LociScan,a tool for screening genetic marker combinations for plant variety discrimination

To reduce the cost and increase the efficiency of plant genetic marker fingerprinting for variety discrimination,it is desirable to identify the optimal marker combinations.We describe a marker combination screening model based on the genetic algorithm(GA)and implemented in a software tool,Loci Scan.Ratio-based variety discrimination power provided the largest optimization space among multiple fitness functions.Among GA parameters,an increase in population size and generation number enlarged optimization depth but also calculation workload.Exhaustive algorithm afforded the same optimization depth as GA but vastly increased calculation time.In comparison with two other software tools,Loci Scan accommodated missing data,reduced calculation time,and offered more fitness functions.In large datasets,the sample size of training data exerted the strongest influence on calculation time,whereas the marker size of training data showed no effect,and target marker number had limited effect on analysis speed.

Discrimination of polysorbate 20 by high-performance liquid chromatography-charged aerosol detection and characterization for components by expanding compound database and library

Analyzing polysorbate 20(PS20)composition and the impact of each component on stability and safety is crucial due to formulation variations and individual tolerance.The similar structures and polarities of PS20 components make accurate separation,identification,and quantification challenging.In this work,a high-resolution quantitative method was developed using single-dimensional high-performance liquid chromatography(HPLC)with charged aerosol detection(CAD)to separate 18 key components with multiple esters.The separated components were characterized by ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry(UHPLC-Q-TOF-MS)with an identical gradient as the HPLC-CAD analysis.The polysorbate compound database and library were expanded over 7-time compared to the commercial database.The method investigated differences in PS20 samples from various origins and grades for different dosage forms to evaluate the composition-process relationship.UHPLC-Q-TOF-MS identified 1329 to 1511 compounds in 4 batches of PS20 from different sources.The method observed the impact of 4 degradation conditions on peak components,identifying stable components and their tendencies to change.HPLC-CAD and UHPLC-Q-TOF-MS results provided insights into fingerprint differences,distinguishing quasi products.

In situ non-destructive measurement of Josephson junction resistance using fritting contact technique

Conventional four-probe methods for measuring the resistance of Josephson junctions can damage superconducting thin films,making them unsuitable for frequency measurements of superconducting qubits.In this study,we present a custom probe station measurement system that employs the fritting contact technique to achieve in situ,non-destructive measurements of Josephson junction resistance.Our experimental results demonstrate that this method allows for accurate prediction of qubit frequency with an error margin of 17.2 MHz.Moreover,the fritting contact technique does not significantly affect qubit coherence time or the integrity of the superconducting film,confirming its non-destructive nature.This innovative approach provides a dependable foundation for frequency tuning and addressing frequency collision issues,thus supporting the advancement and practical deployment of superconducting quantum computing.

The Analysis of the Correlation between SPT and CPT Based on CNN-GA and Liquefaction Discrimination Research

The objective of this study is to investigate themethods for soil liquefaction discrimination. Typically, predicting soilliquefaction potential involves conducting the standard penetration test (SPT), which requires field testing and canbe time-consuming and labor-intensive. In contrast, the cone penetration test (CPT) provides a more convenientmethod and offers detailed and continuous information about soil layers. In this study, the feature matrix based onCPT data is proposed to predict the standard penetration test blow count N. The featurematrix comprises the CPTcharacteristic parameters at specific depths, such as tip resistance qc, sleeve resistance f s, and depth H. To fuse thefeatures on the matrix, the convolutional neural network (CNN) is employed for feature extraction. Additionally,Genetic Algorithm (GA) is utilized to obtain the best combination of convolutional kernels and the number ofneurons. The study evaluated the robustness of the proposed model using multiple engineering field data sets.Results demonstrated that the proposed model outperformed conventional methods in predicting N values forvarious soil categories, including sandy silt, silty sand, and clayey silt. Finally, the proposed model was employedfor liquefaction discrimination. The liquefaction discrimination based on the predicted N values was comparedwith the measured N values, and the results showed that the discrimination results were in 75% agreement. Thestudy has important practical application value for foundation liquefaction engineering. Also, the novel methodadopted in this research provides new ideas and methods for research in related fields, which is of great academicsignificance.

