Search

Abstract Quantum networks comprised of entangled end nodes serve stronger than the classical correlation for unparalleled quantum internet applications. However, practical quantum networking is affected by noise, which at its worst, causes end nodes to be described by pre-existing classical data. In such untrusted networks, determining quantum network fidelity and genuine multi-node entanglement becomes crucial. Here, we show that determining quantum network fidelity and genuine N-node entanglement in an untrusted star network requires only N + 1 measurement settings. This method establishes a semi-trusted framework, allowing some nodes to relax their assumptions. Our network determination method is enabled by detecting genuine N-node Einstein-Podolsky-Rosen steerability. Experimentally, using spontaneous parametric down-conversion entanglement sources, we demonstrate the determinations of genuine 3-photon and 4-photon quantum networks and the false positives of the widely used entanglement witness, the fidelity criterion of 1/2. Our results provide a scalable method for the determination of multipartite entanglement in realistic quantum networks.

Abstract Health monitoring and fault diagnosis of rolling bearings are crucial for the continuous and effective operation of mechanical equipment. In order to improve the accuracy of BP neural network in fault diagnosis of rolling bearings, a feature model is established from the vibration signals of rolling bearings, and an improved genetic algorithm is used to optimize the initial weights, biases, and hyperparameters of the BP neural network. This overcomes the shortcomings of BP neural network, such as being prone to local minima, slow convergence speed, and sample dependence. The improved genetic algorithm fully considers the degree of concentration and dispersion of population fitness in genetic algorithms, and adaptively adjusts the crossover and mutation probabilities of genetic algorithms in a non-linear manner. At the same time, in order to accelerate the optimization efficiency of the selection operator, the elite retention strategy is combined with the hierarchical proportional selection operation. Using the rolling bearing dataset from Case Western Reserve University in the United States as experimental data, the proposed algorithm was used for simulation and prediction. The experimental results show that compared with the other seven models, the proposed IGA-BPNN exhibit superior performance in both convergence speed and predictive performance.

Abstract Entanglement and coherence are fundamental properties of quantum systems, promising to power near-future quantum technologies, such as quantum computation, quantum communication, and quantum metrology. Yet, their quantification, rather than mere detection, generally requires reconstructing the spectrum of quantum states, i.e., experimentally challenging measurement sets that increase exponentially with the system size. Here, we demonstrate quantitative bounds to operationally useful entanglement and coherence that are universally valid, analytically computable, and experimentally friendly. Specifically, our main theoretical results are lower and upper bounds to the coherent information and the relative entropy of coherence in terms of local and global purities of quantum states. To validate our proposal, we experimentally implement two purity detection methods in an optical system: shadow estimation with random measurements and collective measurements on pairs of state copies. The experiment shows that both the coherent information and the relative entropy of coherence of pure and mixed unknown quantum states can be bounded by purity functions. Our research offers an efficient means of verifying large-scale quantum information processing.

Objective To report and analyze the clinical features of the first documented case in China of anti-neurofilament heavy chain (NfH) antibody-associated encephalitis. Methods and Results A 63-year-old female presented with cognitive impairment, recurrent epileptic episodes, and aphasia. Head MRI showed multiple abnormal signals in bilateral frontal and parietal lobes, and slight swelling in some gyri, dilatation of the supratentorial ventricular system and brain atrophy, especially in bilateral temporal lobes and hippocampus. 18F-DPA714 PET/MRI showed abnormal uptake in multiple brain regions in the frontal, temporal, parietal and occipital lobes, suggesting neuroinflammation in the brain. EEG showed frequent epileptiform discharges. Cerebrospinal fluid white cell count and glucose were elevated. Serum and cerebrospinal fluid antibodies related to autoimmune encephalitis were negative. Serum tissue-based indirect immunofluorescence assay showed cerebellar neurofilament-like fluorescence surrounding Purkinje cells, further detection of neurofilament protein-related antibodies, it suggested high titer of serum NfH-IgG (1∶1000). The patient was diagnosed with anti-NfH antibody-associated encephalitis. After treatment with intravenous immunoglobulin and methylprednisolone, the prognosis was improved. Conclusions Anti-NfH antibody - associated encephalitis is a rare and clinically complex condition. The myriad of symptoms complicates early diagnosis and differential identification. Early initiation of immunomodulatory therapy may offer prognostic advantages.

Abstract Metasurface enables the generation and manipulation of multiphoton entanglement with flat optics, providing a more efficient platform for large-scale photonic quantum information processing. Here, we show that a single metasurface optical device would allow more efficient characterizations of multiphoton entangled states, such as shadow tomography, which generally requires fast and complicated control of optical setups to perform information-complete measurements, a demanding task using conventional optics. The compact and stable device here allows implementations of general positive operator valued measures with a reduced sample complexity and significantly alleviates the experimental complexity to implement shadow tomography. Integrating self-learning and calibration algorithms, we observe notable advantages in the reconstruction of multiphoton entanglement, including using fewer measurements, having higher accuracy, and being robust against experimental imperfections. Our work unveils the feasibility of metasurface as a favorable integrated optical device for efficient characterization of multiphoton entanglement, and sheds light on scalable photonic quantum technologies with ultra-thin optical devices.

