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.

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.

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.

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.

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.

Response of Photosynthesis, Growth, Carbon Isotope Discrimination and Osmotic Tolerance of Rice to Elevated CO_2

Four rice ( Oryza sativa L.) cultivars 'IR72', 'Tesanai 2', 'Guichao 2' and 'IIyou 4480' were grown in two plastic house (15 m×3 m) with 35 μmol/mol and 60 μmol/mol CO 2 concentration which was controlled by computer. As compared with rice at ambient 35 μmol/mol CO 2, the changes in photosynthetic rate at elevated CO 2 showed up_regulation ('IR72' and 'Tesanai 2'), stable (unchanged) in 'Guichao 2' and down_regulation type ('IIyou 4480'). Growth rate, panicle weight, integrated water use efficiency (WUE) calculated from Δ 13 C and the capacity of scavenging DPPH · (1,1_diphenyl_2_picrylhydrazyl) free radical were increased at elevated CO 2. An increment in total biomass was observed in three cultivars by elevated CO 2, with the exception of 'IIyou 4480'. Ratios of panicle weight/total biomass were altered to different extents in tested cultivars by elevated CO 2. When leaf segments were subjected to PEG osmotic stress, the electrolyte leakage rate from leaves grown at elevated CO 2 was less than that at 35 μmol/mol CO 2. Those intraspecific variations of rice imply a possibility for selecting cultivars with maximal productivity and high tolerance to stresses adapted to elevated CO 2 in the future.

Perceptual Learning of Grate Orientation Discrimination in Cats

Perceptual learning of orientation discrimination was investigated using cats. Two adult cats （Cat 1 and 2） were trained to monocularly discriminate between two static striped sinusoidal grates with 30° orientation difference. After greater than 80% correct performance was reached, cats were then required to monocularly perform a discrimination between two grates with consecutively shifting orientation difference（2°, 4°, 6°, 8°, 10°, 12°, 16°, 20°, 24°, 30°） . The staircase method （two correct-down and one error-up） was applied throughout the training to track the threshold of orientation difference that cats could detect. The performance of detecting grates with varied orientation difference was measured respectively for beth trained and untrained eyes before and after training. Our results showed that the learning effect of discrimination for grates with a fixed orientation difference transferred completely from the trained eye to the untrained eye, whereas the inter-eye transfer for detecting °ates with gradually reducing orientation difference was almost nonegrates. The two opposite learning effects in the same subject strongly suggest that different information processing mechanisms might mediate the learning processes.

Anti-noise performance of the pulse coupled neural network applied in discrimination of neutron and gamma-ray

In this study,the anti-noise performance of a pulse-coupled neural network(PCNN)was investigated in the neutron and gamma-ray(n-γ)discrimination field.The experiments were conducted in two groups.In the first group,radiation pulse signals were pre-processed using a Fourier filter to reduce the original noise in the signals,whereas in the second group,the original noise was left untouched to simulate an extremely high-noise scenario.For each part,artificial Gaussian noise with different intensity levels was added to the signals prior to the discrimination process.In the aforementioned conditions,the performance of the PCNN was evaluated and compared with five other commonly used methods of n-γdiscrimination:(1)zero crossing,(2)charge comparison,(3)vector projection,(4)falling edge percentage slope,and(5)frequency gradient analysis.The experimental results showed that the PCNN method significantly outperforms other methods with outstanding FoM-value at all noise levels.Furthermore,the fluctuations in FoM-value of PCNN were significantly better than those obtained via other methods at most noise levels and only slightly worse than those obtained via the charge comparison and zerocrossing methods under extreme noise conditions.Additionally,the changing patterns and fluctuations of the FoMvalue were evaluated under different noise conditions.Hence,based on the results,the parameter selection strategy of the PCNN was presented.In conclusion,the PCNN method is suitable for use in high-noise application scenarios for n-γdiscrimination because of its stability and remarkable discrimination performance.It does not rely on strict parameter settings and can realize satisfactory performance over a wide parameter range.

Discrimination of neutron and gamma ray using the ladder gradient method and analysis of filter adaptability

This study proposes a ladder gradient method for neutron and gamma-ray discrimination.The proposed method exhibited state-of-the-art performance with low time consumption,which incorporates two parts:information extraction and discrimination factor calculation.A quasi-continuous spiking cortical model was proposed to extract information from the radiation pulse signals,thus generating an ignition map corresponding to each pulse signal.The ignition map can be used to calculate the discrimination factor.A ladder gradient calculation was introduced to obtain a discrimination factor with low computational complexity.The proposed method was compared with five other discrimination methods to evaluate its robustness and efficacy.Furthermore,the filter adaptability of the pulse-coupled neural network and ladder gradient methods was investigated.Possible reasons for adapting the conditions with different discrimination methods and filters were analyzed.Experiments were conducted in 20 filtering situations with 11 types of filters to determine the most suitable filters for discrimination methods.The experimental results revealed that the three most adaptive filters of the pulse-coupled neural networks and ladder gradient methods are the wavelet,elliptic,and median filters and the elliptic,moving average,and wavelet filters,respectively.

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.

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.

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.

