Animal Transport: Developing optimum animal handling procedures and effective transport strategies in the food production chain to improve animal welfare and food quality

End of project reportA series of studies were performed to investigate the effect of transport on liveweight, physiological and haematological responses of cattle. The first study was carried out over a 6 week period in the Spring of 2004. Eighty-four continental x bulls (mean weight (s.d.) 367 (35) kg), naïve to transport, were randomly assigned to one of six journey (J) times of 0, 6, 9, 12, 18 and 24h transport at a stocking density of 1.02m2/bull. Blood samples were collected by jugular venipuncture before, immediately after and at 1, 2, 4, 6, 8, 12 and 24h and bulls were weighed before, immediately after, and at 4, 12 and 24h. Bulls travelling for 6h (280 km), 9h (435 km), 12h (582 km), 18h (902 km) and 24h (1192 km) lost 4.7, 4.5, 5.7 (P=0.05), 6.6 (P=0.05) and 7.5 (P=0.05) percentage liveweight compared with baseline. During the 24h recovery period liveweight was regained to pre-transport levels. Lymphocyte percentages were lower (P=0.001) and neutrophil percentages were higher (P=0.05) in all animals. Blood protein and creatine kinase, glucose and NEFA concentrations were higher (P=0.05) in the bulls following transport and returned to baseline within 24h. In conclusion, liveweight and some physiological and haematological responses of bulls returned to pre-transport levels within 24h having had access to feed and water. Transport of bulls from 6 – 24hours did not impact negatively on animal welfare

Nonterrestrial utilization of materials: Automated space manufacturing facility

Four areas related to the nonterrestrial use of materials are included: (1) material resources needed for feedstock in an orbital manufacturing facility, (2) required initial components of a nonterrestrial manufacturing facility, (3) growth and productive capability of such a facility, and (4) automation and robotics requirements of the facility

Automated Measurement of Heavy Equipment Greenhouse Gas Emission: The case of Road/Bridge Construction and Maintenance

Road/bridge construction and maintenance projects are major contributors to greenhouse gas (GHG) emissions such as carbon dioxide (CO2), mainly due to extensive use of heavy-duty diesel construction equipment and large-scale earthworks and earthmoving operations. Heavy equipment is a costly resource and its underutilization could result in significant budget overruns. A practical way to cut emissions is to reduce the time equipment spends doing non-value-added activities and/or idling. Recent research into the monitoring of automated equipment using sensors and Internet-of-Things (IoT) frameworks have leveraged machine learning algorithms to predict the behavior of tracked entities. In this project, end-to-end deep learning models were developed that can learn to accurately classify the activities of construction equipment based on vibration patterns picked up by accelerometers attached to the equipment. Data was collected from two types of real-world construction equipment, both used extensively in road/bridge construction and maintenance projects: excavators and vibratory rollers. The validation accuracies of the developed models were tested of three different deep learning models: a baseline convolutional neural network (CNN); a hybrid convolutional and recurrent long shortterm memory neural network (LSTM); and a temporal convolutional network (TCN). Results indicated that the TCN model had the best performance, the LSTM model had the second-best performance, and the CNN model had the worst performance. The TCN model had over 83% validation accuracy in recognizing activities. Using deep learning methodologies can significantly increase emission estimation accuracy for heavy equipment and help decision-makers to reliably evaluate the environmental impact of heavy civil and infrastructure projects. Reducing the carbon footprint and fuel use of heavy equipment in road/bridge projects have direct and indirect impacts on health and the economy. Public infrastructure projects can leverage the proposed system to reduce the environmental cost of infrastructure project

A robust digital image watermarking using repetition codes against common attacks

Digital watermarking is hiding the information inside a digital media to protect for such documents against malicious intentions to change such documents or even claim the rights of such documents. Currently the capability of repetition codes on various attacks in not sufficiently studied. In this project, a robust frequency domain watermarking scheme has been implemented using Discrete Cosine Transform (DCT). The idea of this scheme is to embed an encoded watermark using repetition code (3, 1) inside the cover image pixels based on Discrete Cosine Transform (DCT) embedding technique. The proposed methods have undergone several simulation attacks tests in order to check up and compare their robustness against various attacks, like salt and pepper, speckle, compress, Gaussian, image contrast, resizing and cropping attack. The robustness of the watermarking scheme has been calculated using Peak Signal-To-Noise Ratio (PSNR), Mean Squared Error (MSE) and Normalized Correlations (NC). In our experiments, the results show that the robustness of a watermark with repetition codes is much better than without repetition code

Design of tool management systems for flexible manufacturing systems

The objective of this thesis is to study the design and analysis of tool management system in the automated manufacturing systems. The thesis is focused on two main areas, namely design and experiment. In the first part of the thesis, the design facility created has been reported. The model has been designed using a hybrid approach in which the power of both algorithmic and knowledge based approaches is utilised. Model permits detail, more accurate and complete solutions for the management of tools in a generic manufacturing system. In the second part of the thesis, to add more understanding to the tool management problems, the interactions of the major tool management design parameters have been investigated using a well known design technique, the Taguchi method. For this purpose, a large number of design experiments have been configured where some have been suggested by the Taguchi method and some have been created by the author to add more confidence, using a large body of real industrial data. The experiments results give deeper understanding of TMS problems and allow design guide-lines to be drawn for the designers. The design approach and the experiments have been proven to be an accurate and valid tool for the design of tool management systems for automated manufacturing systems. This is indicated in the conclusion of the thesis

Urban Scaffold for Renewing Derelict Fabric

In a historic Italian town, opportune innovations in automated parking provide solutions to multifaceted challenges in urban renewal and economic revitalization. A single automated parking facility alleviates pressure in urban infrastructure, simultaneously stabilizing an endangered building shell, and becoming a catalyst for phased urban redevelopment. Strategic opportunities in geography and context are pursued to produce upfront and long-term project feasibility