NB) has a significantly lower survival rate compared to low- and intermediate-risk NB (LIR-NB) due to the lack of risk classification diagnostic models and effective therapeutic targets. The present study aims to characterize the differences between neuroblastomas with different risks through transcriptomic and metabolomic, and establish an early diagnostic model for risk classification of neuroblastoma.Plasma samples from 58 HR-NB and 38 LIR-NB patients were used for metabolomics analysis. Meanwhile, NB tissue samples from 32 HR-NB and 23 LIR-NB patients were used for transcriptomics analysis. In particular, integrative metabolomics and transcriptomic analysis was performed between HR-NB and LIR-NB. A total of 44 metabolites (P 1.5) were altered, including 12 that increased and 32 that decreased in HR-NB. A total of 1,408 mRNAs (P 1) showed significantly altered in HR-NB, of which 1,116 were upregulated and 292 were downregulated. Joint analysis of both omic data identified 4 aberrant pathways (P

With the increasing penetration of electric vehicles (EV) and distributed generations (DG) in distribution networks, operation and control of distribution networks (DN) is faced with many new challenges. Considering the remarkable characteristics of distribution network layered by voltage level and the spatial and temporal distribution of EV charging load, a hierarchical and distributed optimization method for DN is proposed. Firstly, the spatial and temporal distribution prediction model of EV charging load is established, which is composed of three parts: the resident travel probability model, the vehicle mobility model and traffic network model. Secondly, these three parts runs jointly to simulate the charging demand generated by EV, and then the charging load is connected to the charging station in the low-voltage distribution station area. Thirdly, considering the operation characteristics of the new energy units, energy storage system (ESS), DG and EV charging station, a dynamic economic dispatching model is established. Fourthly, according to the hierarchical operation characteristics of the medium- and low-voltage distribution network (MLV), a distributed optimization algorithm of DN is proposed to realize the hierarchical and decoupled calculation, which transforms the traditional centralized serial computing mode into distributed parallel computing mode, and improves the computing efficiency of the system. At last, the effectiveness and superiority of the proposed strategy are verified by the simulation test.

Abstract In this paper, an integral‐type half‐tangent phase‐locked loop (IHTan‐PLL) is proposed for the varying‐frequency AC grids of more electric aircraft (MEA). First, the performance of the synchronous reference frame phase‐locked loop (SRF‐PLL) and half‐tangent phase‐locked loop (HTan‐PLL) is discussed from a large‐signal viewpoint. When the frequency changes largely, the SRF‐PLL has many oscillations and endures a rather long transient process. The HTan‐PLL can address the large frequency jumps. However, for the varying‐frequency AC grids with harmonic disturbances, the HTan‐PLL still has oscillations in estimating the frequency when the frequency jumps largely. To enhance the performance of the SRF‐PLL and HTan‐PLL, the IHTan‐PLL is developed for the varying‐frequency AC grids with harmonic disturbances. The IHTan‐PLL performs better than the SRF‐PLL and HTan‐PLL in addressing the large frequency jumps. Specifically, under any large frequency changes, the IHTan‐PLL still converges to the equilibrium point (0,0), and no oscillations emerge. In addition, the IHTan‐PLL has better steady‐state accuracy in estimating the frequency than the SRF‐PLL and HTan‐PLL. The experimental results also demonstrate the superior performance of the proposed IHTan‐PLL.

Abstract Background Apatinib, a highly selective VEGFR2 inhibitor, significantly improved efficacy versus placebo as a third- and later-line treatment for advanced gastric cancer in phase 2 and 3 trials. This prospective, single-arm, multicenter phase IV AHEAD study was conducted to verify the safety and efficacy of apatinib in patients with advanced or metastatic gastric or gastroesophageal adenocarcinoma after at least two lines of systematic therapy in clinical practice settings. Methods Patients with advanced gastric cancer who had previously failed at least two lines of chemotherapy received oral apatinib until disease progression, death or unacceptable toxicity. The primary endpoint was safety. The secondary endpoints included objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS) and overall survival (OS). Adverse events were summarized by the incidence rate. Median OS and PFS were estimated using the Kaplan–Meier method. ORR, DCR, OS at 3 and 6 months, and PFS at 3 and 6 months were calculated, and their 95% CIs were estimated according to the Clopper-Pearson method. Results Between May 2015 and November 2019, a total of 2004 patients were enrolled, and 1999 patients who received at least one dose of apatinib were assessed for safety. In the safety population, 87.9% of patients experienced treatment-related adverse events (TRAEs), with the most common hypertension (45.2%), proteinuria (26.5%), and white blood cell count decreased (25.3%). Additionally, 51% of patients experienced grade ≥ 3 TRAEs. Fatal TRAEs occurred in 57 (2.9%) patients. No new safety concerns were reported. Among the 2004 patients included in the intention-to-treat population, the ORR was 4.4% (95% CI, 3.6–5.4%), and DCR was 35.8% (95% CI, 33.7–38.0%). The median PFS was 2.7 months (95% CI 2.2–2.8), and the median OS was 5.8 months (95% CI 5.4–6.1). Conclusions The findings in the AHEAD study confirmed the acceptable and manageable safety profile and clinical benefit of apatinib in patients with advanced gastric cancer as a third- or later-line of treatment. Trial registration This study was registered with ClinicalTrials.gov NCT02426034. Registration date was April 24, 2015.

Cracks are one of the most common diseases of tunnel lining, and the structural dynamic response can be used to assess the health of a tunnel. Hence, this paper investigates the dynamic response of shield tunnel lining with a partly circumferential crack. The shield tunnel lining is regarded as a thin cylindrical shell and analyzed independently. The research methodology integrates the wave propagation method, the local flexibility matrix, the line spring model and the wave superposition principle. The results show that the position and depth of a partly circumferential crack can influence the natural frequency of the shield tunnel lining. Under the fixed-position load, as the distance from the monitoring point to the crack increases, the difference in displacement response amplitude between the undamaged and cracked linings diminishes. Moreover, deepening cracks enlarge the magnitude of amplitude differences. When the load approaches the crack, the radial amplitude difference first increases and then decreases as the monitor moves away from the crack. This finding helps determine the required monitor position. The displacement response of the selected monitor indicates that the closer the load position is to the crack, the larger the amplitude difference. The results aid in identifying the crack position and selecting corresponding load and monitor locations.

