Fund budget model for multipurpose transit smart card systems

The fund budget of multipurpose transit smart card systems is studied by stochastic programming to assign limited funds to different applications reasonably. Under the constraints of a gross fund, models of chance-constrained and dependentchance for the fund budget of multipurpose transit smart card systems are established with application scale and social demand as random variables, respectively aiming to maximize earnings and satisfy the service requirements the furthest; and the genetic algorithm based on stochastic simulation is adopted for model solution. The calculation results show that the fund budget differs greatly with different system objectives which can cause the systems to have distinct expansibilities, and the application scales of some applications may not satisfy user demands with limited funds. The analysis results indicate that the forecast of application scales and application future demands should be done first, and then the system objective is determined according to the system mission, which can help reduce the risks of fund budgets.

CARD6在脓毒症心肌损伤中的机制研究

目的:探讨CARD6在脓毒症心肌损伤中的调节机制。方法:将C57BL/6小鼠随机分为对照组与模型组。采用LPS腹腔注射法构建脓毒症心肌损伤小鼠模型,RT-qPCR检测小鼠心肌组织中CARD6、caspase-1的mRNA水平。体外实验则采用LPS刺激转染CARD6过表达质粒的H9C2细胞,检测caspase-1、细胞活力、CK-MB水平。结果:与对照组相比,模型组小鼠心肌组织中LDH、CK-MB含量,caspase-1表达水平上升,提示脓毒症导致小鼠心肌损伤,而CARD6表达水平明显降低。体外实验发现CARD6表达水平呈下降趋势,转染含有CARD6的过表达质粒后细胞释放CK-MB的水平降低、细胞活力增加、caspase-1水平降低。结论:CARD6可改善脓毒症所致心肌损伤,其机制可能与caspase-1介导的细胞焦亡相关。

Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

Credit Card Fraud Detection Using Improved Deep Learning Models

Fraud of credit cards is a major issue for financial organizations and individuals.As fraudulent actions become more complex,a demand for better fraud detection systems is rising.Deep learning approaches have shown promise in several fields,including detecting credit card fraud.However,the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters.This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data,thereby improving fraud detection.Three deep learning models:AutoEncoder(AE),Convolution Neural Network(CNN),and Long Short-Term Memory(LSTM)are proposed to investigate how hyperparameter adjustment impacts the efficacy of deep learning models used to identify credit card fraud.The experiments conducted on a European credit card fraud dataset using different hyperparameters and three deep learning models demonstrate that the proposed models achieve a tradeoff between detection rate and precision,leading these models to be effective in accurately predicting credit card fraud.The results demonstrate that LSTM significantly outperformed AE and CNN in terms of accuracy(99.2%),detection rate(93.3%),and area under the curve(96.3%).These proposed models have surpassed those of existing studies and are expected to make a significant contribution to the field of credit card fraud detection.

A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.

CARD11 serves as a therapeutic biomarker for the drug therapies of ccRCC

Background:The incidence of clear cell renal cell carcinoma(ccRCC)is globally high;however,despite the introduction of innovative drug therapies,there remains a lack of effective biomarkers for evaluating treatment response.Recently,Caspase recruiting domain-containing protein 11(CARD11)has garnered attention due to its significant association with tumor development and the immune system.Methods:The expression of CARD11 mRNA and protein in ccRCC were analyzed by public database and immunohistochemistry.The focus of this study is on the epigenomic modifications of CARD11,its expression of ccRCC immunophenotype,and its correlation with response to immunotherapy and targeted therapy.Furthermore,to investigate the mechanism of this molecule’s influence on different biological behaviors of cells,cell tests in vitro have been conducted to observe the impact of its expression level.Results:CARD11 expression was upregulated which may be mainly modified by body methylation and was correlated with poor prognosis in ccRCC.In the tumor microenvironment of ccRCC,CARD11 expression was positively correlated with increased T lymphocyte infiltration and increased expression of inhibitory immune checkpoints.Moreover,ccRCC patients with high CARD11 expression had a better response to immunotherapy and targeted therapy.The knockdown of CARD11 ultimately suppressed the proliferation,migration,and invasion capabilities of ccRCC cells while simultaneously enhancing tumor cell apoptosis.Conclusion:We identified CARD11 as a novel therapeutic biomarker for immunotherapy and targeted therapy in ccRCC.

