Research collaboration between academia and industry is a form of knowledge creation in construction industry project management. This research collaboration is motivated by the intent to provide solutions to issues and problems that industry faces through research expertise and a scientific approach. Notwithstanding the potential benefits acknowledged by researchers, collaborative academia-industry research has not been sufficiently explored and there only exist a few studies addressing research success and success factors. Several main reasons for this include; 1) the success of collaborative academia-industry research has not been well defined, 2) there exist limited empirical studies, and 3) the research process of collaborative academia-industry research has not been systematically investigated. The primary purpose of this study is to improve the process of the collaborative academia-industry research for construction industry project management by identifying key process attributes and success factors. First, this study suggests a definition of the success and success criteria of collaborative academia-industry research based on literature review. Then this study evaluated more than 150 research efforts of the Construction Industry Institute (CII), a non-profit research organization sponsoring academia-industry collaborative research for more than 30 years, against the established success criteria to identify successful and less than successful research efforts. Multiple methods were adopted for the evaluation including web-based surveys, research product dissemination data, journal citation counts, and expert group assessment. By analysis and triangulation of the data collected from those multiple sources, this study identified 11 research efforts for further analyses. In-depth cases studies on the 11 research efforts were conducted focusing on the research process through interviews with a total of 39 academics and industry practitioners who participated in those research efforts. Information from interviews and other relevant data were analyzed for each case as well as across the 11 cases to identify key process attributes and factors contributing to research success. Consolidated findings from the cross-case analyses generated 9 key process attributes and associated success factors with significant potential to improve the research process of collaborative academia-industry research. / text

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Orientador: João Eduardo de Morais Pinto Furtado / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Geociências / Made available in DSpace on 2018-08-17T06:34:14Z (GMT). No. of bitstreams: 1 Murakami_ThaysGoncalvesdeLima_M.pdf: 2907868 bytes, checksum: e5aa147043b4802330ef644d2292229c (MD5) Previous issue date: 2010 / Resumo: Esta pesquisa tem por objetivo investigar a estrutura da produção de conhecimentos que são incorporados à avicultura de corte e de postura brasileira, dando ênfase à atuação das universidades e institutos de pesquisa nacionais (ICTs). A escolha da avicultura como objeto de análise se deve ao fato do Brasil apresentar grande competitividade nesta atividade, posicionando-se, no caso do segmento de corte, como o terceiro maior produtor e o maior exportador mundial de carne de frango. A motivação para a realização desta pesquisa está na importância que a geração de novos insumos, técnicas e conhecimentos tem exercido sobre a avicultura no que tange ao incremento de produtividade, à redução de custos e à melhoria da qualidade dos produtos. Conhecimentos e inovações provenientes de esforços isolados e combinados não somente das empresas dos elos industriais insumidores de genética, nutrição e medicamentos, mas também de universidades e institutos de pesquisa, todos inseridos no que se denominou de 'redes de valor do conhecimento'. Partindo-se do reconhecimento da grande contribuição que estes grupos de atores concedem à avicultura, foi realizado o mapeamento das empresas e organizações relacionadas a esta atividade que atuam no Brasil através do uso da fonte estatística da RAIS e de fontes especializadas em avicultura. As universidades e os institutos de pesquisa, em particular, exercem um papel crucial dentro destas redes, formando capital humano qualificado, produzindo pesquisas científicas e tecnológicas de referência, prestando serviços técnicos e laboratoriais e até mesmo dando apoio técnico à produção de novos produtos e processos desenvolvidos pelas empresas insumidoras e que serão posteriormente introduzidos no mercado. Com vistas a atender aos propósitos desta pesquisa - de investigar a estrutura da produção de conhecimentos com enfoque na atuação das ICTs - foram analisados os artigos científicos relacionados à avicultura publicados na base Scopus entre 1970 e 2009. Com o auxílio de um programa computacional chamado Pajek foram construídas as redes de co-autoria. Através da análise destas redes foram identificadas as ICTs mais importantes em termos de geração de conhecimentos à avicultura brasileira e as que mais interagem com fontes externas, inclusive com o setor industrial / Abstract: This research purposes to investigate the knowledge production structure that is embodied in the Brazilian aviculture (broiler and layer), giving emphasis on the performance of national universities and research institutes (STIs). The investigation of the aviculture is related to the large Brazilian competitiveness in this activity. In the case of broiler sector, Brazil displays the third position in broiler meat production and the first position in exportation. What motivates this research is the importance that the generation of new inputs, practices and knowledge has carried out into the aviculture, increasing productivity, minimizing costs and improving products quality. Knowledge and innovations deriving from isolated and combined efforts not merely from genetic, nutrition and drug industries, but also from universities and research institutes, all of them immersed in what we called 'knowledge value networks'. Recognizing the great contribution that this groups of actors has granted to aviculture we mapped the companies and the organizations related to this activity in Brazil through the use of the Brazilian statistical source called RAIS and specialized poultry sources. Universities and research institutes, in particular, exert a crucial role inside these networks, forming qualified human capital, bearing remarkable scientific and technological researches, rendering technical and laboratorial services and even giving technical support to the production of new products and processes developed by supplier companies that, subsequently, will be introduced in the market. For the purpose of this research - to investigate the knowledge production structure focusing on the performance of the STIs - we analyzed aviculture related scientific papers published in Scopus database between 1970 and 2009. With the support of computational software called Pajek we generated co-authorship networks. Through the investigation of these networks we identified the most important STIs in terms of knowledge generation to the Brazilian aviculture and the STIs that presented a larger number of interactions with external sources, including the industrial sector / Mestrado / Mestre em Política Científica e Tecnológica

