Investigation of wind damage in the metropolitan Washington, D.C. area, April 3-4, 1975 /

Mode of access: Internet.

Investigation of the Skyline Plaza collapse in Fairfax County, Virginia /

Includes bibliographical references.

Abnormal loading on buildings and progressive collapse : an annotated bibliography /

Includes indexes.

Design methods for reducing the risk of progressive collapse in buildings /

Errata sheet at end.

After the flood

Aftermath of the Great Flood of 1908 in Albion, Mich.

Building a microscope for the data center

Managing the physical and compute infrastructure of a large data center is an embodiment of a Cyber-Physical System (CPS). The physical parameters of the data center (such as power, temperature, pressure, humidity) are tightly coupled with computations, even more so in upcoming data centers, where the location of workloads can vary substantially due, for example, to workloads being moved in a cloud infrastructure hosted in the data center. In this paper, we describe a data collection and distribution architecture that enables gathering physical parameters of a large data center at a very high temporal and spatial resolutionof the sensor measurements. We think this is an important characteristic to enable more accurate heat-flow models of the data center andwith them, _and opportunities to optimize energy consumption. Havinga high resolution picture of the data center conditions, also enables minimizing local hotspots, perform more accurate predictive maintenance (pending failures in cooling and other infrastructure equipment can be more promptly detected) and more accurate billing. We detail this architecture and define the structure of the underlying messaging system that is used to collect and distribute the data. Finally, we show the results of a preliminary study of a typical data center radio environment.

Building a Culture of Patient Safety - Report of the Commission on Patient Safety and Quality Assurance

The Commission on Patient Safety and Quality Assurance was established in January 2007 and reported to the Minister in July 2008. The report was considered by government in January 2009 which agreed the implementation process. The overall objective of the Commission was to develop clear and practical recommendations to ensure that safety and quality of care for patients is paramount within the healthcare system. The Commission’s report set out a wide range of policy measures that will drive the safety and quality agenda in Irish healthcare in the coming years. The establishment of the Commission was prompted by an increasing awareness of patient safety issues in general and high profile health service system failures at home and abroad and in particular by the Lourdes Hospital Inquiry. These have underlined the need for an increased focus on patient safety and quality. Download document here Download summary document on the Report

Nation building in Bosnia and Herzegovina: Cooperation, coordination and collaboration

Cross-sectoral interorganizational relationships in post-conflict situations occur regularly. Whether formal task forces, advisory groups or other ad hoc arrangements, these relations take place in chaotic and dangerous situations with urgent and turbulent political, economic and social environments. Furthermore, they typically involve a large number of players from many different nations, operating across sectors, and between multiple layers of bureaucracy and diplomacy. The organizational complexity staggers many participants and observers, as do the tasks they are charged with completing. Reform efforts in Bosnia and Herzegovina starting in 1995 may serve as the archetype model of conflict, transition and development for the 21st century. It wins this honor due not to its particular programmatic successes and failures, rather to the interorganizational complexity of the International Community. From the massive response to the crisis, to the modern nation-building policies it spawned, and the development assistance practices and institutional arrangements it created, the Bosnian development experience has much to offer by way of lessons learned. This manuscript frames the unique Bosnian development situation, and provides lessons learned from the experience of nation building given local realities. Pettigrew (1992) called this "contextualizing." While network and/or organizational structure, strategy and process explain many interorganizational relationship issues, the development variables identified in this manuscript prove equally important, yet elusive and difficult to measure despite their very real and overt presence.

