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We demonstrated that supply air filtration as well as recirculating air filtration technique can easily be implemented in an already existing ventilation system and that recirculating air filtration resulted in enhanced lung health compared to supply air-filtered and non-filtered barns.
A more prominent effect might have been obtained in a breeding facility because of the longer life span of sows and a higher biosecurity level with air filtration as an add-on measure. Standard biosecurity protocols mainly include measures to prevent infections via direct or indirect transmission routes like pigs, semen, human beings, transport vehicles, insects, and fomites. However, treatment of air is often not included in these measures. Air filtration as a biosecurity measure can be used in different fields of application.
Supply air filtration has already been demonstrated to efficiently reduce porcine reproductive and respiratory syndrome virus PRRSV and Mycoplasma M.
The concept of filtering indoor air in pig production [ 8 — 11 ] is still new to pig production facilities [ 12 ]. Indoor airborne particles and microorganisms mainly originate from animals, food, and bedding [ 13 ].
Pathogens are often attached to dust particles [ 15 , 16 ] and this may lead to a faster spread in and between animal buildings. Furthermore, dust may impair the respiratory tract thus increasing susceptibility to infections with high and low pathogenic microorganisms [ 17 , 10 ].
Recently, methicillin-resistant Staphylococcus aureus MRSA has been detected bound to dust particles and in high concentrations in the air of pig facilities [ 18 , 19 ] posing a health threat to animals and caretakers [ 20 , 21 ]. Hence, indoor air quality can also be improved by filtering dust and its associated hazards. A high filtering efficacy In summary, the above projects address many requirements in Grid data management, but their components can not serve as a framework for Grid-based applications in managing data.
This leads us to develop a Grid Data Management GDM framework which supports services for applications in Grid computing environment to manage their data in an effective and secure manner.
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These services include distributing, accessing and integrating data, performance monitoring, and billing service. The detail of this framework will be presented in section 3.
Architecture and functional descriptions of each component of the framework are shown in Section 3.
Section 3. The following four basic goals drive the design of our GDM framework. Data Distribution supports the distribution of data to data resources while Data Integration is concerned with the integration of data from various data resources.
Data Access is responsible for providing access to data in a consistent, data resource-independent way such as querying, updating, transforming and delivering data. Another desired requirement is user management with the ability to map Grid users to database role.
Using these parameters, we can improve the performance of the system by many methods such as adjusting the data distribution policy, predicting the response time for a specific operation, etc.
Each data resource corresponds to a node in Grid. Most if not all of the data of Data Store are reflected in data resources. Data Store contains many data sources and a data source is usually distributed into many data resources.
Therefore, every operation on data source will be replaced by translated into equivalent operations on corresponding data resources in Grid.
GDM core is responsible for: storing and updating metadata of data resources in the system; supplying information about the distribution of data sources in data resources as well as data in data resources such as data type, table schema, data resource name, etc; authenticating and issuing roles for users roles for accessing data about data resources, roles for querying and operating in a specific data resource ; managing the issuing and execution of a ticket which represents a specific request on data; monitoring and analyzing the performance of the system.
Application will operate on data through the services supplied by GDM core. Figure 1. GDM framework architecture. The former deals with supplying all services of the framework and the latter helps applications interact with the former. The GDM core module comprises of the following major services: Data Accessor Service: this service is responsible for interacting with Grid data resources.
All requests of accessing and manipulating data in Grid data resources are handled by this service. OGSA- DAI services can be employed to implement Data Assessor Service for its ability to interact with different data resources to query, update, transform and deliver data. Data Distribution Service: the task of this service is to load data in external data sources into data resources in Grid environment. All queries and subsequent analyses of the data are executed directly in data resources.
Information about the distribution of data sources into data resources and information about data resources in Grid are stored in databases of GDM core. The data distribution strategy depends on many factors: requirement of a specific application or job, the number of data resources allocated, capacity and connectivity of every data resource, the frequency of accessing data and operations on data resource, system performance, etc.
The simplest solution here is equal distribution. Data in a data source is distributed equally into all allocated data resources. We can also assign a parameter to each factor which affects the data distribution policy to calculate the priority of data resources. These parameters will be adjusted incrementally by learning from experimental results. Data Resource Management Service: this service serves all requests for the properties of data resources such as data resource identification, database schema, data size, data distribution, etc.
It will ask the User Management Service to authenticate and authorize user before executing any requests by querying the database of GDM core to get the security related information. Hence, an application will interact with GDM through a valid Grid user identified by a credential. An application can manipulate on many data sources.
To work with a data source, it must be given an account username, password granted with some specific role. Information about roles of applications and users can be stored in a database of the GDM core. In this case, each resource provider has to maintain a role-map file to authorize access to its resources.
The CAS records user groups and their permissions on resources and it targets access control for computational and file-based storage resources. In this method, the CAS grants users memberships on VO roles and then authorizes them in those roles. The resource providers need to maintain only the mapping information from VO roles to local database roles.
We can improve the flexibility of our service by using this solution. Ticket Service: in our system, every request for manipulating data in data resources, except for the requests for metadata of data resources, is expressed in a ticket form.
Each ticket represents a specific request sent to the system such as data access, data update, etc. A ticket contains main information such as ticket identification, valid time, identification of ticket booking person, ticket type and the number of times in use. The remaining information will depend on the ticket type.
Information pertaining to this ticket are column names, table name, and data resource name. In this case, it just describes the table name and the data resource name. An application which desires to manipulate on data must carry out two phases: booking ticket and executing ticket. In the first phase, depending on the type of ticket, application will send appropriate information. If the request is accepted, it will receive the ticket identification ticket-id.
In the second phase, to execute a ticket the application needs to send to Ticket Service a token which contains the ticket-id and the encrypted identification of the delegate.Tom Spurgeon. Over a period of three consecutive fattening periods, we evaluated various parameters including air quality indices, environmental and operating parameters, and pig performance.
Most downloaded recipe The functionality descriptions of classes and interfaces depicted in the above class diagram of GDM implementation are listed below in table 1 and table 2.
Weekly Shonen Jump. Besides, we also have to consider the following problems concerning with booking and executing tickets.
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