Open innovation that Henry Chesbrough introduced in 2003 promotes the usage of the input of outsiders to strengthen internal innovation processes and the search for outside commercialization opportunities for what is developed internally. Open innovation has enabled both academics and practitioners to design innovation strategies based on the reality of our connected world. Although the literature has identified and explored a variety of benefits and costs, to the best of our knowledge, no study has reviewed the benefits and costs of open innovation in terms of their importance for strategic performance. To conduct such a study, we need to take into account two main issues. First, the number of benefits and costs of open innovation are multifold; so, to have a comprehensive comparison, a large number of benefits and costs must be compared. Second, to have a fair comparison, benefits and costs must be compared in terms of different performance criteria, including financial and non-financial. Concerning the issues above, we will face a complex process of exploring benefits and costs. In this regard, we use multiple criterion decision-making (MCDM) methods that have shown promising solutions to complex exploratory problems. In particular, we present how using a stochastic multi-criteria clustering algorithm that is one of the recently introduced MCDM methods can bring promising results when it comes to exploring the strategic importance of benefits and costs of open innovation. Since there is no comprehensive understanding of the nature of the benefits and costs of open innovation, the proposed model aims to cluster them into hierarchical groups to help researchers identify the most crucial benefits and costs concerning different dimensions of performance. In addition, the model is able to deal with uncertainties related to technical parameters such as criteria weights and preference thresholds. We apply the model in the context of open innovation for universities concerning their research collaboration with industries. An online survey was conducted to collect experts' opinions on the open-innovation benefits and costs of university-industry research collaboration, given different performance dimensions. The results obtained through the cluster analysis specify that university researchers collaborate with industry mainly because of knowledge-related and research-related reasons rather than economic reasons. This research also indicates that the most important benefits of university-industry research collaboration for universities are implementing the learnings, increased know-how, accessing specialized infrastructures, accessing a greater idea and knowledge base, sensing and seizing new technological trends, and keeping the employees engaged. In addition, the results show that the most important costs are the lack of necessary resources to monitor activities between university and industry, an increased resistance to change among employees, conflict of interest (different missions), an increased employees' tendency to avoid using the knowledge that they do not create themselves, paying time costs associated with bureaucracy rules, and loss of focus. The research's findings enable researchers to analyze open innovation's related issues for universities more effectively and define their research projects on these issues in line with the priorities of universities.