The cross-line operation (CO) of trains in urban rail transit is an effective method to efficiently satisfy transfer passenger travel demand as well as relieve the pressure of transfer stations. The primary problem of CO is designing train services to satisfy travel demand with an uneven spatial distribution of passengers. This study constructs a nonlinear integer programming model with a novel train operation scheme, i.e., virtual coupling (VC) technology, which allows the coupling/decoupling of trains on different lines at both ends of each operation zone. This scheme makes the train capacity equitably distributed in each operation zone, thereby balancing train capacity utilization over the whole CO system. Regarding the nonlinear characteristics of the proposed model, an adaptive simulated annealing genetic algorithm (ASA-GA) was designed to quickly generate high-quality solutions. Based on real-world data from the Beijing Changping Line and Line 13, the effectiveness of the proposed model and algorithm were verified. The computation results show that in comparison to a single grouping train composition scheme without CO, a VC scheme with CO would reduce operation costs by 46.8%, with 80.6% savings of train capacity equity. Furthermore, the average passenger residence time would be reduced by 25.9%.

Abstract Aim The association between composite dietary antioxidant index (CDAI) and diabetes remains unknown. Our study was to investigate the association of CDAI with diabetes. Methods A total of 11,956 participants were enrolled from the National Health and Nutrition Examination Surveys (NHANES). The CDAI was calculated from the intake of six dietary antioxidants. Multivariable logistic regressions were performed to explore the associations between CDAI and the prevalence of diabetes and glycemic index. Non-linear associations were explored using restricted cubic splines. Results In the multivariate logistic regression model, the odds ratio (95% confidence interval) of CDAI associating with obesity was 0.98 (0.97-1.00; p = 0.033). Compared with the lowest quartile, the highest quartile was related to 0.84-fold risk of diabetes (0.71–0.99; p = 0.035). However, CDAI was not independently associated with fasting glucose and hemoglobin A1c. Conclusion CDAI was negatively associated with diabetes and the relationship was independent of other traditional risk factors.

We report the results of time series analysis of blazar PKS 1440-389, observed by the Transiting Exoplanet Survey Satellite (TESS) in two sectors. We find that the source has a quasi-periodic oscillation (QPO) of about 3.1 days for sector 11 and around 3.7 days for sector 38 in the optical band. We use two methods to assess the QPO and its confidence level: Lomb–Scargle periodogram and weighted wavelet Z-transforms. We explore various potential explanations for these rapid quasi-periodic variations and propose that their source most likely resides within the innermost region of the accretion disk. Within this framework, we estimate the mass of the central black hole of this blazar. We obtain black hole masses of 6.65 × 108M⊙ (Schwarzschild black hole) and 4.22 × 109M⊙ (maximally rotating Kerr black hole), with a main period of 3.7 days. Finally, we utilize the kink instability model to explain the QPO.

The Yellow River Delta wetlands in China belong to the coastal wetland ecosystem, which is one of the youngest and most characteristic wetlands in the world. The Yellow River Delta wetlands are constantly changed by inland sediment and the influence of waves and storm surges, so the accurate classification of the coastal wetlands in the Yellow River Delta is of great significance for the rational utilization, development and protection of wetland resources. In this study, the Yellow River Delta sentinel-2 multispectral data were processed by super-resolution synthesis, and the feature bands were optimized. The optimal feature-band combination scheme was screened using the OIF algorithm. A deep learning model attention mechanism ResNet based on feature optimization with attention mechanism integration into the ResNet network is proposed. Compared with the classical machine learning model, the AM_ResNet model can effectively improve the classification accuracy of the wetlands in the Yellow River Delta. The overall accuracy was 94.61% with a Kappa of 0.93, and they were improved by about 6.99% and 0.1, respectively, compared with the best-performing Random Forest Classification in machine learning. The results show that the method can effectively improve the classification accuracy of the wetlands in the Yellow River Delta.

The accurate prediction of highway passenger volume is very important for China’s transportation planning and economic development. Based on a neural network, this paper establishes a prediction model by using historical road passenger traffic and related influencing factor data, aiming to provide an accurate road passenger traffic prediction. Firstly, the historical highway passenger volume data and the factor data affecting passenger volume are collected. Then, a multilayer perceptron neural network is established by using SPSS software (PASW Statistics 18) to analyze the significant relationship between highway passenger volume and influencing factors. Then, through the training and verification of the model by MATLAB software (R2021a), the reliability of the prediction model is proved. Finally, the model is used to predict the passenger traffic volume in 2020–2022, and the actual passenger traffic volume is compared and analyzed. It is concluded that the highway passenger traffic volume decreased significantly in 2020–2022 due to various factors such as the epidemic situation and policies, which have had an impact on China’s economic development.

Abstract Large foreign object transporting by coal mine conveyor belt may lead to production safety hazards. To reduce safety accidents during coal mining, a large foreign object detection method based on machine vision is proposed in this paper. An adaptive weighted multi-scale Retinex (MSR) image enhancement algorithm is proposed to improve the captured image quality of the belt conveyor line. An improved multi-scale template matching algorithm is designed by combining the frame difference and area methods to screen and identify large foreign objects mixed in coals. The multi-layer perceptron (MLP) network optimized by the Gray Wolf algorithm is introduced to identify the large objects. Experimental results show that the identification accuracy reaches 98.8% for the large foreign objects. Furthermore, industrial field test is carried out in the Gaoyang coal mine, and the filed test results demonstrate that the identification accuracy of the proposed method is more than 95%. Hence, the proposed method meets the industrial detection requirements and can be used in practices for detecting the large foreign objects.

Abstract This paper studies the extraction of information from unstructured text data of medical literature and electronic medical records in the field of medicine, and proposes a TCM-KR method of knowledge reasoning based on electronic medical records to enhance association rules, and carries out a study on association characteristics in the field of the electronic medical record. This method abstracts the word bag representation mode of text semantics from the unstructured data representation and integrates the correlation information of the knowledge graph of the medicine domain. The method based on a graph convolutional network was used to predict the unknown associations' relations between viscera, channel tropism, and channel distribution. The experimental results show that the TCM-KR method can efficiently infer a large amount of high-quality triple knowledge from the unstructured text data of medicine, and predict the correlation characteristics of Syndromes-Viscera, Chinese medicinal-Channel tropism, Acupoints-Channel distribution in treating lumbar intervertebral disc prolapse and provide a dedicated machine learning model and guidance for clinical diagnosis and treatment.

