WEB-BASED EMERGENCY RESPONSE COMMUNITY

Framework and Case Study on Fire Response Community

Alexander Smirnov, Tatiana Levashova, Nikolay Shilov and Alexey Kashevnik

St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences,

39, 14th line, St. Petersburg, 199178, Russian Federation

Keywords: Web-based Communities, Smart Space: Web-services, Service-oriented Architecture, Emergency Response.

Abstract: The research addresses the problem of exploitation of smart space’s facilities for the organization of

resources of a smart space into an emergency response community. The members of such a community and

people involved in emergency are enabled to use Web-based interface for their communications and making

decisions, i.e. they organise a Web-based community. The paper proposes a framework that incorporates

concepts of smart space, Web-based communities, and Web-services. The framework replaces real-world

resources of the smart space with their service-based representations. As a result of this replacement, the

emergency response community comprises Web-services representing resources providing emergency

response services. Service-oriented architecture serves to coordinate interactions of the Web-services the

framework deals with. The applicability of the proposed framework is demonstrated by a simulated case

study on organization of a fire response community.

1 INTRODUCTION

Recently, technologies of smart space environments,

Web-services, and Web-based communities have

received much attention due to facilities offered by

them. The smart environments provide efficient

facilities for organization of their resources in a

context-aware way to assist people in their needs

(Lamorte and Venezia, 2009); (Özçelebi et al.,

2010). Web-services offer advantages of seamless

information exchange between the resources of

smart environments (Schroth, 2007) and a potential

for lower integration costs and greater flexibility

(Microsoft Corp., 2003); (Oracle, 2005). Web-based

communities are beneficial in instant information

exchange and online decision making.

The present research addresses the problem of

exploitation of smart space’s facilities for the

organization of resources of a smart space into a

community aiming at emergency response. The

members of this community and people involved in

emergency are enabled to use Web-based interface

for their communications and making decisions. In

this way they constitute a Web-based community.

The paper proposes a framework that

incorporates concepts of smart space, Web-based

communities, and Web-services. This framework

replaces real-world resources of the smart space with

their service-based representations. As a result of

this replacement, the emergency response

community in made up of Web-services representing

resources that provide emergency response services.

Various sensors, actuators, electronic devices with

computational capabilities, etc. as well as humans

and organisations are considered as various kinds of

resources comprising the smart space.

An emergency response community is organised

based on emergency response plan. The problem of

planning emergency response actions is solved by

computational resources of the smart space. Service-

oriented architecture serves to coordinate

interactions of the Web-services the framework

deals with. The applicability of the proposed

framework is demonstrated by a simulated case

study on organization of a fire response community.

The rest of the paper is structured as follows.

Section 2 presents a brief survey of related research.

Section 3 introduces the framework intended for

organisation of emergency response communities in

smart spaces. The service-oriented architecture and

interactions of Web-services constituting the

framework are discussed in Section 4. Section 5

demonstrates the applicability of the framework via

482

Smirnov A., Levashova T., Shilov N. and Kashevnik A..

WEB-BASED EMERGENCY RESPONSE COMMUNITY - Framework and Case Study on Fire Response Community.

DOI: 10.5220/0003909704820491

In Proceedings of the 8th International Conference on Web Information Systems and Technologies (WEBIST-2012), pages 482-491

ISBN: 978-989-8565-08-2

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

organization of a fire response community. Main

concluding remarks are summarized in Section 6.

2 RELATED RESEARCH

There is no extensive literature on the subject of

organization of Web-based communities in smart

spaces or involvement of members of such

communities in joint actions. An example of

coordination of different users doing collaborative

activities from diverse locations through different

devices is the use of a hypermedia model to describe

and support group activities in intelligent

environments (Arroyo et al., 2008).

The role of social media and online communities

is being thoroughly investigated within the research

area of crisis informatics (Hagar, 2010). Online

forums (Palen et al., 2007), Web portals (Mandel et

al, 2010), Tweeter (Starbird and Palen, 2011);

(Starbird and Palen, 2012), micro-blogging (Vieweg

et al., 2010), social networks (Armour, 2010);

(Krakovsky, 2010), and other forms of social media

are believed to be powerful tools enabling

collaboration of different parties to respond more

effectively to emergencies.