This dissertation presents an in depth investigation of the office furniture industry and of the factors that influence selection and purchase of office furniture. It also utilizes data obtained in a national survey of nearly 270 office furniture buyers to investigate several general conceptual marketing issues. The industry-specific investigation includes a look at the history of the office and at events during the past 2-1/2 decades that have impacted the market for office furniture. It also includes a comparative look at the relative performance of wood (SIC 2521) and metal (SIC 2522) industry sectors. The performance of the office furniture industry is also compared with the wood household furniture industry (SIC 2511). This report includes a detailed look at the industry's changing product mix and use of wood-based materials. While the total use of wood-based material inputs by the industry was at record levels for all material categories in 1987, use on a per unit of output basis declined in several material categories. Material preferences, as expressed by survey respondents, indicated that solid wood is still rated highly and is the material of choice for interior and exterior applications in conventional office furniture manufacture. Twenty-six attributes of office furniture and of dealer/manufacturer services were rated on dual 7- and 5-point Likert scales for importance and difference, respectively. The most important attribute was the ability of the dealer manufacturer to provide products free of defects. However, when differences in the performance of suppliers or products were taken into account, the top determinant attribute was the ability to deliver on schedule. Comparisons of quality and selection and purchase ratings suggested that respondents tended to rate attributes on the bases of their use in selection and purchase higher overall than they rated their use in assessing quality. However, the relative ranking of attributes within the two sets of ratings were highly correlated. Respondent ratings of the 26 attributes were utilized in a multivariate study of quality dimensions employing confirmatory and exploratory factor analyses. Results of these analyses supported operationalization of most of Garvin's eight dimensions of quality. / Ph. D.

Most current machine vision systems for industrial inspection were developed with one specific task in mind. Due to the requirement for real-time operation, these systems are typically implemented in special purpose hardware that performs very specific operations. Hence, these systems inflexible in the sense that they cannot easily be adapted to other applications. However, current trends in computer technology suggests that low-cost general-purpose computers will be available in the very near future that are fast enough to meet the speed requirements of many industrial inspection problems. If this low-cost computing power is to be effectively utilized on industrial inspection problems, more general-purpose vision systems must be developed, vision systems that can be easily adapted to a variety of applications. Unfortunately, little research has gone into creating such general-purpose industrial inspection systems. In this dissertation, a general vision system framework has been developed that can be easily adapted to a variety of industrial web inspection problems. The objective of this system is to automatically locate and identify "defects" on the surface of the material being inspected. This framework is designed to be robust, to be flexible, and to be as computationally simple as possible. To assure robustness this framework employs a combined strategy of top-down and bottom-up control, hierarchical defect models, and uncertain reasoning methods. To make this framework flexible, a modular Blackboard framework is employed. To minimize computational complexity the system incorporates a simple multi-thresholding segmentation scheme, a fuzzy logic focus of attention mechanism for scene analysis operations, and a partitioning of knowledge that allows concurrent parallel processing during recognition. Based on the proposed vision system framework, a computer vision system for automated lumber grading has been developed. The purpose of this vision system is to locate and identify grading defects on rough hardwood lumber in a species independent manner. This problem seems to represent one of the more difficult and complex web inspection problems. The system has been tested on approximately 100 boards from several different species. Three different methods for performing label verification were tested and compared. These are a rule-based approach, a k-nearest neighbor approach, and a neural network approach. The results of these tests together with other considerations suggest that the neural network approach is the better choice and hence is the one selected for use in the vision system framework. Also, a new back-propagation learning algorithm using a steep activation function was developed that is much faster and more stable than the standard back-propagation learning algorithm. This algorithm was designed to speed the learning process involved in training a neural network to do label verification. However this algorithm seems to have general applicability. / Ph. D.