In this work, we report for the first time two repeated transient quasiperiodic oscillations (QPOs) in the γ -ray light curve of the TeV blazar 3C 279. We search for the periodicity in the light curve and estimate its confidence level using the weighted wavelet Z-transform, the Lomb–Scargle periodogram, and the REDFIT techniques. The main results are as follows: (1) a QPO of ∼33 days (>2.5 σ ) is found during the flare of 117 days (MJD 55008–55125) from 2009 June to November. Interestingly, the same QPO (∼39 days) reappeared in the flaring duration from MJD 59430 to 59585, with the confidence level of > 4 σ . (2) Another transient QPO of ∼91 days with a significance of >3.8 σ is found during a period with 455 days (MJD 58430–58985) from 2019 February to 2020 May. Under the premise of considering the QPOs reported in the literature, the QPO of ∼40 days is repeated three times and the QPO of ∼91 days is repeated twice. We discuss several physical models explaining the QPOs of this blazar. Our study may suggest that the two QPOs originate from the twin jets of the binary black holes at the center of 3C 279. The repeated occurrence of QPOs of a similar scale strongly supports the geometric scenario of a blob spiraling within the jet. Furthermore, the hypothesis of a sheath in the jet may also be a potential explanation for the repetitive γ -ray flare patterns observed in the light curve.

Background: The potential benefits of intervention with empagliflozin or dapagliflozin for patients with heart failure with preserved ejection fraction (HFpEF) were first demonstrated in the EMPEROR-Preserved and DELIVER studies. However, the cost-effectiveness of this intervention (empagliflozin or dapagliflozin) is yet to be established.Methods: In the context of Chinese healthcare, a Markov model was proposed, which incorporates clinical outcomes from the EMPEROR-Preserved and DELIVER studies, to predict the utility and costs over a lifetime. The time horizon was 20 years, and a 5% discount rate was applied to the costs and utilities. The incremental cost-effectiveness ratio (ICER) threshold against willingness to pay (WTP) was set as the primary outcome. The robustness of the decision was evaluated using sensitivity analyses.Results: After a simulated 20-year lifetime, a 72-year-old patient with HFpEF in the intervention group (empagliflozin) showed an increase of 0.44 quality-adjusted life years (QALYs) and $1,623.58 with an ICER of $3,691.56 per QALY, which was lower than the WTP threshold of $12,032.10 per QALY. A 72-year-old patient with HFpEF in the intervention group (dapagliflozin) showed an increase of 0.34 QALYs and $2,002.13 with an ICER of $5,907.79 per QALY, which was lower than the WTP threshold of $12,032.10 per QALY. One-way sensitivity analyses showed that cardiovascular (CV) mortality in the intervention and comparator groups was the most sensitive to the decision. Cost-effectiveness was demonstrated in the intervention group (empagliflozin or dapagliflozin) in 67.9% or 62.2% of 1000 Monte Carlo simulations, respectively.Conclusion: In Chinese healthcare, the interventions (empagliflozin or dapagliflozin) for HFpEF were more cost-effective than the comparators. Our study has provided a quantitative evaluation of the costs and benefits of such interventions for a lifetime using the model.

Objective: Long-acting injections (LAIs) of paliperidone palmitate have been shown to improve medication adherence and relieve psychotic symptoms. However, the specific cost-utility analysis of these LAIs in schizophrenia in China remains unclear.Methods: A multi-state Markov model was constructed to simulate the economic outcomes of patients with schizophrenia in China who received paliperidone palmitate 1-month formulation (PP1M), paliperidone palmitate 3-month formulation (PP3M), and paliperidone extended-release (ER). A cost-utility analysis was conducted, mostly derived from published literature and clinical databases. All costs and utilities were discounted at a rate of 5% per annum. The primary outcome measure was the incremental cost-effectiveness ratios (ICERs). A series of sensitivity analyses were also applied.Results: After 20 years, compared to ER, using PP1M resulted in an increased discounted cost from $36,252.59 to $43,207.28. This increased cost was associated with a gain in quality-adjusted life years (QALYs) from 8.60 to 9.45. As a result, the ICER for PP1M was estimated to be $8,247.46/QALY, which was lower than the willingness-to-pay (WTP) threshold of $12,756.55/QALY. When using PP3M instead of ER, the incremental cost was $768.81 and the incremental utility was 0.88 QALYs, projecting an ICER of $873.13/QALY, which was also lower than the WTP threshold of $12,756.55/QALY. The univariate sensitivity analysis showed that the costs of PP1M, PP3M, and ER had the greatest impact on ICERs. The probability sensitivity analysis (PSA) revealed that when the WTP thresholds were $12,756.55/QALY, the probability of PP1M and PP3M being cost-effective was 59.2% and 66.0%, respectively.Conclusion: From the Chinese healthcare system perspective, PP3M and PP1M are both more cost-effective compared to ER, and PP3M has notable cost-utility advantages over PP1M.