To some extent potentialities of smart spaces in

emergency have been used in an architecture that

intends to improve the collaboration of rescue

operators in emergency management via their

assistance by a Process Management System

(Catarci et al., 2010). This system is installed on the

smart phones and PDAs of the rescue operators. It

manages the execution of emergency-management

processes by orchestrating the human operators with

their software applications and some automatic

services to access the external data sources and

sensors.

The idea close to the integration of Web-services

into an emergency response community has been

studied in research addressing the investigation of

effectiveness of actor-agent communities in context

of incident management (Gouman et al., 2010).

Although the preliminary research results are

inconclusive, they allow ones to suggest that agents,

at least, can efficiently support humans in achieving

a common goal.

The idea beyond the present research of treating

emergency response as the problem of planning

emergency response actions in an efficient manner is

shared by many studies, e.g., (Ng and Chiu, 2006);

(Ling, 2009) and many others.

The approaches above address different aspects

of organisation of communities of actors (including

emergency response communities) sharing a

common goal. They integrate various emerging

technologies to achieve their goals. But no one of

them investigates jointly both the problems of

planning response actions and involvement of the

participants of these actions into Web-based

communities.

3 FRAMEWORK

The proposed framework is intended to coordinate

operations of various resources of a smart space in

context aware way to assist people in attaining their

objectives. The framework distinguishes two kinds

of resources in the smart space: information and

acting. The information resources are various kinds

of sensors and electronic devices that provide data &

information and perform computations. Particularly,

some information resources are responsible for

problem solving. The acting resources are people

and /or organisations that can be involved in the

response actions, i.e., emergency responders.

3.1 Emergency Response Community

As known from e-Government practice,

participation of different stakeholders in e-

Government’s activities can result in broader

(integrated) solutions (Rainford, 2006); (Chourabi

and Mellouli, 2011). So, the framework assumes

partnerships of different stakeholders in emergency

response actions. It integrates emergency services

the smart space provides and voluntary sector as the

partnerships (Figure 1). It is considered that the

smart space provides emergency response services

on first aid, emergency control, and people

evacuation. The services on first aid and emergency

control are services rendered by professional

emergency responders, whereas the evacuation

services are provided by the voluntary sector. This

sector is represented by car drivers – they are the

volunteers ready to evacuate the potential victims.

Access to the emergency response services is

achieved through wire or wireless Internet-

accessible devices. Communications between the

participants of emergency response actions are

supported by Web-based interface. In this way these

participants organize a Web-based community.

The professional emergency responders and the

volunteers make up the emergency response

community. They use Web-based interface to make

decisions on action plans, to exchange information

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during the response actions, and to communicate

with victims.

Emergencyresponse

community

Smartspace’s

emergencyservices

Voluntarysector

Serviceconsumers

Cardrivers

Professionalemergency

responders

Potentialvictims

Injuredpeople

WireorwirelessInternet‐accessibledevices

Figure 1: Emergency response community in smart space.

3.2 Generic Scheme of Framework

The idea behind the framework is to represent the

resources of the smart space, emergency responders,

and people in any way involved in the emergency by

sets of Web-services. Each of the listed objects is

characterized by its profile. A profile, besides typical

information characterising an object (object’s name,

address, etc.), holds a set of context sensitive

properties, e.g., the object’s location, its availability,

role, etc. The Web-services provide data stored in

the profiles and implement the resources’

functionalities. As a result of the representation

used, the emergency response community comprises

Web-services representing emergency responders

taking the response actions.

The framework guides the emergency response

as follows (Figure 2). Whenever an emergency event

occurs, resources of the smart space recognize the

type of event and determine other event

characteristics (the location, intensity and severity of

the event, etc.). Based on the type of emergency and

knowledge represented in an application ontology of

the emergency management domain, special

developed services create an abstract context. This

context is an ontology-based model of the

emergency situation of the given type at the abstract

(non-instantiated) level. It represents knowledge

relevant to the emergency situation, i.e., kinds of

services required in the given emergency situation

and other knowledge related to these services.

The abstract context is instantiated by resources

of the smart space. The resources continuously fill

up the abstract context with real-world information

characterising the emergency situation. In this way

an operational context is produced, which is a model

of the emergency situation representing fully-

instantiated real-world objects relevant to it.

