The Agricultural Irrigation District Information System Based on Multi-Agent and GSM

2013 ◽  
Vol 433-435 ◽  
pp. 1853-1856
Author(s):  
Ting Hong Zhao ◽  
Peng Fei Zhang ◽  
Hui Min Hou
Keyword(s):  
Communication Network ◽  
Information System ◽  
Large Scale ◽  
Irrigation Management ◽  
Irrigation District ◽  
Agricultural Irrigation ◽  
Complex Data ◽  
Data Types ◽  
Good Communication ◽  
Multi Agent

rrigation district informative construction is an effective way to improve the management and to rational allocate and effectively utility irrigation water resources. This paper is directed against the characteristics such as large-scale monitoring data amount, complex data types, high real-time requirement, strong spatial correlation, etc. combine Multi-Agent theory with irrigation district information system together, and use GSM communication network as the communication network of system, established an agricultural irrigation district information system based on Multi-Agent and GSM, which can full utility intelligent of Agent and the good communication coordination of Multi-Agent system, so to provide comprehensive technical support for irrigation management and decision making.

scholarly journals Scalable analysis of multi-modal biomedical data

GigaScience ◽  
2021 ◽  
Vol 10 (9) ◽  
Author(s):  
Jaclyn Smith ◽  
Yao Shi ◽  
Michael Benedikt ◽  
Milos Nikolic
Keyword(s):  
Data Integration ◽  
Large Scale ◽  
Treatment Options ◽  
Complex Data ◽  
Biomedical Data ◽  
Data Types ◽  
Large Scale Data Processing ◽  
Scalable Analysis ◽  
Targeted Medicine ◽  
The Impact

Abstract Background Targeted diagnosis and treatment options are dependent on insights drawn from multi-modal analysis of large-scale biomedical datasets. Advances in genomics sequencing, image processing, and medical data management have supported data collection and management within medical institutions. These efforts have produced large-scale datasets and have enabled integrative analyses that provide a more thorough look of the impact of a disease on the underlying system. The integration of large-scale biomedical data commonly involves several complex data transformation steps, such as combining datasets to build feature vectors for learning analysis. Thus, scalable data integration solutions play a key role in the future of targeted medicine. Though large-scale data processing frameworks have shown promising performance for many domains, they fail to support scalable processing of complex datatypes. Solution To address these issues and achieve scalable processing of multi-modal biomedical data, we present TraNCE, a framework that automates the difficulties of designing distributed analyses with complex biomedical data types. Performance We outline research and clinical applications for the platform, including data integration support for building feature sets for classification. We show that the system is capable of outperforming the common alternative, based on “flattening” complex data structures, and runs efficiently when alternative approaches are unable to perform at all.

scholarly journals MATSim-T

2009 ◽  
pp. 57-78 ◽  
Author(s):  
Michael Balmer ◽  
Marcel Rieser ◽  
Konrad Meister ◽  
David Charypar ◽  
Nicolas Lefebvre ◽  
...  
Keyword(s):  
Large Scale ◽  
Computation Time ◽  
Gps Tracking ◽  
Complex Data ◽  
Aggregated Data ◽  
Computer Performance ◽  
Time Dynamic ◽  
Traffic Forecasting ◽  
Microscopic Models ◽  
Multi Agent

Micro-simulations for transport planning are becoming increasingly important in traffic simulation, traffic analysis, and traffic forecasting. In the last decades the shift from using typically aggregated data to more detailed, individual based, complex data (e.g. GPS tracking) andthe continuously growing computer performance on fixed price level leads to the possibility of using microscopic models for large scale planning regions. This chapter presents such a micro-simulation. The work is part of the research project MATSim (Multi Agent Transport Simulation, http://matsim.org). In the chapter here the focus lies on design and implementation issues as well as on computational performance of different parts of the system. Based on a study of Swiss daily traffic – ca. 2.3 million individuals using motorized individual transport producing about 7.1 million trips, assigned to a Swiss network model with about 60,000 links, simulated and optimized completely time-dynamic for a complete workday – it is shown that the system is able to generate those traffic patterns in about 36 hours computation time.

scholarly journals Scalable Analysis of Multi-Modal Biomedical Data

2020 ◽  
Author(s):  
Jaclyn M Smith ◽  
Yao Shi ◽  
Michael Benedikt ◽  
Milos Nikolic
Keyword(s):  
Data Integration ◽  
Large Scale ◽  
Treatment Options ◽  
Complex Data ◽  
Biomedical Data ◽  
Data Types ◽  
Large Scale Data Processing ◽  
Scalable Analysis ◽  
Targeted Medicine ◽  
The Impact