肺炎支原体CARDS毒素依赖KELED序列调控炎性反应

目的细胞内逆向运输系统是微生物毒素蛋白导致细胞毒性的关键。KELED序列是肺炎支原体CARDS毒素逆向转运至内质网,而后发挥细胞毒性作用的关键氨基酸基序。在小鼠模型中,体外重组的CARDS蛋白可以重现肺炎支原体肺炎患者的特征性症状。然而,CARDS是否依赖于KELED序列调控肺组织炎性反应仍不清楚。方法在大肠杆菌中表达并纯化CARDS及其KELED序列突变蛋白;随后在细胞水平验证其致空泡化活性;最后,在小鼠模型中评价KELED序列突变的CARDS蛋白是否影响炎性因子的表达以及是否造成肺组织病理学损伤。结果KELED序列氨基酸发生突变,减弱CARDS蛋白致细胞空泡化效应,下调肺组织炎性因子mRNA表达,减轻肺组织的病理学损伤。结论KELED序列对CARDS蛋白致细胞空泡化及炎性损伤活性的维持至关重要。

A Study on the Problem of Employee Punch Card Substitution in Enterprise Management:The Case of Jingdong Enterprise

With economic progress and the continuous advancement of science and technology,the issue of employees substituting punch cards has gradually become a significant challenge in enterprise management.The purpose of this paper is to discuss the causes,effects,and countermeasures of the employee punch card phenomenon,with the aim of providing effective management recommendations for Chinese enterprises.In practice,enterprises should flexibly apply the countermeasures proposed in this paper according to their specific circumstances to prevent substitute punch card incidents and improve overall management efficiency.

CARD8基因新发P367L失功能突变患者自身炎症性疾病的临床及免疫学特征

目的报道1例CARD8基因新发突变导致自身炎症性疾病患儿及其家系的临床特征、致病性鉴定与免疫致病机制。方法收集并分析1例CARD8基因突变患儿的突变位点以及临床资料,随访患儿治疗及预后;采用全外显子测序发现可疑致病基因并利用Sanger测序验证家系突变位点;利用体外实验进行致病性验证及免疫学分析。结果先证者为1名男性患儿,主要临床表现为极早发的食物蛋白过敏性肠炎、特应性皮炎及反复发作的上消化道出血。实验室检查提示血清IgE升高。全外显子测序发现CARD8基因新发杂合突变c.1100C>T(p.P367L),且该突变位点经Sanger测序验证证实来源于其父亲。该突变致病性不明确,且既往未见报道。实验室检查提示患儿血浆中多种促炎细胞因子水平显著升高。患儿原代外周血单个核细胞(PBMCs)及转染相同突变质粒的293T细胞CARD8蛋白表达均显著降低。患儿PBMCs中NLRP3炎性小体过度激活,而NF-κB通路无异常。以上研究均提示患儿的CARD8基因p.P367L突变为失功能性致病突变。结论本文报道全球第2例,中国第1例CARD8基因杂合突变患儿及家系,并明确CRAD8基因c.1100C>T的突变致病性。该CARD8基因失功能突变可导致NLRP3炎性小体的过度激活而诱发患儿发生以消化道症状起病的自身炎症性疾病。

Digital signature systems based on smart card and fingerprint feature

Two signature systems based on smart cards and fingerprint features are proposed. In one signature system, the cryptographic key is stored in the smart card and is only accessible when the signer＇s extracted fingerprint features match his stored template. To resist being tampered on public channel, the user＇s message and the signed message are encrypted by the signer＇s public key and the user＇s public key, respectively. In the other signature system, the keys are generated by combining the signer＇s fingerprint features, check bits, and a rememberable key, and there are no matching process and keys stored on the smart card. Additionally, there is generally more than one public key in this system, that is, there exist some pseudo public keys except a real one.

On the Study of Multi-Gas Data Acquisition System Based on PCI1711 Card

A data acquisition system for testing gas sensor array response to multi-gas is presented.The testing system is based on the character of the gas response of metal oxide semiconductor gas sensor array.The data acquisition is realized automatically through the real time controlling of the data acquisition card PCI1711.This system is highly attractive for electronic nose,which is a powerful tool for the discrimination of gases.

Exploring the Evolution of Passenger Flow and Travel Time Reliability with the Expanding Process of Metro System Using Smartcard Data

Metro system has experienced the global rapid rise over the past decades. However,few studies have paid attention to the evolution in system usage with the network expanding. The paper's main objectives are to analyze passenger flow characteristics and evaluate travel time reliability for the Nanjing Metro network by visualizing the smart card data of April 2014,April 2015 and April 2016. We performed visualization techniques and comparative analyses to examine the changes in system usage between before and after the system expansion. Specifically,workdays,holidays and weekends were specially segmented for analysis.Results showed that workdays had obvious morning and evening peak hours due to daily commuting,while no obvious peak hours existed in weekends and holidays and the daily traffic was evenly distributed. Besides,some metro stations had a serious directional imbalance,especially during the morning and evening peak hours of workdays. Serious unreliability occurred in morning peaks on workdays and the reliability of new lines was relatively low,meanwhile,new stations had negative effects on exiting stations in terms of reliability. Monitoring the evolution of system usage over years enables the identification of system performance and can serve as an input for improving the metro system quality.