The use of immune checkpoint inhibitors (ICIs) has shown remarkable efficacy in the treatment of various malignancies, significantly reshaping cancer treatment. However, as a result of the widespread use of ICIs, several immune-related adverse events (iRAEs) have emerged, some of which can be rare and potentially fatal. In this paper, we reported the earliest case of Sintilimab used in the treatment of esophageal cancer with severe inflammatory myopathy (involving the cardiac, respiratory, and skeletal muscles)in China. This patient was an elderly female who presented to our institution with progressive limb weakness and ptosis. Prior to the onset of symptoms, the patient had undergone a radical esophagectomy for esophageal cancer, experienced several cycles of of radiotherapy and chemotherapy, as well as two doses of Sintilimab treatment. Shortly after initiating immunotherapy, the patient developed symptoms including bilateral ptosis, limb weakness, and difficulty swallowing and breathing. The levels of creatine kinase and troponin I in the patient’s blood were significantly elevated, and positive results were observed for anti-skeletal and anti-cardiac muscle antibodies, indicating that the patient might be developing ICIs-related inflammatory myopathy. Fortunately, the patient responded well to treatment including corticosteroids, plasmapheresis, intravenous immunoglobulin, and other supportive therapies. Here, we discuss the incidence, mechanisms, and management strategies of fatal iRAEs. Early detection and timely intervention may be critical in reducing the incidence and mortality rates of iRAEs and improving patient outcomes.

Abstract Grouting is an effective method to improve the integrity and stability of fractured rocks that surround deep roadways. After years of research and practice, various theories and a complete set of grouting technologies for deep roadways with fractured rocks have been developed and are widely applied in Chinese coal mining production. This paper systematically summarizes and analyzes the research results concerning the theory, design, materials, processes, and equipment for the grouting and reinforcement of fractured rocks surrounding deep roadways. Specifically, in terms of grouting methods, pregrouting, grouting‐while‐excavation, and postgrouting methods are explored; in terms of grouting theory, backfill grouting, compaction grouting, infiltration grouting, and fracture grouting theories are studied. In addition, this paper also studies grouting borehole arrangement, water‐cement ratio, grouting pressure, grouting volume, grout diffusion radius, and other grouting parameters and their determination methods. On this basis, this paper explores the physical and mechanical properties of organic and organic‐inorganic composite grouting materials, and assess grouting reinforcement quality testing methods and instruments. Taken as the field cases, the application of pregrouting in front of heading faces, grouting‐while‐excavation, and postgrouting in the Kouzidong coal mine are then introduced, and the effects of the grouting reinforcements are evaluated. This paper proposes a development direction for grouting technology based on problems existing in the grouting reinforcement of fractured rocks surrounding deep roadways.

In this paper, the large time behavior of globally smooth solutions of the Cauchy problem for the three dimensional Euler-Poisson system of compressible fluids coupled to a magnetic field is studied. We prove that the smooth solutions (near a given constant equilibrium state) of the problem converge asymptotically to a stationary solution exponentially fast as t goes to ∞.

Abstract Background Verticillium wilt, caused by the fungus Verticillium dahliae, leads to significant losses in cotton yield worldwide. Biocontrol management is a promising means of suppressing verticillium wilt. The purpose of the study was to obtain and analyze endophytic bacteria with Verticillium wilt-resistant activities from the roots of Gossypium barbadense ‘Xinhai15’ and to explore the interactions between the soil and plants. Results An endophytic bacterium Bacillus sp. T6 was obtained from the Verticillium wilt-resistant cotton G. barbadense ‘Xinhai15’, which showed significant antagonistic abilities against cotton Verticillium wilt. The bioassay results indicated that the strain possessed strong antagonistic abilities that inhibited V. dahliae spore germination and mycelial growth without contact, and thus it was speculated that the active factor of the bacteria might be volatile compounds. A total of 46 volatile substances were detected via headspace solid-phase microextraction and gas chromatography–mass spectrometry analysis. The pure product verification experiment confirmed that the styrene produced by the T6 strain was the main virulence factor. Transcriptome analysis showed that following styrene induction, 247 genes in V. dahliae, including four hydrolase genes, eight dehydrogenase genes, 11 reductase genes, 17 genes related to transport and transfer were upregulated. Additionally, 72 genes, including two chitinase genes, two protease genes, five transport-related genes, and 33 hypothetical protein genes, were downregulated. The quantitative real-time PCR results confirmed that the expression of the four genes VDAG_02838, VDAG_09554, VDAG_045572, and VDAG_08251 was increased by 3.18, 78.83, 2.71, and 2.92 times, respectively, compared with the uninduced control group. Conclusions The research provides a new reference for the development and application of the volatile compounds of endophytic bacteria as new biocontrol agents for the control of Verticillium wilt and as biological preservatives for agricultural products.

Due to the suddenness, diversity, randomness and complexity of various earth's surface anomalies caused by natural and human factors, the time-efficiency of traditional satellite remote sensing technology is seriously lagging behind the actual needs of the earth's surface anomalies early warning and emergency handling. Real-time earth's surface anomaly detection has become an important issue for achieving ecological security, human safety and health, and high-quality socioeconomic development. Therefore, this study proposed a real-time detection framework for earth's surface anomalies using a single temporal remote sensing data based on a priori knowledge base. The framework first selected an appropriate remote sensing feature system and constructed a priori knowledge base effectively reflecting the natural evolutionary law of the normal earth's surface. Then, the earth's surface anomalies were identified by comparing the real-time acquired remote sensing data to the priori knowledge base with the designed detection rules. Finally, the intensity of earth's surface anomalies was evaluated comprehensively using pixel- and patch-scale information. The preliminary analysis of several specific cases indicated that the proposed framework was reliable and could effectively identify the earth's surface anomalies with high accuracy. This proposed framework is fully applicable to the real-time detection of most earth's surface anomalies using a single temporal remote sensing data, which is not limited to a single earth's surface anomaly event. Meanwhile, the framework presents great potential for on-orbit processing of the earth's surface anomaly detection and realizing real-time remote sensing services for the early warning of the earth's surface anomalies.

