Research on the construction path of the megacity from the perspective of Nudge Theory:Empirical evidence of the“Park City”construction in Chengdu Tianfu New Area

The transformation of Chengdu Tianfu New Area,a state-level new district,from the first site of constructing the“Park City”to the representative of the“Park City”,contains a unique path of“Chinese Style Governance”.To expand the diverse paths of constructing megacities,the current research references the nudge theory of behavioral science,adopts the extended case method,and takes Chengdu Tianfu New Area as a typical case to summarize the“nudge”path of“Party City”construction.The study found that the government achieves the lock-in effect by the priming mechanism and uses the nudge strategy of improving environment and guiding public opinion,thereby helping the people make free choices.It realizes the framework effect by the salience mechanism and uses the strategies of highlighting key information and optimizing information delivery methods,which directly affects the behavior of the public.It achieves the commitment effect by the comparison mechanism,including the horizontal comparison and vertical comparison strategies,and gradually realizes the policy goals.It realizes the social norm effect by the imitation mechanism and uses the strategies of creating peer pressure and playing celebrity effect,which in turn influence people’s choices.With the help of the“nudging”mechanism,the construction of the park city has realized the interactive cycle of“government output-citizen feedback”and achieved the goal of“making great change through small actions”.As a new behavioral intervention tool,“nudging”provides an alternative explanation for the construction of the park city in Tianfu New Area and opens up new ideas for the construction of megacities.This study concludes that the construction of megacities can achieve the goal of“common return”through different ways and provides practical inspiration for future urban construction.

“Scrambling”: Logic of horizontal competition between local governments based on three cases of interprovincial disaster counterpart support

Interprovincial counterpart support is a cooperative system used by local governments to achieve horizontal flow of resources based on cross-regional cooperation.Existing research has mainly focused on governance ef‐ficiency,institutional advantages,and ranking incentives while ignoring the scrambling behavior and opera‐tional mechanisms of local governments formed by ranking incentives and territorial responsibilities.This study selected the Wenchuan earthquake,Yushu earthquake,and COVID-19 as three typical cases.We con‐structed a theoretical framework for competition among provincial local governments and found that competi‐tion in interprovincial disaster counterpart support followed a dual behavioral logic of“striving to be first”and“fear of being last”.Specifically,local governments will choose striving to be first under the logic of time coercion,content games,and territorial responsibility;they will choose fear of being last under the logic of re‐sponsibility avoidance and moral pressure.This type of scrambling-based horizontal competition reflects the logic of local government competition tournaments.This study further revealed the specific processes,mecha‐nisms,and results of horizontal local government competition,which can provide inspiration for cross regional and provincial cooperation.

Storage and Parallel Loading System Based on Mode Network for Multimode Medical Image Data

Since Multimode data is composed of many modes and their complex relationships,it cannot be retrieved or mined effectively by utilizing traditional analysis and processing techniques for single mode data.To address the challenges,we design and implement a graph-based storage and parallel loading system aimed at multimode medical image data.The system is a framework designed to flexibly store and rapidly load these multimode data.Specifically,the system utilizes the Mode Network to model the modes and their relationships in multimode medical image data and the graph database to store the data with a parallel loading technique.

A New Method for Medical Image Retrieval Based on Markov Random Field

The development of medical images acquisition and storage technology has led to the rapid growth of the relevant data.Retrieval of similar medical images can effectively help doctors to diagnose diseases more accurately.But because of the particularity of medical images,traditional contentbased image retrieval(CBIR)method such as bag-of-words(BOW)cannot be applied to medical images.For example,when retrieving a diseased image,we should not only consider the similar characteristics but also need to consider the type of lesion.And for medical images,images with the same lesion may have different image features,similar images may have different types of lesions.In this paper,a Markov random field(MRF)is structured,and an approximate belief propagation algorithm is used to retrieval images.An adjust-ranking step after initial retrieval is incorporated to further improve the retrieval performance.This paper uses the real brain CT images.The experimental results show that the proposed method can significantly improve the retrieval accuracy and has good efficiency.

A Joint Link Prediction Method for Social Network

The popularity of social network services has caused the rapid growth of the users. To predict the links between users has been recognized as one of the key tasks in social network analysis. Most of the present link prediction methods either analyze the topology structure of social network graph or just concern the user’s interests. These will lead to the low accuracy of prediction.Furthermore, the large amount of user interest information increases the difficulties for common interest extraction. In order to solve the above problems, this paper proposes a joint social network link prediction method-JLPM.Firstly, we give the problem formulation. Secondly, we define a joint prediction feature model(JPFM) to describe user interest topic feature and network topology structure feature synthetically, and present corresponding feature extracting algorithm. JPFM uses the LDA topic model to extract user interest topics and uses a random walk algorithm to extract the network topology features. Thirdly,by transforming the link prediction problem to a classification problem, we use the typical SVM classifier to predict the possible links. Finally, experimental results on citation data set show the feasibility of our method.

A Range-Threshold Based Medical Image Classification Algorithm for Crowdsourcing

Medical images are important for medical research and clinical diagnosis.The research of medical images includes image acquisition,processing,analysis and other related research fields.Crowdsourcing is attracting growing interests in recent years as an effective tool.It can harness human intelligence to solve problems that computers cannot perform well,such as sentiment analysis and image recognition.Crowdsourcing can achieve higher accuracies in medical image classification,but it cannot be widely used for its low efficiency and the monetary cost.We adopt a hybrid approach which combines computer’s algorithm and crowdsourcing system for image classification.Medical image classification algorithms have a high error rate near the threshold.And it is not significant by improving these classification algorithms to achieve a higher accuracy.To address the problem,we propose a hybrid framework,which can achieve a higher accuracy significantly than only use classification algorithms.At the same time,it only processes the images that classification algorithms perform not well,so it has a lower monetary cost.In the framework,we device an effective algorithm to generate a range-threshold that assign images to crowdsourcing or classification algorithm.Experimental results show that our method can improve the accuracy of medical images classification and reduce the crowdsourcing monetary cost.