携带罕见突变的CARD14相关丘疹鳞屑性疾病一例

患者,男,41岁。因“全身红斑鳞屑40天”来我院就诊,临床、组织病理学具有毛发红糠疹和银屑病的交叉表现,无家族史,首次发病年龄为41岁。初步诊断为I型毛发红糠疹,口服阿维A有显著改善。经过全外显子测序和Sanger测序发现患者携带CARD14基因的罕见致病性突变位点c.1799 C>T(p.Pro600Leu),综合临床、组织病理学表现,以及基因检测的结果,最终诊断为CARD14相关的丘疹鳞屑性疾病。

A Credit Card Fraud Model Prediction Method Based on Penalty Factor Optimization AWTadaboost

With the popularity of online payment, how to perform creditcard fraud detection more accurately has also become a hot issue. And withthe emergence of the adaptive boosting algorithm (Adaboost), credit cardfraud detection has started to use this method in large numbers, but thetraditional Adaboost is prone to overfitting in the presence of noisy samples.Therefore, in order to alleviate this phenomenon, this paper proposes a newidea: using the number of consecutive sample misclassifications to determinethe noisy samples, while constructing a penalty factor to reconstruct thesample weight assignment. Firstly, the theoretical analysis shows that thetraditional Adaboost method is overfitting in a noisy training set, which leadsto the degradation of classification accuracy. To this end, the penalty factorconstructed by the number of consecutive misclassifications of samples isused to reconstruct the sample weight assignment to prevent the classifierfrom over-focusing on noisy samples, and its reasonableness is demonstrated.Then, by comparing the penalty strength of the three different penalty factorsproposed in this paper, a more reasonable penalty factor is selected.Meanwhile, in order to make the constructed model more in line with theactual requirements on training time consumption, the Adaboost algorithmwith adaptive weight trimming (AWTAdaboost) is used in this paper, so thepenalty factor-based AWTAdaboost (PF_AWTAdaboost) is finally obtained.Finally, PF_AWTAdaboost is experimentally validated against other traditionalmachine learning algorithms on credit card fraud datasets and otherdatasets. The results show that the PF_AWTAdaboost method has betterperformance, including detection accuracy, model recall and robustness, thanother methods on the credit card fraud dataset. And the PF_AWTAdaboostmethod also shows excellent generalization performance on other datasets.From the experimental results, it is shown that the PF_AWTAdaboost algorithmhas better classification performance.

ERCS: An Efficient and Robust Card Recognition System for Camera-Based Image

Cards Recognition Systems,(CRSs)are representative computer vision-based applications.They have a broad range of usage scenarios.For example,they can be used to recognize images containing business cards,personal identification cards,and bank cards etc.Even though CRSs have been studied for many years,it is still difficult to recognize cards in camera-based images taken by ordinary devices,e.g.,mobile phones.Diversity of viewpoints and complex backgrounds in the images make the recognition task challenging.Existing systems employing traditional image processing schemes are not robust to varied environment,and are inefficient in dealing with natural images,e.g.,taken by mobile phones.To tackle the problem,we propose a novel framework for card recognition by employing a Convolutional Neutral Network(CNN)based approach.The system localizes the foreground of the image by utilizing a Fully Convolutional Network(FCN).With the help of the foreground map,the system localizes the corners of the card region and employs perspective transformation to alleviate the effects from distortion.Text lines in the card region are detected and recognized by utilizing CNN and Long Short Term Memory,(LSTM).To evaluate the proposed scheme,we collect a large dataset which contains 4,065 images in a variety of shooting scenarios.Experimental results demonstrate the efficacy of the proposed scheme.Specifically,it is able to achieve an accuracy of 90.62%in the end-toend test,outperforming the state-of-the-art.

A Credit Card Fraud Detection Model Based on Multi-Feature Fusion and Generative Adversarial Network

Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD,which significantly impact classification models’performance.To address these issues,this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks(MFGAN).The MFGAN model consists of two modules:a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space,and a balance module based on the generative adversarial network to decrease the class imbalance ratio.The effectiveness of theMFGAN model is validated on two actual credit card datasets.The impacts of different class balance ratios on the performance of the four resamplingmodels are analyzed,and the contribution of the two different modules to the performance of the MFGAN model is investigated via ablation experiments.Experimental results demonstrate that the proposed model does better than state-of-the-art models in terms of recall,F1,and Area Under the Curve(AUC)metrics,which means that the MFGAN model can help banks find more fraudulent transactions and reduce fraud losses.