Particularly, the operational context contains

Emergency

situation

Services

Web-service

interface

Constraint

satisfaction

problem solving

Smart s

ace

Victims and

resources

Decision making

Emergency response

plan

Application ontology Abstract context Operational context

Web-based

community

Profile

Ontology-

ased

resource

representation

Emergency response

community

Relationship

Corres

ondence

Reference

Information flow

Figure 2: Generic scheme of the framework.

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information about the locations of potential

emergency responders along with some other their

characteristics (their availabilities, capacities, etc.).

The operational context serves as the basis for

producing a plan of response actions. An emergency

response plan is a set of emergency responders with

transportation routes for the mobile responders,

required helping services, and schedules for the

responders’ activities. The problem of plan

producing is solved as a constraint satisfaction

problem, the result of which is a set of feasible

action plans.

From the set of feasible plans an efficient plan is

selected. For this, some efficiency criteria are

applied. For professional emergency responders the

following efficiency criteria are provided for:

minimal time of arriving of professional emergency

responders at the emergency location, minimal time

and cost of transportation of injured people to

hospitals, and minimal number of mobile teams

involved in the response actions. For car drivers

efficiency criteria are minimum evacuation time and

maximum evacuation capacity.

The efficient plan is displayed on the Internet-

accessible devices of emergency responders that are

in this plan for making decisions if they are ready to

act according to the plan or not. The procedure of

making decisions is provided for two reasons.

Firstly, emergency situations are rapidly changing

ones – something may happen between the moment

when a plan is selected and time when the possible

community members receive this plan. Secondly,

resources of the smart space may be disabled in

emergency and because of this operational informa-

tion may be not available; therefore the operational

context may not meet the real state of the situation.

The approved plan is thought to be the guide for

the response actions. The emergency responders

scheduled in this plan organise the emergency

response community.

3.3 Decision Making

Decisions on action plans are made online using

Internet-accessible devices and Web-based interface.

But procedures of making decisions by professional

emergency responders and volunteers are different.

For the professional emergency responders an

emergency response plan is a set of professional

emergency responders (emergency teams, fire

brigades, rescue parties, hospitals, etc.), a set of

services these responders have to provide in the

emergency situation (fire extinguishing,

transportation, first aid, etc.), a set of transportation

routes to go to the emergency location and to

transport injured people to hospitals, and schedules

for the responders’ activities.

The procedure of making decisions by

professional emergency responders is as follows

(Figure 3). If the plan is approved by all the

responders this plan is supposed to be the plan for

actions. Otherwise, either this plan is adjusted (so

that the potential participant who refused to act

according to the plan does not appear in the adjusted

plan) or another set of plans is produced.

The plan adjustment is in a redistribution of the

actions among emergency responders that are

contained in the set of feasible plans. If such a

distribution does not lead to a considerable loss of

time (particularly, the estimated time of the

transportation of the injured people to hospitals does

not exceed “The Golden Hour”) then the adjusted

plan is submitted to the renewed set of emergency

responders for approval. If a distribution is not

possible or leads to loss of response time a new set

of plans is produced, from which a new efficient

plan is selected and submitted to approval.

For the car drivers the emergency response plan

is a plan for evacuation of potential victims from the

dangerous area. Such a plan for a driver is a

ridesharing route and transportation schedule.

Decision making on an evacuation plan is in

making agreement between the driver and the

evacuee to go according to the scheduled ridesharing

route (Figure 4). In case, when there is no agreement

between a driver and an evacuee, another car for

evacuation of this passenger is sought for. At that,

the confirmed routes are not revised.

The emergency responders that are in the

approved plan intended for professional emergency

responders and the drivers participating in the

evacuation organise the emergency response

community.

Figure 3: Decision making by professional emergency

responders.

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Figure 4: Decision making by car drivers and evacuees.

4 SERVICE-ORIENTED

ARCHITECTURE

To coordinate the interactions of Web-services, the

framework deals with, service-oriented architecture

has been designed.

4.1 Architecture Components

The architecture comprises three groups of services

(Figure 5). The first group is made up of core

services responsible for the registration of the Web-

services in the service register and producing the

real-world model of the emergency situation, i.e. the

creation of the abstract and operational contexts.

Services belonging to this group are as follows:

 registration service registers the Web-services in

the service register;

 application ontology service provides access to

the application ontology;

 abstract context service creates, stores,

maintains, and reuses the abstract contexts;

 operational context service produces the

operational contexts.

