Evaluating conservation and fisheries management strategies by linking spatial prioritization software and ecosystem and fisheries modelling tools

Well-designed marine protected area (MPA) networks can deliver a range of ecological, economic and social benefits, and so a great deal of research has focused on developing spatial conservation prioritization tools to help identify important areas. However, whilst these software tools are designed to identify MPA networks that both represent biodiversity and minimize impacts on stakeholders, they do not consider complex ecological processes. Thus, it is difficult to determine the impacts that proposed MPAs could have on marine ecosystem health, fisheries and fisheries sustainability. Using the eastern English Channel as a case study, this paper explores an approach to address these issues by identifying a series of MPA networks using the Marxan and Marxan with Zones conservation planning software and linking them with a spatially explicit ecosystem model developed in Ecopath with Ecosim. We then use these to investigate potential trade-offs associated with adopting different MPA management strategies. Limited-take MPAs, which restrict the use of some fishing gears, could have positive benefits for conservation and fisheries in the eastern English Channel, even though they generally receive far less attention in research on MPA network design. Our findings, however, also clearly indicate that no-take MPAs should form an integral component of proposed MPA networks in the eastern English Channel, as they not only result in substantial increases in ecosystem biomass, fisheries catches and the biomass of commercially valuable target species, but are fundamental to maintaining the sustainability of the fisheries. Synthesis and applications. Using the existing software tools Marxan with Zones and Ecopath with Ecosim in combination provides a powerful policy-screening approach. This could help inform marine spatial planning by identifying potential conflicts and by designing new regulations that better balance conservation objectives and stakeholder interests. In addition, it highlights that appropriate combinations of no-take and limited-take marine protected areas might be the most effective when making trade-offs between long-term ecological benefits and short-term political acceptability.

Design of composites for thermal management: Aluminum reinforced with diamond-containing bimodal particle mixtures

A new design route is proposed in order to fabricate aluminum matrix diamond-containing composite materials with optimized values of thermal conductivity (TC) for thermal management applications. The proper size ratio and proportions of particulate diamond–diamond and diamond–SiC bimodal mixtures are selected based on calculations with predictive schemes, which combine two main issues: (i) the volume fraction of the packed particulate mixtures, and (ii) the influence of different types of particulates (with intrinsically different metal/reinforcement interfacial thermal conductances) on the overall thermal conductivity of the composite material. The calculated results are validated by comparison with measurements on composites fabricated by gas pressure infiltration of aluminum into preforms of selected compositions of particle mixtures. Despite the relatively low quality (low price) of the diamond particles used in this work, outstanding values of TC are encountered: a maximum of 770 W/m K for Al/diamond–diamond and values up to 690 W/m K for Al/diamond–SiC.

Product/Brand co-creation methodology crossing marketing, design thinking, creativity and management: ideas(r)evolution

