The forest products industry is a significant contributor to the U.S. economy contributing six percent of the total U.S. manufacturing gross domestic product (GDP), placing it on par with the U.S. automotive and plastics industries. Sustaining business competitiveness by reducing costs and maintaining product quality will be essential in the long term for this industry. Improved production efficiency and business competitiveness is the primary rationale for this work. A challenge facing this industry is to develop better knowledge of the complex nature of process variables and their relationship with final product quality attributes. Quantifying better the relationships between process variables (e.g., press temperature) and final product quality attributes plus predicting the strength properties of final products are the goals of this study. Destructive lab tests are taken at one to two hour intervals to estimate internal bond (IB) tensile strength and modulus of rupture (MOR) strength properties. Significant amounts of production occur between destructive test samples. In the absence of a real-time model that predicts strength properties, operators may run higher than necessary feedstock input targets (e.g., weight, resin, etc.). Improved prediction of strength properties using boosted regression tree (BRT) models may reduce the costs associated with rework (i.e., remanufactured panels due to poor strength properties), reduce feedstocks costs (e.g., resin and wood), reduce energy usage, and improve wood utilization from the valuable forest resource. Real-time, temporal process data sets were obtained from a U.S. particleboard manufacturer. In this thesis, BRT models were developed to predict the continuous response variables MOR and IB from a pool of possible continuous predictor variables. BRT model comparisons were done using the root mean squared error for prediction (RMSEP) and the RMSEP relative to the mean of the response variable as a percent (RMSEP%) for the validation data set(s). Overall, for MOR, RMSEP values ranged from 0.99 to 1.443 MPa, and RMSEP% values ranged from 7.9% to 11.6%. Overall, for IB, RMSEP values ranged from 0.074 to 0.108 MPa, and RMSEP% values ranged from 12.7% to 18.6%.

Little is known about the distribution of black corals in the northwestern Gulf of Mexico. Of thirty-nine species of black coral documented in the Western Atlantic, thirty have been previously documented by various studies in the Gulf of Mexico. This study proposes potential range extensions for four black coral species, including Stichopathes gracilis, Stichopathes semiglabra, Tanacetipathes paula, and Tanacetipathes spinescens, to include the Gulf of Mexico. The validation of in situ identifications of black coral species is evaluated, and recommendations for species identifications and species groupings are made. Black coral associated fauna are documented, supporting known associations and documenting potentially new associations and species. Habitat suitability models for the distribution of black coral species at selected banks in the northwestern Gulf of Mexico were generated. Presence-only models made using the MaxEnt modeling program were compared to presence-absence models made using Boosted Regression Tree modeling techniques. Presence-absence models were documented to have greater predictive accuracy than the presence-only models, which showed evidence of model overfitting. The model was projected to five similar salt-dome features in the region, highlighting extensive habitat for multiple black coral species in these unexplored habitats. This study presents habitat suitability maps as a testable hypothesis for black coral distribution in the mesophotic zone of this region.