Web-services comprising the second group are

responsible for the organization of an emergency

response community. This group contains:

 emergency response service integrates

information provided by the city’s resources;

 routing service generates a set of feasible plans

for emergency response actions;

 smart logistics service implements functions of

the ridesharing technology;

 decision making service selects an efficient plan

from the set of feasible plans and coordinates the

(re)planning procedure.

The third group comprises sets of services

responsible for the representation of the smart

space’s resources, implementation of their functions,

and representation of emergency responders and

people in any way involved in the emergency. This

group includes:

 resource services provide data stored in the

Core services

Create model of emergency situation

Smart space’s services

Represent resources, emergency responders, and victims;

fulfill resources’ functions

Emergency response services

Organize emergency response community

Emergency

response

service

Routing

service

Decision

making

service

Smart

logistics

service

Resource services

Registration service

Registers Web-services in the service register

Communication bus

Acting services

Figure 5: Service-oriented architecture.

Application

ontology service

Abstract context

service

Operational

context service

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profiles of information resources and implement

functions of these resources;

 acting services provide data stored in the profiles

of acting resources (emergency responders) and

other people involved in emergency.

4.2 Service Interactions

Service interactions in Web-based community are

demonstrated by two scenarios. These scenarios

introduce the service interactions in making

decisions on the plan for actions intended for

professional emergency responders.

Figure 6 shows Web-service interactions when

all the emergency responders agree to participate in

the joint actions according to the plan selected by

decision making service (in the figure the emergency

responders are represented by vehicles that they use

– ambulance, fire truck, and rescue helicopter). It is

seen that decision making service sends

simultaneous messages to all the emergency

responders with the plan for each responder, waits

their replays on plan acceptance (Ready), and sends

them simultaneous messages to take the response

actions (Start).

Figure 7 demonstrates Web-service interactions

in case when all the ambulances selected for the

response actions are not ready to participate in them

and routing service does not manage to adjust the

selected plan. Two ambulances (Ambulance 1 and

Ambulance 2) replay “Not ready” to the messages of

decision making service. This replay is accompanied

with the messages to decision masking service and

operational service with the reasons of their

refusals. Examples of such reasons are the road has

been destroyed, the ambulance has blocked, etc.

Decision making

service

Ambulance Rescue

helicopter

Fire truck

URL_AmbulServ, Route_Ambulance

URL_FTServ, Route_FireTruck

URL_RHServ, Destination

Ready

Start

Figure 6: Emergency responders accept emergency

response plan.

Decision making service duplicates the messages

with the reasons for operational service. The

duplication is a guarantee that operational service

will receive information that it was unaware of up to

this moment. As well decision making service sends

the message on excluding the two ambulances from

the list of available emergency responders to routing

service.

Operational service corrects the operational

context according to the information contained in the

reasons. Routing service requests operation service

of the operational context that represents the up-to-

date information of the emergency situation,

generates a new set of plans, and sends it to decision

making service.

5 CASE STUDY: FIRE RESPONSE

COMMUNITY

An applicability of the proposed framework is

demonstrated via the organization of an emergency

response community aimed at response to a fire

event happened in a smart space. The event was

simulated using an internal platform that supports a

GIS-based simulation. The platform is capable to

generate random failures and locations of emergency

responders, and random route availabilities; it allows

ones to input contextual information on types of

emergency events, number of victims, etc.

According to the framework resource services

recognize the fire event, fix the fire location, classify

the fire severity, and registers number of victims to

be transported to hospitals. In the test case it is

simulated that the fire has happened in a building, its

level of severity is low, 9 injured people have to be

transported to hospitals. This information is sent to

emergency response service. This service concludes

that to extinguish the fire 1 fire brigade is required.

Based on the type of emergency (fire) abstract

context service creates an abstract context. This

context represents the following kinds of response

services required in the fire situation: fire service,

emergency service, and transportation service. These

services are provided by the following kinds of

emergency responders: fire brigades, emergency

teams, hospitals, and car drivers. Besides the listed

knowledge, the abstract context represents various

kinds of vehicles that may be used by the emergency

responders, and kinds of roles of the individuals

involved in the fire situation (e.g., leader of a team,

driver, victim, passenger, etc.).

