From 80a657fcd8bbefa9dc98e2c9285e25e48bb883d2 Mon Sep 17 00:00:00 2001 From: "Dr. Christoph \"Schorsch\" Jung" Date: Fri, 3 May 2024 20:06:02 +0200 Subject: [PATCH] fix: linting errors in ka kit --- .../knowledge-agents/adoption-view/intro.md | 1 + .../development-view/Arc42.md | 1 - .../development-view/architecture.md | 20 ++-- .../development-view/modelling.md | 113 +++++++++--------- .../knowledge-agents/modelling_federated.jpg | Bin 0 -> 7896054 bytes 5 files changed, 65 insertions(+), 70 deletions(-) create mode 100644 static/img/knowledge-agents/modelling_federated.jpg diff --git a/docs-kits/kits/knowledge-agents/adoption-view/intro.md b/docs-kits/kits/knowledge-agents/adoption-view/intro.md index 5db145affb0..89cddfef1b0 100644 --- a/docs-kits/kits/knowledge-agents/adoption-view/intro.md +++ b/docs-kits/kits/knowledge-agents/adoption-view/intro.md @@ -66,6 +66,7 @@ Knowledge agents introduce an ecosystem of efficient services (for data handling We also define bridges to other digital twin approaches, such as AAS (Asset Administration Shell), such that data and service provisioning into multiple use cases will be as effortless as possible. Our Implementations can be accessed as [Eclipse Tractus-X Repositories](https://github.com/eclipse-tractusx): + * [Agent Plane(s) for EDC (KA-EDC)](https://github.com/eclipse-tractusx/knowledge-agents-edc) see also [KA-EDC Operation View](../operation-view/agent_edc) * [Binding Agent(s) for Providers (KA-RI)](https://github.com/eclipse-tractusx/knowledge-agents) see also [KA-RI Operation View](../operation-view/provider) * [Agent Bridges for AAS (KA-AAS)](https://github.com/eclipse-tractusx/knowledge-agents-aas-bridge) see also [KA-AAS Operation View](../operation-view/bridge) diff --git a/docs-kits/kits/knowledge-agents/development-view/Arc42.md b/docs-kits/kits/knowledge-agents/development-view/Arc42.md index 638214e4b8e..12b00160701 100644 --- a/docs-kits/kits/knowledge-agents/development-view/Arc42.md +++ b/docs-kits/kits/knowledge-agents/development-view/Arc42.md @@ -30,7 +30,6 @@ title: Detailed Architecture ![Agents Kit Banner](/img/knowledge-agents/AgentsKit-Icon.png) - **Arc42 for federated queries over the whole data space.** For more information see diff --git a/docs-kits/kits/knowledge-agents/development-view/architecture.md b/docs-kits/kits/knowledge-agents/development-view/architecture.md index 15f74c1ff16..3c43250fec1 100644 --- a/docs-kits/kits/knowledge-agents/development-view/architecture.md +++ b/docs-kits/kits/knowledge-agents/development-view/architecture.md @@ -44,9 +44,9 @@ For more information see ## Compute-To-Data -We describe a semantically-driven and state-of-the-art compute-to-data architecture for automotive use cases (and beyond), the so-called Knowledge Agents (KA) approach. +We describe a semantically-driven and state-of-the-art compute-to-data architecture for automotive use cases (and beyond), the so-called Knowledge Agents (KA) approach. -It builds on well-established standards and best practices around [GAIA-X](https://gaia-x.eu), [W3C](https://www.w3.org/) and [Big Data](https://en.wikipedia.org/wiki/Big_data) in order to empower powerful queries to the data space (Skills). Those Skills can be used to answer business questions directly (comparable to a search engine) or they can be embedded in apps to include query results (Knowledge) into workflows with more advanced visualization etc. +It builds on well-established standards and best practices around [GAIA-X](https://gaia-x.eu), [W3C](https://www.w3.org/) and [Big Data](https://en.wikipedia.org/wiki/Big_data) in order to empower powerful queries to the data space (Skills). Those Skills can be used to answer business questions directly (comparable to a search engine) or they can be embedded in apps to include query results (Knowledge) into workflows with more advanced visualization etc. To reach this aim, full semantic integration, search and query with focus on relations between entities and data sovereignty is focused. In contrast to a simple file-based data transfer, this shifts the responsibility for the @@ -64,7 +64,7 @@ The App in the figure serves the consumer by gathering, analyzing, and presentin ### Skill -To help collecting the data over the network, Skills are introduced. A Skill is a pre-formulated query (or: procedure) with limited scope such as: List all vehicle series that contain ``material`` produced in ``location``. The Skill is used to access all federated data instances via the tenant (=authentication and authorization scope) of the caller. +To help collecting the data over the network, Skills are introduced. A Skill is a pre-formulated query (or: procedure) with limited scope such as: List all vehicle series that contain ``material`` produced in ``location``. The Skill is used to access all federated data instances via the tenant (=authentication and authorization scope) of the caller. A skill receives input in the form of a data set (we use a JSON notation in the following example): @@ -85,7 +85,7 @@ which drives the control flow, the filtering and aggregating of the information In order to obtain the correct results in a federated system, all the participants of the skill execution need to have common understanding (Semantic Model, in KA this is mechanized by a Federated Catalogue based on the Web Ontology Language OWL) over the vocabulary (Data Model, in KA this is represented generically by sets, i.e., graphs of Resource Description Framework RDF triples). A guideline for how this vocabulary should be formulated can be found [here](modelling). -Relying on these conventions, a Skill executor can calculate which Providers are able to contribute or yield the necessary information in which sequence such that the resulting distributed operation will be performant. +Relying on these conventions, a Skill executor can calculate which Providers are able to contribute or yield the necessary information in which sequence such that the resulting distributed operation will be performant. ### Matchmaking Agent @@ -93,7 +93,7 @@ This coordinating job is taken over by the Matchmaking Agent, an endpoint that i In turn, upon successful transfer of the Sub-Skill, the Provider-Side Matchmaking Agent(s) will be activated by their respective EDC. The precondition for this activation is of course that the Provider EDC first needs to offer a so-called Graph Asset: -### Graph Assets +### Graph Assets are a variant of ordinary Data Assets in the Catena-X EDC Standard; while Data Assets typically refer to an actual backend system (e.g., an Blob in an Object Store, an AAS server, a REST endpoint), Graph Assets introduce another intermediary instance, the so-called Binding Agent. @@ -103,11 +103,11 @@ In turn, upon successful transfer of the Sub-Skill, the Provider-Side Matchmakin Simply put, the **Binding Agent** is a restricted version of the Matchmaking Agent (which speaks a profile, i.e., a subset of SPARQL specification, see the ANNEX) which is just focused on translating Sub-Skills of a particular business domain (Bill-Of-Material, Chemical Materials, Production Sites, etc.) into proper SQL- or REST based backend system calls. This scheme has several advantages: -- For different types of backend systems, business domains and usage scenarios, different Binding Agent implementations (Caching Graph Store, SQL Binding Engine, REST Binding Engine) can be switched-in without affecting both the shared dataspace/semantic model and the mostly immutable backend systems/data models as well. -- Access to the backend systems can be optimized by JIT compilation technology. -- The same backend system/data model can be used in various Graph Assets/Use Cases and different roles and policies. -- Access to the backend system is decoupled by another layer of security, such that additional types of policies (role-based row-level and attribute-level access) can be implemented in the interplay of Matchmaking and Binding Agents. -- There is a clear distinction between advanced graph operations (including type inference and transitive/recursive traversal also via EDC) on the Matchmaking Level and efficient, but more restricted and secure graph operations on the Binding/Data Level. +* For different types of backend systems, business domains and usage scenarios, different Binding Agent implementations (Caching Graph Store, SQL Binding Engine, REST Binding Engine) can be switched-in without affecting both the shared dataspace/semantic model and the mostly immutable backend systems/data models as well. +* Access to the backend systems can be optimized by JIT compilation technology. +* The same backend system/data model can be used in various Graph Assets/Use Cases and different roles and policies. +* Access to the backend system is decoupled by another layer of security, such that additional types of policies (role-based row-level and attribute-level access) can be implemented in the interplay of Matchmaking and Binding Agents. +* There is a clear distinction between advanced graph operations (including type inference and transitive/recursive traversal also via EDC) on the Matchmaking Level and efficient, but more restricted and secure graph operations on the Binding/Data Level. ### Federated Catalogue diff --git a/docs-kits/kits/knowledge-agents/development-view/modelling.md b/docs-kits/kits/knowledge-agents/development-view/modelling.md index c81300b2902..528417b1a08 100644 --- a/docs-kits/kits/knowledge-agents/development-view/modelling.md +++ b/docs-kits/kits/knowledge-agents/development-view/modelling.md @@ -54,17 +54,17 @@ This document gives fundamental guidelines to create ontology models for being u ## Semantics & Ontology -Semantics is the art and science of understanding what data means. In the context of Catena-X this topic has a significant importance in the sense that data provider and consumer need to have the same understanding what data needs to be shared (see also [FAIR data](https://en.wikipedia.org/wiki/FAIR_data)). +Semantics is the art and science of understanding what data means. In the context of Catena-X this topic has a significant importance in the sense that data provider and consumer need to have the same understanding what data needs to be shared (see also [FAIR data](https://en.wikipedia.org/wiki/FAIR_data)). -In the knowledge agent approach, ontologies are used to describe the semantics of data. Ontologies are similar to data models or data schemas in which the semantics and structure of data are described. In addition, ontologies provide a high degree of semantic expressiveness, e.g. by supporting inference on data like "p-123" is_a part & part is_a physical object => "p-123" is_a physical object. Such inference enables strong typing of data. This way a machine, an hence the dataspace participants, can understand the meaning of the vocabulary 'part', that it is a physical object and not a document part or a design model part. +In the knowledge agent approach, ontologies are used to describe the semantics of data. Ontologies are similar to data models or data schemas in which the semantics and structure of data are described. In addition, ontologies provide a high degree of semantic expressiveness, e.g. by supporting inference on data like "p-123" is_a part & part is_a physical object => "p-123" is_a physical object. Such inference enables strong typing of data. This way a machine, an hence the dataspace participants, can understand the meaning of the vocabulary 'part', that it is a physical object and not a document part or a design model part. -It could be that similar vocabularies are used for different things in the business partners sovereign data models. Hence the formal semantics of ontologies prevents such misunderstandings in data caused by synonymity or homonymity, etc. and lead to better collaboration and interoperability among data space participants. +It could be that similar vocabularies are used for different things in the business partners sovereign data models. Hence the formal semantics of ontologies prevents such misunderstandings in data caused by synonymity or homonymity, etc. and lead to better collaboration and interoperability among data space participants. On the other hand, the ontology-based approach represents the underlying data in assets as a generlized graph structure. This allows flexible querying of typed and linked data - no matter of its physical storage and retrieval. In applying the ontology-based data modelling in Catena-X, the following fundamentals should be achieved: -- Maximise Semantics -- Reduce Complexity -- Minimise Redundancy +* Maximise Semantics +* Reduce Complexity +* Minimise Redundancy ### Accessing Catena-X Ontology and Taxonomy @@ -73,35 +73,34 @@ On the other hand, the ontology-based approach represents the underlying data in * The current Catena-X Ontology can be found under the namespace [https://w3id.org/catenax/ontology](https://w3id.org/catenax/ontology) * The current Catena-X Taxonomy can be found under the namespace [https://w3id.org/catenax/taxonomy](https://w3id.org/catenax/taxonomy) * Older versions of ontologies and taxonomies can also be referenced: - - [https://w3id.org/catenax/v24.03/ontology](https://w3id.org/catenax/v24.03/ontology) - - [https://w3id.org/catenax/v24.03/taxonomy](https://w3id.org/catenax/v24.03/taxonomy) - - [https://w3id.org/catenax/v23.09/ontology](https://w3id.org/catenax/v23.09/ontology) - - [https://w3id.org/catenax/v23.09/taxonomy](https://w3id.org/catenax/v23.09/taxonomy) + * [https://w3id.org/catenax/v24.03/ontology](https://w3id.org/catenax/v24.03/ontology) + * [https://w3id.org/catenax/v24.03/taxonomy](https://w3id.org/catenax/v24.03/taxonomy) + * [https://w3id.org/catenax/v23.09/ontology](https://w3id.org/catenax/v23.09/ontology) + * [https://w3id.org/catenax/v23.09/taxonomy](https://w3id.org/catenax/v23.09/taxonomy) * Upcoming versions of ontologies and taxonomies can also be referenced: - - [https://w3id.org/catenax/next/ontology](https://w3id.org/catenax/next/ontology) - - [https://w3id.org/catenax/next/taxonomy](https://w3id.org/catenax/next/taxonomy) - + * [https://w3id.org/catenax/next/ontology](https://w3id.org/catenax/next/ontology) + * [https://w3id.org/catenax/next/taxonomy](https://w3id.org/catenax/next/taxonomy) ## Concept -Th concept of ontology-based