Phosphatidylserine:An overview on functionality,processing techniques,patents,and prospects

Phosphatidylserine(PS)is the part of cell structure in the body and has many beneficial functions especially in brain-related aging diseases.Although daily foods can provide PS to human body,the amount is very limited due to its poverty in most foods.To overcome the issue,numerous studies based on PS have been reported to develop PS-related supplements.In this review,PS was comprehensively and critically reviewed from the view of resources,functions,processing techniques,patents,and prospects.For resources,animal,plant,and microorganism origins were all covered with their differences in composition profiles.For functions,benefits regarding memory,cognitive enhancement,exercise performance,reducing Alzheimer’s disease,and attention-deficit hyperactivity disorder symptoms were covered as well as the functional differences among animal-,plant-,and microorganism-based PS-related supplements.For processing techniques,traditional extracting methods from animal,plant,and microorganism tissues were comparatively discussed with enzymatic synthesis based on different reaction systems.Finally,patents of PS-related supplements were evaluated as well as their applications.This review could provide scientific and valuable support for PS industry.

Algebraic processing technique for extracting frequency-dependent shear-wave splitting parameters in an anisotropic medium

Based on the dual source cumulative rotation technique in the time-domain proposed by Zeng and MacBeth（1993）,a new algebraic processing technique for extracting shear-wave splitting parameters from multi-component VSP data in frequency-dependent medium has been developed.By using this dual source cumulative rotation technique in the frequency-domain（DCTF）,anisotropic parameters,including polarization direction of the shear-waves and timedelay between the fast and slow shear-waves,can be estimated for each frequency component in the frequency domain.It avoids the possible error which comes from using a narrow-band filter in the current commonly used method.By using synthetic seismograms,the feasibility and validity of the technique was tested and a comparison with the currently used method was also given.The results demonstrate that the shear-wave splitting parameters frequency dependence can be extracted directly from four-component seismic data using the DCTF.In the presence of larger scale fractures,substantial frequency dependence would be found in the seismic frequency range,which implies that dispersion would occur at seismic frequencies.Our study shows that shear-wave anisotropy decreases as frequency increases.

Processing Technique of Instant Flavored Daylily(Hemerocallis citrina Baroni)

This study was conducted to investigate the recipe and process of instant pickle by multiple steps with dry daylily as a raw material, and an orthogonal test was adopted to obtain the optimal recipe and process. The pickling process of the instant flavored daylily was conducted at an optimal crisp-keeping Ca Cl2 concentration at 0.050%, cooking time of 5 min, pickling time of 6 h and a salt concentration of 4%. The effects of various factors on product taste were in order of salt concentrationcooking timepickling timeCa Cl2 concentration.The obtained product has the characteristics of strong fragrance, crisp delicious taste and unique flavor with stomachic effect.

The State-of-the-Art Review on Applications of Intrusive Sensing,Image Processing Techniques,and Machine Learning Methods in Pavement Monitoring and Analysis

In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users.Therefore,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation safety.Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions.Advanced technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning methods.This review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.

FUNDAMENTAL COMMUNICATIONS THEORIES AND SIGNAL PROCESSING TECHNIQUES FOR AMORPHOUS CELLULAR SYSTEMS

The rapid developing of the fourth generation(4G)wireless communications has aroused tremendous demands for high speed data transmission due to the dissemination of various types of the intelligent user terminals as well as the wireless multi-media services.It is predicted that the network throughput will increase

A Systematic Review of Computer Vision Techniques for Quality Control in End-of-Line Visual Inspection of Antenna Parts