Sales of cottage cheese have been on the decline since 1972. Several factors have contributed towards this decline, including limited shelf-life. Cottage cheese shelf-life is estimated to be 10-21 days, in standard, non-barrier containers held at refrigeration temperatures. Shelf-life is shortened when aerobic, psychrotrophic microorganisms grow at refrigeration temperatures, producing changes which are undesirable. Previous studies have demonstrated that modified atmosphere packaging (MAP) is able to maintain cottage cheese quality and extend shelf-life over air packaging. The objectives of our study were to evaluate the ability of MAP to maintain cottage cheese quality, while establishing the proper atmosphere to be used. Further, we wanted to determine the potential for discoloration and development of undesirable acid flavors in cottage cheese by elevated CO₂ levels. Direct-set cottage cheese was packaged in barrier containers and flushed with 100% CO₂, 75% CO₂:25% N₂, 100% N₂, and air, and stored at 4°C for 28 days. Product quality was assessed by sensory evaluation. Microbiological and chemical tests were conducted to obtain a better understanding of the effects of MAP on cottage cheese. Results obtained demonstrated that there was no change during storage for headspace gas composition. Psychrotrophic and lactic acid bacteria increased for air treated samples. Counts for MAP cottage cheese remained unchanged. In contradiction to previous studies, elevated CO₂ levels did not cause product discoloration. Acidity increased over storage life; however, the increase in acidity was not perceived organoleptically. These results contradicted previous studies which demonstrated that elevated CO₂ levels imparted a sharp acid flavor to the food product. Lactic acid did not contribute towards increased acidity. Sensory evaluation demonstrated that air treatment was inadequate in maintaining product quality past day 19. Cottage cheese packaged under 100% CO₂ was judged most acceptable, followed by 75% CO₂ - 25% N₂, and 100% N₂ treatments. / Master of Science

This purpose of this study was to assess ground disturbance from harvesting hardwood stands with conventional rubber-tired skidders on slopes greater than 30 percent in Virginia. Special emphasis was placed on erosion, compaction and soil movement. Ten randomly selected study areas were clear-cut between September 1988 and August 1989; measurements followed between March 1989 and August 1989. Potential erosion was estimated using the Universal Soil Loss Equation and soil mechanical strength was measured with a cone penetrometer. Volumes of soil movement resulting from skid trails, landings, and waterbars were measured. Circular plots were used to estimate the percentage of each tract in seven disturbance classes. Descriptive data documented during the study includes land ownership, precipitation records, soil survey information, equipment (make, model, tire size), and volume of the products removed during harvesting. Results show a relatively small amount of soil disturbance associated with harvesting these tracts. Erosion estimates for seven of the ten tracts were below 1.08 tons/acre/year and only one was greater than 3.0 tons/acre/year. The erosion potential for these areas will decrease with time as vegetation increases. The primary source of ground disturbance within the harvested areas was due to skid trails, which occupied 3 to 10 percent of the ground surface. Tracts using overland skid trails experienced far less disturbance than those with bladed skid trails. Following harvest, the undisturbed area ranged from 73 to 81 percent on the ten study tracts. Scheduling practices, tract layout, and tract closure techniques concentrated in high risk spots, can greatly reduce the impact of harvesting steep slopes. / Master of Science

Master of Science

During the past 10 years, construction was the leading industry of occupational fatalities when compared to other goods producing industries in the US. This is partially attributed to ineffective safety management strategies, specifically lack of automated construction equipment and worker monitoring. Currently, worker safety performance is measured and recorded manually, assessed subjectively, and the resulting performance information is infrequently shared among selected or all project stakeholders. Accurate and emerging remote sensing technology provides critical spatio-temporal data that has the potential to automate and advance the safety monitoring of construction processes. This doctoral research focuses on pro-active safety utilizing radio-frequency location tracking (Ultra Wideband) and real-time three-dimensional (3D) immersive data visualization technologies. The objective of the research is to create a model that can automatically analyze the spatio-temporal data of the main construction resources (personnel, materials, and equipment), and automatically measure, assess, and visualize worker's safety performance. The research scope is limited to human-equipment interaction in a complex construction site layout where proximities among construction resources are omnipresent. In order to advance the understanding of human-equipment proximity issues, extensive data has been collected in various field trials and from projects with multiple scales. Computational algorithms developed in this research process the data to provide spatio-temporal information that is crucial for construction activity monitoring and analysis. Results indicate that worker's safety performance of selected activities can be automatically and objectively measured using the developed model. The major contribution of this research is the creation of a proximity hazards assessment model to automatically analyze spatio-temporal data of construction resources, and measure, evaluate, and visualize their safety performance. This research will significantly contribute to transform safety measures in construction industry, as it can determine and communicate automatically safe and unsafe conditions to various project participants located on the field or remotely.