data modelling of the knowledge agent approach is based on the following building blocks: +The concept of ontology-based data modelling of the knowledge agent approach is based on the following building blocks: -- [OWL 2 QL Profile](https://www.w3.org/TR/owl2-profiles/#OWL_2_QL) for modelling ontologies, -- [OWL 2 RL Profile](https://www.w3.org/TR/owl2-profiles/#OWL_2_QL) for modelling common dataspace assets, -- [SKOS](https://www.w3.org/2004/02/skos/) for vocabularies, -- [RML](https://rml.io/specs/rml/) & [R2RML](https://www.w3.org/TR/2012/REC-r2rml-20120927/) for mapping data source model to asset model to provide data in an asset, -- [SHACL](https://www.w3.org/TR/shacl/) for data quality check and for describing which data is available in the asset and in which form, -- [RDF](https://www.w3.org/TR/rdf12-concepts/) for description and exchange of graph data, -- [Turtle](https://www.w3.org/TR/turtle/) for persisting, -- [SPARQL](https://www.w3.org/TR/sparql11-query/) profiles for transferring logic and querying catalogues, data and functions. +* [OWL 2 QL Profile](https://www.w3.org/TR/owl2-profiles/#OWL_2_QL) for modelling ontologies, +* [OWL 2 RL Profile](https://www.w3.org/TR/owl2-profiles/#OWL_2_QL) for modelling common dataspace assets, +* [SKOS](https://www.w3.org/2004/02/skos/) for vocabularies, +* [RML](https://rml.io/specs/rml/) & [R2RML](https://www.w3.org/TR/2012/REC-r2rml-20120927/) for mapping data source model to asset model to provide data in an asset, +* [SHACL](https://www.w3.org/TR/shacl/) for data quality check and for describing which data is available in the asset and in which form, +* [RDF](https://www.w3.org/TR/rdf12-concepts/) for description and exchange of graph data, +* [Turtle](https://www.w3.org/TR/turtle/) for persisting, +* [SPARQL](https://www.w3.org/TR/sparql11-query/) profiles for transferring logic and querying catalogues, data and functions. In the Knowledge Agent approach, the data is modelled with the Semantic Web Ontology language [OWL](https://www.w3.org/TR/2012/REC-owl2-primer-20121211/). This enables object-oriented modelling of domains with classes, attributes and relationships. Object-oriented modelling allows the modelling of a domain from general to specific by creating subclasses. The first step is to define the main classes that describe the domain. In the case of Catena-X, five main classes were defined. -- Activity: This class comprises actions that are intentionally performed by instances of the actor over the course of the product life cycle and result in state changes in physical and conceptual objects. -- Actor: This class comprises organization, device or people, either individually or in groups, who have the potential to perform intentional actions of kinds for which someone may be held responsible. -- Physical object: This class includes objects of a material nature, which are documentation units and have physical boundaries. -- Conceptual object: This class includes non-material products, human-produced data related to physical objects. The production of such information may have been supported by the use of technical tools. -- Place: The class Place is determined by reference to the position of objects such as buildings, cities, or special geographic markers. +* Activity: This class comprises actions that are intentionally performed by instances of the actor over the course of the product life cycle and result in state changes in physical and conceptual objects. +* Actor: This class comprises organization, device or people, either individually or in groups, who have the potential to perform intentional actions of kinds for which someone may be held responsible. +* Physical object: This class includes objects of a material nature, which are documentation units and have physical boundaries. +* Conceptual object: This class includes non-material products, human-produced data related to physical objects. The production of such information may have been supported by the use of technical tools. +* Place: The class Place is determined by reference to the position of objects such as buildings, cities, or special geographic markers. [![Modelling Concept](/img/knowledge-agents/modelling_concept.jpg)](/img/knowledge-agents/modelling_concept.jpg) @@ -109,14 +108,14 @@ In the Knowledge Agent approach, the data is modelled with the Semantic Web Onto A well-known approach is the activity-centered or event-based modelling. The [CIDOC CRM](https://www.cidoc-crm.org/) ontology provides this idea and the main classes based on this concept. In this approach the classes actor, place, physical object and conceptual object are connected through activities. Instead of assigning all information to the physical object. This distributed representation has several