New Science Bldg [Building]

Looking west from roof of adjacent building. Albert Kahn, architect. Irwin & Leighton, contractors. Construction 1914-1915. Building named for Edward H. Kraus. Image mounted on linen. One of series of construction photos probably taken by Lyndon for contractor and given to UM Building & Grounds. On image: New Science Bldg. Irwin + Leighton, contractors. June 18, 1914.

Irwin-Lighton [sic] Building Contractors ... [Natural Science Building]

Albert Kahn, architect. Irwin & Leighton, contractors. Construction 1914-1915. Building named for Edward H. Kraus. On image: Schmidt photo - 14

The New Natural Science Building

Albert Kahn, architect. Irwin & Leighton, contractors. Construction 1914-1915. Building named for Edward H. Kraus. From south / southwest.

Natural Science Building and Burton Memorial Tower

Albert Kahn, architect. Irwin & Leighton, contractors. Construction 1914-1915. Building named for Edward H. Kraus.

Space Science Building

The Architects Collective, Inc., architect. On verso: Ann Arbor News shot. NASA Space Sciences Bldg. 7-64

Excavation for new Physical Science Building [Randall Physics Laboratory]

Albert Kahn, architect. Built 1924. East University on site of old medical building which was razed in 1914. Also called New Physics and East Physics.

Excavation for new Physical Science Building [Randall Physics Laboratory]

Albert Kahn, architect. Built 1924. East University on site of old medical building which was razed in 1914. Also called New Physics and East Physics.

Excavation for new Physical Science Building [Randall Physics Laboratory]

Albert Kahn, architect. Built 1924. East University on site of old medical building which was razed in 1914. Also called New Physics and East Physics.

Building a 'Repository of Science': the importance of integrating Biobanks within molecular pathology programmes

Repositories containing high quality human biospecimens linked with robust and relevant clinical and pathological information are required for the discovery and validation of biomarkers for disease diagnosis, progression and response to treatment. Current molecular based discovery projects using either low or high throughput technologies rely heavily on ready access to such sample collections. It is imperative that modern biobanks align with molecular diagnostic pathology practices not only to provide the type of samples needed for discovery projects but also to ensure requirements for ongoing sample collections and the future needs of researchers are adequately addressed. Biobanks within comprehensive molecular pathology programmes are perfectly positioned to offer more than just tumour derived biospecimens; for example, they have the ability to facilitate researchers gaining access to sample metadata such as digitised scans of tissue samples annotated prior to macrodissection for molecular diagnostics or pseudoanonymised clinical outcome data or research results retrieved from other users utilising the same or overlapping cohorts of samples. Furthermore, biobanks can work with molecular diagnostic laboratories to develop standardized methodologies for the acquisition and storage of samples required for new approaches to research such as ‘liquid biopsies’ which will ultimately feed into the test validations required in large prospective clinical studies in order to implement liquid biopsy approaches for routine clinical practice. We draw on our experience in Northern Ireland to discuss how this harmonised approach of biobanks working synergistically with molecular pathology programmes is key for the future success of precision medicine.

Modeling of Distributed Control Systems in Intelligent Building based on Colored Petri Nets

Distributed control systems consist of sensors, actuators and controllers, interconnected by communication networks and are characterized by a high number of concurrent process. This work presents a proposal for a procedure to model and analyze communication networks for distributed control systems in intelligent building. The approach considered for this purpose is based on the characterization of the control system as a discrete event system and application of coloured Petri net as a formal method for specification, analysis and verification of control solutions. With this approach, we develop the models that compose the communication networks for the control systems of intelligent building, which are considered the relationships between the various buildings systems. This procedure provides a structured development of models, facilitating the process of specifying the control algorithm. An application example is presented in order to illustrate the main features of this approach.

Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption

There are several ways to attempt to model a building and its heat gains from external sources as well as internal ones in order to evaluate a proper operation, audit retrofit actions, and forecast energy consumption. Different techniques, varying from simple regression to models that are based on physical principles, can be used for simulation. A frequent hypothesis for all these models is that the input variables should be based on realistic data when they are available, otherwise the evaluation of energy consumption might be highly under or over estimated. In this paper, a comparison is made between a simple model based on artificial neural network (ANN) and a model that is based on physical principles (EnergyPlus) as an auditing and predicting tool in order to forecast building energy consumption. The Administration Building of the University of Sao Paulo is used as a case study. The building energy consumption profiles are collected as well as the campus meteorological data. Results show that both models are suitable for energy consumption forecast. Additionally, a parametric analysis is carried out for the considered building on EnergyPlus in order to evaluate the influence of several parameters such as the building profile occupation and weather data on such forecasting. (C) 2008 Elsevier B.V. All rights reserved.