Fretting fatigue is a specific fatigue phenomenon. Due to the complex mechanisms and multitude of influencing factors, it is still hard to predict fretting fatigue life accurately, despite there being many works on this topic. This paper developed a particle-swarm-optimized back propagation neural network to predict the fretting fatigue life of aluminum alloys using the test data gathered from the published literature. A commonly used critical plane model, the Smith, Watson, and Topper criterion, was used as a contrast. The analysis result shows that the proposed fretting fatigue life prediction neural network model achieves a higher prediction accuracy compared to the traditional SWT model. Experimental validation demonstrates the effectiveness of the model in improving the accuracy of fretting fatigue life prediction. This research provides a new data-driven methodology for fretting fatigue life prediction.

A bearing is an important and easily damaged component of mechanical equipment. For early fault diagnosis of ball bearings, acoustic emission signals are more sensitive and less affected by mechanical background noise. To cope with the large amount of data brought by the high sampling frequency and high sampling points of acoustic emission signals, a compressed sensing processing framework is introduced to research data compression and feature extraction, and a wavelet sparse convolutional network is proposed for resolved diagnosis and evaluation. The main research objective of this paper is to maximize the compression rate of the signal under the constraint of ensuring the reconstruction error of the acoustic emission signal, which can reduce the data volume of the acoustic emission signal and reduce the pressure of data analysis for subsequent fault diagnosis. At the same time, a wide convolution kernel based on a continuous wavelet is introduced when designing the neural network, and the energy information of different frequency bands of the signal is extracted by the wavelet convolution kernel to characterize the fault characteristics of the equipment. The energy pooling layer is designed to enhance the deep mining ability of compressed features, and the regularized loss function is introduced to improve the diagnostic accuracy and robustness through feature sparseness. The experimental results show that the method can effectively extract the fault characteristics of the bearing acoustic emission signal, improve the analysis efficiency and accurately classify the bearing faults.

Abstract Objective To evaluate the diagnostic value of plasma β-amyloid (Aβ) seeding activity measured using a newly developed instrument to distinguish Alzheimer’s disease (AD) from other forms of dementia. Methods Seventy-nine AD patients, 64 non-AD dementia (NADD) patients, and 75 cognitively normal (NC) subjects were recruited in the study. To measure the levels of Aβ seeding activity in the plasma samples, we have developed an AD-seeds protein analyzer. We used receiver operating characteristic (ROC) curves to quantify the ability of plasma Aβ seeding activity to distinguish between AD and NADD or NC individuals. Spearman’s correlation was used to examine the associations between plasma Aβ seeding activity and global cognitive function or conventional AD biomarkers. Results The Aβ seeding activities were 0.83 (0.58–1.16) A.U. in AD, 0.42 (0.04–0.74) A.U. in NADD and 0.42 (0.09–0.69) A.U. in NC, respectively. The Aβ seeding activity was able to identify AD patients and distinguish them from NC or NADD with high accuracy (AUC = 0.85–0.86). In addition, the plasma Aβ seeding activity showed a strong correlation with cognitive performance (mini-mental state examination, r = − 0.188; Montreal cognitive assessment, r = − 0.189; clinical dementia rating, r = 0.205) and conventional biomarkers (cerebrospinal fluid [CSF] Aβ42/40, r = -0.227; CSF T-tau/Aβ42, r = 0.239; CSF P-tau/Aβ42, r = 0.259). Conclusion Our results confirmed that plasma Aβ seeding activity is an antibody-free and low-cost biomarker for the diagnosis of AD. Trial registration Trial registration number NCT04850053

The reliable knowledge of seasonal snow volume and its trend is very important to understand Earth’s climate system. Thus, a long-time snow water equivalent (SWE) dataset is necessary. This work presents a daily SWE product of 1980–2020 with a linear unmixing method through passive microwave data including SMMR, SSM/I and SSMIS over China after cross-calibration and bias-correction. The unbiased root-mean-square error of snow depth is about 5–7 cm, corresponding to 10–15 mm for SWE, when compared with stations measurements and field snow course data. The spatial patterns and trends of SWE over China present significant regional differences. The overall slope trend presented an insignificant decreasing pattern during 1980–2020 over China; however, there is an obvious fluctuation, i.e. a significant decrease trend during the period 1980–1990, an upward trend from 2005 to 2009, a significant downward trend from 2009 to 2018. The increase of SWE occurred in the Northeast Plain, with an increase trend of 0.2 mm per year. Whereas in the Hengduan Mountains, it presented a downward trend of SWE, up to −0.3 mm per year. In the North Xinjiang, SWE has an increasing trend in the Junggar Basin, while it shows a decreasing trend in the Tianshan and Altai Mountains.

Abstract In wind farms, the uncertainty of wind power and the incoordination of multiple time scales increase the difficulty of voltage/var control (VVC), and result in the reactive power output of VVC devices becoming unreasonable. This will decrease the power quality and even cause the voltage collapse of wind farms at large voltage deviations. For these problems, hierarchical control of VVC devices is a promising solution, as VVC devices can adjust the reactive power output in different control strategies. Therefore, this paper proposes a three‐stage robust voltage/var optimal control strategy for wind farms. The robust optimization model is introduced to solve the VVC problem caused by the uncertainty of wind power in this strategy. Besides, compared with traditional VVC strategies, the proposed strategy can coordinate different control stages and VVC devices at multiple time scales by combining centralized and local VVC strategies, which achieves voltage/var optimal control objectives. Simulation results demonstrate the good performance of the proposed strategy in mitigating system voltage violation rate and power loss of the wind farm.