Balancing the minimum error rate and minimum copy consumption in quantum state discrimination

Extracting more information and saving quantum resources are two main aims for quantum measurements.However,the optimization of strategies for these two objectives varies when discriminating between quantum states |ψ_(0)> and |ψ_(1)> through multiple measurements.In this study,we introduce a novel state discrimination model that reveals the intricate relationship between the average error rate and average copy consumption.By integrating these two crucial metrics and minimizing their weighted sum for any given weight value,our research underscores the infeasibility of simultaneously minimizing these metrics through local measurements with one-way communication.Our findings present a compelling trade-off curve,highlighting the advantages of achieving a balance between error rate and copy consumption in quantum discrimination tasks,offering valuable insights into the optimization of quantum resources while ensuring the accuracy of quantum state discrimination.

FPGA implementation of 500-MHz high-count-rate high-time-resolution real-time digital neutron-gamma discrimination for fast liquid detectors

Fast neutron flux measurements with high count rates and high time resolution have important applications in equipment such as tokamaks.In this study,real-time neutron and gamma discrimination was implemented on a self-developed 500-Msps,12-bit digitizer,and the neutron and gamma spectra were calculated directly on an FPGA.A fast neutron flux measurement system with BC-501A and EJ-309 liquid scintillator detectors was developed and a fast neutron measurement experiment was successfully performed on the HL-2 M tokamak at the Southwestern Institute of Physics,China.The experimental results demonstrated that the system obtained the neutron and gamma spectra with a time accuracy of 1 ms.At count rates of up to 1 Mcps,the figure of merit was greater than 1.05 for energies between 50 keV and 2.8 MeV.

Flexible pavement longitudinal joint quality evaluation using non-destructive testing

Longitudinal joint construction quality is critical to the life of flexible pavements.Maintaining deteriorated longitudinal joints has become a challenge for many highway agencies.Improving the joint's quality through better compaction during construction can help achieve flexible pavements with longer service lives and less maintenance.Current quality control(QC)and quality assurance(QA)plans provide limited coverage.Consequently,the risk of missing areas with poor joint compaction is significant.A density profiling system(DPS)is a non-destructive alternative to conventional destructive evaluation methods.It can provide quick and continuous real-time coverage of the compaction during construction in dielectrics.The paper presents several case studies comparing various types of longitudinal joints and demonstrating the use of DPS to evaluate the joint's compaction quality.The paper shows that dielectric measurements can provide valuable insight into the ability of various construction techniques to achieve adequate levels of compaction at the longitudinal joint.The paper proposes a dielectric-based longitudinal joint quality index(LJQI)to evaluate the relative compaction of the joint during construction.It also shows that adopting DPS for assessing the compaction of longitudinal joints can minimize the risk of agencies accepting poorly constructed joints,identify locations of poor quality during construction,and achieve better-performing flexible pavements.

The Influence of Price Discrimination from Airports on the Route Development Behavior of Airlines

Given the prominence and magnitude of airport incentive schemes,it is surprising that literature hitherto remains silent as to their effectiveness.In this paper,the relationship between airport incentive schemes and the route development behavior of airlines is analyzed.Because of rare and often controversial findings in the extant literature regarding relevant influencing variables for attracting airlines at an airport,expert interviews are used as a complement to formulate testable hypotheses in this regard.A fixed effects regression model is used to test the hypotheses with a dataset that covers all seat capacity offered at the 22 largest German commercial airports in the week 46 from 2004 to 2011.It is found that incentives from primary choice,as well as secondary choice airports,have a significant influence on Low Cost Carriers.Furthermore,Low Cost Carriers,in general,do not leave any of both types of airports when the incentives cease.In the case of Network Carriers,no case is found where one joins a primary choice airport and receives an incentive.Insufficient data between Network Carriers and secondary choice airports in the time when incentives have ceased means that no statement can be given.

Acoustic Non-Destructive Testing Technology in Concrete Bridge Inspection and Pile Foundation Detection

This article takes the actual construction project of a certain concrete bridge project as an example to analyze the application of acoustic non-destructive testing technology in its detection.It includes an overview of a certain bridge construction project studied and acoustic non-destructive testing technology and the application of acoustic non-destructive testing technology in actual testing.This analysis hopes to provide some guidelines for acoustic non-destructive testing of modern concrete bridge projects.