The rapid evolution of wireless communication technologies has underscored the critical role of antennas in ensuring seamless connectivity.Antenna defects,ranging from manufacturing imperfections to environmental wear,pose significant challenges to the reliability and performance of communication systems.This review paper navigates the landscape of antenna defect detection,emphasizing the need for a nuanced understanding of various defect types and the associated challenges in visual detection.This review paper serves as a valuable resource for researchers,engineers,and practitioners engaged in the design and maintenance of communication systems.The insights presented here pave the way for enhanced reliability in antenna systems through targeted defect detection measures.In this study,a comprehensive literature analysis on computer vision algorithms that are employed in end-of-line visual inspection of antenna parts is presented.The PRISMA principles will be followed throughout the review,and its goals are to provide a summary of recent research,identify relevant computer vision techniques,and evaluate how effective these techniques are in discovering defects during inspections.It contains articles from scholarly journals as well as papers presented at conferences up until June 2023.This research utilized search phrases that were relevant,and papers were chosen based on whether or not they met certain inclusion and exclusion criteria.In this study,several different computer vision approaches,such as feature extraction and defect classification,are broken down and analyzed.Additionally,their applicability and performance are discussed.The review highlights the significance of utilizing a wide variety of datasets and measurement criteria.The findings of this study add to the existing body of knowledge and point researchers in the direction of promising new areas of investigation,such as real-time inspection systems and multispectral imaging.This review,on its whole,offers a complete study of computer vision approaches for quality control in antenna parts.It does so by providing helpful insights and drawing attention to areas that require additional exploration.

Fresh Processing Technology in Polygonatum odoratum Production Area and Its Comparison with Traditional Processing

[Objectives]To compare the effects of traditional processing and fresh processing on the quality of Polygonatum odoratum decoction piece.[Methods]The effects of fresh processing and traditional processing on the quality of P.odoratum decoction piece were compared and analyzed with appearance characteristics,total ash content,extract content,total polysaccharides content,and total flavonoids content as the evaluation indexes.[Results]Fresh processing method in different production areas has different effects on P.odoratum decoction piece.P.odoratum was dried in oven of 50℃.When moisture content was 41.44%-59.67%,it was cut.After complete drying at 50℃,the moisture content of dried P.odoratum was 8.94%-9.60%,and ethanol-soluble extract content was 77.29%-78.20%,and water-soluble extract was 77.7%-78.14%.At this time,the appearance characteristics of section of P.odoratum decoction piece were better than that of traditional processing,which was yellowish white.The total polysaccharide content was higher than that of traditional processing,and the content of total flavonoids was statistically significant different from that of traditional processing.[Conclusions]The quality of P.odoratum decoction piece by fresh processing is better than that of the traditional processing,and it is feasible to use fresh processing.

Machine Learning-based Identification of Contaminated Images in Light Curve Data Preprocessing

Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal.Analyzing light curves to determine attitude is the most commonly used method.In photometric observations,outliers may exist in the obtained light curves due to various reasons.Therefore,preprocessing is required to remove these outliers to obtain high quality light curves.Through statistical analysis,the reasons leading to outliers can be categorized into two main types:first,the brightness of the object significantly increases due to the passage of a star nearby,referred to as“stellar contamination,”and second,the brightness markedly decreases due to cloudy cover,referred to as“cloudy contamination.”The traditional approach of manually inspecting images for contamination is time-consuming and labor-intensive.However,we propose the utilization of machine learning methods as a substitute.Convolutional Neural Networks and SVMs are employed to identify cases of stellar contamination and cloudy contamination,achieving F1 scores of 1.00 and 0.98 on a test set,respectively.We also explore other machine learning methods such as ResNet-18 and Light Gradient Boosting Machine,then conduct comparative analyses of the results.

Development of HPLC Method to Evaluate Drug-processing Technique of Eucommiae Cortex

Objective To establish a RP-HPLC method investigate the processing technique and mechanism of Eucommiae Cortex.Methods The RP-HPLC method was applied to simultaneously determining six ingredients,geniposidic acid,geniposide,genipin,chlorogenic acid,(+)-pinoresinol-di-β-D-glucopyranoside,and(+)-syringaresinol-di-β-D-glucopyranoside,in the different processed barks of Eucommia ulmoides.Results The valid method with good accuracy could be well used to study the processing technique of E.ulmoides;Besides,target ingredients in E.ulmoide were decreased within 6 h when they were processed.Conclusion Established RP-HPLC is a reliable method which could be used to research the processing technique of the barks of E.ulmoides.Moreover,the result of this study could be provided with significant evidence of processed barks of E.ulmoides.