Proceso metodológico para optimizar la toma de decisiones en el proyecto de envolventes arquitectónicas multicapa = Methodological process for the optimization of the decision making in multilayer building envelope projects

La presente tesis doctoral se enmarca dentro del concepto de la sistematización del conocimiento en arquitectura, más concretamente en el campo de las construcciones arquitectónicas y la toma de decisiones en la fase de proyecto de envolventes arquitectónicas multicapa. Por tanto, el objetivo principal es el establecimiento de las bases para una toma de decisiones informadas durante el proyecto de una envolvente multicapa con el fin de colaborar en su optimización. Del mismo modo que la historia de la arquitectura está relacionada con la historia de la innovación en construcción, la construcción está sujeta a cambios como respuesta a los fracasos anteriores. En base a esto, se identifica la toma de decisiones en la fase de proyecto como el estadio inicial para establecer un punto estratégico de reflexión y de control sobre los procesos constructivos. La presente investigación, conceptualmente, define los parámetros intervinientes en el proyecto de envolventes arquitectónicas multicapa a partir de una clasificación y sistematización de todos los componentes (elementos, unidades y sistemas constructivos) utilizados en las fachadas multicapa. Dicha sistematización se materializa en una hoja matriz de datos en la que, dentro de una organización a modo de árbol, se puede acceder a la consulta de cada componente y de su caracterización. Dicha matriz permite la incorporación futura de cualquier componente o sistema nuevo que aparezca en el mercado, relacionándolo con aquellos con los que comparta ubicación, tipo de material, etc. Con base en esa matriz de datos, se diseña la sistematización de la toma de decisiones en la fase de proyecto de una envolvente arquitectónica, en concreto, en el caso de una fachada. Operativamente, el resultado se presenta como una herramienta que permite al arquitecto o proyectista reflexionar y seleccionar el sistema constructivo más adecuado, al enfrentarse con las distintas decisiones o elecciones posibles. La herramienta se basa en las elecciones iniciales tomadas por el proyectista y se estructura, a continuación y sucesivamente, en distintas aproximaciones, criterios, subcriterios y posibilidades que responden a los distintos avances en la definición del sistema constructivo. Se proponen una serie de fichas operativas de comprobación que informan sobre el estadio de decisión y de definición de proyecto alcanzados en cada caso. Asimismo, el sistema permite la conexión con otros sistemas de revisión de proyectos para fomentar la reflexión sobre la normalización de los riesgos asociados tanto al proprio sistema como a su proceso constructivo y comportamiento futuros. La herramienta proporciona un sistema de ayuda para ser utilizado en el proceso de toma de decisiones en la fase de diseño de una fachada multicapa, minimizando la arbitrariedad y ofreciendo una cualificación previa a la cuantificación que supondrá la elaboración del detalle constructivo y de su medición en las sucesivas fases del proyecto. Al mismo tiempo, la sistematización de dicha toma de decisiones en la fase del proyecto puede constituirse como un sistema de comprobación en las diferentes fases del proceso de decisión proyectual y de definición de la envolvente de un edificio. ABSTRACT The central issue of this doctoral Thesis is founded on the framework of the concept of the systematization of knowledge in architecture, in particular, in respect of the field of building construction and the decision making in the design stage of multilayer building envelope projects. Therefore, the main objective is to establish the bases for knowledgeable decision making during a multilayer building envelope design process, in order to collaborate with its optimization. Just as the history of architecture is connected to the history of innovation in construction, construction itself is subject to changes as a response to previous failures. On this basis, the decisions made during the project design phase are identified as the initial state to establish an strategic point for reflection and control, referred to the constructive processes. Conceptually, this research defines the parameters involving the multilayer building envelope projects, on the basis of a classification and systematization for all the components (elements, constructive units and constructive systems) used in multilayer façades. The mentioned systematization is materialized into a data matrix sheet in which, following a tree‐like organization, the access to every single component and its characterization is possible. The above data matrix allows the future inclusion of any new component or system that may appear in the construction market. That new component or system can be put into a relationship with another, which it shares location, type of material,… with. Based on the data matrix, the systematization of the decision making process for a building envelope design stage is designed, more particularly in the case of a façade. Putting this into practice, it is represented as a tool which allows the architect or the designer, to reflect and to select the appropriate building system when facing the different elections or the different options. The tool is based on the initial elections taken by the designer. Then and successively, it is shaped on the form of different operative steps, criteria, sub‐criteria and possibilities which respond to a different progress in the definition of the building construction system. In order to inform about the stage of the decision and the definition reached by the project in every particular case, a range of operative sheets are proposed. Additionally, the system allows the connection with other reviewing methods for building projects. The aim of this last possibility is to encourage the reflection on standardization of the associated risks to the building system itself and its future performance. The tool provides a helping system to be used during the decision making process for a multilayer façade design. It minimizes the arbitrariness and offers a qualification previous to the quantification that will be done with the development of the construction details and their bill of quantities, that in subsequent project stages will be executed. At the same time, the systematization of the mentioned decision making during the design phase, can be found as a checking system in the different stages of the decision making design process and in the different stages of the building envelope definition.