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Decision making

service

Ambulance 1 Fire truck Rescue

helicopter

Ambulance 2 Routing

service

Operational

service

URL_Ambul1Serv, Route_Ambulance1

URL_FTServ, Route_FireTruck

URL_RHServ, Destination

Not ready, Reason 1

Ready

Exclude URL_Ambul1Serv, URL_Ambul2Serv

1: URL11_Route11, URL21_Route12,...; 2: URL21, Route_21, URL_22, Route_22,...

Reason 1

Ready

URL_Ambul2Serv, Route_Ambulance2

Not ready, Reason 2

Reason 2

CurrentSituation_Req

CurrentSituation_Rep

Figure 7: Plan regeneration.

Operational context service instantiates the kinds of

concepts represented in the abstract context and

produces in that way an operational context. For the

instantiation operational context service uses the

information provided by the resource services and

acting services:

 the location and severity of the fire event;

 the number of victims;

 the current locations, availabilities, and

capacities of the mobile emergency responders, i.e.,

fire brigades, emergency teams, and car drivers;

 the types of vehicles the mobile emergency

responders use;

 the addresses, contact information, availabilities,

and free capacities of the hospitals;

 the destinations of cars passing by the fire place

and the cars’ properties (free capacities,

availabilities of baby car seats, etc.);

 the current locations of the uninjured people to

be evacuated from the fire area;

 the transportation network, the route

availabilities, and the traffic situation.

Operational context service passes the operational

context to routing service. Routing service analyses

types of routes (roads, waterways, etc.) that the

emergency teams and fire brigades can follow

depending on the vehicles they use. Then, routing

service selects feasible fire brigades, emergency

teams, and hospitals that can be involved in the

response operation. They are selected depending on

1) their availabilities; 2) the types of vehicles they

use and 3) the routes available for these types; and

4) the hospitals’ free capacities.

In the simulated area 7 available fire brigades, 8

emergency teams, 5 hospitals having free capacities

for 4, 4, 2, 3, and 3 patients correspondingly are

found; 6 fire trucks and 1 fire helicopter are

allocated to the fire brigades, 7 ambulances and 1

rescue helicopter are allocated to the emergency

teams.

For the found emergency responders routing

service generates a set of feasible plans for actions.

A plan for actions produced for the emergency

teams supposes that one vehicle can house one

injured person.

Routing service passes the operational context

and the set of plans to decision making service. The

latter selects an efficient plan (Figure 8). At that,

minimal time of victim transportations is used as the

efficiency criterion. In Figure 8 the big dot denotes

the fire location; dotted lines depict routes to be used

for transportations of the emergency teams and fire

brigades.

As it is seen from the figure, the set of

emergency responders comprises 1 fire brigade

going by 1 fire helicopter, 7 emergency teams

allocated to 1 rescue helicopter and 6 ambulances,

and 3 hospitals having free capacities for 4, 2, and 3

patients. 1 ambulance (encircled in the figure) and

the rescue helicopter are planned to go from the fire

location to hospitals twice. The estimated time of the

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Figure 8: Plan for actions for fire brigades and emergency teams.

operation of transportations of all the victims to

hospitals is 1 h. 25 min.

Decision making service submits the plan to the

emergency responders that are in it for making

decisions on this plan. The plan is displayed on the

Internet-accessible devices of these responders. The

view of the plan depends on the roles the emergency

responders fulfill in the current emergency situation.

Figure 9 shows part of the plan displayed on the

Tablet PC of the leader of an emergency team.

In the test case all the emergency responders

represented in Figure 8 are supposed to agree on the

plan and therefore have become the members of the

emergency response community.

Concurrently with the planning of the response

actions activities on evacuation of people from the

fire area are undertaken. Persons who need to be

evacuated from the fire area invoke smart logistics

service. This service scans cars passing the person

locations. Based on the information about the person

locations and destinations, and the locations and

destinations of the found cars, routing service

produces a set of feasible ridesharing routes for the

person transportations. Decision making service

selects efficient routes.

The selected efficient routes are displayed on

Internet-accessible devices of the drivers and the

evacuees to confirm their intentions to go according

to the proposed to them ridesharing routes. Besides

the routes, the passengers are informed of the model,

color, and license plate number of the car intended

for their transportation.