This thesis introduce a new innovation methodology called IDEAS(R)EVOLUTION that was developed according to an on-going experimental research project started in 2007. This new approach to innovation has initial based on Design thinking for innovation theory and practice. The concept of design thinking for innovation has received much attention in recent years. This innovation approach has climbed from the design and designers knowledge field towards other knowledge areas, mainly business management and marketing. Human centered approach, radical collaboration, creativity and breakthrough thinking are the main founding principles of Design thinking that were adapted by those knowledge areas due to their assertively and fitness to the business context and market complexity evolution. Also Open innovation, User-centered innovation and later on Living Labs models emerge as answers to the market and consumers pressure and desire for new products, new services or new business models. Innovation became the principal business management focus and strategic orientation. All this changes had an impact also in the marketing theory. It is possible now to have better strategies, communications plans and continuous dialogue systems with the target audience, incorporating their insights and promoting them to the main dissemination ambassadors of our innovations in the market. Drawing upon data from five case studies, the empirical findings in this dissertation suggest that companies need to shift from Design thinking for innovation approach to an holistic, multidimensional and integrated innovation system. The innovation context it is complex, companies need deeper systems then the success formulas that “commercial “Design thinking for innovation “preaches”. They need to learn how to change their organization culture, how to empower their workforce and collaborators, how to incorporate external stakeholders in their innovation processes, hoe to measure and create key performance indicators throughout the innovation process to give them better decision making data, how to integrate meaning and purpose in their innovation philosophy. Finally they need to understand that the strategic innovation effort it is not a “one shot” story it is about creating a continuous flow of interaction and dialogue with their clients within a “value creation chain“ mindset; RESUMO: Metodologia de co-criação de um produto/marca cruzando Marketing, Design Thinking, Criativity and Management - IDEAS(R)EVOLUTION. Esta dissertação apresenta uma nova metodologia de inovação chamada IDEAS(R)EVOLUTION, que foi desenvolvida segundo um projecto de investigação experimental contínuo que teve o seu início em 2007. Esta nova abordagem baseou-se, inicialmente, na teoria e na práctica do Design thinking para a inovação. Actualmente o conceito do Design Thinking para a inovação “saiu” do dominio da area de conhecimento do Design e dos Designers, tendo despertado muito interesse noutras áreas como a Gestão e o Marketing. Uma abordagem centrada na Pessoa, a colaboração radical, a criatividade e o pensamento disruptivo são principios fundadores do movimento do Design thinking que têm sido adaptados por essas novas áreas de conhecimento devido assertividade e adaptabilidade ao contexto dos negócios e à evolução e complexidade do Mercado. Também os modelos de Inovação Aberta, a inovação centrada no utilizador e mais tarde os Living Labs, emergem como possiveis soluções para o Mercado e para a pressão e desejo dos consumidores para novos productos, serviços ou modelos de negócio. A inovação passou a ser o principal foco e orientação estratégica na Gestão. Todas estas mudanças também tiveram impacto na teoria do Marketing. Hoje é possivel criar melhores estratégias, planos de comunicação e sistemas continuos de diálogo com o público alvo, incorporando os seus insights e promovendo os consumidores como embaixadores na disseminação da inovação das empresas no Mercado Os resultados empiricos desta tese, construídos com a informação obtida nos cinco casos realizados, sugerem que as empresas precisam de se re-orientar do paradigma do Design thinking para a inovação, para um sistema de inovação mais holistico, multidimensional e integrado. O contexto da Inovação é complexo, por isso as empresas precisam de sistemas mais profundos e não apenas de “fórmulas comerciais” como o Design thinking para a inovação advoga. As Empresas precisam de aprender como mudar a sua cultura organizacional, como capacitar sua força de trabalho e colaboradores, como incorporar os públicos externos no processo de inovação, como medir o processo de inovação criando indicadores chave de performance e obter dados para um tomada de decisão mais informada, como integrar significado e propósito na sua filosofia de inovação. Por fim, precisam de perceber que uma estratégia de inovação não passa por ter “sucesso uma vez”, mas sim por criar um fluxo contínuo de interação e diálogo com os seus clientes com uma mentalidade de “cadeia de criação de valor”

Development of super early genotypes for the dry bean (Phaseolus vulgaris) as affected by nitrogen management.

The super early genotypes (SEG) of dry bean (Phaseolus vulgaris L.) have a shorter life cycle (65-75 days) when compared with the season length of traditional cultivars (90-100 days). Timing of nitrogen top-dressing fertilization could be different because of this reduction in length of the SEG life cycle. This study aimed at characterizing, by using growth analysis and vegetation index, super early genotypes of dry bean development as affected by timing of nitrogen application. Field experiments were conducted in the 2014 and 2015 growing seasons in central Brazil with a randomized block experimental design with split plots scheme and four replicates. The plots comprised the dry bean genotypes (Colibri ? check cultivar, CNFC 15873, CNFC 15874, and CNFC 15875), and subplots comprised applications of N at different timings: 90 kg of N at sowing, 90 kg N at top-dressing; 45 kg of N at sowing plus 45 kg at top-dressing, with urea as the source of N. We also used a control treatment without N application. The CNFC 15874 super early genotype of dry bean had the higher grain yield (2776 kg ha-1) and differed from the CNFC 15873 genotype (2492 kg ha-1). Nitrogen fertilization allowed higher grain yield (2619 kg ha-1, when applied N at sowing, 2605 kg ha-1, when applied N at sowing and at top-dressing, and 2680 kg ha-1, when applied N at top-dressing) than the control, 2360 kg ha-1 (no N fertilization). The time of N fertilization in super early genotype of dry bean did not affect grain yield.

MILK : the Mobile Informal Learning Kit. Collaborating to design successful mobile learning applications

This paper suggests ways for educators and designers to understand and merge priorities in order to inform the development of mobile learning (m-learning) applications that maximise user experiences and hence learning opportunities. It outlines a User Experience Design (UXD) theory and development process that requires designers to conduct a thorough initial contextual inquiry into a particular domain in order to set project priorities and development guidelines. A matrix that identifies the key contextual considerations namely the social, cultural, spatial, technical and temporal constructs of any domain is presented as a vital tool for achieving successful UXD. The frame of reference provided by this matrix ensures that decisions made throughout the design process are attributable to a desired user experience. To illustrate how the proposed UXD theory and development process supports the creation of effective m-learning applications, this paper documents the development process of MILK (Mobile Informal Learning Kit). MILK is a support tool that allows teachers and students to develop event paths that consist of a series SMS question and answer messages that lead players through a series of checkpoints between point A and point B. These event paths can be designed to suit desired learning scenarios and can be used to explore a particular place or subject. They can also be designed to facilitate formal or informal learning experiences.

