Tourism Development Mode of Historical and Cultural Blocks from the Perspective of Urban Renewal Based on Online Review Data:A Case Study of Nanluoguxiang in Beijing

Under the background of urban renewal, this paper re-explores the tourism development modeof Nanluoguxiang historical and cultural block based on online review data, and puts forward correspondingdevelopment strategies. As a cultural label of a city, historical and cultural blocks should be updated first inorder to achieve sustainable development. By using multi-source big data review and qualitative researchmethods, the perception evaluation of tourists in Nanluoguxiang is obtained, and the shortcomings ofcurrent tourism development mode are analyzed. Furthermore, corresponding improvement strategies andsuggestions are put forward, in order to provide some effective ideas for the sustainable development ofhistorical and cultural blocks in the future.

Integration of Sensor Network Synthesis and Steady-State Online Data Reconciliation

Sensor network synthesis means to select certain variables to be measured, to select suitable sensors, to observe all the important variables, and to get reliable and accurate process data. The steady state online data reconciliation can remove the influences of errors in the measurements on the acquired data, and get consistent process data that satisfy the process constraints. These two techniques have been studied often but separately. This paper proposed to integrate these two techniques into a unified system to get better information about the process data. An industrial application of the integrated system to a crude oil distillation unit was also described, where valid process data were obtained and applied for online process optimization system, which led to a profit increase of about four million RMB annually.

Artificial intelligence ecosystem for computational psychiatry:Ideas to practice

Computational psychiatry is an emerging field that not only explores the biological basis of mental illness but also considers the diagnoses and identifies the underlying mechanisms.One of the key strengths of computational psychiatry is that it may identify patterns in large datasets that are not easily identifiable.This may help researchers develop more effective treatments and interventions for mental health problems.This paper is a narrative review that reviews the literature and produces an artificial intelligence ecosystem for computational psychiatry.The artificial intelligence ecosystem for computational psychiatry includes data acquisition,preparation,modeling,application,and evaluation.This approach allows researchers to integrate data from a variety of sources,such as brain imaging,genetics,and behavioral experiments,to obtain a more complete understanding of mental health conditions.Through the process of data preprocessing,training,and testing,the data that are required for model building can be prepared.By using machine learning,neural networks,artificial intelligence,and other methods,researchers have been able to develop diagnostic tools that can accurately identify mental health conditions based on a patient’s symptoms and other factors.Despite the continuous development and breakthrough of computational psychiatry,it has not yet influenced routine clinical practice and still faces many challenges,such as data availability and quality,biological risks,equity,and data protection.As we move progress in this field,it is vital to ensure that computational psychiatry remains accessible and inclusive so that all researchers may contribute to this significant and exciting field.

Requirement-driven remote sensing metadata planning and online acquisition method for large-scale heterogeneous data

Remote sensing data acquisition is one of the most essential processes in the field of Earth observation.However,traditional methods to acquire data do not satisfy the requirements of current applications because large-scale data processing is required.To address this issue,this paper proposes a data acquisition framework that carries out remote sensing metadata planning and then realizes the online acquisition of large amounts of data.Firstly,this paper establishes a unified metadata cataloging model and realizes the catalog of metadata in a local database.Secondly,a coverage calculation model is presented,which can show users the data coverage information in a selected geographical region under the data requirements of a specific application.Finally,according to the data retrieval results and the coverage calcula-tion,a machine-to-machine interface is provided to acquire target remote sensing data.Experiments were conducted to verify the availability and practicality of the proposed frame-work,and the results show the strengths and powerful capabilities of our framework by overcoming deficiencies in traditional methods.It also achieved the online automatic acquisi-tion of large-scale heterogeneous remote sensing data,which can provide guidance for remote sensing data acquisition strategies.

Data-based intelligent modeling and control for nonlinear systems

With the ever increasing complexity of industrial systems,model-based control has encountered difficulties and is facing problems,while the interest in data-based control has been booming.This paper gives an overview of data-based control,which divides it into two subfields,intelligent modeling and direct controller design.In the two subfields,some important methods concerning data-based control are intensively investigated.Within the framework of data-based modeling,main modeling technologies and control strategies are discussed,and then fundamental concepts and various algorithms are presented for the design of a data-based controller.Finally,some remaining challenges are suggested.

Convergence Analysis of a Kind of Deterministic Discrete-Time PCA Algorithm

We proposed a generalized adaptive learning rate (GALR) PCA algorithm, which could be guaranteed that the algorithm’s convergence process would not be affected by the selection of the initial value. Using the deterministic discrete time (DDT) method, we gave the upper and lower bounds of the algorithm and proved the global convergence. Numerical experiments had also verified our theory, and the algorithm is effective for both online and offline data. We found that choosing different initial vectors will affect the convergence speed, and the initial vector could converge to the second or third eigenvectors by satisfying some exceptional conditions.

Insights into CODE-DE–Germany’s Copernicus data and exploitation platform

This article presents and analyses the modular architecture and capabilities of CODE-DE(Copernicus Data and Exploitation Platform–Deutschland,www.code-de.org),the integrated German operational environment for accessing and processing Copernicus data and products,as well as the methodology to establish and operate the system.Since March 2017,CODE-DE has been online with access to Sentinel-1 and Sentinel-2 data,to Sentinel-3 data shortly after this time,and since March 2019 with access to Sentinel-5P data.These products are available and accessed by 1,682 registered users as of March 2019.During this period 654,895 products were downloaded and a global catalogue was continuously updated,featuring a data volume of 814 TByte based on a rolling archive concept supported by a reload mechanism from a long-term archive.Since November 2017,the element for big data processing has been operational,where registered users can process and analyse data themselves specifically assisted by methods for value-added product generation.Utilizing 195,467 core and 696,406 memory hours,982,948 products of different applications were fully automatically generated in the cloud environment and made available as of March 2019.Special features include an improved visualization of available Sentinel-2 products,which are presented within the catalogue client at full 10 m resolution.

A Scalable Infrastructure for Online Performance Analysis on CFD Application

The fast-growing demand of computational fluid dynamics（CFD） application for computing resources stimulates the development of high performance computing（HPC） and meanwhile raises new requirements for the technology of parallel application performance monitor and analysis.In response to large-scale and long-time running for the application of CFD,online and scalable performance analysis technology is required to optimize the parallel programs as well as to improve their operational efficiency.As a result,this research implements a scalable infrastructure for online performance analysis on CFD application with homogeneous or heterogeneous system.The infrastructure is part of the parallel application performance monitor and analysis system（PAPMAS） and is composed of two modules which are scalable data transmission module and data storage module.The paper analyzes and elaborates this infrastructure in detail with respect to its design and implementation.Furthermore,some experiments are carried out to verify the rationality and high efficiency of this infrastructure that could be adopted to meet the practical needs.

Spatial Distribution Characteristics and Influencing Factors of Taobao Villages in Guangzhou

Through a case study on 24 Taobao villages in Guangzhou,this paper analyzes the relationship between the spatial distribution characteristics and influencing factors of Taobao villages using the online data mining methodology,and compares e-commerce-based industry clusters with traditional industry clusters.The result shows that Taobao villages in Guangzhou are mainly distributed in peripheral areas of the central city,with low population and employment density; villages running the same kind of business present the trend of concentration.And the formation of Taobao villages is highly relevant to the distribution of factories,wholesale markets,express services,and low-rent housing,as well as the learning and demonstration effect among subjects.It further proves that,under the influence of e-commerce,the importance of economies of scale and economies of scope brought by geographic proximity has been weakened,while the importance of interaction and communication effect among subjects have been obviously strengthened.