O recrutamento é extremamente importante no ambiente recifal, sendo o principal responsável pelo reabastecimento de populações adultas de peixes. Esse fenômeno é altamente complexo, não sendo claro se é influenciado apenas por processos estocásticos ou também por processos determinísticos. No presente estudo avaliamos a dinâmica temporal do recrutamento de diversas espécies de peixes recifais, identificando sítios de berçário (i.e. recrutamento estável e alto) e a influência de variáveis ambientais. Para tal, utilizamos dados de um monitoramento de médio prazo (i.e. 2001 a 2014) realizado no Banco dos Abrolhos (BA-Brasil). Foram amostrados mais de 45 sítios, sendo levantados dados sobre a comunidade de peixes, comunidade bentônica e outras variáveis ambientais. A partir desses dados, avaliamos a variação do recrutamento por sítio em dois períodos distintos (i.e. 2001-2008/2006-2014) e a influência de variáveis ambientais no recrutamento, através da técnica Boosted Regression Trees. Constatamos que diversas espécies de peixe apresentam-se com recrutamento estável em distintos sítios de amostragem. Também observamos um efeito positivo da densidade de peixes recifais coespecíficos adultos e da cobertura relativa de algas frondosas no recrutamento de diversas espécies analisadas. No geral, observamos que há certa espécie especificidade no processo de recrutamento, porém, em escalas espaciais maiores, os padrões podem estar ligados a características mais gerais, relacionadas a um grupo taxonômico mais elevado. Em relação aos sítios de berçário, um se destacou, sendo berçário de 5 diferentes espécies, incluindo Scarus trispinosus, uma das espécies prioritárias para conservação na região de Abrolhos. Assim, recomendamos a criação de uma área marinha de proteção integral que englobe o sítio em questão. Além disso, as descobertas deste trabalho nos permitem reforçar a teoria de que o recrutamento de peixes recifais pode ser influenciado por fenômenos determinísticos e não varia simplesmente de maneira estocástica. / Recruitment is extremely important in the reef environment, because it is the main source of population replenishment. Reef fish recruitment is a highly complex process and it is not clear whether it is influenced only by stochastic processes or also by deterministic processes. Herein, we aimed to investigate temporal dynamics of reef fish recruitment, identify nursery sites (i.e. predictably high recruitment sites) and evaluate the influence of environmental variables on recruitment. We used data from a medium-term time series (i.e. 2001-2014) of scientific surveys in Abrolhos Bank (BA-Brazil). We sampled more than 45 sites, for several consecutive years and recorded data about fish community, benthic community and other environmental variables. We assessed the variation of recruitment on each site, during two distinct periods (i.e. 2001-2008 / 2006-2014), and used the Boosted Regression Trees technique to evaluate the influence of environmental variables in recruitment. We found that several reef fish species present a low variable recruitment at different sampling sites. BRT showed a positive effect of the coverage of flesh algae and abundance of conspecific in the abundance of recruits (i.e. young-of-year) of many species. Overall, we notice that the recruitment traits seems to be species specific, but we also found indications that in larger spatial scales, recruitment spatial and temporal patterns may be related to general characteristics among species of the higher taxa. Nursery sites varied among species and one site was a nursery to 5 different reef fish species, including Scarus trispinosus, a species that require priority conservation in the Abrolhos Bank. Therefore, we recommend the creation of a new no-take marine protected area that encompasses this site. Our results also indicated that reef fish recruitment may be influenced by deterministic processes and do not vary only stochastically.

O recrutamento é extremamente importante no ambiente recifal, sendo o principal responsável pelo reabastecimento de populações adultas de peixes. Esse fenômeno é altamente complexo, não sendo claro se é influenciado apenas por processos estocásticos ou também por processos determinísticos. No presente estudo avaliamos a dinâmica temporal do recrutamento de diversas espécies de peixes recifais, identificando sítios de berçário (i.e. recrutamento estável e alto) e a influência de variáveis ambientais. Para tal, utilizamos dados de um monitoramento de médio prazo (i.e. 2001 a 2014) realizado no Banco dos Abrolhos (BA-Brasil). Foram amostrados mais de 45 sítios, sendo levantados dados sobre a comunidade de peixes, comunidade bentônica e outras variáveis ambientais. A partir desses dados, avaliamos a variação do recrutamento por sítio em dois períodos distintos (i.e. 2001-2008/2006-2014) e a influência de variáveis ambientais no recrutamento, através da técnica Boosted Regression Trees. Constatamos que diversas espécies de peixe apresentam-se com recrutamento estável