A framework for analyzing system intelligence of the building control system : a study of the integrated building management system

The lack of satisfactory consensus for characterizing the system intelligence and structured analytical decision models has inhibited the developers and practitioners to understand and configure optimum intelligent building systems in a fully informed manner. So far, little research has been conducted in this aspect. This research is designed to identify the key intelligent indicators, and develop analytical models for computing the system intelligence score of smart building system in the intelligent building. The integrated building management system (IBMS) was used as an illustrative example to present a framework. The models presented in this study applied the system intelligence theory, and the conceptual analytical framework. A total of 16 key intelligent indicators were first identified from a general survey. Then, two multi-criteria decision making (MCDM) approaches, the analytic hierarchy process (AHP) and analytic network process (ANP), were employed to develop the system intelligence analytical models. Top intelligence indicators of IBMS include: self-diagnostic of operation deviations; adaptive limiting control algorithm; and, year-round time schedule performance. The developed conceptual framework was then transformed to the practical model. The effectiveness of the practical model was evaluated by means of expert validation. The main contribution of this research is to promote understanding of the intelligent indicators, and to set the foundation for a systemic framework that provide developers and building stakeholders a consolidated inclusive tool for the system intelligence evaluation of the proposed components design configurations.

Life cycle modelling and design knowledge development in 3D virtual environments : final report

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Improving the management of building life cycle : a data mining approach

The construction industry has adapted information technology in its processes in terms of computer aided design and drafting, construction documentation and maintenance. The data generated within the construction industry has become increasingly overwhelming. Data mining is a sophisticated data search capability that uses classification algorithms to discover patterns and correlations within a large volume of data. This paper presents the selection and application of data mining techniques on maintenance data of buildings. The results of applying such techniques and potential benefits of utilising their results to identify useful patterns of knowledge and correlations to support decision making of improving the management of building life cycle are presented and discussed.

Quality-based benefit design in health insurance : the impact of a product benefit design change on the utilisation of oral health services by members of a private health insurance fund in regional and rural New South Wales, Australia

Objective: To examine the impact on dental utilisation following the introduction of a participating provider scheme (Regional and Rural Oral Health Program {RROHP)). In this model dentists receive higher third party payments from a private health insurance fund for delivering an agreed range of preventive and diagnostic benefits at no out-ofpocket cost to insured patients. Data source/Study setting: Hospitals Contribution Fund of Australia (HCF) dental claims for all members resident in New South Wales over the six financial years from l99811999 to 200312004. Study design: This cohort study involves before and after analyses of dental claims experience over a six year period for approximately 81,000 individuals in the intervention group (HCF members resident in regional and rural New South Wales, Australia) and 267,000 in the control group (HCF members resident in the Sydney area). Only claims for individuals who were members of HCF at 31 December 1997 were included. The analysis groups claims into the three years prior to the establishment of the RROHP and the three years subsequent to implementation. Data collection/Extraction methods: The analysis is based on all claims submitted by users of services for visits between 1 July 1988 and 30 June 2004. In these data approximately 1,000,000 services were provided to the intervention group and approximately 4,900,000 in the control group. Principal findings: Using Statistical Process Control (SPC) charts, special cause variation was identified in total utilisation rate of private dental services in the intervention group post implementation. No such variation was present in the control group. On average in the three years after implementation of the program the utilisation rate of dental services by regional and rural residents of New South Wales who where members of HCF grew by 12.6%, over eight times the growth rate of 1.5% observed in the control group (HCF members who were Sydney residents). The differences were even more pronounced in the areas of service that were the focus of the program: diagnostic and preventive services. Conclusion: The implementation of a benefit design change, a participating provider scheme, that involved the removal of CO-payments on a defined range of preventive and diagnostic dental services combined with the establishment and promotion of a network of dentists, appears to have had a marked impact on HCF members' utilisation of dental services in regional and rural New South Wales, Australia.

Developing Software Agents for Data Mining : [AIMM] Agents for Improving Maintenance Management

This report demonstrates the development of: • Development of software agents for data mining • Link data mining to building model in virtual environments • Link knowledge development with building model in virtual environments • Demonstration of software agents for data mining • Populate with maintenance data

Demonstration of Software Agents Prototype System : [AIMM] Agents for Improving Maintenance Management

This report presents the demonstration of software agents prototype system for improving maintenance management [AIMM] including: • Developing and implementing a user focused approach for mining the maintenance data of buildings. This report presents the demonstration of software agents prototype system for improving maintenance management [AIMM] including: • Developing and implementing a user focused approach for mining the maintenance data of buildings. • Refining the development of a multi agent system for data mining in virtual environments (Active Worlds) by developing and implementing a filtering agent on the results obtained from applying data mining techniques on the maintenance data. • Integrating the filtering agent within the multi agents system in an interactive networked multi-user 3D virtual environment. • Populating maintenance data and discovering new rules of knowledge.

Life Cycle Modelling and Design Knowledge Development in 3D Virtual Environments

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.