Egg recognition and nestling discrimination in the Crested Myna(Acridotheres cristatellus):Size matters

Most studies exploring abilities of hosts to detect brood parasitism are based on detecting colour and/or pattern differences among parasitic and host eggs or nestlings,while only few were focused on size differences.True recognition and recognition by discordancy are used to explain cognitive mechanisms of host egg recognition;however,only a few studies have found that hosts use recognition by discordancy.This study investigated:1)whether egg and nestling recognitions in the Crested Myna(Acridotheres cristatellus) are based on size cues;2)whether the egg cognitive mechanism is recognition by discordancy based on size cues;and 3) whether the longer the experiment time,the higher the egg recognition rate.Our results showed that the Crested Myna uses egg or nestling size as a recognition cue while the egg and nestling colour and patterning are not associated with egg or nestling rejection,thus the cognitive mechanism of egg recognition in the Crested Myna is recognition by discordancy based on egg size cues.Furthermore,there is a rejection delay in time of egg rejection behaviour of the Crested Myna.Therefore,we suggest that the periodicity of egg rejection experiments could be appropriately extended,especially for species with relatively low egg recognition ability.

Performance of real‑time neutron/gamma discrimination methods

Nuclear security usually requires the simultaneous detection of neutrons and gamma rays.With the development of crystalline materials in recent years,Cs2LiLaBr6(CLLB)dual-readout detectors have attracted extensive attention from researchers,where real-time neutron/gamma pulse discrimination is the critical factor among detector performance parameters.This study investigated the discrimination performance of the charge comparison,amplitude comparison,time comparison,and pulse gradient_(m)ethods and the effects of a Sallen–Key filter on their performance.Experimental results show that the figure of merit(FOM)of all four methods is improved by proper filtering.Among them,the charge comparison method exhibits excellent noise resistance;moreover,it is the most_(s)uitable method of real-time discrimination for CLLB detectors.However,its discrimination performance depends on the parameters t_(s),t_(m),and t_(e).When t_(s)corresponds to the moment at which the pulse is at 10%of its peak value,t_(e)requires a delay of only 640–740 ns compared to t_(s),at which time the potentially optimal FOM of the charge comparison method at 3.1–3.3 MeV is greater than 1.46.The FOM obtained using the t_(m)value calculated by a proposed maximized discrimination difference model(MDDM)and the potentially optimal FOM differ by less than 3.9%,indicating that the model can provide good guidance for parameter selection in the charge comparison method.

Sex effect on growth performance and marker-aided sex discrimination of seedlings of Populus deltoides

Sex has a significant effect on various traits among dioecious plants.In this analysis of the sex effect on the radial growth and wood density of numerous 20-yearold trees of Populus deltoides growing in a common garden,male trees performed better than the females in radial growth,but sex did not significantly affect wood density.Growth rate and wood density were weakly negatively correlated.Sex selection is also critical for controlling seed-hair pollution from P.deltoides plantations.However,because the juvenile period of P.deltoides lasts for years,a reliable technique to determine the sex of juveniles has been needed.Here we developed a marker-aided technique to discriminate the sexes of P.deltoides seedlings.This study provides essential information on target traits and a highly desirable genetic toolkit for accelerate breeding programs for this important tree species.

Multi-Path Attention Inverse Discrimination Network for Offline Signature Verification

Signature verification,which is a method to distinguish the authenticity of signature images,is a biometric verification technique that can effectively reduce the risk of forged signatures in financial,legal,and other business envir-onments.However,compared with ordinary images,signature images have the following characteristics:First,the strokes are slim,i.e.,there is less effective information.Second,the signature changes slightly with the time,place,and mood of the signer,i.e.,it has high intraclass differences.These challenges lead to the low accuracy of the existing methods based on convolutional neural net-works(CNN).This study proposes an end-to-end multi-path attention inverse dis-crimination network that focuses on the signature stroke parts to extract features by reversing the foreground and background of signature images,which effectively solves the problem of little effective information.To solve the problem of high intraclass variability of signature images,we add multi-path attention modules between discriminative streams and inverse streams to enhance the discriminative features of signature images.Moreover,a multi-path discrimination loss function is proposed,which does not require the feature representation of the samples with the same class label to be infinitely close,as long as the gap between inter-class distance and the intra-class distance is bigger than the set classification threshold,which radically resolves the problem of high intra-class difference of signature images.In addition,this loss can also spur the network to explore the detailed infor-mation on the stroke parts,such as the crossing,thickness,and connection of strokes.We respectively tested on CEDAR,BHSig-Bengali,BHSig-Hindi,and GPDS Synthetic datasets with accuracies of 100%,96.24%,93.86%,and 83.72%,which are more accurate than existing signature verification methods.This is more helpful to the task of signature authentication in justice and finance.