em distintos sítios de amostragem. Também observamos um efeito positivo da densidade de peixes recifais coespecíficos adultos e da cobertura relativa de algas frondosas no recrutamento de diversas espécies analisadas. No geral, observamos que há certa espécie especificidade no processo de recrutamento, porém, em escalas espaciais maiores, os padrões podem estar ligados a características mais gerais, relacionadas a um grupo taxonômico mais elevado. Em relação aos sítios de berçário, um se destacou, sendo berçário de 5 diferentes espécies, incluindo Scarus trispinosus, uma das espécies prioritárias para conservação na região de Abrolhos. Assim, recomendamos a criação de uma área marinha de proteção integral que englobe o sítio em questão. Além disso, as descobertas deste trabalho nos permitem reforçar a teoria de que o recrutamento de peixes recifais pode ser influenciado por fenômenos determinísticos e não varia simplesmente de maneira estocástica. / Recruitment is extremely important in the reef environment, because it is the main source of population replenishment. Reef fish recruitment is a highly complex process and it is not clear whether it is influenced only by stochastic processes or also by deterministic processes. Herein, we aimed to investigate temporal dynamics of reef fish recruitment, identify nursery sites (i.e. predictably high recruitment sites) and evaluate the influence of environmental variables on recruitment. We used data from a medium-term time series (i.e. 2001-2014) of scientific surveys in Abrolhos Bank (BA-Brazil). We sampled more than 45 sites, for several consecutive years and recorded data about fish community, benthic community and other environmental variables. We assessed the variation of recruitment on each site, during two distinct periods (i.e. 2001-2008 / 2006-2014), and used the Boosted Regression Trees technique to evaluate the influence of environmental variables in recruitment. We found that several reef fish species present a low variable recruitment at different sampling sites. BRT showed a positive effect of the coverage of flesh algae and abundance of conspecific in the abundance of recruits (i.e. young-of-year) of many species. Overall, we notice that the recruitment traits seems to be species specific, but we also found indications that in larger spatial scales, recruitment spatial and temporal patterns may be related to general characteristics among species of the higher taxa. Nursery sites varied among species and one site was a nursery to 5 different reef fish species, including Scarus trispinosus, a species that require priority conservation in the Abrolhos Bank. Therefore, we recommend the creation of a new no-take marine protected area that encompasses this site. Our results also indicated that reef fish recruitment may be influenced by deterministic processes and do not vary only stochastically.

No description available.

Computer numerical control (CNC) machining is an integral element to the manufacturing industry for production of components with requirements to meet several outcome conditions. The surface roughness (Ra) of a workpiece is one of the most important outcomes in finish machining processes due to it’s direct impact on the functionality and lifespan of components in their intended applications. Several factors contribute to the creation of Ra in machining including, but not limited to, the machining parameters, properties of the workpiece, tool geometry and wear. Alternative to traditional selection of machining parameters using existing standards and/or expert knowledge, current studies in literature have examined methods to consider these factors for prediction and optimization of machining parameters to minimize Ra. These methods span many approaches including theoretical modelling and simulation, design of experiments, statistical and machine learning methods. Despite the abundance of research in this area, challenges remain regarding the generalizability of models for multiple machining conditions, and lengthy training requirements of methods based solely on machine learning methods. Furthermore, many machine learning methods focus on static cutting parameters rather than consideration of properties of the tool and workpiece, and dynamic factors such as tool wear. The main contribution of this research was to develop a prediction and optimization model framework to minimize Ra for finish turning that combines theoretical and machine learning methods, and can be practically utilized by CNC machine operators for parameter v decision making. The presented research work was divided into four distinct objectives. The first objective of this research focused on analyzing the relationship between the machining parameters and Ra for three different materials with varying properties (AISI 4340, AISI 316, and CGI 450). This was followed by the second objective that targeted the development of an Ra prediction framework that utilized a kinematics-based prediction model with an ensemble gradient boosted