Search

Earth Science community depends on the exploration, analysis and reprocessing of high volumes of data as well as the modeling and simulation of complex coupled systems on multiple scales. The main aim of this article is to introduce a new hydrological modeling service based on the Soil and Water Assessment Tool (SWAT) (Arnold et al. J American Water Resour Assoc 34(1), 73-89, 1998 ; Arnold and Fohrer Hydrol Process 19(3), 563-572, 2005) model using high efficiency, resource sharing and low cost cloud computing resources (Astsatryan et al. International Journal of Scientific & Engineering Research 1(1), 1130-1133, 2014). Such a Desktop as a Service (DaaS) approach allowing users to work from anywhere, and gives centralized desktop management and great performance. Within the Spatial Data Infrastructure (SDI) and cloud platform, the DaaS service gives secure access to the model and a centralized data storage to get a SWAT model input. The article illustrates the analyses of the implementation of the SWAT model for the Sotk watershed of Lake Sevan in Armenia (Sargsyan 2007). [ABSTRACT FROM AUTHOR]

Over time, the population and anthropogenic activities increase, adversely affecting our atmosphere. In particular, an increase in air temperature leads to a change in the number of various gases that fill the atmosphere, the growth and decrease of which can significantly affect the environment. Nitrogen dioxide (NO2) is a common air pollutant from natural and human activities, with potential respiratory irritant effects at high levels. To effectively address these issues, there is a need for advanced tools and services that can analyze and visualize NO2 air pollution data comprehensively. This article presents the development and implementation of a Data Analytics and Visualization Service tailored to Armenia and Belarus's unique air quality dynamics. The service aims to provide actionable insights for policymakers, researchers, and the public. The studies show that NO2 excesses in their troposphere for Armenia and Belarus. This increase is correlated with the rise in urban population, causing dramatic adverse effects on the atmosphere. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to introduce a portal of geoprocessing services that can be used to compute either a single vegetation index or a combination of vegetation indices, as a workflow. High Performance Computing (HPC) resources are used for the calculations, and the Web Processing Service (WPS) standard is used to handle the requests from and the responses to the portal. In case of a workflow, a single node of the cluster is dedicated to each index, and the number of used cores depends on the complexity of the task. In addition, based on a series of experiments made to accelerate remote sensing image processing, a parallelization method within the computational node is automatically chosen depending on the complexity of the operations and the amount of data. The suggested algorithm optimizes the processing by selecting the best methodology (serial or parallel) and the number of cores to efficiently manipulate and distribute the data. The interoperable web portal, Spatial Data Infrastructure (SDI) and the heterogeneous resources of HPC cluster are located in the same local area network, and the cluster nodes have access to the data via network file system sharing. The use of standardized web services makes it possible to use remote data as inputs. [ABSTRACT FROM AUTHOR]

The study presents a comparative analysis of algorithms of traditional numerical methods and methods using neural networks for solving ordinary differential equations. The evaluation includes both mathematical aspects and aspects of machine learning. Using the Julia programming language, neural networks and the necessary algorithms for conducting practical experiments are implemented. These experiments demonstrate outstanding achievements in the field of quantum computing. Moreover, comparing the efficiency metrics of solving differential equations using numerical methods and neural networks reveals interesting results, depending on the chosen architecture and network parameters. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) swarms offer a resource-effective and time-efficient method of collecting and analyzing data for a range of applications. The paper presents a cutting-edge collective artificial intelligence-powered self-organizing UAV swarm mission planning and preparing platform designed to enhance task performance with a fleet of UAVs and facilitate terrain monitoring. Users can create requests easily from the QT interface due to the cloud-based multiuser platform's interactive capabilities, which enable smooth user collaboration and real-time video viewing for group study of dynamic landscape imagery. Optimizing the behavior and performance of UAV swarm navigation maps, the UAV map configurator makes it easier to create and modify them. Additionally, the QT service layer guarantees safe data transfer to cloud servers, and the parameter gossip system encourages coordination and communication among swarm members. This integrated data sets significant parameters such as the number of swarm participants, starting relative coordinates, and statuses (imager and/or strike), and drives the establishment of critical swarm and target jobs. The server uses complex algorithms, such as the research road graph, which is based on the rotor-router model, and the complete information exchange graph, which uses the gossip/broadcast model, to carry out these tasks. These algorithms complement each other in the server environment, allowing the UAV swarm to plan and coordinate tasks more effectively. In addition, the platform provides the smooth transfer of the created goal tasks to each member of the swarms' memory, benefiting decision-making skills and swarm performance as an entire. The QT-based user interface also provides the capability to modify the specific member task type of UAVs. This implies that UAVs can be used for specific area consideration, information gathering, transport, or, in the case of a military use, as an enemy position attacker. A server environment and QT-based interface can be used as a data sharing transponder to accomplish all of these functional changes. The task categories within the mentioned swarm shift when alterations occur in the initial transfer graph of UAVs, necessitating the inclusion of new locations for completing the modified tasks. Additionally, ensuring a unified primary administrative domain and coordinated data transmission among users across multiple platforms are crucial aspects of the suggested method. This domain ensures secure connections and offers additional security features. [ABSTRACT FROM AUTHOR]

National Research and Education Networks (NRENs) are pivotal in modern research and education, providing a dynamic ecosystem for academic and scientific communities. These networks form the backbone for crucial infrastructures and services, propelling research, education, and innovation. This article explores the role of monitoring and advanced emulation in enhancing NREN performance. The experiences of ASNET-AM, the Armenian NREN, in leveraging these techniques are presented, highlighting the benefits and challenges. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAV) swarms offer a cost-effective, time-efficient data collection and analysis solution across various applications. The study presents a cutting-edge self-organizing UAV swarm simulation platform empowered by collective artificial intelligence designed to facilitate terrain monitoring and optimize task performance using a fleet of UAVs. The cloud-based multi-user platform provides users with interactive features for seamless user collaboration and real-time video viewing for collective exploration of dynamic terrain imagery, allowing users to generate requests seamlessly from the QT interface. The UAV map configurator facilitates the creation and modification of UAV swarm navigation maps, optimizing their behavior and performance. Additionally, the parameter gossip system fosters communication and coordination among swarm members, while the QT service layer ensures secure data transfer to cloud servers. This integrated data fuels the formation of essential swarm and target tasks, determining key parameters such as swarm participant count, initial relative coordinate positions, and statuses (imager and/or strike). The server employs advanced algorithms to achieve these functionalities, including the research road graph based on the rotor-router model and the comprehensive information exchange graph using the gossip/broadcast model. These algorithms work synergistically within the server environment, enabling efficient task planning and coordination among the UAV swarm. Furthermore, the platform allows for the seamless transmission of the formed target tasks to the memory of individual swarm participants, enhancing their decision-making capabilities and overall swarm performance. [ABSTRACT FROM AUTHOR]