With high penetration of wind power, the apparent inertia of power systems is decreased, which seriously affects the safety and stability of the system frequency. Therefore, a novel sliding mode frequency control (SMFC) scheme is proposed for the wind turbines participating in frequency regulation support (FRS) in power systems, which can significantly improve the dynamic characteristics of the system frequency. At first, a power system frequency response model considering wind turbines participation in FRS is derived. In this model, the load disturbance, wind power disturbance and synchronous generator frequency regulation dynamics are combined as a lumped disturbance. Then, the design of the proposed SMFC scheme, which combines the super-twisting sliding-mode disturbance observer (STSMDO) with the super-twisting sliding-mode frequency controller (STSMFC), is introduced. The proposed STSMFC has ability to tune the system dynamics easily by choosing suitable sliding surface matrix via eigenvalue assignment method or optimal sliding manifold design. In such a frequency control scheme, the stability of the system frequency is guaranteed by the STSMFC, whereas the STSMDO is employed to estimate the lumped disturbance. Finally, numerical simulations verify the effectiveness and superiority of the proposed SMFC scheme.

Background. Neonatal sepsis is an extremely dangerous and fatal disease among neonates, and its timely diagnosis is critical to treatment. This research is aimed at evaluating the clinical significance of the lymphocyte-to-C-reactive protein ratio (LCR) as an early sepsis indicator in neonates with suspected sepsis. Methods. Between January 2016 and December 2021, 1269 neonates suspected of developing sepsis were included in this research. Among them, sepsis was diagnosed in 819 neonates, with 448 severe cases, as per the International Pediatric Sepsis Consensus. Data related to clinical and laboratory tests were obtained via electronic medical records. LCR was calculated as total lymphocyte (109 cells/L)/C-reactive protein (mg/L). Multivariate logistic regression analysis was employed to evaluate the effectiveness of LCR as an independent indicator for determining sepsis in susceptible sepsis neonates. Receiver operating characteristic (ROC) curve analysis was conducted for investigating the diagnostic significance of LCR in sepsis. When suitable, the statistical tool SPSS 24.0 was used for statistical analyses. Results. LCR decreased significantly in the control, mild, and severe sepsis groups. Further analyses exhibited that there was a substantially greater incidence of sepsis in neonates in the low-LCR group (LCR≤3.94) as opposed to the higher LCR group (LCR>3.94) (77.6% vs. 51.4%, p

Abstract Background The most common biomarkers of Alzheimer’s disease (AD) are amyloid β (Aβ) and tau, detected in cerebrospinal fluid (CSF) or with positron emission tomography imaging. However, these procedures are invasive and expensive, which hamper their availability to the general population. Here, we report a panel of microRNAs (miRNAs) in serum that can predict P-tau/Aβ42 in CSF and readily differentiate AD from other dementias, including vascular dementia (VaD), Parkinson disease dementia (PDD), behavioral variant frontotemporal dementia (bvFTD), and dementia with Lewy body (DLB). Methods RNA samples were extracted from the participant’s blood. P-tau/Aβ42 of CSF was examined for diagnostic purposes. A pilot study (controls, 21; AD, 23), followed by second (controls, 216; AD, 190) and third groups (controls, 153; AD, 151), is used to establish and verify a predictive model of P-tau/Aβ42 in CSF. The test is then applied to a fourth group of patients with different dementias (controls, 139; AD,155; amnestic mild cognitive impairment [aMCI], 55; VaD, 51; PDD, 53; bvFTD, 53; DLB, 52) to assess its diagnostic capacity. Results In the pilot study, 29 upregulated and 31 downregulated miRNAs in the AD group were found. In Dataset 2, these miRNAs were then included as independent variables in the linear regression model. A seven-microRNA panel (miR-139-3p, miR-143-3p, miR-146a-5p, miR-485-5p, miR-10a-5P, miR-26b-5p, and miR-451a-5p) accurately predicted values of P-tau/Aβ42 of CSF. In Datasets 3 and 4, by applying the predicted P-tau/Aβ42, the predictive model successfully differentiates AD from controls and VaD, PDD, bvFTD, and DLB. Conclusions This study suggests that the panel of microRNAs is a promising substitute for traditional measurement of P-tau/Aβ42 in CSF as an effective biomarker of AD.

Abstract Background Larotinib is a new first-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor. This open-label, phase 1b study is aimed to evaluate the efficacy, safety of larotinib in patients with advanced esophageal squamous cell carcinoma (ESCC) with EGFR overexpression or amplification pretreated with one or more system regimens, and to recommend an appropriate dose for its further study. Methods Patients received larotinib orally at 3 doses (250, 300, 350 mg), once daily. Clinical response was evaluated every 8 weeks according to RECIST v1.1 criteria by both investigators and independent radiology review (IRC). Results 81 patients were enrolled. The investigator-assessed overall response rate (ORR) was 13.7% (10/73), all responses were observed in the 350 mg group of which ORR up to 20.0% (10/50), with 10 of them having EGFR overexpression and 4 having EGFR amplification. Per IRC assessment, ORR for all patients and 350 mg group were 13.9% (10/72) and 16.3% (8/50). In the 350 mg group, median overall survival (OS) and progression-free survival (PFS) were 8.0 (95% CI 4.9–10.2) months and 3.4 (95% CI 2.4–3.7) months, respectively. The most common treatment-related adverse events (TRAEs) were diarrhea, rash, and palmar-plantar erythrodysesthesia syndrome, elevated AST/ALT, vomiting, similarly with other EGFR TKIs. Conclusions Larotinib demonstrated promising antitumor activity and manageable safety profiles in patients with pre-treated advanced ESCC with EGFR overexpression or amplification, especially at the dose of 350 mg, which showed better efficacy and acceptable safety. A phase 3 study is underway on 350 mg larotinib in ESCC patients with EGFR overexpression. Trial registration This trial was retrospectively registered on 25/03/2019, NCT03888092. https://clinicaltrials.gov/ct2/show/NCT03888092 .