The pore-structure characteristics of hybrid basalt–polypropylene fibre-reinforced concrete were investigated by using mercury intrusion porosimetry. The applicability of various fractal models in characterising the HBPRC pore structure was compared. The results show that the types and content of fibres show different influences on the cumulative pore volume and fractal characteristics of hybrid basalt-polypropylene fibre-reinforced concrete. Compared with pore-volume and solid mass, the fractal characteristics of pore surface area of hybrid basalt-polypropylene fibre-reinforced concrete is more significant. Furthermore, the pore-surface fractal model that established based on the energy conservation relationship during mercury intrusion is more accurate and effective for the characterization of fractal dimension of hybrid basalt-polypropylene fibre-reinforced concrete. The research results can provide important theoretical guidance for the study of pore structure and fractal characteristics of fibre-reinforced concrete.

Glial fibrillary acidic protein astrocytopathy (GFAP-A) is a rare autoimmune disease of the central nervous system that was newly reported in 2016. Previous studies have speculated that the pathological mechanism and clinical outcome of GFAP-A lie in the demyelination of the central nervous system, but due to the limitations of MR, this conclusion has not been further confirmed from the perspective of neuroimaging. A non-invasive, quantitative measurement of demyelination would be clinically valuable, given its critical role in mediating GFAP-A. Here, we report a case in which we use 18F-florbetapir positron emission tomography-magnetic resonance imaging (PET/MRI) to evaluate myelin recovery with follow-up in the patient with GFAP-A. Our patient displayed a decreased uptake of PET tracer 18F-florbetapir in the brain lesions and lower distribution volume ratio in the damaged white matter lesions compared to the normal-appearing white matter, indicating significant intracranial demyelination. After treatment, the 18F-florbetapir PET/MRI examination showed a significant increase in the uptake of 18F-florbetapir in the brain lesions, along with a reduced Expanded Disability Status Scale score. Although only a small number of patients have been validated, this case first reported 18F-florbetapir PET/MRI could quantitatively and non-invasively assess the myelin recovery in GFAP-A patients, which may lead to improvements in the early diagnosis and long-term prognosis.

As one of the main pieces of equipment in coal transportation, the belt conveyor with its detection system is an important area of research for the development of intelligent mines. Occurrences of non-coal foreign objects making contact with belts are common in complex production environments and with improper human operation. In order to avoid major safety accidents caused by scratches, deviation, and the breakage of belts, a foreign object detection method is proposed for belt conveyors in this work. Firstly, a foreign object image dataset is collected and established, and an IAT image enhancement module and an attention mechanism for CBAM are introduced to enhance the image data sample. Moreover, to predict the angle information of foreign objects with large aspect ratios, a rotating decoupling head is designed and a MO-YOLOX network structure is constructed. Some experiments are carried out with the belt conveyor in the mine’s intelligent mining equipment laboratory, and different foreign objects are analyzed. The experimental results show that the accuracy, recall, and mAP50 of the proposed rotating frame foreign object detection method reach 93.87%, 93.69%, and 93.68%, respectively, and the average inference time for foreign object detection is 25 ms.

Extraction of the laser fringe center line is a key step in the 3D reconstruction of linear structured light, the accuracy of which is directly related to the quality of the 3D model. A laser center line extraction method based on an improved gray center of gravity method is proposed to solve the problem of low extraction accuracy. Firstly, a smoothing method is used to eliminate the flat top of the laser line, and the Gaussian curve is adopted to fit the peak position of the curve. Then, the gray threshold is set to automatically extract the laser linewidth, and based on the window opening, the grayscale center of gravity method is improved to extract the coordinates of the center pixel for the second time. Finally, experiments show that the average absolute error of the improved laser line extraction method is 0.026 pixels, which is 2.3 times lower than the gray center of gravity method, 1.9 times lower than the curve fitting method, and the standard error can reach 0.005 pixels. Compared with the gray center of gravity method and the curve fitting method, the influence of gray value change on the center line extraction is more fully considered, and the center of the light strip can be extracted more accurately, achieving sub-pixel accuracy.

Sea fog is a weather hazard along the coast and over the ocean that seriously threatens maritime activities. In the deep learning approach, it is difficult for convolutional neural networks (CNNs) to fully consider global context information in sea fog research due to their own limitations, and the recognition of sea fog edges is relatively vague. To solve the above problems, this paper puts forward an ECA-TransUnet model for daytime sea fog recognition, which consists of a combination of a CNN and a transformer. By designing a two-branch feed-forward network (FFN) module and introducing an efficient channel attention (ECA) module, the model can effectively take into account long-range pixel interactions and feature channel information to capture the global contextual information of sea fog data. Meanwhile, to solve the problem of insufficient existing sea fog detection datasets, we investigated sea fog events occurring in the Yellow Sea and Bohai Sea and their territorial waters, extracted remote sensing images from Moderate Resolution Imaging Spectroradiometer (MODIS) data at corresponding times, and combined data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), cloud and sea fog texture features, and waveband feature information to produce a manually annotated sea fog dataset. Our experiments showed that the proposed model achieves 94.5% accuracy and an 85.8% F1 score. Compared with the existing models relying only on CNNs such as UNet, FCN8s, and DeeplabV3+, it achieves state-of-the-art performance in sea fog recognition.

IntroductionHigh intake of dietary fiber is associated with lower incidence of cardiovascular diseases. Dietary fiber, functions as a prebiotic, has a significant impact on intestinal bacteria composition and diversity. The intestinal flora and metabolites generated by fermentation of dietary fiber not only affect the health of intestine but also play a role in many extra-intestinal diseases, such as obesity, diabetes and atherosclerosis. However, the role and the mechanism by which a high fiber diet contributes to the development of myocardial infarction is still unclear.Methods and resultsHere we used an in vivo mouse model to investigate whether dietary fiber intake could protect against myocardial infarction. Our study demonstrated high fiber diet significantly improved cardiac function, reduced infarct size and prevented adverse remodeling following myocardial infarction. The protective effects of high fiber diet had a strong relation with its attenuation of inflammation. Moreover, we observed that high fiber diet could modulate the composition of intestinal flora and differentially impacted metabolites production, including the biosynthesis of bile acids and linoleic acid metabolism.ConclusionOverall, the findings of this study provided mechanistic insights into the curative effect of dietary fiber on myocardial infarction with a specific emphasis on the potential role of microbiota-metabolism-immunity interactions.