advantages: -- The life cycles of products and documents are represented separately and clearly. -- The activities have a start and end date so that the data can be queried chronologically. -- The data can be viewed and queried from different perspectives. - - Identifies all actors (e.g. companies) involved in an activity (e.g. manufacturing). - - Identifies all activities performed on a physical object (vehicle). - - Finds all activities that have been performed at a place. - - Searches for all physical objects (e.g. material) used in an activity (e.g. manufacturing). - - Search for conceptual objects related to a physical object in an activity. +* The life cycles of products and documents are represented separately and clearly. +* The activities have a start and end date so that the data can be queried chronologically. +* The data can be viewed and queried from different perspectives. + * Identifies all actors (e.g. companies) involved in an activity (e.g. manufacturing). + * Identifies all activities performed on a physical object (vehicle). + * Finds all activities that have been performed at a place. + * Searches for all physical objects (e.g. material) used in an activity (e.g. manufacturing). + * Search for conceptual objects related to a physical object in an activity. [![Activity-Centered Model](/img/knowledge-agents/modelling_activity.jpg)](/img/knowledge-agents/modelling_activity.jpg) @@ -126,16 +125,16 @@ The concept described above is the basic modelling pattern in Knowledge Agent. W Benefits of the basic modelling pattern: -- Easy for domain experts to understand and create new domain ontologies. -- Data is represented in datasets using the same pattern. -- Data can be queried with general queries such as 'Get All Actors' or specific queries such as 'Get Manufacturer'. +* Easy for domain experts to understand and create new domain ontologies. +* Data is represented in datasets using the same pattern. +* Data can be queried with general queries such as 'Get All Actors' or specific queries such as 'Get Manufacturer'. [![Modelling Domains](/img/knowledge-agents/modelling_domain.jpg)](/img/knowledge-agents/modelling_domain.jpg) * The current (merged) Catena-X Ontology can be found under the namespace [https://w3id.org/catenax/ontology](https://w3id.org/catenax/ontology) -* The current Catena-X Core Ontology can be found under the namespace [https://w3id.org/catenax/ontology/core](https://w3id.org/catenax/ontology/core) +* The current Catena-X Core Ontology can be found under the namespace [https://w3id.org/catenax/ontology/core](https://w3id.org/catenax/ontology/core) * The current Catena-X Common Ontology (caring about dataspace entities and catalogues) can be found under the namespace [https://w3id.org/catenax/ontology/common](https://w3id.org/catenax/ontology/common) -* For more domain ontologies, see the complete namespace [https://w3id.org/catenax](https://w3id.org/catenax) +* For more domain ontologies, see the complete namespace [https://w3id.org/catenax](https://w3id.org/catenax) ## Data Serialization as a Graph @@ -147,7 +146,7 @@ The RDF graphs are stored in [RDF databases](https://www.w3.org/wiki/LargeTriple Serialization of data based on Turtle syntax: -``` +```ttl @prefix rdf: . @prefix rdfs: . @prefix owl: . @@ -180,9 +179,9 @@ Although using a different representation and API, this concept is similar to th ## Taxonomy -A taxonomy is a collection of terms and their meaning. The terms are structured in a hierarchical form, whereby the relationships become broader and narrower. Taxonomies can be seen as a dictionary that ensures the correct use of terms. Ontologies use the terms to describe a domain. Domain ontologies can grow very quickly and become confusing. Therefore, it is not recommended to define classes for all types of a domain in an ontology. Instead of defining new subclasses, the instances can be typed using terms from the taxonomy. For this purpose, a Catena-X taxonomy is created with SKOS, which can be extended based on the classes of the core ontology. For example, the class Vehicle in the Vehicle Ontology describes vehicles. A small car is a special type of vehicle for which there it is not necessary to create a separate class. Instead, the term can be included in the taxonomy and the instances can be specified from there. +A taxonomy is a collection of terms and their meaning. The terms are structured in a hierarchical form, whereby the relationships become broader and narrower. Taxonomies can be seen as a dictionary that ensures the correct use of terms. Ontologies use the terms to describe a domain. Domain ontologies can grow very quickly and become confusing. Therefore, it is not recommended to define classes for all types of a domain in an ontology. Instead of