BUILDING EFFICIENT AND COST-EFFECTIVE CLOUD-BASED BIG DATA MANAGEMENT SYSTEMS

In today’s big data world, data is being produced in massive volumes, at great velocity and from a variety of different sources such as mobile devices, sensors, a plethora of small devices hooked to the internet (Internet of Things), social networks, communication networks and many others. Interactive querying and large-scale analytics are being increasingly used to derive value out of this big data. A large portion of this data is being stored and processed in the Cloud due the several advantages provided by the Cloud such as scalability, elasticity, availability, low cost of ownership and the overall economies of scale. There is thus, a growing need for large-scale cloud-based data management systems that can support real-time ingest, storage and processing of large volumes of heterogeneous data. However, in the pay-as-you-go Cloud environment, the cost of analytics can grow linearly with the time and resources required. Reducing the cost of data analytics in the Cloud thus remains a primary challenge. In my dissertation research, I have focused on building efficient and cost-effective cloud-based data management systems for different application domains that are predominant in cloud computing environments. In the first part of my dissertation, I address the problem of reducing the cost of transactional workloads on relational databases to support database-as-a-service in the Cloud. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availability, and tolerating failures gracefully. I have designed, built and evaluated SWORD, an end-to-end scalable online transaction processing system, that utilizes workload-aware data placement and replication to minimize the number of distributed transactions that incorporates a suite of novel techniques to significantly reduce the overheads incurred both during the initial placement of data, and during query execution at runtime. In the second part of my dissertation, I focus on sampling-based progressive analytics as a means to reduce the cost of data analytics in the relational domain. Sampling has been traditionally used by data scientists to get progressive answers to complex analytical tasks over large volumes of data. Typically, this involves manually extracting samples of increasing data size (progressive samples) for exploratory querying. This provides the data scientists with user control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. I propose a new progressive data-parallel computation framework, NOW!, that provides support for progressive analytics over big data. In particular, NOW! enables progressive relational (SQL) query support in the Cloud using unique progress semantics that allow efficient and deterministic query processing over samples providing meaningful early results and provenance to data scientists. NOW! enables the provision of early results using significantly fewer resources thereby enabling a substantial reduction in the cost incurred during such analytics. Finally, I propose NSCALE, a system for efficient and cost-effective complex analytics on large-scale graph-structured data in the Cloud. The system is based on the key observation that a wide range of complex analysis tasks over graph data require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph; examples include ego network analysis, motif counting in biological networks, finding social circles in social networks, personalized recommendations, link prediction, etc. These tasks are not well served by existing vertex-centric graph processing frameworks whose computation and execution models limit the user program to directly access the state of a single vertex, resulting in high execution overheads. Further, the lack of support for extracting the relevant portions of the graph that are of interest to an analysis task and loading it onto distributed memory leads to poor scalability. NSCALE allows users to write programs at the level of neighborhoods or subgraphs rather than at the level of vertices, and to declaratively specify the subgraphs of interest. It enables the efficient distributed execution of these neighborhood-centric complex analysis tasks over largescale graphs, while minimizing resource consumption and communication cost, thereby substantially reducing the overall cost of graph data analytics in the Cloud. The results of our extensive experimental evaluation of these prototypes with several real-world data sets and applications validate the effectiveness of our techniques which provide orders-of-magnitude reductions in the overheads of distributed data querying and analysis in the Cloud.