Bearing as the core components of high-grade CNC machine tools, with the constant change of internal clearance of bearing wear increasingly aggravated, spindle rotation accuracy reduced. In this paper, the idea of control variable method is used to explore the dynamic change of bearing clearance from different speeds and different radial loads. Starting with the dynamic model of bearing vibration, the theoretical model of rolling bearing with five degrees of freedom is established in this paper. The interaction force between steel shaft and ceramic bearing inner ring is calculated by Runge-Kutta method with elastic wall thickness ring theory, and the reduction of radial clearance of bearing is obtained. Therefore, a dynamic model of ceramic bearing considering the extrusion force of ceramic bearing inner ring is proposed. At the same time, the vibration test of steel shaft-all-ceramic bearing is designed and carried out. The test results show that under the same load, the higher the rotating speed, the shorter the time for the bearing-rotor system to reach temperature stability, and the root mean square of ceramic bearing-rotor system is obviously reduced. At the same speed, the greater the load, the more obvious the root mean square increase of the bearing-rotor system.

Over the last two decades, spaceborne polarimetric synthetic aperture radar (PolSAR) has been widely used to penetrate sea ice surfaces to achieve fully polarimetric high-resolution imaging at all times of day and in a range of weather conditions. Model-based polarimetric decomposition is a powerful tool used to extract useful physical and geometric information about sea ice from the matrix datasets acquired by PolSAR. The volume scattering of sea ice is usually modeled as the incoherent average of scatterings of a large volume of oriented ellipsoid particles that are uniformly distributed in 3D space. This uniform spatial distribution is often approximated as a uniform orientation distribution (UOD), i.e., the particles are uniformly oriented in all directions. This is achieved in the existing literature by ensuring the canting angle φ and tilt angle τ of particles uniformly distributed in their respective ranges and introducing a factor cos⁡τ in the ensemble average. However, we find this implementation of UOD is not always effective, while a real UOD can be realized by distributing the solid angles of particles uniformly in 3D space. By deriving the total solid angle of the canting-tilt cell spanned by particles and combining the differential relationship between solid angle and Euler angles φ and τ, a complete expression of the joint probability density function pφ,τ that can always ensure the uniform orientation of particles of sea ice is realized. By ensemble integrating the coherency matrix of φ,τ-oriented particle with pφ,τ, a generalized modeling of the volume coherency matrix of 3D uniformly oriented spheroid particles is obtained, which covers factors such as radar observation geometry, particle shape, canting geometry, tilt geometry and transmission effect in a multiplicative way. The existing volume scattering models of sea ice constitute special cases. The performance of the model in the characterization of the volume behaviors was investigated via simulations on a volume of oblate and prolate particles with the differential reflectivity ZDR, polarimetric entropy H and scattering α angle as descriptors. Based on the model, several interesting orientation geometries were also studied, including the aligned orientation, complement tilt geometry and reflection symmetry, among which the complement tilt geometry is specifically highlighted. It involves three volume models that correspond to the horizontal tilt, vertical tilt and random tilt of particles within sea ice, respectively. To match the models to PolSAR data for adaptive decomposition, two selection strategies are provided. One is based on ZDR, and the other is based on the maximum power fitting. The scattering power that reduces the rank of coherency matrix by exactly one without violating the physical realizability condition is obtained to make full use of the polarimetric scattering information. Both the models and decomposition were finally validated on the Gaofen-3 PolSAR data of a young ice area in Prydz Bay, Antarctica. The adaptive decomposition result demonstrates not only the dominant vertical tilt preference of brine inclusions within sea ice, but also the subordinate random tilt preference and non-negligible horizontal tilt preference, which are consistent with the geometric selection mechanism that the c-axes of polycrystallines within sea ice would gradually align with depth. The experiment also indicates that, compared to the strategy based on ZDR, the maximum power fitting is preferable because it is entirely driven by the model and data and is independent of any empirical thresholds. Such soft thresholding enables this strategy to adaptively estimate the negative ZDR offset introduced by the transmission effect, which provides a novel inversion of the refractive index of sea ice based on polarimetric model-based decomposition.

Aiming at the influence of matched filter (e.g.root raised cosine filter) on the coherent optical fiber communication system, the effect of roll-off factor (Rf) are studied under different value of optical bandwidth, residual dispersion and nonlinearity. Simulations are carried out for 256 Gbit/s Polarization Division Multiplexing-16 Quadrature Amplitude Modulation (PDM-16QAM) signals when the optical bandwidth are 33, 50 and 100 GHz, the residual dispersion ranging from-300 to 300 ps/nm and launched power ranging from-3 to 4 dBm in a 1 200 km optical fiber link. The simulations show that a good transmission performance can be achieved for limited optical bandwidth system when Rfis set to the value of (1+Rf) B (Bis the Baud rate of the signal) that similar to the system optical bandwidth. When optical bandwidth near or exceed 2 times Baud rate of signals, Rfshould be set between 0.2 and 0.5 to achieve a better transmission performance.

