Research on the Status Quo and Supervision Mechanism of Food Safety in China

Food safety supervision mechanism is a strong guarantee to promote the smooth implementation of China's food safety laws and regulations,and it is implemented through legal,administrative,economic,moral and other integrated policy instruments,as well as media publicity,quality traceability,network tracking,information disclosure and other non-administrative means. Along with strengthening supervision and control means,the people's food safety in China is safeguarded,and the healthy development of the food industry is promoted.

Analysis of problems and policies of management reform in higher education

Under the background of globalization and informatization,China’s higher education management reform faces many challenges,including cumbersome administrative management,lack of transparency,and unequal distribution of resources.This paper aims to analyze these problems and put forward effective countermeasures.First of all,it is proposed to simplify the management process and enhance the transparency to improve the efficiency of decision-making and the fairness of resource allocation.Secondly,it emphasizes strengthening cooperation among universities,breaking barriers,promoting resource sharing and improving education quality.In addition,it is recommended to establish a comprehensive educational evaluation and supervision mechanism to ensure that colleges and universities follow rules and regulations and improve the credibility of education.This paper also discusses the key areas such as the reform of”management service”,the integration of science and education,and the reform of personnel system,and puts forward specific reform measures in order to promote the modernization of higher education management system and enhance the international competitiveness of China’s higher education.

A new method for the extraction of tailing ponds from very high-resolution remotely sensed images:PSVED

Automatic extraction of tailing ponds from Very High-Resolution(VHR)remotely sensed images is vital for mineral resource management.This study proposes a Pseudo-Siamese Visual Geometry Group Encoder-Decoder network(PSVED)to achieve high accuracy tailing ponds extraction from VHR images.First,handcrafted feature(HCF)images are calculated from VHR images based on the index calculation algorithm,highlighting the tailing ponds'signals.Second,considering the information gap between VHR images and HCF images,the Pseudo-Siamese Visual Geometry Group(Pseudo-Siamese VGG)is utilized to extract independent and representative deep semantic features from VHR images and HCF images,respectively.Third,the deep supervision mechanism is attached to handle the optimization problem of gradients vanishing or exploding.A self-made tailing ponds extraction dataset(TPSet)produced with the Gaofen-6 images of part of Hebei province,China,was employed to conduct experiments.The results show that the proposed'method_achieves the best visual performance and accuracy for tailing ponds extraction in all the tested methods,whereas the running time of the proposed method maintains at the same level as other methods.This study has practical significance in automatically extracting tailing ponds from VHR images which is beneficial to tailing ponds management and monitoring.