regression tree (GBRT) to create a multi-material model with justified results, while strengthening accuracy with the machine learning component. The results demonstrated the multi-material model was able to provide predictions with a root-mean-square error (RMSE) of 0.166 μm and attained 70% of testing predictions to fall within limits set by the ASME B46.1-2019 standard. This standard was utilized as an efficient evaluation tool for determining if the prediction accuracy was within an acceptable range. The remaining objectives of this research focused on investigating the relationship between tool wear and Ra through a focused study on AISI 316, followed by application of the prediction model framework as the fitness function for testing of three different metaheuristic optimization algorithms to minimize Ra. The results revealed a significant relationship between tool wear and Ra, which enabled improvement in the prediction framework through the use of the tool’s total cutting distance for an indicator of tool wear as an input into the prediction model. Significant prediction improvement was achieved, demonstrated by metrics including RMSE of 0.108 μm and 87% of predictions were within the ASME B46.1-2019 limits. The improved prediction model was used as the fitness function for comparison performance of genetic algorithm (GA), particle swarm vi optimization (PSO), and simulated annealing (SA), under constrained and unconstrained conditions. SA demonstrated superior performance with less than 5% error between the optimal and experimental Ra when constrained to the experimental data set during validation testing. The overall results of this research establish the feasibility of a framework that could be applied in an industrial setting for both prediction of Ra for multiple materials, and supports the determination of parameters for minimizing Ra considering the dynamic nature of tool wear. / Thesis / Master of Applied Science (MASc) / The surface quality produced on a workpiece via computer numerical control (CNC) machining is influenced by many factors, including the machining parameters, characteristics of the workpiece, and the cutting tool’s geometry and wear. When the optimal machining parameters are not used, manufacturing companies may incur unexpected costs associated with scrapped components, as well as time and materials required for re-machining the component. This research focuses on developing a model to indirectly predict surface roughness (Ra) in CNC turning, and to provide operators guidance regarding the optimal machining parameters to ensure the machined surface is within specifications. A multi-material Ra prediction model was produced to allow for use under multiple machining conditions. This was enhanced by comparing three different optimization algorithms to evaluate their suitability with the prediction framework for providing recommendation on the optimal machining parameters, considering an indicator for tool wear as an input factor.

Zahlreiche Studien den Stand der Forschung in Bezug auf die Ökosystemdienstleistungen untersucht. Dennoch wurde die Dimension „Volumen und Höhe“, d.h. die dritte Dimension städtischer Systeme, in den Studien zu Ökosystemdienstleistungen in städtischen Gebieten ignoriert. Die Forschungsziele und Fragestellungen dieser Dissertation lauten: i) Stand der aktuellen Forschung zur dritten Dimension von Ökosystemdienstleistungen im städtischen Raum, ii) Beurteilung des Zusammenhangs von urbanen mehrdimensionalen Indikatoren (zwei- und dreidimensionalen Indikatoren) für die Oberflächentemperatur in der Stadt und iii) Unterschiede zwischen Innen- und Außentemperaturen in urbanen Räumen. Diese Dissertation ist in vier Kapitel gegliedert. Im ersten und zweiten Kapitel werden die Forschungslücken und das Ziel der vorliegenden Untersuchung erläutert. Kapitel 3 enthält die veröffentlichten Artikel. Das letzte Kapitel behandelt die Ergebnisse der veröffentlichten Artikel. Diese Dissertation betont die Bedeutung von dreidimensionalen Studien in urbanen Ökosystemen, um das Konzept der Nachhaltigkeit in Städten voranzutreiben. Deshalb werden kontinentübergreifende Forschungen für weitere Studien empfohlen, die die dreidimensionale Struktur aller städtischen Komponenten und ihre Auswirkungen auf die Außen- und Innentemperatur berücksichtigen. / Among the studies on ecosystem services undertaken in urban areas, a ‎dimension ‘volume and height’, i.e., the third-dimension of urban environment is largely ignored. More specific, three-dimensional spatial models will increase the knowledge of how complex environment ‎shape the micro-climate in urban ‎environment. The research objectives and questions of this dissertation is: i) the status of the current research addressing the third-dimension of ‎ecosystem services in urban area, ii) assessing the association of urban multi-dimensional (two- and three- ‎dimensional) indicators on urban surface temperature and iii) variation of indoor and outdoor urban temperature pattern. This