The quantum component in uncertainty relation can be naturally characterized by the quantum coherence of a quantum state, which is of paramount importance in quantum information science. Here, we experimentally investigate quantum uncertainty relations construed with relative entropy of coherence, l1 norm of coherence, and coherence of formation. Instead of quantum state tomographic technology, we employ the classical shadow algorithm for the detection of lower bounds in quantum uncertainty relations. With an all-optical setup, we prepare a family of quantum states whose purity can be fully controlled. We experimentally explore the tightness of various lower bounds in different reference bases on the prepared states. Our results indicate that the tightness of quantum coherence lower bounds depends on the reference bases and the purity of the quantum state.

Due to the poor lighting conditions and the presence of a large amount of suspended dust in coal mines, obtained video has problems with uneven lighting and low differentiation of facial features. In order to address these problems, an improved image enhancement method is proposed. Firstly, the characteristics of underground coal mine images are analyzed, and median filtering is selected for noise removal. Then, the gamma function and fractional order operator are introduced, and an image enhancement algorithm based on particle swarm optimization is proposed. Finally, several experiments are conducted, and the results show that the proposed improved algorithm outperforms classical image enhancement algorithms, such as MSR, CLAHE and HF. Compared with the original image, the evaluation metrics of the enhanced Yale face images, including average local standard deviation, average gradient, information entropy and contrast, are improved by 113.1%, 63.8%, 22.8% and 24.1%, respectively. Moreover, the proposed algorithm achieves a superior enhancement effect in the simulated coal mine environment.

To reduce engine vibration, a semi-active controlled magnetorheological suspension is designed. First, the magnetic circuit of the new magnetorheological suspension is simulated. Furthermore, the non-parametric forward and inverse dynamic models of the magnetorheological suspension with the Elman neural network are established, where the modeling results and accuracy are analyzed. Second, according to the characteristics of semi-active vibration control of engine suspension, an improved fruit fly optimized proportional-integral-differential (PID) controller is proposed. Compared with the original fruit fly optimized PID controller and particle swarm optimization PID controller, the control strategies based on the proposed methods are simulated by MATLAB. Then, a vibration control experiment is carried out under different control strategies. The simulation and experiment results show that the PID controller based on the improved fruit fly optimization algorithm has the advantages of short adjustment time and fast dynamic response.

Abstract Background The high expression of BLM (Bloom syndrome) helicase in tumors involves its strong association with cell expansion. Bisbenzylisoquinoline alkaloids own an antitumor property and have developed as candidates for anticancer drugs. This paper aimed to screen potential antiproliferative small molecules from 12 small molecules (the derivatives of bisbenzylisoquinoline alkaloids tetrandrine and fangchinoline) by targeting BLM642–1290 helicase. Then we explore the inhibitory mechanism of those small molecules on proliferation of MDA-MB-435 breast cancer cells. Methods Fluorescence polarization technique was used to screen small molecules which inhibited the DNA binding and unwinding of BLM642–1290 helicase. The effects of positive small molecules on the ATPase and conformation of BLM642–1290 helicase were studied by the malachite green-phosphate ammonium molybdate colorimetry and ultraviolet spectral scanning, respectively. The effects of positive small molecules on growth of MDA-MB-435 cells were studied by MTT method, colony formation and cell counting method. The mRNA and protein levels of BLM helicase in the MDA-MB-435 cells after positive small molecule treatments were examined by RT-PCR and ELISA, respectively. Results The compound HJNO (a tetrandrine derivative) was screened out which inhibited the DNA binding, unwinding and ATPase of BLM642–1290 helicase. That HJNO could bind BLM642–1290helicase to change its conformationcontribute to inhibiting the DNA binding, ATPase and DNA unwinding of BLM642–1290 helicase. In addition, HJNO showed its inhibiting the growth of MDA-MB-435 cells. The values of IC50 after drug treatments for 24 h, 48 h and 72 h were 19.9 μmol/L, 4.1 μmol/L and 10.9 μmol/L, respectively. The mRNA and protein levels of BLM helicase in MDA-MB-435 cells increased after HJNO treatment. Those showed a significant difference (P

In order to improve the response characteristics of magnetorheological fluid brake (MRB) and reduce the braking fluctuation rate, an improved grey wolf optimization algorithm was proposed to adjust the parameters of the proportion integration differentiation (PID) controller. Firstly, an MRB system was designed and constructed, and its transfer function was determined. Moreover, by adopting the iterative method of logistic curve, an enhanced grey wolf optimization algorithm (EGWOA) was presented. Using the EGWOA, the parameters of the PID controller were optimized to improve the control performance of the system. Finally, the simulation and experiment were carried out. The results showed that EGWOA has a faster response output and overall better performance without overshoot compared with the conventional PID and grey wolf optimization algorithm (GWOA) PID controller.

Owing to some of its specific advantages, magnetorheological fluid (MRF) has drawn significant attention in a broad range of modern precision machining fields. With the diversification and increase in demand, many novel structural configurations and processing methods have been applied to mechanical machining equipment. Although different applications using MRF have been proposed in the existing literature, the classification, latest approaches, and further trend are not understood clearly for the machining field. Therefore, the current applications such as machining auxiliary equipment and surface polishing equipment that used MRF are summarized from 2016 to 2020, in this article. Especially, some detailed structures of equipment are investigated, and relative limitations are analyzed based on the characteristics of MRF. Finally, in view of the current equipment, advantages and defects are briefly reported; the developing trends of modern precision machining with MRF are discussed. Therefore, in the state-of-the-art review, the significant role of MRF in the machining field is emphasized, which paves the way to innovative development and market selection.

