Alternate Reinforcement for Structural Concrete

Concrete has been utilized on construction projects for thousands of years, ranging from the Pyramids to the foundation your home is built on. Innovated upon, concrete and its properties have changed over the years, one of the greatest being the discovery of tensile reinforcement. Rebar, commonly known as reinforcing steel or reinforcement bars, have been used in the construction industry for decades. Ranging in size and application, rebar’s unmatched tensile strength and anti-corrosive properties make it the champion of reinforcing structural concrete. Though the advancement in technology has created a new era where other materials may take its place. Attempting to utilize other materials that are lighter but acceptable strong, I wanted to evaluate alternatives of readily available materials that could take over rebar’s place in the construction industry

How to support growth with less energy

There is considerable potential to support growth with less use of primary energy and lower carbon emissions. This can be achieved through technical solutions (existing and new), as well as behavioural change. The goal of securing growth with lower carbon emissions is just one of several strategic goals that need to be satisfied. Of the others, the need to develop alternatives to an energy system heavily dependent on oil and natural gas and to maintain security of energy supply are likely to be the most important. The strategic goals are to achieve major reductions in the energy intensity of transport, buildings in use, and to achieve corresponding reductions in energy intensity of the major building materials. Key challenges associated with these strategic goals include: • the development of technologies to produce carbon-free cement, carbon-free steel, carbon-free glass • enabling infrastructural developments that provide a framework for a wide range of low-carbon technologies and increase energy diversity and security of supply • identification of key energy-efficiency tipping points and the construction of technology policy • development of methane-fired modular fuel cells • improved capabilities to model whole energy systems, i.e. adequately modelling both demand and supply, social/economic as well as technical, and assessing the impact outside of the UK system boundary • better low-carbon planning and improved co-ordination of planning, building control and other policy tools • better monitoring and feedback on the real performance of energy efficient technologies. The implication of the Energy White Paper goal of reducing CO2 emissions by 60% by 2050 is a six-fold reduction in the carbon intensity of the UK economy. In the longer run, it is clear that we will move towards a carbon-free economy. Within this transition, developments in supply, distribution and end-use technologies will be multiplicative, while action to constrain demand growth is crucial to the rate of the overall transition

The Random Forest Algorithm with Application to Multispectral Image Analysis

The need for computers to make educated decisions is growing. Various methods have been developed for decision making using observation vectors. Among these are supervised and unsupervised classifiers. Recently, there has been increased attention to ensemble learning--methods that generate many classifiers and aggregate their results. Breiman (2001) proposed Random Forests for classification and clustering. The Random Forest algorithm is ensemble learning using the decision tree principle. Input vectors are used to grow decision trees and build a forest. A classification decision is reached by sending an unknown input vector down each tree in the forest and taking the majority vote among all trees. The main focus of this research is to evaluate the effectiveness of Random Forest in classifying pixels in multispectral image data acquired using satellites. In this paper the effectiveness and accuracy of Random Forest, neural networks, support vector machines, and nearest neighbor classifiers are assessed by classifying multispectral images and comparing each classifier\u27s results. As unsupervised classifiers are also widely used, this research compares the accuracy of an unsupervised Random Forest classifier with the Mahalanobis distance classifier, maximum likelihood classifier, and minimum distance classifier with respect to multispectral satellite data

Multispectral Image Analysis Using Random Forest

Classical methods for classification of pixels in multispectral images include supervised classifiers such as the maximum-likelihood classifier, neural network classifiers, fuzzy neural networks, support vector machines, and decision trees. Recently, there has been an increase of interest in ensemble learning – a method that generates many classifiers and aggregates their results. Breiman proposed Random Forestin 2001 for classification and clustering. Random Forest grows many decision trees for classification. To classify a new object, the input vector is run through each decision tree in the forest. Each tree gives a classification. The forest chooses the classification having the most votes. Random Forest provides a robust algorithm for classifying large datasets. The potential of Random Forest is not been explored in analyzing multispectral satellite images. To evaluate the performance of Random Forest, we classified multispectral images using various classifiers such as the maximum likelihood classifier, neural network, support vector machine (SVM), and Random Forest and compare their results

