Content of this work is oriented on mechanical testing of PWB. Deals with standards related to mechanical testing and quality evaluation of PWB. This works is engaged into industry standards, for example IPC or JEDEC. Studies principle and methods of chosen vibration tests. Further aim of this work is vibration fundamentals of PWB assembly. This work describes some research studies, which were conducted in past years and dealt with vibration stress issues. Shows individual factors, which has effect on vibration response of DPS and its destruction, like component position, acceleration difference between component and substrate, around temperature or material constants. In practical part was chosen method of vibration test and experiment in Labview programming interface was performed to verify these findings from vibration theory.

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The aim of the given study is to investigate the impact of the legal as well as the sociocultural systems of Iran on the secondary victimization of rape victims in the country. As a theoretical background of the study, two feminist theories, Feminist criminology and Radical Feminist, will be used. By the help of these theories the influences of patriarchy on secondary victimization of rape victims in Iran will be explained. While case study is used to frame the design of the study, qualitative content analysis will also be used to collect the needed data through analyzing different sources and materials. The findings show that the lack of comprehensive laws regarding rape crime in Iran and the way service providers view and perceive rape victims have led to re-victimization of rape victims. Moreover, analyzing the sociocultural system of the country demonstrated that the prevalence of rape myths and gender inequality in the society have also contributed to re-victimization of such victims. The author believes that establishing new comprehensive laws regarding rape crime and changing the service providers and people’s attitudes regarding rape crime and rape victims through education are the most appropriate solutions for this problem.

Occupational therapy (OT) and applied behavior analysis (ABA) practitioners often collaborate when working with children and young adults with disabilities (McGinnis, 2013). OT and ABA practitioners are primed to collaborate due to many areas of overlap among each respective scope of practice; however, there is limited research to guide best practice for this collaborative partnership (Welch & Polatajko, 2016). According to a review of OT and ABA literature, in addition to the literature of other community-based, social services, and health care professions, there are four major barriers to interprofessional collaboration (IPC): (1) biases, (2) differing cultures, values, and professional languages, (3) overlaps in scopes of practice, and (4) poor communication and relationship-building skills (Kim et al., 2016; Peck & Norman, 1999; Rice et al., 2010). Due to a limited amount of accessible OT and ABA training interventions on collaboration, there is a need for an online, interactive, educational training to present evidence-based and theoretically-sound solutions for the barriers to collaboration. The proposed program is called Supporting Interprofessional Partnerships: An Educational Training for Collaboration Between Occupational Therapy and Applied Behavior Analysis Practitioners. The program’s educational content targets: (1) the definition, benefits, and barriers to IPC, (2) context-based information on ABA’s culture, values, professional language, and scope of practice, and (3) strategies to improve collaboration with ABA providers. Supporting Interprofessional Partnerships explores the working relationship between OT and ABA to improve collaboration as well as client, family, provider, and organizational outcomes.

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The patent domain is a very important source of scientific information that is currently not used to its full potential. Searching for relevant patents is a complex task because the number of existing patents is very high and grows quickly, patent text is extremely complicated, and standard vocabulary is not used consistently or doesn’t even exist. As a consequence, pure keyword searches often fail to return satisfying results in the patent domain. Major companies employ patent professionals who are able to search patents effectively, but even they have to invest a lot of time and effort into their search. Academic scientists on the other hand do not have access to such resources and therefore often do not search patents at all, but they risk missing up-to-date information that will not be published in scientific publications until much later, if it is published at all. Document search on PubMed, the pre-eminent database for biomedical literature, relies on the annotation of its documents with relevant terms from the Medical Subject Headings ontology (MeSH) for improving recall through query expansion. Similarly, professional patent searches expand beyond keywords by including class codes from various patent classification systems. However, classification-based searches can only be performed effectively if the user has very detailed knowledge of the system, which is usually not the case for academic scientists. Consequently, we investigated methods to automatically identify relevant classes that can then be suggested to the user to expand their query. Since every patent is assigned at least one class code, it should be possible for these assignments to be used in a similar way as the MeSH annotations in PubMed. In order to develop a system for this task, it is necessary to have a good understanding of the properties of both classification systems. In order to gain such knowledge, we perform an in-depth comparative analysis of MeSH and the main patent classification system, the International Patent Classification (IPC). We investigate the hierarchical structures as well as the properties of the terms/classes respectively, and we compare the assignment of IPC codes to patents with the annotation of PubMed documents with MeSH terms. Our analysis shows that the hierarchies are structurally similar, but terms and annotations differ significantly. The most important differences concern the considerably higher complexity of the IPC class definitions compared to MeSH terms and the far lower number of class assignments to the average patent compared to the number of MeSH terms assigned to PubMed documents. As a result of these differences, problems are caused both for unexperienced patent searchers and professionals. On the one hand, the complex term system makes it very difficult for members of the former group to find any IPC classes that are relevant for their search task. On the other hand, the low number of IPC classes per patent points to incomplete class assignments by the patent office, therefore limiting the recall of the classification-based searches that are frequently performed by the latter group. We approach these problems from two directions: First, by automatically assigning additional patent classes to make up for the missing assignments, and second, by automatically retrieving relevant keywords and classes that are proposed to the user so they can expand their initial search. For the automated assignment of additional patent classes, we adapt an approach to the patent domain that was successfully used for the assignment of MeSH terms to PubMed abstracts. Each document is assigned a set of IPC classes by a large set of binary Maximum-Entropy classifiers. Our evaluation shows good performance by individual classifiers (precision/recall between 0:84 and 0:90), making the retrieval of additional relevant documents for specific IPC classes feasible. The assignment of additional classes to specific documents is more problematic, since the precision of our classifiers is not high enough to avoid false positives. However, we propose filtering methods that can help solve this problem. For the guided patent search, we demonstrate various methods to expand a user’s initial query. Our methods use both keywords and class codes that the user enters to retrieve additional relevant keywords and classes that are then suggested to the user. These additional query components are extracted from different sources such as patent text, IPC definitions, external vocabularies and co-occurrence data. The suggested expansions can help unexperienced users refine their queries with relevant IPC classes, and professionals can compose their complete query faster and more easily. We also present GoPatents, a patent retrieval prototype that incorporates some of our proposals and makes faceted browsing of a patent corpus possible.