Abstract Enterococcus faecalis (E. faecalis), one of the main pathogens responsible for refractory periapical periodontitis and nosocomial infections, exhibits markedly higher pathogenicity in biofilms. Objectives Studies have shown that caseinolytic protease P (ClpP) is involved in biofilm formation. However, to date, few studies have investigated the role of ClpP in the survival of E. faecalis, and in enhancing biofilm formation. Therefore, we investigated the role of ClpP in the formation of E. faecalis biofilms. Methodology In our study, we used homologous recombination to construct clpP deleted and clpP complement strains of E. faecalis ATCC 29212. A viable colony counting method was used to analyze the growth patterns of E. faecalis. Crystal violet staining (CV) and confocal scanning laser microscopy (CLSM) were used to characterize biofilm mass formation and scanning electron microscopy (SEM) was used to observe the biofilm microstructure. Data was statistically analyzed via Student’s t-test or one-way analysis of variance (ANOVA). Results The results exhibited altered growth patterns for the clpP deletion strains and depleted polysaccharide matrix, resulting in reduced biofilm formation capacity compared to the standard strains. Moreover, ClpP was observed to increase biofilm formation in E. faecalis. Conclusion Our study shows that ClpP can increase biofilm formation in E. faecalis and emphasizes the importance of ClpP as a potential target against E. faecalis.

Peer-reviewed published studies on tibial plateau fractures treated with either open reduction with internal fixation (ORIF) or circular external fixation were reviewed to compare functional, radiological outcomes, postoperative complications, and reoperation rates between the two methods. A systematic search of various databases including Medline, Cochrane Controlled Register of Trials (CENTRAL), ScienceDirect, and Google Scholar from inception until June 2019 was performed. 17 studies with 1168 participants were included in the review. Most of the studies (76%) were retrospective in nature and had low or unclear bias risks. Incidence of total infection (Odds ratio [OR], 2.58; 95% CI, 1.33-5.02) and malunions (OR, 2.56; 95% CI, 1.12-5.84) were higher and length of hospital stay was shorter in patients treated with circular external fixator (Mean difference [MD], -6.1; 95% CI, -11.1--1.19). There were no differences in the incidence of secondary osteoarthritis (OR, 1.49; 95% CI, 0.92-2.42), range of motion (MD, 2.28; 95% CI, -11.27-15.82) non-union (OR, 1.44; 95% CI, 0.14-14.27) and reoperation rates (OR, 1.84; 95% CI, 0.90-3.78) between the two groups. Results from this investigation suggest that circular fixation may offer some advantages over ORIF such as a shortened length of hospital stay and early return to preinjury activities. Definitive clinical recommendations cannot be made as it also presents higher rates of postoperative complications than ORIF.

Aiming to study the effect of drive plate with different surface topographies on the wear property of magnetorheological fluid (MRF), some specific experiments are carried out and analyzed in this paper. Firstly, experiment materials and test methods for MRF are elaborated, and four different surface topography drive plates are designed and manufactured. MRF test-bed is built up to test the rheological properties of MRF. Moreover, the surface morphology of particles is taken using a scanning electron microscope (SEM). Finally, experimental results show that there are remarkable influences on tribological properties of carbonyl iron-based MRF with different drive plates. Therefore, the drive plate with specific surface topography can meet the needs of different MRF transmission systems.

Background/Aims: Pancreatic cancer (PC) is an aggressive malignancy with a poor survival rate. Despite advances in the treatment of PC, the efficacy of therapy is limited by the development of chemoresistance. Here, we examined the role of microRNA-29c (miR-29c) and the involvement of autophagy and apoptosis in the chemoresistance of PC cells in vivo and in vitro. Methods: We employed qRT-PCR, western blot and immunofluorescence to examine the expression level of miR-29c, USP22 and autophagy relative protein. In addition, we used MTT assay to detect cell proliferation and transwell assay to measure migration and invasiveness. The apoptosis was determined using annexin V-FITC/PI apoptosis detection kit by flow cytometry. Luciferase reporter assays confirmed the relationship between USP22 and miR-29c. Results: miR-29c overexpression in the PC cell line PANC-1 enhanced the effect of gemcitabine on decreasing cell viability and inducing apoptosis and inhibited autophagy, as shown by western blotting, immunofluorescence staining, colony formation assays, and flow cytometry. Ubiquitin specific peptidase (USP)-22, a deubiquitinating enzyme known to induce autophagy and promote PC cell survival, was identified as a direct target of miR-29c. USP22 knockdown experiments indicated that USP22 suppresses gemcitabine-induced apoptosis by promoting autophagy, thereby increasing the chemoresistance of PC cells. Luciferase reporter assays confirmed that USP22 is a direct target of miR-29c. A xenograft mouse model demonstrated that miR-29c increases the chemosensitivity of PC in vivo by downregulating USP22, leading to the inhibition of autophagy and induction of apoptosis. Conclusions: Taken together, these findings reveal a potential mechanism underlying the chemoresistance of PC cells mediated by the regulation of USP22-mediated autophagy by miR-29c, suggesting potential targets and therapeutic strategies in PC.

Detecting multipartite quantum coherence usually requires quantum state reconstruction, which is quite inefficient for large-scale quantum systems. Along this line of research, several efficient procedures have been proposed to detect multipartite quantum coherence without quantum state reconstruction, among which the spectrum-estimation-based method is suitable for various coherence measures. Here, we first generalize the spectrum-estimation-based method for the geometric measure of coherence. Then, we investigate the tightness of the estimated lower bound of various coherence measures, including the geometric measure of coherence, the l1-norm of coherence, the robustness of coherence, and some convex roof quantifiers of coherence multiqubit GHZ states and linear cluster states. Finally, we demonstrate the spectrum-estimation-based method as well as the other two efficient methods. We observe that the spectrum-estimation-based method outperforms other methods in various coherence measures, which significantly enhances the accuracy of estimation.