Random Forest Algorithm for Land Cover Classification

Since the launch of the first land observation satellite Landsat-1 in 1972, many machine learning algorithms have been used to classify pixels in Thematic Mapper (TM) imagery. Classification methods range from parametric supervised classification algorithms such as maximum likelihood, unsupervised algorithms such as ISODAT and k-means clustering to machine learning algorithms such as artificial neural, decision trees, support vector machines, and ensembles classifiers. Various ensemble classification algorithms have been proposed in recent years. Most widely used ensemble classification algorithm is Random Forest. The Random Forest classifier uses bootstrap aggregating for form an ensemble of classification and induction tree like tree classifiers. A few researchers have used Random Forest for land cover analysis. However, the potential of Random Forest has not yet been fully explored by the remote sensing community. In this paper we compare classification accuracy of Random Forest with other commonly used algorithms such as the maximum likelihood, minimum distance, decision tree, neural network, and support vector machine classifiers

Random Forest Algorithm for Land Cover Classification

Since the launch of the first land observation satellite Landsat-1 in 1972, many machine learning algorithms have been used to classify pixels in Thematic Mapper (TM) imagery. Classification methods range from parametric supervised classification algorithms such as maximum likelihood, unsupervised algorithms such as ISODAT and k-means clustering to machine learning algorithms such as artificial neural, decision trees, support vector machines, and ensembles classifiers. Various ensemble classification algorithms have been proposed in recent years. Most widely used ensemble classification algorithm is Random Forest. The Random Forest classifier uses bootstrap aggregating for form an ensemble of classification and induction tree like tree classifiers. A few researchers have used Random Forest for land cover analysis. However, the potential of Random Forest has not yet been fully explored by the remote sensing community. In this paper we compare classification accuracy of Random Forest with other commonly used algorithms such as the maximum likelihood, minimum distance, decision tree, neural network, and support vector machine classifiers

UC-56 Rendeview

Rendeview is a mobile application designed to allow users to find a physical meeting location equitable for 3+ people, taking into account drive time and traffic conditions.Advisors(s): Dr. Reza PariziTopic(s): Software EngineeringSWE 472

Lost Generation: System Resilience and Flexibility

Whole energy system modelling is a valuable tool to support the development of policy to decarbonise energy systems, and has been used extensively in the UK for this purpose. However, quantitative insights produced by such models methods necessarily omit potentially important features of physical and engineering reality. The authors argue that important socio-technical insights can be gained by studying critical events such as the loss of 2.1 GW generation from the electricity system of Great Britain in August, 2019. The present paper uses this event as a starting point for a discussion of the need for additional tools, drawn from the System Architecture literature, to support the design and realisation of future fully decarbonised systems with high penetrations of renewable energy, capable of providing high levels of resilience and flexibility

Pancytopenia due to Restrictive Food Intake in an Autistic Adult

Autism spectrum disorder (ASD) is a neuro-behavioral syndrome that develops in childhood and can be comorbid with restrictive and avoidant food intake disorder. This case details a young man who was hospitalized with pancytopenia due to restrictive nutritional intake related to his severe ASD. He was found to have undetectable vitamin B12 levels. His blood counts improved with transfusion, nutritional supplementation, and dental care. This report illustrates the importance of understanding ASD and potential medical complications of related behaviors

Initial State Interactions for K−K^--Proton Radiative Capture

The effects of the initial state interactions on the $K^--p$ radiative capture branching ratios are examined and found to be quite sizable. A general coupled-channel formalism for both strong and electromagnetic channels using a particle basis is presented, and applied to all the low energy $K^--p$ data with the exception of the {\it 1s} atomic level shift. Satisfactory fits are obtained using vertex coupling constants for the electromagnetic channels that are close to their expected SU(3) values.Comment: 16 pages, uses revte