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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Biopsychosocial impact of discrimination on cancer risk and outcome: A conceptual review

Discrimination,a major social factor influencing health,can influence both the risk and course of cancer.The medical and psychological mechanisms through which discrimination can impact the onset and spread of cancer are explored in depth in this conceptual evaluation.In addition to investigating the ethical aspects of discrimination in cancer research,it also studies the effects of bias on cancer detection and therapy.In addition,this review provides suggestions for reducing the effect of discrimination on cancer risk and outcomes.Discrimination,in particular,can trigger the growth and spread of cancer via various pathways,including stress,inflammation,and changes in epigenetic patterns.It can also affect the immune system,making the body more vulnerable to the proliferation of cancerous cells.Discrimination can result in hindrances or delays in the process of cancer screening and treatment,and it can influence the quality of care for individuals suffering from cancer.This can contribute to the presence of disparities in terms of cancer vulnerability,occurrence,mortality,and survival rates among different demographic groups.Various measures can be implemented to mitigate the impact of discrimination on cancer vulnerability and outcomes.These measures address the underlying causes of discrimination,ensure that all individuals have access to exceptional cancer care,promote the acquisition of cultural proficiency and anti-bias training by healthcare providers,and develop and implement interventions to reduce discrimination’s impact on cancer vulnerability,screening,and treatment.

铀矿井降氡优势通风方式研究

本文以矿井通风网络入风段和用风段氡析出规律为出发点,由铀矿通风降氡基本方程导出不同通风方式下铀矿降氡需风量的计算公式,并由入风段有效供风能力导出优势通风方式评价指标,给出铀矿通风降氡方式的判别方法。

用矿物元素指纹识别中华绒螯蟹产地及其养殖水体

分析测定体质量为180~200 g左右、产自阳澄湖湖区(YCL)、阳澄湖周边池塘(YCP)、洪泽湖(HZL)、高邮湖(GYL)、固城湖(GCL)湖区的雄性中华绒螯蟹(Eriocheir sinensis)体内Ca、Mg、K、Na、Sr和Fe等35种元素的含量,以开发中华绒螯蟹产地及其养殖水体的识别技术。结果表明,32种元素组间差异显著(P<0.05)。基于35种元素对中华绒螯蟹来源进行判别分析,10种元素进入判别模型,测试集和交叉验证判别准确率分别为98.3%和98.3%,仅有11.1%的HZL蟹被判入地理距离较近的GYL。剔除Ca、Mg、K、Na、Fe、Mn、Zn、Cu这8种在中华绒螯蟹养殖过程中人为输入量较大的元素再行判别分析,则6种元素进入判别模型,测试集和交叉验证判别准确率分别为100.0%和96.7%,仍有11.1%的HZL蟹被判入GYL,还有11.1%的GCL蟹被判入GYL。上述结果表明,采用矿物元素指纹识别中华绒螯蟹产地和养殖水体具有较高的准确率,养殖过程中人为输入量较大的元素对中华绒螯蟹产地与养殖水体的识别无不利影响。

IP属地化政策对舆情评论的影响研究——基于微博用户属地公开的准自然实验

[目的/意义]实证评估公开属地政策能否以及如何对舆情评论产生影响,为网络舆情研究、平台优化和治理提供启示。[研究设计/方法]基于自我监控理论和隐私计算理论构建研究模型和假设,以微博“IP属地”功能的推出为准自然实验,运用双重差分法和断点回归模型探究IP属地化政策对舆情评论的影响。[结论/发现]政策引发用户自我监控效应和风险感知效应,不仅整体改善了评论情感倾向、评论可读性和共情性,也能够显著抑制评论情感极性,该结论在一系列稳健性检验后仍然成立。政策影响在不同用户群体之间存在显著差异,相较处于事件热点中心的用户,属地公开更能促使处于非热点中心的用户做出改变。政策同时加剧评论中的地域歧视现象。[创新/价值]揭示了IP属地化政策对在线评论行为的作用,为清朗网络环境提供新视角,丰富了IP属地化与舆情评论行为影响因素的相关研究。

医疗服务领域中的价格歧视——以特需门诊为例

特需门诊是医院为了提高利润而采取的一种价格歧视策略。本文通过构建博弈模型,分析了医院对普通门诊和特需门诊的资源配置行为和特需门诊对不同类型患者福利的影响,并进一步分析了数量管制政策的效果。该博弈模型的子博弈精炼纳什均衡结果表明,如果政府没有采取任何管制政策,那么医院将把全部医疗资源配置到特需门诊,全体患者的福利受损。当政府采取数量管制政策时,价格歧视对患者福利的损害程度减轻,但是特需门诊患者和普通门诊患者的福利水平依旧是下降的。从保护患者福利的视角出发,政府应该进一步完善管制政策体系,限制特需门诊的发展。

基于外观性状和指标性成分划分枸杞子不同产地

目的 对不同产地枸杞子外观性状与其指标性成分进行分析,并探究两者是否存在关联性,为进一步研究枸杞子质量评价提供方法。方法 依据《中华人民共和国药典》2020版规定对枸杞子中甜菜碱、枸杞多糖的含量进行测定;对枸杞子的外观性状进行测量统计,采用逐步判别法、线性判别分析法(LDA)对枸杞子外观性状数据进行筛选并优化;并分析枸杞子外观性状与其指标性成分之间的关联性。结果 宁夏枸杞多糖含量高达3.43%,远高于其他产地。对枸杞子外观性状与内在品质进行关联性分析,发现果皮厚度与甜菜碱含量呈负相关性(r=-0.485,P<0.01);纵横比与枸杞多糖含量(r=0.420,P<0.05)、甜菜碱含量(r=0.815,P<0.01)呈正相关性。结论 不同产地的枸杞子在外观性状及内在指标上均有差异,该研究可为枸杞子的综合质量评价提供参考。

歧视知觉在高职生童年期创伤与攻击行为之间的中介作用

目的:了解高职生攻击行为、歧视知觉和童年期创伤的关系为相应的心理健康教育提供理论参考。方法:2021年10月,采用Buss-Perry攻击行为问卷、歧视知觉问卷和童年期创伤问卷对635名高职生进行问卷调查。结果:高职生攻击行为与知觉歧视(r=0.622,P<0.01)和童年期创伤(r=0.310,P<0.01)呈正相关。童年期创伤可以直接对高职生攻击行为产生正向预测作用,也可以通过歧视知觉间接影响攻击行为。结论:高职生歧视知觉在攻击行为和童年期创伤之间起部分中介效应。

算法歧视的敏捷治理——以D短视频平台为例

敏捷治理作为社会治理的新范式,是对回应式治理和集中式治理的进阶和超越,是塑造未来数智世界和元宇宙治理的现实路径。以敏捷治理化解算法歧视的技术与伦理风险,实现算法治理成为一项重要的理论和现实议题。通过参与式调查对D短视频平台的内容展开田野调查,深度剖析短视频平台算法运作的逻辑机理,总结归纳算法歧视的形态和成因,探索尝试算法治理的可行举措和实践进路。研究发现,基于用户偏好、平台优先等平台价值观念是D短视频平台算法运作的隐形逻辑;主观设计下歧视性推送和定价是D短视频平台算法歧视的突出表现;灵敏感知、快速响应、持续发展的敏捷治理机制是逾越“算计”陷阱的有效手段。因而,为应对算法歧视的风险挑战,政府亟须以敏捷治理赋能算法歧视治理,塑造多元参与、多管齐下的治理格局,实现整体智治、协同共治、动态管治,从而实现算法“善治”,优化算法治理的中国方案。