Quantitative Method of Classification and Discrimination of a Porous Carbonate Reservoir Integrating K-means Clustering and Bayesian Theory

Reservoir classification is a key link in reservoir evaluation.However,traditional manual means are inefficient,subjective,and classification standards are not uniform.Therefore,taking the Mishrif Formation of the Western Iraq as an example,a new reservoir classification and discrimination method is established by using the K-means clustering method and the Bayesian discrimination method.These methods are applied to non-cored wells to calculate the discrimination accuracy of the reservoir type,and thus the main reasons for low accuracy of reservoir discrimination are clarified.The results show that the discrimination accuracy of reservoir type based on K-means clustering and Bayesian stepwise discrimination is strongly related to the accuracy of the core data.The discrimination accuracy rate of TypeⅠ,TypeⅡ,and TypeⅤreservoirs is found to be significantly higher than that of TypeⅢand TypeⅣreservoirs using the method of combining K-means clustering and Bayesian theory based on logging data.Although the recognition accuracy of the new methodology for the TypeⅣreservoir is low,with average accuracy the new method has reached more than 82%in the entire study area,which lays a good foundation for rapid and accurate discrimination of reservoir types and the fine evaluation of a reservoir.

Discrimination and quantification of scar tissue by Mueller matrix imaging with machine learning

Scarring is one of the biggest areas of unmet need in the long-term success of glaucoma filtration surgery.Quantitative evaluation of the scar tissue and the post-operative structure with micron scale resolution facilitates development of anti-fibrosis techniques.However,the distinguishment of conjunctiva,sclera and the scar tissue in the surgical area still relies on pathologists'experience.Since polarized light imaging is sensitive to anisotropic properties of the media,it is ideal for discrimination of scar in the subconjunctival and episcleral area by characterizing small differences between proportion,organization and the orientation of the fibers.In this paper,we defined the conjunctiva,sclera,and the scar tissue as three target tissues after glaucoma filtration surgery and obtained their polarization characteristics from the tissue sections by a Mueller matrix microscope.Discrimination score based on parameters derived from Mueller matrix and machine learning was calculated and tested as a diagnostic index.As a result,the discrimination score of three target tissues showed significant difference between each other(p<0.001).The visualization of the discrimination results showed significant contrast between target tissues.This study proved that Mueller matrix imaging is effective in ocular scar discrimination and paves the way for its application on other forms of ocular fibrosis as a substitute or supplementary for clinical practice.

Do Chinese Women Face Subtle Discrimination in Job Hiring?

Gender discrimination has long been the problem that exists in the entire world,including China.However,as more and more people are focusing on the general gender discrimination,the group recognized an extension of such discrimination-pregnancy discrimination in job hiring(the discrimination that women who are pregnant or have the inclination to pregnancy receive in hiring).The possible subtle pregnancy discrimination may be one of the main causes that lead to the declining fertility rate in China.The group decided to conduct an experiment to determine whether women are receiving more subtle discrimination in job hiring due to their identity as mother than men with children,men without children,and women without children.Using the form of questionary and resumes,the team found that Chinese women are indeed enduring subtle discrimination in work hiring.The experiment highlights the importance to promote further equality among women.However,there are some confounding variables,like the level of patient of the HR who reads the resume,affecting the ultimate result of the study.Keywords:gender discrimination,women’s career,Chinese job market,pregnancy discrimination,fertility rate in China.