defining new subclasses, the instances can be typed using terms from the taxonomy. For this purpose, a Catena-X taxonomy is created with SKOS, which can be extended based on the classes of the core ontology. For example, the class Vehicle in the Vehicle Ontology describes vehicles. A small car is a special type of vehicle for which there it is not necessary to create a separate class. Instead, the term can be included in the taxonomy and the instances can be specified from there. -``` +```ttl @prefix dct: . @prefix rdf: . @@ -223,12 +222,11 @@ The current Catena-X Asset Ontology (caring about dataspace terms) can be found For more taxonomies, see the complete namespace [https://w3id.org/catenax](https://w3id.org/catenax) - ## Asset Content Description The Common Ontology contains classes and properties to describe assets on a meta-level. The asset class describes the URL from which the SPRAQL endpoint can be reached, which ontology was used to describe the data, what form the data has based on SHACL, which business partner provides the asset, and so on. The descriptions are helpful for ordinary users and necessary for application configuration. -``` +```ttl @prefix rdf: . @prefix rdfs: . @@ -270,7 +268,7 @@ exp:VehicleShape a sh:NodeShape ; In the Knowledge Agent approach, in addition to publishing static data, it is also possible to offer services that perform calculations. These services can be called as functions with parameters. The functions can be modelled using the ontology function. The function parameters are defined by the cx-fx:argument. The ontology also contains function configuration properties that are required to configure the service. -``` +```ttl @prefix rdf: . @prefix rdfs: . @@ -296,7 +294,7 @@ exp:ModelYear rdf:type cx-fx:Result; Functions are defined in a similar way to data in SHACL. In addition, the **cx-sh:hasAsArgument** property can be used to define which data property is required for a function argument to execute the function. -``` +```ttl # Function Ontology @@ -319,7 +317,7 @@ The RDF graphs can be accessed using the SPARQL query language via a SPARQL HTTP General query example: Returns all physical objects and their name and activities. -``` +```sparql PREFIX cx: PREFIX rdf: PREFIX exp: @@ -377,7 +375,7 @@ The same result (for mime-type "application/sparql-results+json"): Specific query example: Returns all vehicles and their name and manufacturing activities. -``` +```sparql PREFIX cx: PREFIX rdf: @@ -407,15 +405,11 @@ Query result is the same as the first one: An important advantage of SPARQL is that multiple repositories can be accessed from a single query. This feature gives Catena-X the great advantage of being able to query distributed data in different assets at the same time. -
-
- image -
-
+[![Federated Query Example](/img/knowledge-agents/modelling_federated.jpg)](/img/knowledge-agents/modelling_federated.jpg) Federated query example: Returns all vehicle names from OEM Asset and for the same vehicles the names of the diagnosis results from Service Asset. -``` +```sparql PREFIX cx: PREFIX rdf: @@ -455,10 +449,11 @@ Federated Query result (for mime-type "text/csv"): ## Skill -A Skill in the Knowledge Agent approach is any Data/Federated Query which -- is parameterizable by a set of input variables -- is published in the dataspace as a contractible asset with a unique id -- maybe invoked either on consumer-side (by downloading the query test) or provider-side (by execution of the query) +A Skill in the Knowledge Agent approach is any Data/Federated Query which + +* is parameterizable by a set of input variables +* is published in the dataspace as a contractible asset with a unique id +* maybe invoked either on consumer-side (by downloading the query test) or provider-side (by execution of the query) For example, above federated query could be invoked under the assetname `SkillAsset?supplier=ListDiagnosis` and the following input parameter set (using contenttype "application/sparql-results+json") diff --git a/static/img/knowledge-agents/modelling_federated.jpg b/static/img/knowledge-agents/modelling_federated.jpg new file mode 100644 index 0000000000000000000000000000000000000000..dfda9de70616b356fbcd13a31c9c45e50f71b1aa GIT binary patch literal 7896054 zcmeF42bdhixvotPCJ5|929r%VwlQFWP0l76``Fh8d;nj-Hej&9U;{!RfyhYYoP-Dh z0RkitNGMCIyvjLC2#JhI4w!T9{bp*qrn)*z&-TvltoEI!G|%j)rl;rK>Dv1L?+-_u z^!JXJf30xaPycq)zvI^b+SlG$<7;1A?Q2^Up8ndf(*GBKDBb-(ZV-R~1Rwwb2tWV= z5P$##AOHafKmY;|fB*y_009U<00Izz00bZa0SG_<0uX=z1Rwwb2tWV=5P$##AOHaf zKmY;|fB*y_009U<00Izz00bZa0SG_<0uX=z1Rwwb2tWV=5P$##AOHafKmY;|fB*y_ z009U<00Izz00bZa0SG_<0uX=z1Rwwb2tWV=5P$##AOHafKmY;|fB*y_009U<00Izz z00bZa0SG_<0uX=z1Rwwb2tWV=5P$##AOHafKmY;|fB*y_009U<00Izz00bZa0SG_< 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