ObjectiveTo evaluate the surgical outcomes of modified combined fascia sheath (CFS) and levator muscle (LM) complex suspension for the correction of severe congenital blepharoptosis in pediatric patients.MethodsPediatric patients with severe congenital blepharoptosis were enrolled form July 2017 to July 2021. All patients were divided into two groups according to their age (group A ≤ 7 years; group B > 7 years) and received CFS + LM suspension surgery. Main surgical outcome indexes include margin reflex distance 1 (MRD1) and MRD1 regression. Postoperative complications such as lagophthalmos (LAG), conjunctival prolapse, exposure keratopathy and trichiasis were documented.ResultsFifty patients (60 eyes) were enrolled, including 17 patients (18 eyes) in group A and 33 patients (42 eyes) in group B. The MRD1 in group A was 3.06 ± 0.64 mm at 6 months after the operation, and the MRD1 in group B was 2.64 ± 0.69 mm 6 months postoperatively which is significantly lower than that of group A (P = 0. 044). At the last visit, however, the MRD1 in group A was 3.00 ± 0.69 mm and the MRD1 in group B was 2.64 ± 0.70 mm. There was no significant difference in MRD1 between two groups in long term (P = 0.255). Additionally, there were a variety of degrees of MRD1 regression, especially in the first month after the operation in both groups (both P

Despite significant advancements in remote sensing object tracking (RSOT) in recent years, achieving accurate and continuous tracking of tiny-sized targets remains a challenging task due to similar object interference and other related issues. In this paper, from the perspective of feature enhancement and a better feature matching strategy, we present a tracker SiamTM specifically designed for RSOT, which is mainly based on a new target information enhancement (TIE) module and a multi-level matching strategy. First, we propose a TIE module to address the challenge of tiny object sizes in satellite videos. The proposed TIE module goes along two spatial directions to capture orientation and position-aware information, respectively, while capturing inter-channel information at the global 2D image level. The TIE module enables the network to extract discriminative features of the targets more effectively from satellite images. Furthermore, we introduce a multi-level matching (MM) module that is better suited for satellite video targets. The MM module firstly embeds the target feature map after ROI Align into each position of the search region feature map to obtain a preliminary response map. Subsequently, the preliminary response map and the template region feature map are subjected to the Depth-wise Cross Correlation operation to get a more refined response map. Through this coarse-to-fine approach, the tracker obtains a response map with a more accurate position, which lays a good foundation for the prediction operation of the subsequent sub-networks. We conducted extensive experiments on two large satellite video single-object tracking datasets: SatSOT and SV248S. Without bells and whistles, the proposed tracker SiamTM achieved competitive results on both datasets while running at real-time speed.

Abstract This paper proposes a novel distributed bilayer coordinated control scheme (BCCS) to realize the autonomous economic operation of islanded hybrid AC/DC microgrids, which only requires a sparse communication link. In the lower layer, the optimal power reference iterative algorithm (OPRIA)‐based droop control is proposed to realize the economic dispatch of each subgrid individually, while the frequency of AC subgrid and the bus voltage of DC subgrid are regulated to their rated values. Then, the upper‐layer control for the interlinking converter is designed to achieve the global economic operation of the hybrid AC/DC microgrid through optimizing the exchanging power between AC and DC subgrids. The proposed BCCS has the advantage of reaching the economic operation and frequency/voltage regulation simultaneously for the hybrid AC/DC microgrid. Besides, the proposed BCCS realizes the stable and economic operation with less communication, flexible controllability, and good compatibility of renewable energy sources. Numerical simulation results based on Matlab/Simulink verify the effectiveness and excellent performance of the proposed BCCS even with the integration of renewable energy sources, and the test based on RT‐LAB illustrates the good real‐time performance of the proposed control scheme.

The treatment for tuberculosis (TB), especially multidrug-resistant TB (MDR-TB), has a prolonged cycle which can last up to a year. This is partially due to the lack of effective therapies. The development of novel anti-TB drugs from the perspective of host immune regulation can provide an important supplement for conventional treatment strategies. Salidroside (SAL), a bioactive component from the Tibetan medicine Rhodiola rosea, has been used in the treatment of TB, although its mechanism remains unclear. Here, the bacteriostatic effect of SAL in vivo was first demonstrated using a zebrafish–M. marinum infection model. To further investigate the underlying mechanism, we then examined the impact of SAL on immune cell recruitment during wound and infection. Increased macrophage and neutrophil infiltrations were found both in the vicinity of the wound and infection sites after SAL treatment compared with control, which might be due to the elevated chemokine expression levels after SAL treatment. SAL treatment alone was also demonstrated to improve the survival of infected zebrafish larvae, an effect that was amplified when combining SAL treatment with isoniazid or rifampicin. Interestingly, the reduced bacterial burden and improved survival rate under SAL treatment were compromised in tnfα-deficient embryos which suggests a requirement of Tnfα signaling on the anti-mycobacterial effects of SAL. In summary, this study provides not only the cellular and molecular mechanisms for the host anti-mycobacterial effects of the Tibetan medicine SAL but also proof of concept that combined application of SAL with traditional first-line anti-TB drugs could be a novel strategy to improve treatment efficacy.

