A Distributed Approach of Big Data Mining for Financial Fraud Detection in a Supply Chain

Supply Chain Finance(SCF)is important for improving the effectiveness of supply chain capital operations and reducing the overall management cost of a supply chain.In recent years,with the deep integration of supply chain and Internet,Big Data,Artificial Intelligence,Internet of Things,Blockchain,etc.,the efficiency of supply chain financial services can be greatly promoted through building more customized risk pricing models and conducting more rigorous investment decision-making processes.However,with the rapid development of new technologies,the SCF data has been massively increased and new financial fraud behaviors or patterns are becoming more covertly scattered among normal ones.The lack of enough capability to handle the big data volumes and mitigate the financial frauds may lead to huge losses in supply chains.In this article,a distributed approach of big data mining is proposed for financial fraud detection in a supply chain,which implements the distributed deep learning model of Convolutional Neural Network(CNN)on big data infrastructure of Apache Spark and Hadoop to speed up the processing of the large dataset in parallel and reduce the processing time significantly.By training and testing on the continually updated SCF dataset,the approach can intelligently and automatically classify the massive data samples and discover the fraudulent financing behaviors,so as to enhance the financial fraud detection with high precision and recall rates,and reduce the losses of frauds in a supply chain.

Improvement of a highly sensitive and specific whole-cell biosensor by adding a positive feedback amplifier

In this study,we designed a Cd^(2+)whole-cell biosensor with both positive and negative feedback cascade am-plifiers in Pseudomonas putida KT2440(LTCM)based on our previous design with only a negative feedback amplifier(TCM).The results showed that the newly developed biosensor LTCM was greatly improved compared to TCM.Firstly,the linear response range of LTCM was expanded while the maximum linear response range was raised from 0.05 to 0.1μM.Meanwhile,adding a positive feedback amplifier further increased the fluorescence output signal of LTCM 1.11–2.64 times under the same culture conditions.Moreover,the response time of LTCM for detection of practical samples was reduced from 6 to 4 h.At the same time,LTCM still retained very high sensitivity and specificity,while its lowest detection limit was 0.1 nM Cd^(2+)and the specificity was 23.29(compared to 0.1 nM and 17.55 in TCM,respectively).In summary,the positive and negative feedback cascade amplifiers effectively improved the performance of the biosensor LTCM,resulting in a greater linear response range,higher output signal intensity,and shorter response time than TCM while retaining comparable sensitivity and specificity,indicating better potential for practical applications.