PROCESSINGTECHNIQUESANDCHARACTERIZATIONOFCERAMICFIBERREINFORCEDALUMINIUMCOMPOSITES

This paper relates to the fabrication of aluminium matrix composites with various amounts of Al 2O 3 fiber and SiC whiskers by rheocasting, powder metallurgy process and liquid metal infiltration process. To analyze wetting characteristics, the cross sections of composites are examined by scanning electron microscopy(SEM). The bending tests and microhardness tests are performed to evaluate mechanical properties of composites. The results show that the composites produced by liquid metal infiltration give better properties than those produced by rheocasting or powder metallurgy process, primarily due to the decrease of porosity and reinforcement cluster. For liquid metal infiltration composites, a good adhesion between the fiber and matrix is found. Three points bending test results show that there is an increase in flexural modulus with reinforcement contents. In addition, a series of microhardness tests are conducted to determine the effect of heat treatment on the mechanical property of Al 2O 3/Al composites.

Effects of Different Curing Techniques on Material and Aroma of Flue-cured Tobacco

[Objective] The aim was to compare the effects of three-phase technique, quality and aromatic-flavor improvement technique and Zimbabwe technique on to-bacco quality to select a suitable technique in tobacco production. [Method] Tobacco leaves in down, middle and upper parts of a tobacco variety KRK26 were col ected to analyze raw tobacco quality characters. [Result] With quality and aromatic-flavor improvement technique, the proportions of high-quality and middle/high quality tobac-cos improved by 4.68% and 1.97% and average price enhanced by 1.15 yuan/kg, increasing by 10.33%, 9.07% and 3.12 yuan/kg, compared with three-phase tech-nique. It is proved that the technique would better improve tobacco fragrance and quality and coordinate tobacco fragrance, taste and throat-irritation. [Conclusion] Quality and aromatic-flavor improvement technique provides references for tobacco production.

Research progress of heterogeneous structure magnesium alloys:A review

In recent years,a new class of metallic materials featuring heterogeneous structures has emerged.These materials consist of distinct soft and hard domains with significant differences in mechanical properties,allowing them to maintain high strength while offering superior ductility.Magnesium(Mg)alloys,renowned for their low density,high specific strength,exceptional vibration damping,and electromagnetic shielding properties,exhibit tremendous potential as lightweight and functional materials.Despite their advantageous properties,high-strength Mg alloys often suffer from limited ductility.However,the emergence of heterogeneous materials provides a fresh perspective for the development of Mg alloys with both high strength and ductility.This article provided a fundamental overview of heterostructured materials and systematically reviewed the recent research progress in the design of Mg alloys with strength-ductility balance based on heterostructure principles.The review encompassed various aspects,including preparation methods,formation mechanisms of diverse heterostructures,and mechanical properties,both within domestic and international contexts.On this basis,the article discussed the challenges encountered in the design and fabrication of heterostructured Mg alloys,as well as the urgent issues that require attention and resolution in the future.

Super-resolution Imaging of Telescopic Systems based on Optical-neural Network Joint Optimization

Optical telescopes are an important tool for acquiring optical information about distant objects,and resolution is an important indicator that measures the ability to observe object details.However,due to the effects of system aberration,atmospheric seeing,and other factors,the observed image of ground-based telescopes is often degraded,resulting in reduced resolution.This paper proposes an optical-neural network joint optimization method to improve the resolution of the observed image by co-optimizing the point-spread function(PSF)of the telescopic system and the image super-resolution(SR)network.To improve the speed of image reconstruction,we designed a generative adversarial net(LCR-GAN)with light parameters,which is much faster than the latest unsupervised networks.To reconstruct the PSF trained by the network in the optical path,a phase mask is introduced.It improves the image reconstruction effect of LCR-GAN by reconstructing the PSF that best matches the network.The results of simulation and verification experiments show that compared with the pure deep learning method,the SR image reconstructed by this method is rich in detail and it is easier to distinguish stars or stripes.