Abstract Background Monoclonal antibodies targeting programmed death ligand 1 (PD-L1) signaling currently approved for defective mismatch repair (dMMR)/microsatellite instability high (MSI-H) tumors must be delivered by intravenous infusion. Envafolimab, a humanized single-domain anti-PD-L1 antibody fused to an Fc fragment, represents a potential advance because it can be conveniently administered subcutaneously. Methods This open-label, single-arm, phase 2 study evaluated the efficacy and safety of envafolimab in patients with previously treated advanced dMMR/MSI-H tumors from 25 clinical sites across China. Adults with histologically confirmed locally advanced or metastatic malignant dMMR/MSI-H solid tumors received weekly 150 mg subcutaneous envafolimab injections in a 28-day treatment cycle. The primary efficacy endpoint was the objective response rate (assessed by a blinded independent review committee). Secondary efficacy outcomes were disease control rate, duration of response, progression-free survival, and overall survival. Results One hundred and three patients (65 with colorectal cancer, 18 with gastric cancer, and 20 with other solid tumors) were enrolled. Median follow-up was 11.5 months. The objective response rate was 42.7% (95% confidence interval [CI] 33.0–52.8), and the disease control rate was 66.0% (95% CI 56.0–75.1). Median duration of response was not reached; the duration of response rate at 12 months was 92.2% (95% CI 77.5–97.4). Median progression-free survival was 11.1 months (95% CI 5.5 to not evaluable). Overall survival at 12 months was 74.6% (95% CI 64.7–82.1). Sixteen patients (16%) had at least one grade 3 or 4 related treatment-emergent adverse event. No grade 5 treatment-emergent adverse events related to envafolimab were reported. Injection site reactions, all grade 1–2, were reported in nine patients (9%), but there were no infusion reactions. Eight patients (8%) had grade 3 or 4 immune-related adverse events. Conclusions This is the first pivotal phase 2 study to examine the efficacy and safety of a single-domain immune checkpoint antibody in the treatment of cancer. Envafolimab was effective and had acceptable safety in the treatment of previously treated advanced dMMR/MSI-H solid tumors. As the first single-domain PD-L1-targeting antibody administered by rapid subcutaneous injection, envafolimab has the potential to be a significant advance in the treatment of cancer. Trial registration ClinicalTrials.gov, NCT03667170. Registered 10 September 2018—Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03667170 .

Abstract Background Accelerated long-term forgetting has been identified in preclinical Alzheimer’s disease (AD) and is attributed to a selective impairment of memory consolidation in which the hippocampus plays a key role. As blood may contain multiple senescence-related factors that involved in neurogenesis and synaptic plasticity in the hippocampus, we tested whether there is an association between blood-borne factors and accelerated long-term forgetting in asymptomatic individuals from families with autosomal dominant AD (ADAD). Methods We analyzed data of 39 asymptomatic participants (n = 18 ADAD mutation carriers, n = 21 non-carriers) from the Chinese Familial Alzheimer’s Disease Network (CFAN) study. Long-term forgetting rates were calculated based on recall or recognition of two materials (word list and complex figure) at three delays comprising immediate, 30 min, and 7 days. Peripheral blood concentrations of candidate pro-aging factors (CC chemokine ligand 11 [CCL11] and monocyte chemotactic protein 1 [MCP1]) and rejuvenation factors (growth differentiation factor 11 [GDF11], thrombospondin-4 [THBS4], and secreted protein acidic and rich in cysteine like 1 [SPARCL1]) were evaluated in all participants. Results Despite normal performance on standard 30-min delayed testing, mutation carriers exhibited accelerated forgetting of verbal and visual material over 7 days in comparison with matched non-carriers. In the whole sample, lower plasma THBS4 was associated with accelerated long-term forgetting in list recall (β = −0.46, p = 0.002), figure recall (β = −0.44, p = 0.004), and list recognition (β = −0.37, p = 0.010). Additionally, higher plasma GDF11 and CCL11 were both associated with accelerated long-term forgetting (GDF11 versus figure recall: β = 0.39, p = 0.007; CCL11 versus list recognition: β = 0.44, p = 0.002). Conclusions Accelerated long-term forgetting is a cognitive feature of presymptomatic AD. Senescence-related blood-borne factors, especially THBS4, GDF11, and CCL11, may be promising biomarkers for the prediction of accelerated long-term forgetting.

Pt/TiO2 nanoparticles were prepared by a simple two-step aqueous solution method, which consists of a low temperature hydrothermal step and a photoreduction deposition step. The as-prepared samples were characterized by XRD, TEM, XPS, UV-vis, and BET techniques. The as-prepared samples exhibited enhanced photocatalytic activity towards the degradation of rhodamine B (RhB) under visible light irradiation. With increasing Pt content from 0.25 to 1 wt.%, the band gap energies shift from 2.32 to 1.64 eV. The 0.75 wt.% Pt/TiO2 sample showed the best photocatalytic activity, which could be ascribed to the formation of a Schottky barrier and the localized surface plasmon resonance effect of Pt nanoparticles. And the as-prepared samples also displayed excellent stability and reusability in multiple experimental cycles.

This article is a review of models for predicting ultra-low cycle fatigue life. In the article, the life prediction models are divided into three types: (1) microscopic ductile fracture models based on cavity growth and cavity merger; (2) fracture models based on porous plasticity; and (3) ductile fracture models based on continuum damage mechanics. Furthermore, the article provides a critical assessment of the current state of research on ultra-low cycle fatigue life prediction models, highlighting the limitations and challenges faced by each model type. Ultimately, this review aims to provide a comprehensive overview of the different models available for predicting ultra-low cycle fatigue life and to guide future research in this important area of materials science and engineering.