As the drivers and the passengers confirm the

evacuation plans, smart logistics service sends ap-

propriate signals to the drivers included in the

agreed plans. Examples of ways routed for a driver

and a passenger and displayed on their smart phones

are given in Figure 10 and Figure 11. For the passen-

ger the walking path to the locations where the

drivers are offered to pick his/ her up is displayed.

The encircled car in the figures shows the location

where the driver is offered to pick up the passenger.

In the simulated example 26 persons are sup-

posed to have to be evacuated from the fire area. The

results obtained for this are as follows: 22 persons

have been driven directly to the destinations by 16

cars whereas for 4 persons no cars have been found.

These 4 persons are informed through their mobile

devices that they can be evacuated by taxis. If they

agree, smart logistics service makes orders for taxi.

The Web-based community organised in the test

case comprises 1) the professional emergency

responders scheduled in the fire response plan

Figure 9: Plan for actions for an emergency team.

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(Figure 8) in the persons of the leaders of the

emergency teams and fire brigades as well as the

administrators of the hospitals; 2) the cars’ drivers

participated in the confirmation of the ridesharing

routes and 3) the evacuees. The emergency teams,

fire brigades, hospitals, and car drivers constitute the

emergency response community.

Figure 10: Ridesharing route: driver’s view.

The Smart-M3 platform (Honkola et al, 2010)

was used in the scenario execution. Tablet PC Nokia

N810 (Maemo4 OS), smart phone N900 (Maemo5

OS), and different

mobile phones served as the user

devices. Personal PCs based on Pentium IV

processors and running under Ubuntu 10.04 and

Windows XP were used for hosting other services.

In the experiments with different datasets the

execution time from the moment the emergency

event was registered to the moment of producing the

operational context took around 0.0007 s. The time

taken to generate the sets of action plans for

different datasets is shown in Table 1. The

approximating equation is quadratic for the total

amount of objects involved in the response actions.

The experimentation showed that the system already

takes a reasonable time for result generation.

Presented results are based on the usage of a

research prototype running on a desktop PC. In a

production environment the system is aimed to be

run on dedicated servers and it is expected to be

responsive enough to handle a large amount of

objects. The future development of Smart-M3 up to

the production level with a higher capacity could

also contribute to the system performance.

Table 1: Execution results.

Number of

emergency

responders

Number of

victims

Total number

of objects

Time of plan

generations, s.

10 10 20 4.85

10 20 30 9.12

20 20 40 17.51

30 30 60 37.93

40 40 80 66.13

50 50 100 101.29

Figure 11: Ridesharing route: passenger’s view.

6 CONCLUSIONS

The problem of integration of services provided by a

smart space with the purpose of organisation of

emergency response communities was investigated.

A framework that serves to integrate concepts of

smart space, Web-services and Web-based commu-

nities has been proposed. The framework supports

seamless information exchange between the re-

sources of the smart space, allows the members of

an emergency response community to make online

decisions on plans for their actions and to communi-

cate during these actions for coordination of their

activities, enables Web-based communications be-

tween the emergency responders and emergency

victims, supports access the emergency services that

the smart space provides using any wire and wireless

Internet-accessible devices.

An original feature of the way the response

actions are planned is in involvement of ridesharing

technology in planning evacuation activities.

Some limitations of the developed framework are

worth mentioning. The framework does not take into

account cases when it is not found enough available

professional emergency responders or when some

resources become disabled at time of the response

actions. As well, the framework does not address the

problem of lack of passing cars for evacuation of

people from the fire area and the problem of

searching for a route with changes if there are not

any cars nearby the fire area going directly to the

person destination. The listed limitations as well as

real-life testing and implementation will be subjects

for future research and activities.

ACKNOWLEDGEMENTS

The present research was supported partly by pro-

jects funded by grants 10-07-00368, 11-07-00045,

11-07-00058, 12-07-00298 of the Russian

WEBIST2012-8thInternationalConferenceonWebInformationSystemsandTechnologies

490

Foundation for Basic Research, the project 213 of

the research program “Information, control, and

intelligent technologies & systems” of the Russian

Academy of Sciences (RAS), the project 2.2 of the

Nano- & Information Technologies Branch of RAS,

and the contract 14.740.11.0357 of the Ministry of

Education and Science of Russian Federation.

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