Quantification of coherence lies at the heart of quantum information processing and fundamental physics. Exact evaluation of coherence measures generally needs a full reconstruction of the density matrix, which becomes intractable for large-scale multipartite systems. Here, we propose a systematic theoretical approach to efficiently estimating lower and upper bounds of coherence in multipartite states. Under the stabilizer formalism, the lower bound is determined by the spectrum estimation method with a small number of measurements, and the upper bound is determined by a single measurement. We verify our theory with a four-qubit optical quantum system. We experimentally implement various multiqubit entangled states, including the Greenberger-Horne-Zeilinger state, the cluster state, and the W state, and show how their coherences are efficiently inferred from measuring a few observables.

Ideal quantum teleportation transfers an unknown quantum state intact from one party Alice to the other Bob via the use of a maximally entangled state and the communication of classical information. If Alice and Bob do not share entanglement, the maximal average fidelity between the state to be teleported and the state received, according to a classical measure-and-prepare scheme, is upper bounded by a function f_{c} that is inversely proportional to the Hilbert space dimension. In fact, even if they share entanglement, the so-called teleportation fidelity may still be less than the classical threshold f_{c}. For two-qubit entangled states, conditioned on a successful local filtering, the teleportation fidelity can always be activated, i.e., boosted beyond f_{c}. Here, for all dimensions larger than two, we show that the teleportation power hidden in a subset of entangled two-qudit Werner states can also be activated. In addition, we show that an entire family of two-qudit rank-deficient states violates the reduction criterion of separability, and thus their teleportation power is either above the classical threshold or can be activated. Using hybrid entanglement prepared in photon pairs, we also provide the first proof-of-principle experimental demonstration of the activation of teleportation power hidden in this latter family of qubit states. The connection between the possibility of activating hidden teleportation power with the closely-related problem of entanglement distillation is discussed.

When applied in the measurement of coal pyrolysis reaction heat, the differential scanning calorimeter outputs a total heat flow signal which is composed by reaction heat flow and baseline flow.Hence, determination of baseline heat flow is the key to accurately measuring the coal pyrolysis reaction heat.The composition of the baseline heat flow in the process of measuring pyrolysis reaction heat of coal and other heterogeneous organic matters by TG-DSC technique was theoretically analyzed in this paper, then the comparison of empirical methods with the testing approaches to determining the baseline heat flow was conducted.Finally the evaluation of the testing approaching to determining the baseline heat flow for each consisting part was carried out, in which particular attention was focused on their application possibility in the determination method of baseline heat flow.Results show that during the measurement of coal pyrolysis reaction heat by TG-DSC, the baseline heat flow consists of two parts, namely, sensible heat flow and radiation heat flow difference.On the basis of thermal analysis parame-ters which are obtained by TG-DSC in the measurement of coal pyrolysis reaction heat, sensible heat flow could be calculated by using Merrick model, mixture model, empirical formula of ash specific heat, etc.As for radiation heat flow difference, it could be obtained by correction factor λ after relating the process of coal pyrolysis with char pyrolysis.Finally, the baseline heat flow of coal pyrolysis reaction is determined by the sum of sensible heat and radiation heat flow difference.

This paper focuses on the development of regional GDP and proposes a method proposed for forecast of enterprise power consumption data and GDP based on ensemble algorithms. The enterprise power consumption data are used as independent variables and GDP data as dependent variables. A multiple linear regression model is selected as the primary learner for training and its outputs will be sorted into a new dataset of input features to train a secondary learner. The forecast of GDP is thus realized through ensemble learning.

Maximally bipartite entangled state |Ψ^{+}〉, also known as the Einstein-Podolsky-Rosen pair, is the unit resource of entanglement and the key for quantum information processing. An important problem is that how many maximally bipartite entangled states could be distilled from a multipartite entangled state shared among a quantum network. Here, we focus on the distillation of |Ψ^{+}〉 from a single copy of the three-qubit W state. An interesting phenomenon in this case is that the random entanglement distillation between two unspecified parties can yield a strictly higher distillation rate (the average number of |Ψ^{+}〉 distilled from each W state) than the case between two specified parties. In this work, we develop a distillation protocol by introducing weak measurements that do not destroy the global entanglement. We find that the distillation rate can be significantly enhanced with only a few rounds by performing an extra distillation procedure between two specified parties at the final step. Experimentally, we prepare a three-photon W state in the polarization degree of freedom, and employ the path degree of freedom of each photon as probe qubits to realize the weak measurement. As a proof-of-principle demonstration, we show that the distillation rate is enhanced from 2/3, which is the theoretical limit of any distillation scheme between two specified parties, to 0.751±0.030 between two unspecified parties with only one distillation round.

The Bell inequality, and its substantial experimental violation, offers a seminal paradigm for showing that the world is not in fact locally realistic. Here, going beyond the scope of Bell's inequality on physical states, we show that quantum teleportation can be used to quantitatively characterize quantum correlations of physical processes. The validity of the proposed formalism is demonstrated by considering the problem of teleportation through a linear three-node quantum network. A hierarchy is derived between the Bell nonlocality, nonbilocality, steering, and nonlocality-steering hybrid correlations based on a process fidelity constraint. The proposed framework can be directly extended to reveal the nonlocality structure of teleportation through any linear many-node quantum network. The formalism provides a faithful identification of quantum teleportation and demonstrates the use of quantum-information processing as a means of quantitatively discriminating quantum correlations.