The rapid development of cities in recent years has increased the operational pressure of rail vehicles, and due to the characteristics of rail vehicles, including harsh operating environment, frequent starting and braking, resulting in rails and wheels being prone to rail corrugation, polygons, flat scars and other faults. These faults are coupled in actual operation, leading to the deterioration of the wheel–rail contact relationship and causing harm to driving safety. Hence, the accurate detection of wheel–rail coupled faults will improve the safety of rail vehicles’ operation. The dynamic modeling of rail vehicles is carried out to establish the character models of wheel–rail faults including rail corrugation, polygonization and flat scars to explore the coupling relationship and characteristics under variable speed conditions and to obtain the vertical acceleration of the axle box. An APDM time–frequency analysis method is proposed in this paper based on the PDMF adopting Rényi entropy as the evaluation index and employing a WOA to optimize the parameter set. The number of iterations of the WOA adopted in this paper is decreased by 26% and 23%, respectively, compared with PSO and SSA, which means that the WOA performs at faster convergence speed and with a more accurate Rényi entropy value. Additionally, TFR obtained using APDM realizes the localization and extraction of the coupled fault characteristics under rail vehicles’ variable speed working conditions with higher energy concentration and stronger noise resistance corresponding to prominent ability of fault diagnosis. Finally, the effectiveness of the proposed method is verified using simulation and experimental results that prove the engineering application value of the proposed method.

Many pivotal and knotty engineering problems in practical applications boil down to optimization problems, which are difficult to resolve using traditional mathematical optimization methods. Metaheuristics are efficient algorithms for solving complex optimization problems while keeping computational costs reasonable. The carnivorous plant algorithm (CPA) is a newly proposed metaheuristic algorithm, inspired by its foraging strategies of attraction, capture, digestion, and reproduction. However, the CPA is not without its shortcomings. In this paper, an enhanced multistrategy carnivorous plant algorithm called the UCDCPA is developed. In the proposed framework, a good point set, Cauchy mutation, and differential evolution are introduced to increase the algorithm’s calculation precision and convergence speed as well as heighten the diversity of the population and avoid becoming trapped in local optima. The superiority and practicability of the UCDCPA are illustrated by comparing its experimental results with several algorithms against the CEC2014 and CEC2017 benchmark functions, and five engineering designs. Additionally, the results of the experiment are analyzed again from a statistical point of view using the Friedman and Wilcoxon rank-sum tests. The findings show that these introduced strategies provide some improvements in the performance of the CPA, and the accuracy and stability of the optimization results provided by the proposed UCDCPA are competitive against all algorithms. To conclude, the proposed UCDCPA offers a good alternative to solving optimization issues.

Snow density varies spatially, temporally, and vertically within the snowpack and is the key to converting snow depth to snow water equivalent. While previous studies have demonstrated the feasibility of retrieving snow density using a multiple-angle L-band radiometer in theory and in ground-based radiometer experiments, this technique has not yet been applied to satellites. In this study, the snow density was retrieved using the Soil Moisture Ocean Salinity (SMOS) satellite radiometer observations at 43 stations in Quebec, Canada. We used a one-layer snow radiative transfer model and added a τ-ω vegetation model over the snow to consider the forest influence. We developed an objective method to estimate the forest parameters (τ, ω) and soil roughness (SD) from SMOS measurements during the snow-free period and applied them to estimate snow density. Prior knowledge of soil permittivity was used in the entire process, which was calculated from the Global Land Data Assimilation System (GLDAS) soil simulations using a frozen soil dielectric model. Results showed that the retrieved snow density had an overall root-mean-squared error (RMSE) of 83 kg/m3 for all stations, with a mean bias of 9.4 kg/m3. The RMSE can be further reduced if an artificial tuning of three predetermined parameters (τ, ω, and SD) is allowed to reduce systematic biases at some stations. The remote sensing retrieved snow density outperforms the reanalysis snow density from GLDAS in terms of bias and temporal variation characteristics.

Many deep learning models for fault diagnosis have not considered the prior diagnosis knowledge of the rolling bearing. Moreover, some measuring locations cannot collect adequate data to diagnose due to equipment size or installation space problems. This paper proposes a wavelet convolutional deep transfer learning model for rolling bearing fault detection on cross-measurement points. A new convolution layer includes a redesigned convolution kernel and a new energy pooling layer. The convolution kernel is designed based on wavelet construction for mining time-frequency characteristics. The energy pooling layer is proposed to extract the energy of different frequency bands. The associated location’s fault information has been transferred to domain knowledge to enhance the target features. Different domain features have adaptive matches based on multiple kernel variants of maximum mean discrepancy. The experimental results demonstrate that the accuracy of fault detection can reach 99.73%, and the robustness of the proposed method is also verified.

The guidance of EV via a price-based demand response is of great significance to the security and economy of the distribution network. However, the current price-based demand response mechanisms fail to consider the spatial-temporal distribution of large-scale EVs connected to the distribution network. For tackling this challenge, this paper proposes a day-ahead economic dispatch strategy for distribution networks considering total cost price-based demand response. A two-layer model of the day-ahead economic dispatch for the distribution network is utilized to obtain the interactive calculation framework for the total cost price. The total cost price iterates between the economic dispatch model of the distribution network and the total cost price-based demand response model until no significant changes in total cost price are observed. Among them, the price-responsive load considers spatial-temporal shift of the EV charging load. Finally, simulation study case based on a modified IEEE 33-bus system is employed to demonstrate the effectiveness of this strategy for the economic operation of the distribution network.

The NOD-like receptors (NLRs) have been shown to be involved in infection and autoinflammatory disease. Previously, we identified a zebrafish NLR, nlrc3-like, required for macrophage homeostasis in the brain under physiological conditions. Here, we found that a deficiency of nlrc3-like leads to decreased bacterial burden at a very early stage of Mycobacterium marinum infection, along with increased production of pro-inflammatory cytokines, such as il-1β and tnf-α. Interestingly, myeloid-lineage specific overexpression of nlrc3-like achieved the opposite effects, suggesting that the impact of nlrc3-like on the host anti-mycobacterial response is mainly due to its expression in the innate immune system. Fluorescence-activated cell sorting (FACS) and subsequent gene expression analysis demonstrated that inflammasome activation-related genes were upregulated in the infected macrophages of nlrc3-like deficient embryos. By disrupting asc, encoding apoptosis-associated speck-like protein containing a CARD, a key component for inflammasome activation, the bacterial burden increased in asc and nlrc3-like double deficient embryos compared with nlrc3-like single deficient embryos, implying the involvement of inflammasome activation in infection control. We also found extensive neutrophil infiltration in the nlrc3-like deficient larvae during infection, which was associated with comparable bacterial burden but increased tissue damage and death at a later stage that could be alleviated by administration of dexamethasone. Our findings uncovered an important role of nlrc3-like in the negative regulation of macrophage inflammasome activation and neutrophil infiltration during mycobacterial infection. This highlights the importance of a balanced innate immune response during mycobacterial infection and provides a potential molecular basis to explain how anti-inflammatory drugs can improve treatment outcomes in TB patients whose infection is accompanied by a hyperinflammatory response.