dissertation is organized into four chapters. The ‎first and second chapter explain the gaps in literature and the aim of this research. Chapter 3 holds the published articles. The last chapter discusses the results of the published articles. This dissertation emphasizes the importance of three-dimensional studies in urban ecosystems to advance the concept of sustainability in cities. Therefore, cross-continental studies that consider the three-dimensional structure of all the urban components and its impact on outdoor and indoor temperature is recommended for future research. / به جرات می توان گفت که در مطالعات خدمات اکوسیستم، بخصوص خدمات اکوسیستم شهری ، بعد سوم که شامل "ارتفاع و حجم" می باشد اصلا مورد توجه قرار نگرفته است. هدف از این پایان نامه، تلفیق مفهوم بعد سوم در خدمات اکوسیستم شهری و استفاده از فواید آن می باشد. مطالعه بعد سوم دانش ما را در نحوه شکل گیری اقلیم خُرد شهری افزایش می دهد. هدف این پروژه دکتری پاسخ به سوالات ذیل می باشد: 1) سطح آگاهی تحقیقات از بعد سوم خدمات اکوسیستم شهری، 2) ارزیابی ارتباط شاخص های چندبعدی (دو و سه بعدی) با دمای سطح و 3) ارزیابی الگوی دمای درونی و بیرونی در شهر. جهت پاسخ دادن به سوال های مطرح شده، این پژوهش به چهار فصل تقسیم شده است. فصل اول و دوم، که جایگاه خدمات اکوسیستم را در مطالعات شهری بررسی و جای خالی مفهوم بعد سوم در مطالعات خدمات اکوسیستم شهری را جستجو می کند. فصل سوم، شامل سه مقاله چاپ شده در راستای این پروژه دکتری می باشد. فصل چهارم، که نتایج بدست آمده را تجزیه و تحلیل می کند. نتایج بدست آمده نشان می دهد که مطالعات خدمات اکوسیستم شهری از معنی کلی و بنیادی به سمت سازش پذیری شهرها با پدیده تغییر اقلیم در حال تغییر است. همچنین نتایج نشان می دهد که ساختار متفاوت شهری بر شکل گیری الگوی دمای بیرون و داخل ساختمان ها موثر می باشد. استنتاج نتایج بدست آمده از این پایان نامه دو مورد را پیشنهاد می کند. اول، بررسی نقش ساختار های دو بعدی و سه بعدی بر روی دیگر شهر ها و تاثیر آن بر شکل گیری دمای بیرون و درونی ساختمان ها.

Saltmarshes provide a diverse range of ecosystem services and are protected under a number of international designations. Nevertheless they are generally declining in extent in the United Kingdom and North West Europe. The drivers of this decline are complex and poorly understood. When considering mitigation and management for future ecosystem service provision it will be important to understand why, where, and to what extent decline is likely to occur. Few studies have attempted to forecast saltmarsh morphodynamics at a system level over decadal time scales. There is no synthesis of existing knowledge available for specific site predictions nor is there a formalised framework for individual site assessment and management. This project evaluates the extent to which machine learning model approaches (boosted regression trees, neural networks and Bayesian networks) can facilitate synthesis of information and prediction of decadal-scale morphological tendencies of saltmarshes. Importantly, data-driven predictions are independent of the assumptions underlying physically-based models, and therefore offer an additional opportunity to crossvalidate between two paradigms. Marsh margins and interiors are both considered but are treated separately since they are regarded as being sensitive to different process suites. The study therefore identifies factors likely to control morphological trajectories and develops geospatial methodologies to derive proxy measures relating to controls or processes. These metrics are developed at a high spatial density in the order of tens of metres allowing for the resolution of fine-scale behavioural differences. Conventional statistical approaches, as have been previously adopted, are applied to the dataset to assess consistency with previous findings, with some agreement being found. The data are subsequently used to train and compare three types of machine learning model. Boosted regression trees outperform the other two methods in this context. The resulting models are able to explain more than 95% of the variance in marginal changes and 91% for internal dynamics. Models are selected based on validation performance and are then queried with realistic future scenarios which represent altered input conditions that may arise as a consequence of future environmental change. Responses to these scenarios are evaluated, suggesting system sensitivity to all scenarios tested and offering a high degree of spatial detail in responses. While mechanistic interpretation of some responses is challenging, process-based justifications are offered for many of the observed behaviours, providing confidence that the results are realistic. The work demonstrates a potentially powerful alternative (and complement) to current morphodynamic models that can be applied over large areas with relative ease, compared to numerical implementations. Powerful analyses with broad scope are now available to the field of coastal geomorphology through the combination of spatial data streams and machine learning. Such methods are shown to be of great potential value in support of applied management and monitoring interventions.