Gender Discrimination in English

Gender discrimination in language is usually defined as discrimination based on sex,especially discrimination against women.With the rise of women’s liberation movement in the 1960s and 1970s,and the improvement of women’s social status in recent years,gender discrimination in English attracts more and more attention.Based on previous studies,this thesis first discusses the manifestations of gender discrimination in English vocabulary and address terms,then analyzes the factors of gender discrimination in English from social and cultural perspectives,finally puts forward some methods that are good for avoiding or eliminating gender discrimination in English.

A Brief Analysis of Discrimination in Language Classroom——from the Perspective of Sociolinguistics

It is widely acknowledged that sociolinguistics is the study of language in certain context concerned with our society.Sociolinguistics and linguistics are intrinsically related to each other,but there has been difference as well.Linguistics research deals with language system itself,which belongs to the micro level on the one hand;many phenomena reflect discrimination in language classroom,these discrimination are caused by social factors to a certain degree.This paper makes a brief analysis of discrimination in language classroom from the perspective of sociolinguistics,which deals with many issues such as depiction of language discrimination、analysis of phenomenon and accordinglysolved measures.

Experimental validation of material discrimination ability of muon scattering tomography at the TUMUTY facility

Muon scattering tomography is believed to be a promising technique for cargo container inspection, owing to the ability of natural muons to penetrate into dense materials and the absence of artificial radiation. In this work, the material discrimination ability of muon scattering tomography is evaluated based on experiments at the Tsinghua University cosmic ray muon tomography facility,with four materials: flour(as drugs substitute), aluminum,steel, and lead. The features of the different materials could be discriminated with cluster analysis and classifiers based on support vector machine. The overall discrimination precisions for these four materials could reach 70, 95, and 99% with 1-, 5-, and 10-min-long measurement,respectively.

HIV/AIDS Related Discrimination in Health Care Service:a Cross-sectional Study in Gejiu City, Yunnan Province

Objectives To investigate discrimination against people living with HIV （PLWH） and its impacts. Methods Forty people who were either HIV positive （7/40） or had high risk behavior （33/40） were interviewed. Focus group discussion was held in the interview with people who were suspected to be infected with HIV, and in-depth interview was conducted in the survey of HIV positive persons whose privacy was strictly protected to ensure the confidentiality of the collected information. Results It was identified that six forms of discrimination against people living with HIV occurred in health care service in Gejiu, including speaking to patients in an insulting manner, refusing to provide health care service, delaying treatment, treating differently, uncovering patients＇ privacy, and over-protecting themselves against patients. Discrimination against people living with HIV greatly affected their health conditions. Conclusions Discrimination against people living with HIV in health service has negative impact on their physical and mental health.

MUPS identification based on discrimination rules

The minimal unsatisfiability-preserving sub-TBoxes（MUPS）of an unsatisfiable class C identified by two equivalent transformations,axiom splitting and ontology reduction,and three discrimination rules comprise minimal sets of axioms which support the unsatisfiability.Discrimination rules classify all MUPS into three types based on the transitivity of unsatisfiability,fully dependent on C（MUPSf）,transitively dependent on C（MUPSt）and uncertainly dependent on C（MUPSu）.The results show that the number of MUPSt is frequently a large fraction of the total number of all MUPS,but only MUPSf catches the root error of C.Modelers and domain experts conduct iterative repair work effectively,considering only MUPSf in each iteration.The classification shows the significance for the evaluation of the quality of ontologies from the perspective of maintenance and for repair work.

Discrimination of neutrons and gamma rays in plastic scintillator based on pulse-coupled neural network

Neutron and gamma ray pulse signal discrimination technology is an essential part of many modern scientific fields,such as biology,geology,radiation imaging,and nuclear medicine.Neutrons are always accompanied by gamma rays due to their unique penetration characteristic;thus,the development of n-γdiscrimination methods is especially crucial.In the present study,a novel n-γdiscrimination method is proposed that implements a pulse-coupled neural network for n-γdiscrimination.In addition,experiments were conducted on the pulse signals detected by an EJ299-33 plastic scintillator,which is especially suitable for n-γdiscrimination.The proposed method was compared to three other discrimination methods,including the back-propagation neural network(BPNN),the fractal spectrum method,and the charge comparison method,with respect to two aspects:(i)the figure of merit(FoM)and(ii)discrimination time.The experimental results showed that the pulse-coupled neural network(PCNN)has a 26.49%improvement in FoM-value compared to the charge comparison method,a72.80%improvement compared to the BPNN,a 66.24%improvement compared to the fractal spectrum method,and the second-fastest discrimination time of 2.22 s.In conclusion,the PCNN treats the input signal as a whole for analysis and processing,imparting it with an excellent antinoise effect and the ability to process the dynamic information contained in a pulse signal.

Novel discrimination method of digital deceptive jamming in mono-pulse radar

A new polarization measurement algorithm by using the sum and difference beam differential property of mono-pulse radar is given.Based on the generation mechanism differences between the target scattering and multi-false-target jamming,the signal models of real targets and digital deceptive false target jamming for sum and delta channel are presented.The polarization discrimination parameters are designed,and the discrimination method and its performance are discussed.This novel method does not need the accurate estimation of the absolute value of full target polarization scattering matrix,but only requires the relative estimation of the orthogonal polarized component of the targets.Without the need to add additional polarization channels,the proposed method is more suitable for engineering realization.The simulation experiment verifies that the correctly identifying probability can be better than 90%.