High-Precision Doppler Frequency Estimation Based Positioning Using OTFS Modulations by Red and Blue Frequency Shift Discriminator

Orthogonal Time Frequency and Space(OTFS) modulation is expected to provide high-speed and ultra-reliable communications for emerging mobile applications, including low-orbit satellite communications. Using the Doppler frequency for positioning is a promising research direction on communication and navigation integration. To tackle the high Doppler frequency and low signal-to-noise ratio(SNR) in satellite communication, this paper proposes a Red and Blue Frequency Shift Discriminator(RBFSD) based on the pseudo-noise(PN) sequence.The paper derives that the cross-correlation function on the Doppler domain exhibits the characteristic of a Sinc function. Therefore, it applies modulation onto the Delay-Doppler domain using PN sequence and adjusts Doppler frequency estimation by red-shifting or blue-shifting. Simulation results show that the performance of Doppler frequency estimation is close to the Cramér-Rao Lower Bound when the SNR is greater than -15dB. The proposed algorithm is about 1/D times less complex than the existing PN pilot sequence algorithm, where D is the resolution of the fractional Doppler.

Compressive Strength of Basic Magnesium Sulfate Cement Coral Aggregate Concrete(MCAC)on Non-Destructive Testing

Basic magnesium sulfate cement coral aggregate concrete(MCAC)is a new type of concrete consisting of basic magnesium sulfate cement,coarse coral aggregate,coral reef sand and seawater.The rebound hammer(RH),the ultrasonic pulse velocity(UPV)and the compressive strength(fcu)tests of 14 sets of cube specimens of the MCAC after 28 d of aging were conducted.The impact of the content and length of sisal fiber on the relationship between the fcu-RH and the fcu-UPV was determined.A mathematical model was established to predict the strength of the MCAC using the UPV,RH,and comprehensive UPV/RH methods and to obtain the curves of test strength.The applicability of the test strength curves of ordinary portland concrete(OPC),light-weight aggregate concrete(LAC),and coral aggregate concrete(CAC)to MCAC was assessed.The results showed that the test strength curves of OPC,LAC and CAC were inappropriate to determine the strength of MCAC using non-destructive method.The relative standard error of the curves of test strength of the RH method and the comprehensive method met the specifications,whereas that of the UPV method did not.

Artificial neural network algorithm for pulse shape discrimination in 2πα and 2πβ particle surface emission rate measurements

To enhance the accuracy of 2πα and 2πβ particle surface emission rate measurements and address the identification issues of nuclides in conventional methods, this study introduces two artificial neural network(ANN) algorithms: back-propagation(BP) and genetic algorithm-based back-propagation(GA-BP). These algorithms classify pulse signals from distinct α and β particles. Their discrimination efficacy is assessed by simulating standard pulse signals and those produced by contaminated sources, mixing α and β particles within the detector. This study initially showcases energy spectrum measurement outcomes, subsequently tests the ANNs on the measurement and validation datasets, and contrasts the pulse shape discrimination efficacy of both algorithms. Experimental findings reveal that the proportional counter's energy resolution is not ideal, thus rendering energy analysis insufficient for distinguishing between 2πα and 2πβ particles. The BP neural network realizes approximately 99% accuracy for 2πα particles and approximately 95% for 2πβ particles, thus surpassing the GA-BP's performance. Additionally, the results suggest enhancing β particle discrimination accuracy by increasing the digital acquisition card's threshold lower limit. This study offers an advanced solution for the 2πα and 2πβ surface emission rate measurement method, presenting superior adaptability and scalability over conventional techniques.

Pulse-shaping method for real-time neutron/gamma discrimination at low sampling rates

The Na I:Tl scintillator is an innovative material for dual-gamma-ray and neutron detection with a low ^(6)Li concentration.To achieve real-time n/γ discrimination,a zero-crossing time comparison algorithm based on trapezoidal pulse shaping was developed.The algorithm can operate efficiently at low sampling rates and was implemented on a single-probe portable digital n/γ discriminator based on a field-programmable gate array.The discriminator and Na I:Tl,^(6)Li detector were tested in a neutron-gamma mixed field produced by an ^(241)Am-Be neutron source to evaluate the performance of the algorithm.The figure of merits was measured as 2.88 at a sampling rate of 50 MHz,indicating that the discriminator with its embedded algorithm has a promising n/γ discrimination capability.Efficient discrimination at sampling rates of 40 and 25 MHz demonstrates that the capability of this method is not limited by low sampling rates.