Targeted diagnosis and treatment options are dependent on insights drawn from multi-modal analysis of large-scale biomedical datasets. Advances in genomics sequencing, image processing, and medical data management have supported data collection and management within medical institutions. These efforts have produced large-scale datasets and have enabled integrative analyses that provide a more thorough look of the impact of a disease on the underlying system. The integration of large-scale biomedical data commonly involves several complex data transformation steps, such as combining datasets to build feature vectors for learning analysis. Thus, scalable data integration solutions play a key role in the future of targeted medicine. Though large-scale data processing frameworks have shown promising performance for many domains, they fail to support scalable processing of complex datatypes. To address these issues and achieve scalable processing of multi-modal biomedical data, we present TraNCE, a framework that automates the difficulties of designing distributed analyses with complex biomedical data types. We outline research and clinical applications for the platform, including data integration support for building feature sets for classification. We show that the system is capable of outperforming the common alternative, based on flattening complex data structures, and runs efficiently when alternative approaches are unable to perform at all.

The use of intelligent agents in modeling an integrated information system of transport logistics

2020 ◽  
Vol 5 ◽  
pp. 59-66
Author(s):  
Y.M. Iskanderov ◽  
Keyword(s):  
Information System ◽  
Supply Chains ◽  
Intelligent Agents ◽  
Technological Processes ◽  
Transport Services ◽  
Wide Range ◽  
Integrated Information System ◽  
Multi Agent ◽  
Transport Logistics ◽  
Integrated Information

Aim. The use of intelligent agents in modeling an integrated information system of transport logistics makes it possible to achieve a qualitatively new level of design of control systems in supply chains. Materials and methods. The article presents an original approach that implements the possibilities of using multi-agent technologies in the interests of modeling the processes of functioning of an integrated information system of transport logistics. It is shown that the multi-agent infrastructure is actually a semantic shell of the information system, refl ecting the rules of doing business and the interaction of its participants in the supply chains. The characteristic of the model of the class of an intelligent agent, which is basic for solving problems of management of transport and technological processes, is given. Results. The procedures of functioning of the model of integration of information resources of the participants of the transport services market on the basis of intelligent agents are considered. The presented procedures provide a wide range of network interaction operations in supply chains, including traffi c and network structure “fl exible” control, mutual exchange of content and service information, as well as their distributed processing, and information security. Conclusions. The proposed approach showed that the use of intelligent agents in modeling the functioning of an integrated information system makes it possible to take into account the peculiarities of transport and technological processes in supply chains, such as the integration of heterogeneous enterprises, their distributed organization, an open dynamic structure, standardization of products, interfaces and protocols.

scholarly journals Advancing Drug Discovery for Neurological Disorders Using iPSC-Derived Neural Organoids

2021 ◽  
Vol 22 (5) ◽  
pp. 2659
Author(s):  
Gianluca Costamagna ◽  
Giacomo Pietro Comi ◽  
Stefania Corti
Keyword(s):  
Drug Discovery ◽  
Drug Screening ◽  
Neural Development ◽  
Neurological Disorders ◽  
Large Scale ◽  
Three Dimensional ◽  
Induced Pluripotent Stem Cell ◽  
Cellular Level ◽  
Complex Data ◽  
Complex Data Sets

In the last decade, different research groups in the academic setting have developed induced pluripotent stem cell-based protocols to generate three-dimensional, multicellular, neural organoids. Their use to model brain biology, early neural development, and human diseases has provided new insights into the pathophysiology of neuropsychiatric and neurological disorders, including microcephaly, autism, Parkinson’s disease, and Alzheimer’s disease. However, the adoption of organoid technology for large-scale drug screening in the industry has been hampered by challenges with reproducibility, scalability, and translatability to human disease. Potential technical solutions to expand their use in drug discovery pipelines include Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) to create isogenic models, single-cell RNA sequencing to characterize the model at a cellular level, and machine learning to analyze complex data sets. In addition, high-content imaging, automated liquid handling, and standardized assays represent other valuable tools toward this goal. Though several open issues still hamper the full implementation of the organoid technology outside academia, rapid progress in this field will help to prompt its translation toward large-scale drug screening for neurological disorders.

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