A Preliminary Comparative Study on the Centering Algorithms for CassiniISS NAC Images

Obtaining high precision is an important consideration for astrometric studies using images from the Narrow Angle Camera(NAC)of the Cassini Imaging Science Subsystem(ISS).Selecting the best centering algorithm is key to enhancing astrometric accuracy.In this study,we compared the accuracy of five centering algorithms:Gaussian fitting,the modified moments method,and three point-spread function(PSF)fitting methods(effective PSF(ePSF),PSFEx,and extended PSF(x PSF)from the Cassini Imaging Central Laboratory for Operations(CICLOPS)).We assessed these algorithms using 70 ISS NAC star field images taken with CL1 and CL2 filters across different stellar magnitudes.The ePSF method consistently demonstrated the highest accuracy,achieving precision below 0.03 pixels for stars of magnitude 8-9.Compared to the previously considered best,the modified moments method,the e PSF method improved overall accuracy by about 10%and 21%in the sample and line directions,respectively.Surprisingly,the xPSF model provided by CICLOPS had lower precision than the ePSF.Conversely,the ePSF exhibits an improvement in measurement precision of 23%and 17%in the sample and line directions,respectively,over the xPSF.This discrepancy might be attributed to the xPSF focusing on photometry rather than astrometry.These findings highlight the necessity of constructing PSF models specifically tailored for astrometric purposes in NAC images and provide guidance for enhancing astrometric measurements using these ISS NAC images.

Resolution Enhancement of SOHO/MDI Magnetograms

Research on the solar magnetic field and its effects on solar dynamo mechanisms and space weather events has benefited from the continual improvements in instrument resolution and measurement frequency.The augmentation and assimilation of historical observational data timelines also play a significant role in understanding the patterns of solar magnetic field variation.Within the realm of astronomical data processing,super-resolution(SR)reconstruction refers to the process of using a substantial corpus of training data to learn the nonlinear mapping between low-resolution(LR)and high-resolution(HR)images,thereby achieving higherresolution astronomical images.This paper is an application study in high-dimensional nonlinear regression.Deep learning models were employed to perform SR modeling on SOHO/MDI magnetograms and SDO/HMI magnetograms,thus reliably achieving resolution enhancement of full-disk SOHO/MDI magnetograms and enhancing the image resolution to obtain more detailed information.For this study,a data set comprising 9717pairs of data from 2010 April to 2011 February was used as the training set,1332 pairs from 2011 March were used as the validation set and 1034 pairs from 2011 April were used as the test set.After data preprocessing,we randomly cropped 128×128 sub-images as the LR cases from the full-disk MDI magnetograms,and the corresponding 512×512 sub-images as HR ones from the HMI full-disk magnetograms for model training.The tests conducted have shown that the study successfully produced reliable 4×SR reconstruction of full-disk MDI magnetograms.The MESR model's results(0.911)were highly correlated with the target HMI magnetographs as indicated by the correlation coefficient values.Furthermore,the method achieved the best PSNR,SSIM,MAE and RMSE values,indicating that the MESR model can effectively reconstruct magnetograms.

Preparation for CSST:Star-galaxy Classification using a Rotationally Invariant Supervised Machine Learning Method

Most existing star-galaxy classifiers depend on the reduced information from catalogs,necessitating careful data processing and feature extraction.In this study,we employ a supervised machine learning method(GoogLeNet)to automatically classify stars and galaxies in the COSMOS field.Unlike traditional machine learning methods,we introduce several preprocessing techniques,including noise reduction and the unwrapping of denoised images in polar coordinates,applied to our carefully selected samples of stars and galaxies.By dividing the selected samples into training and validation sets in an 8:2 ratio,we evaluate the performance of the GoogLeNet model in distinguishing between stars and galaxies.The results indicate that the GoogLeNet model is highly effective,achieving accuracies of 99.6% and 99.9% for stars and galaxies,respectively.Furthermore,by comparing the results with and without preprocessing,we find that preprocessing can significantly improve classification accuracy(by approximately 2.0% to 6.0%)when the images are rotated.In preparation for the future launch of the China Space Station Telescope(CSST),we also evaluate the performance of the GoogLeNet model on the CSST simulation data.These results demonstrate a high level of accuracy(approximately 99.8%),indicating that this model can be effectively utilized for future observations with the CSST.

