Delineation of within-site terroir effects using soil and vine water measurement. Investigation of Cabernet Franc

. The influence of vine water status was studied in commercial vineyard blocks of Vilis vinifera L. cv. Cabernet Franc in Niagara Peninsula, Ontario from 2005 to 2007. Vine performance, fruit composition and vine size of non-irrigated grapevines were compared within ten vineyard blocks containing different soil and vine water status. Results showed that within each vineyard block water status zones could be identified on GIS-generated maps using leaf water potential and soil moisture measurements. Some yield and fruit composition variables correlated with the intensity of vine water status. Chemical and descriptive sensory analysis was performed on nine (2005) and eight (2006) pairs of experimental wines to illustrate differences between wines made from high and low water status winegrapes at each vineyard block. Twelve trained judges evaluated six aroma and flavor (red fruit, black cherry, black current, black pepper, bell pepper, and green bean), thr~e mouthfeel (astringency, bitterness and acidity) sensory attributes as well as color intensity. Each pair of high and low water status wine was compared using t-test. In 2005, low water status (L WS) wines from Buis, Harbour Estate, Henry of Pelham (HOP), and Vieni had higher color intensity; those form Chateau des Charmes (CDC) had high black cherry flavor; those at RiefEstates were high in red fruit flavor and at those from George site was high in red fruit aroma. In 2006, low water status (L WS) wines from George, Cave Spring and Morrison sites were high in color intensity. L WS wines from CDC, George and Morrison were more intense in black cherry aroma; LWS wines from Hernder site were high in red fruit aroma and flavor. No significant differences were found from one year to the next between the wines produced from the same vineyard, indicating that the attributes of these wines were maintained almost constant despite markedly different conditions in 2005 and 2006 vintages. Partial ii Least Square (PLS) analysis showed that leaf \}' was associated with red fruit aroma and flavor, berry and wine color intensity, total phenols, Brix and anthocyanins while soil moisture was explained with acidity, green bean aroma and flavor as well as bell pepper aroma and flavor. In another study chemical and descriptive sensory analysis was conducted on nine (2005) and eight (2006) medium water status (MWS) experimental wines to illustrate differences that might support the sub-appellation system in Niagara. The judges evaluated the same aroma, flavor, and mouthfeel sensory attributes as well as color intensity. Data were analyzed using analysis of variance (ANOVA), principal component analysis (PCA) and discriminate analysis (DA). ANOV A of sensory data showed regional differences for all sensory attributes. In 2005, wines from CDC, HOP, and Hemder sites showed highest. r ed fruit aroma and flavor. Lakeshore and Niagara River sites (Harbour, Reif, George, and Buis) wines showed higher bell pepper and green bean aroma and flavor due to proximity to the large bodies of water and less heat unit accumulation. In 2006, all sensory attributes except black pepper aroma were different. PCA revealed that wines from HOP and CDC sites were higher in red fruit, black currant and black cherry aroma and flavor as well as black pepper flavor, while wines from Hemder, Morrison and George sites were high in green bean aroma and flavor. ANOV A of chemical data in 2005 indicated that hue, color intensity, and titratable acidity (TA) were different across the sites, while in 2006, hue, color intensity and ethanol were different across the sites. These data indicate that there is the likelihood of substantial chemical and sensory differences between clusters of sub-appellations within the Niagara Peninsula iii

Automatic Structure Generation using Genetic Programming and Fractal Geometry

Three dimensional model design is a well-known and studied field, with numerous real-world applications. However, the manual construction of these models can often be time-consuming to the average user, despite the advantages o ffered through computational advances. This thesis presents an approach to the design of 3D structures using evolutionary computation and L-systems, which involves the automated production of such designs using a strict set of fitness functions. These functions focus on the geometric properties of the models produced, as well as their quantifiable aesthetic value - a topic which has not been widely investigated with respect to 3D models. New extensions to existing aesthetic measures are discussed and implemented in the presented system in order to produce designs which are visually pleasing. The system itself facilitates the construction of models requiring minimal user initialization and no user-based feedback throughout the evolutionary cycle. The genetic programming evolved models are shown to satisfy multiple criteria, conveying a relationship between their assigned aesthetic value and their perceived aesthetic value. Exploration into the applicability and e ffectiveness of a multi-objective approach to the problem is also presented, with a focus on both performance and visual results. Although subjective, these results o er insight into future applications and study in the fi eld of computational aesthetics and automated structure design.