Design of Experimental System of USB Data Acquisition Card-based Temperature Acquisition

In order to design a more efficient and more convenient temperature acquisition system, an approach combining USB data acquisition card with K type thermocouple temperature sensor is proposed under the circumstance of LabVIEW 2012 programming software. Firstly, the LabVIEW 2012 programming software is used to complete a temperature acquisition control program. Secondly, K type thermocouple temperature sensor is employed to transfer the temperature information. Thirdly, Then the USB data acquisition card can collect the voltage of K type thermocouple temperature sensor and convert it to a temperature scale. And, the simplification of experimental procedure can reduce the cost of development greatly. Finally, the experimental results illustrate that the range of measurement temperature is more wide and the temperature scale is more accurate.

Sound Card Based ASK Communication System for Teaching Communication Principle Course

This paper deals with the issue of using the MATLAB tool in teaching the course of communication principles via constructing an Amplitude Shift Keying (ASK) communication system. Different from conventional MATLAB based simulations, the constructed system transmits modulated signals through a wire audio channel by exploiting sound card. Synchronization is required before the received signal being demodulated. Many practical problems should be considered as in real system. The designed system can be extended easily, and not only stimulates students’ interest in communication course, but also helps them understanding the principles from system viewpoints.

Study on Licker-In and Flat Speeds of Carding Machine and Its Effects on Quality of Cotton Spinning Process

Spinning has a significant influence on all textile processes. Combinations of all the capital equipment display the process’ critical condition. By transforming unprocessed fibers into carded sliver and yarn, the carding machine serves a critical role in the textile industry. The carding machine’s licker-in and flat speeds are crucial operational factors that have a big influence on the finished goods’ quality. The purpose of this study is to examine the link between licker-in and flat speeds and how they affect the yarn and carded sliver quality. A thorough experimental examination on a carding machine was carried out to accomplish this. The carded sliver and yarn produced after experimenting with different licker-in and flat speed combinations were assessed for important quality factors including evenness, strength, and flaws. To account for changes in material qualities and machine settings, the study also took into consideration the impact of various fiber kinds and processing circumstances. The findings of the investigation showed a direct relationship between the quality of the carded sliver and yarn and the licker-in and flat speeds. Within a limited range, greater licker-in speeds were shown to increase carding efficiency and decrease fiber tangling. On the other hand, extremely high speeds led to more fiber breakage and neps. Higher flat speeds, on the other hand, helped to enhance fiber alignment, which increased the evenness and strength of the carded sliver and yarn. Additionally, it was discovered that the ideal blend of licker-in and flat rates varied based on the fiber type and processing circumstances. When being carded, various fibers displayed distinctive behaviors that necessitated adjusting the operating settings in order to provide the necessary quality results. The study also determined the crucial speed ratios between the licker-in and flat speeds that reduced fiber breakage and increased the caliber of the finished goods. The results of this study offer useful information for textile producers and process engineers to improve the quality of carded sliver and yarn while maximizing the performance of carding machines. Operators may choose machine settings and parameter adjustments wisely by knowing the impacts of licker-in and flat speeds, which will increase textile industry efficiency, productivity, and product quality.

Effect of a low-cost instruction card for automated external defibrillator operation in lay rescuers: a randomized simulation study

BACKGROUND:To evaluate whether a simplified self-instruction card can help potential rescue providers use automated external defibrillators(AEDs)more accurately and quickly.METHODS:From June 1,2018,to November 30,2019,a prospective longitudinal randomized controlled simulation study was conducted among 165 laypeople(18–65 years old)without prior AED training.A self-instruction card was designed to illuminate key AED operation procedures.Subjects were randomly divided into the card(n=83)and control(n=82)groups with age stratification.They were then individually evaluated in the same simulated scenario to use AED with(card group)or without the self-instruction card(control group)at baseline,posttraining,and at the 3-month follow-up.RESULTS:At baseline,the card group reached a significantly higher proportion of successful defibrillation(31.1%vs.15.9%,P=0.03),fully baring the chest(88.9%vs.63.4%,P<0.001),correct electrode placement(32.5%vs.17.1%,P=0.03),and resuming cardiopulmonary resuscitation(CPR)(72.3%vs.9.8%,P<0.001).At post-training and follow-up,there were no significant differences in key behaviors,except for resuming CPR.Time to shock and time to resume CPR were shorter in the card group,while time to power-on AED was not different in each phase of tests.In the 55–65 years group,the card group achieved more skill improvements over the control group compared to the other age groups.CONCLUSION:The self-instruction card could serve as a direction for first-time AED users and as a reminder for trained subjects.This could be a practical,cost-effective way to improve the AED skills of potential rescue providers among different age groups,including seniors.