Abstract The variational mode decomposition （VMD） is widely used in fault diagnosis. Extracting fault characteristics from vibration signals is a critical part in the bearing fault diagnosis. It is difficult to extract early fault signatures under strong background noise and pulse interference. Thus， a new fault diagnosis method is proposed based on the variational mode decomposition and fruit fly optimization algorithm （FOA）. Firstly， the fruit fly optimization algorithm is used to optimize the penalty parameter α and the decomposition number K of the VMD adaptively to obtain the optimal parameter combination. Then， the VMD decomposition of the signal is performed to find K modal components. Finally， the optimal modal component is selected based on the kurtosis maximization criterion for envelope demodulation analysis and extracting the frequency of the fault feature. The effectiveness of the presented method is verified by simulation signal analysis， detecting bearing fault signal and comparing with multi-resolution singular value decomposition （MRSVD） approach based on fruit fly optimization algorithm.

Due to their simplicity and flexibility, the unsupervised statistical models such as Gaussian mixture model (GMM) are powerful tools to address the brain magnetic resonance (MR) images segmentation problems. However, the GMM is based only on the intensity information, which makes it sensitive to noise. Lots of GMM-based segmentation algorithms have the low segmentation accuracy because of the influence caused by noise and intensity inhomogeneity. To further improve the segmentation accuracy, a robust spatial information-theoretic GMM algorithm is proposed in this paper to simultaneously estimate the intensity inhomogeneity and segment brain MR images. First, a novel spatial factor containing the non-local spatial information is incorporated to reduce the impact of noise. The proposed spatial factor not only takes into account the local neighboring information, but also considers the spatial structure information of pixels. Thus, our algorithm can retain more image details while reducing the influence of noise. Second, the mutual information (MI) maximization method is used to identify and eliminate outliers. Finally, to overcome the impact of intensity inhomogeneity, we use a linear combination of a set of orthogonal polynomials to approximate the bias field. The objective function is integrated with the bias field estimation model to segment the images and estimate the bias field simultaneously. The experimental results on both synthetic and clinical brain MR images show that the proposed algorithm can overcome the influence of noise and intensity inhomogeneity, and achieve more accurate segmentation results.

The intelligent diagnosis of wheel flat based on vibration image classification is a promising research subject for performance maintenance of railway vehicles. However, the image representation method of vibration signal and classification network construction under small samples have become two obstacles to intelligent diagnosis of wheel flat. This paper presents a novel frequency-domain Gramian angular field (FDGAF) algorithm to encode the vibration signal of wheel flat to featured images. Furthermore, a modified transfer learning network is introduced to classify these featured images under small samples without any involvement of prior knowledge. The proposed FDGAF can calculate the Gramian angular matrix of axle box acceleration signal in frequency domain and assign frequency position dependence to the featured images to preserve original characteristic information. Then, these featured images can be intelligent classified by a transfer learning network under the condition of 30 sample without require of prior knowledge. To verify the efficiency of this proposed method, 12 cases of artificial wheel flats are processed on a scaled railway test rig, and their axle box acceleration signals are collected to obtain visual diagnosis results. The verfication proves that FDGAF is able to obtain accurate diagnostic results with high separability, for separability indexes of FDGAF reaches 10.8, 8.7, 14.9, and 5.8. We anticipate that this method will find use in the performance maintenance of railway vehicles and the improvement of industrial condition monitoring.

Magnetorheological dampers (MRDs) use the controlled rheological properties of MRF with the action of an applied magnetic field to achieve lower current regulation and control larger damping. MRD technology is applied to the defense industry, vehicle engineering, civil engineering, traffic engineering, aerospace, and other equipment. In this paper, the structural design and latest engineering applications of three types of MRDs including internal, bypass, and hybrid are investigated in terms of the number of coils, piston structure, damping channels, and operating modes to obtain the latest developments of MRDs and analyze potential applications. This study is essential for understanding and developing magnetorheological damping technology and applications.

Deep learning methods have been widely studied in the field of polarimetric synthetic aperture radar (PolSAR) ship detection over the past few years. However, the backscattering of manmade targets, including ships, is sensitive to the relative geometry between target orientation and radar line of sight, which makes the diversity of polarimetric and spatial features of ships. The diversity of scattering leads to a relative increase in the scarcity of PolSAR-labeled samples, which are difficult to obtain. To solve the abovementioned issue and extract the polarimetric and spatial features of PolSAR images better, this paper proposes a few-shot PolSAR ship detection method based on the combination of constructed polarimetric input data selection and improved contrastive self-supervised learning (CSSL) pre-training. Specifically, eight polarimetric feature extraction methods are adopted to construct deep learning network input data with polarimetric features. The backbone is pre-trained with un-labeled PolSAR input data through an improved CSSL method without negative samples, which enhances the representation capability by the multi-scale feature fusion module (MFFM) and implements a regularization strategy by the mix-up auxiliary pathway (MUAP). The pre-trained backbone is applied to the downstream ship detection network; only a few labeled samples are used for fine-tuning and the construction method of polarimetric input data with the best detection effect is studied. The comparison and ablation experiment results on the self-established PolSAR ship detection dataset verify the superiority of the proposed method, especially in the case of few-shot learning.