Lossless Compression Method for the Magnetic and Helioseismic Imager(MHI)Payload

The Solar Polar-orbit Observatory(SPO),proposed by Chinese scientists,is designed to observe the solar polar regions in an unprecedented way with a spacecraft traveling in a large solar inclination angle and a small ellipticity.However,one of the most significant challenges lies in ultra-long-distance data transmission,particularly for the Magnetic and Helioseismic Imager(MHI),which is the most important payload and generates the largest volume of data in SPO.In this paper,we propose a tailored lossless data compression method based on the measurement mode and characteristics of MHI data.The background out of the solar disk is removed to decrease the pixel number of an image under compression.Multiple predictive coding methods are combined to eliminate the redundancy utilizing the correlation(space,spectrum,and polarization)in data set,improving the compression ratio.Experimental results demonstrate that our method achieves an average compression ratio of 3.67.The compression time is also less than the general observation period.The method exhibits strong feasibility and can be easily adapted to MHI.

On the Relation Between Symmetry of Radio Galaxies and Their Physical Parameters

Gravity as a fundamental force plays a dominant role in the formation and evolution of cosmic objects and leaves its effect in the emergence of symmetric and asymmetric structures.Thus,analyzing the symmetry criteria allows us to uncover mechanisms behind the gravity interaction and understand the underlying physical processes that contribute to the formation of large-scale structures such as galaxies.We use a segmentation process using intensity thresholding and the k-means clustering algorithm to analyze radio galaxy images.We employ a symmetry criterion and explore the relation between morphological symmetry in radio maps and host galaxy properties.Optical properties(stellar mass,black hole mass,optical size(R_(50)),concentration,stellar mass surface density(μ_(50)),and stellar age)and radio properties(radio flux density,radio luminosity,and radio size)are considered.We found that there is a correlation between symmetry and radio size,indicating larger radio sources have smaller symmetry indices.Therefore,size of radio sources should be considered in any investigation of symmetry.Weak correlations are also observed with other properties,such as R_(50)for FRI galaxies and stellar age.We compare the symmetry differences between FRI and FRII radio galaxies.FRII galaxies show higher symmetry in 1.4 GHz and 150 MHz maps.Investigating the influence of radio source sizes,we discovered that this result is independent of the sizes of radio sources.These findings contribute to our understanding of the morphological properties and analyses of radio galaxies.

How to Coadd Images.Ⅱ.Anti-aliasing and PSF Deconvolution

We have developed a novel method for co-adding multiple under-sampled images that combines the iteratively reweighted least squares and divide-and-conquer algorithms.Our approach not only allows for the anti-aliasing of the images but also enables Point-Spread Function(PSF)deconvolution,resulting in enhanced restoration of extended sources,the highest peak signal-to-noise ratio,and reduced ringing artefacts.To test our method,we conducted numerical simulations that replicated observation runs of the China Space Station Telescope/the VLT Survey Telescope(VST)and compared our results to those obtained using previous algorithms.The simulation showed that our method outperforms previous approaches in several ways,such as restoring the profile of extended sources and minimizing ringing artefacts.Additionally,because our method relies on the inherent advantages of least squares fitting,it is more versatile and does not depend on the local uniformity hypothesis for the PSF.However,the new method consumes much more computation than the other approaches.

Three-Dimensional Rigid-Plastic FEM Simulation of Bulk Forming Processes with New Contact and Remeshing Techniques

Some techniques such as die surface description, contact judgement algorithm and remeshing are proposed to improve the robustness of the numerical solution. Based on these techniques, a three-dimensional rigid-plastic FEM code has been developed. Isothermal forging process of a cylindrical housing has been simulated. The simulation results show that the given techniques and the FEM code are reasonable and feasible for three-dimensional bulk forming processes.