The patent domain is a very important source of scientific information that is currently not used to its full potential. Searching for relevant patents is a complex task because the number of existing patents is very high and grows quickly, patent text is extremely complicated, and standard vocabulary is not used consistently or doesn’t even exist. As a consequence, pure keyword searches often fail to return satisfying results in the patent domain. Major companies employ patent professionals who are able to search patents effectively, but even they have to invest a lot of time and effort into their search. Academic scientists on the other hand do not have access to such resources and therefore often do not search patents at all, but they risk missing up-to-date information that will not be published in scientific publications until much later, if it is published at all. Document search on PubMed, the pre-eminent database for biomedical literature, relies on the annotation of its documents with relevant terms from the Medical Subject Headings ontology (MeSH) for improving recall through query expansion. Similarly, professional patent searches expand beyond keywords by including class codes from various patent classification systems. However, classification-based searches can only be performed effectively if the user has very detailed knowledge of the system, which is usually not the case for academic scientists. Consequently, we investigated methods to automatically identify relevant classes that can then be suggested to the user to expand their query. Since every patent is assigned at least one class code, it should be possible for these assignments to be used in a similar way as the MeSH annotations in PubMed. In order to develop a system for this task, it is necessary to have a good understanding of the properties of both classification systems. In order to gain such knowledge, we perform an in-depth comparative analysis of MeSH and the main patent classification system, the International Patent Classification (IPC). We investigate the hierarchical structures as well as the properties of the terms/classes respectively, and we compare the assignment of IPC codes to patents with the annotation of PubMed documents with MeSH terms. Our analysis shows that the hierarchies are structurally similar, but terms and annotations differ significantly. The most important differences concern the considerably higher complexity of the IPC class definitions compared to MeSH terms and the far lower number of class assignments to the average patent compared to the number of MeSH terms assigned to PubMed documents. As a result of these differences, problems are caused both for unexperienced patent searchers and professionals. On the one hand, the complex term system makes it very difficult for members of the former group to find any IPC classes that are relevant for their search task. On the other hand, the low number of IPC classes per patent points to incomplete class assignments by the patent office, therefore limiting the recall of the classification-based searches that are frequently performed by the latter group. We approach these problems from two directions: First, by automatically assigning additional patent classes to make up for the missing assignments, and second, by automatically retrieving relevant keywords and classes that are proposed to the user so they can expand their initial search. For the automated assignment of additional patent classes, we adapt an approach to the patent domain that was successfully used for the assignment of MeSH terms to PubMed abstracts. Each document is assigned a set of IPC classes by a large set of binary Maximum-Entropy classifiers. Our evaluation shows good performance by individual classifiers (precision/recall between 0:84 and 0:90), making the retrieval of additional relevant documents for specific IPC classes feasible. The assignment of additional classes to specific documents is more problematic, since the precision of our classifiers is not high enough to avoid false positives. However, we propose filtering methods that can help solve this problem. For the guided patent search, we demonstrate various methods to expand a user’s initial query. Our methods use both keywords and class codes that the user enters to retrieve additional relevant keywords and classes that are then suggested to the user. These additional query components are extracted from different sources such as patent text, IPC definitions, external vocabularies and co-occurrence data. The suggested expansions can help unexperienced users refine their queries with relevant IPC classes, and professionals can compose their complete query faster and more easily. We also present GoPatents, a patent retrieval prototype that incorporates some of our proposals and makes faceted browsing of a patent corpus possible.

Enterprise Service Bus (ESB) est actuellement l'approche la plus prometteuse pour l'implémentation d'une architecture orientée services (SOA : Service-Oriented Architecture) par l'intégration des différentes applications isolées dans une plateforme centralisée. De nombreuses solutions d'intégration à base d'ESB on été proposées, elles sont soit open-source comme : Mule, Petals, ou encore Fuse, soit propriétaires tels que : Sonic ESB, IBM WebSphere Message Broker, ou Oracle ESB. Cependant, il n'en existe aucune en mesure de traiter, à la fois des aspects : d'intégration et de traitement massivement parallèle, du moins à notre connaissance. L'intégration du parallélisme dans le traitement est un moyen de tirer profit des technologies multicœurs/multiprocesseurs qui améliorent considérablement les performances des ESBs.Toutefois, cette intégration est une démarche complexe et soulève des problèmes à plusieurs niveaux : communication, synchronisation, partage de données, etc.Dans cette thèse, nous présentons l'étude d'une nouvelle architecture massivement parallèle de type ESB. / Enterprise service bus (ESB) is currently the most promising approach for business application integration in distributed and heterogeneous environments. It allows to deploy a service-oriented architecture (SOA) by the integration of all the isolated applications on a decentralized platform.Several commercial or open source ESB-based solutions have been proposed. However, to the best of our knowledge, none of these solutions has integrated the parallel processing. The integration of parallelism in the treatment allows to take advantage of the multicore/multiprocessor technologies and thus can improve greatly the ESB performance. However, this integration is difficult to achieve, and poses problems at multiple levels (communication, synchronization, etc). In this study, we present a new massively parallel ESB architecture that meets this challenge.