Automatic evolution of conceptual building architectures

This thesis describes research in which genetic programming is used to automatically evolve shape grammars that construct three dimensional models of possible external building architectures. A completely automated fitness function is used, which evaluates the three dimensional building models according to different geometric properties such as surface normals, height, building footprint, and more. In order to evaluate the buildings on the different criteria, a multi-objective fitness function is used. The results obtained from the automated system were successful in satisfying the multiple objective criteria as well as creating interesting and unique designs that a human-aided system might not discover. In this study of evolutionary design, the architectures created are not meant to be fully functional and structurally sound blueprints for constructing a building, but are meant to be inspirational ideas for possible architectural designs. The evolved models are applicable for today's architectural industries as well as in the video game and movie industries. Many new avenues for future work have also been discovered and highlighted.

Delineation of within-site terroir effects using soil and vine water measurements in Riesling vineyards within the Niagara Peninsula

The major focus of this dissertation was to explain terroir effects that impact wine varietal character and to elucidate potential determinants of terroir by testing vine water status (VWS) as the major factor of the terroir effect. It was hypothesized that consistent water status zones could be identified within vineyard sites, and, that differences in vine performance, fruit composition and wine sensory attributes could be related to VWS. To test this hypothesis, ten commercial Riesling vineyards representative of each Vintners Quality Alliance sub-appellation were selected. Vineyards were delineated using global positioning systems and 75 to 80 sentinel vines per vineyard were geo-referenced for data collection. During the 2005 to 2007 growing seasons, VWS measurements [midday leaf water potential ('l')] were collected from a subset of these sentinel vines. Data were collected on soil texture and composition, soil moisture, vine performance (yield components, vine size) and fruit composition. These variables were mapped using global information system (GIS) software and relationships between them were elucidated. Vines were categorized into "low" and "high" water status regions within each vineyard block and replicate wines were made from each. Many geospatial patterns and relationships were spatially and temporally stable within vineyards. Leaf'l' was temporally stable within vineyards despite different weather conditions during each growing season. Generally, spatial relationships between 'l', soil moisture, vine size, berry weight and yield were stable from year to year. Leaf", impacted fruit composition in several vineyards. Through sorting tasks and multidimensional scaling, wines of similar VWS had similar sensory properties. Descriptive analysis further indicated that VWS impacted wine sensory profiles, with similar attributes being different for wines from different water status zones. Vineyard designation had an effect on wine profiles, with certain sensory and chemical attributes being associated from different subappellations. However, wines were generally grouped in terms of their regional designation ('Lakeshore', 'Bench', 'Plains') within the Niagara Peninsula. Through multivariate analyses, specific sensory attributes, viticulture and chemical variables were associated with wines of different VWS. Vine water status was a major contributor to the terroir effect, as it had a major impact on vine size, berry weight and wine sensory characteristics.

Automatic Inference of Graph Models for Complex Networks with Genetic Programming

Complex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.

Illustrations of masonry (1855)

The universal masonic library, a republication, in thirty volumes, of all the standard publications in masonry designed for the libraries of masonic bodies and inviduals…volume III embodying Preston's illustrations of masonry.

Canadian Niagara Power Glass Slide - Sketch Showing Proposed Masonry Lining Near Portal of Tunnel

Drawing by consulting engineer dated October 19, 1901. Scale is noted as 1/4 inch = 1 foot.

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.

Automatic Preparation of ETD Material from the Internet Archive for the DSpace Repository Platform

A big challenge associated with getting an institutional repository off the ground is getting content into it. This article will look at how to use digitization services at the Internet Archive alongside software utilities that the author developed to automate the harvesting of scanned dissertations and associated Dublin Core XML files to create an ETD Portal using the DSpace platform. The end result is a metadata-rich, full-text collection of theses that can be constructed for little out of pocket cost.

Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.

Receipt from E. Riddle and Sons, Contractors of Masonry, St. Catharines

Receipt from E. Riddle and Sons, Contractors of Masonry, St. Catharines for work done, Mar. 13, 1887.