Transition to Product Service Systems: methodology based on scenarios identification, modelling and evaluation

Part 4: Transition Towards Product-Service Systems

Analysis of Dyscalculia Evidences through Artificial Intelligence Systems

Dyscalculia is usually perceived of as a specific learning difficulty for mathematics or, more appropriately, arithmetic. Because definitions and diagnoses of dyscalculia are in their infancy and sometimes are contradictory. However, mathematical learning difficulties are certainly not in their infancy and are very prevalent and often devastating in their impact. Co-occurrence of learning disorders appears to be the rule rather than the exception. Co-occurrence is generally assumed to be a consequence of risk factors that are shared between disorders, for example, working memory. However, it should not be assumed that all dyslexics have problems with mathematics, although the percentage may be very high, or that all dyscalculics have problems with reading and writing. Because mathematics is very developmental, any insecurity or uncertainty in early topics will impact on later topics, hence to need to take intervention back to basics. However, it may be worked out in order to decrease its degree of severity. For example, disMAT, an app developed for android may help children to apply mathematical concepts, without much effort, that is turning in itself, a promising tool to dyscalculia treatment. Thus, this work will focus on the development of a Decision Support System to estimate children evidences of dyscalculia, based on data obtained on-the-fly with disMAT. The computational framework is built on top of a Logic Programming approach to Knowledge Representation and Reasoning, grounded on a Case-based approach to computing, that allows for the handling of incomplete, unknown, or even self-contradictory information.

Business intelligence no suporte às autarquias locais e à sua gestão orçamental

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Pervasive business intelligence: a marketing intelligence framework proposal

Doctoral Thesis in Information Systems and Technologies Area of Information Systems and Technology

Enhancing emancipatory systems methodologies for sustainable development

Our interest lies in applying the principles of critical systems thinking to human activity systems in developing countries in situations where issues of natural resource sustainability constrain the feasible set of long-term strategies. The concept of sustainable development provides an expanded domain for critical systems thinking. The fundamental values underpinning sustainable development are that both intragenerational and intergenerational equity are important. As a consequence, key stakeholders are often excluded from power-sharing within current social systems. Addressing these issues requires renewed focus on emancipatory commitment and methodologies. To date, Ulrich's critical systems heuristics is the only critical systems methodology that offers practicable tools for emancipation. A case study analysis in Tigray, northern Ethiopia, provides insights in relation to the application of critical system heuristics to issues of sustainable development and highlights the need to extend the use of critical systems heuristics beyond the design and monitoring of structured interventions.

Perspectives for Forensic Intelligence in anti-doping: thinking outside the box

Today's approach to anti-doping is mostly centered on the judicial process, despite pursuing a further goal in the detection, reduction, solving and/or prevention of doping. Similarly to decision-making in the area of law enforcement feeding on Forensic Intelligence, anti-doping might significantly benefit from a more extensive gathering of knowledge. Forensic Intelligence might bring a broader logical dimension to the interpretation of data on doping activities for a more future-oriented and comprehensive approach instead of the traditional case-based and reactive process. Information coming from a variety of sources related to doping, whether directly or potentially, would feed an organized memory to provide real time intelligence on the size, seriousness and evolution of the phenomenon. Due to the complexity of doping, integrating analytical chemical results and longitudinal monitoring of biomarkers with physiological, epidemiological, sociological or circumstantial information might provide a logical framework enabling fit for purpose decision-making. Therefore, Anti-Doping Intelligence might prove efficient at providing a more proactive response to any potential or emerging doping phenomenon or to address existing problems with innovative actions or/and policies. This approach might prove useful to detect, neutralize, disrupt and/or prevent organized doping or the trafficking of doping agents, as well as helping to refine the targeting of athletes or teams. In addition, such an intelligence-led methodology would serve to address doping offenses in the absence of adverse analytical chemical evidence.

Perspectives for Forensic Intelligence in anti-doping: Thinking outside of the box.

Today's approach to anti-doping is mostly centered on the judicial process, despite pursuing a further goal in the detection, reduction, solving and/or prevention of doping. Similarly to decision-making in the area of law enforcement feeding on Forensic Intelligence, anti-doping might significantly benefit from a more extensive gathering of knowledge. Forensic Intelligence might bring a broader logical dimension to the interpretation of data on doping activities for a more future-oriented and comprehensive approach instead of the traditional case-based and reactive process. Information coming from a variety of sources related to doping, whether directly or potentially, would feed an organized memory to provide real time intelligence on the size, seriousness and evolution of the phenomenon. Due to the complexity of doping, integrating analytical chemical results and longitudinal monitoring of biomarkers with physiological, epidemiological, sociological or circumstantial information might provide a logical framework enabling fit for purpose decision-making. Therefore, Anti-Doping Intelligence might prove efficient at providing a more proactive response to any potential or emerging doping phenomenon or to address existing problems with innovative actions or/and policies. This approach might prove useful to detect, neutralize, disrupt and/or prevent organized doping or the trafficking of doping agents, as well as helping to refine the targeting of athletes or teams. In addition, such an intelligence-led methodology would serve to address doping offenses in the absence of adverse analytical chemical evidence.

METHODOLOGY FOR AUTOMATICALLY DELIMITING PERMANENT PRESERVATION AREAS ALONG WATER COURSES - THE USE OF GIS IN THE HYDROLOGICAL BASIN OF THE SERGIPE RIVER, BRAZIL

ABSTRACT Permanent Preservation Areas (PPAs) along watercourses have been the focus of numerous studies, not only because of the fragility and ecological relevance of riverine vegetation, but also because of the inefficiency demonstrated in conforming to the legislation protecting it. One of the major difficulties encountered in terms of guaranteeing the effective conservation of these riverside areas is the absence of methodologies that can be used to define them rapidly and accurately without manually determining the widths of the rivers or assigning only uniform linear values for the entire watercourse. The present work sought to develop a spatial analysis methodology capable of automatically defining permanent preservation areas along watercourses using geographic information system (GIS) software. The present study was undertaken in the Sergipe River basin, "considering the river itself and its principal affluents. We used the database of the Digital Atlas of Hydrological Resources (SEMARH/SE), and the delimitations of the PPAs were performed using ArcGIS 10.1 and the XToolPro 9.0 extension. A total of 5,003.82 hectares of Permanent Preservation Areas were delimited along the margins of the rivers analyzed, with a margin of error of <1% in delimiting the widths of the rivers within the entire area considered. The methodology described here can be used to define PPAs efficiently, relatively rapidly, and with very small margins of error, thus representing a technological advance in terms of using GIS for land management.

Enhancing the development of military capabilities by a systems approach

The political environment of security and defence has changed radically in the Western industrialised world since the Cold War. As a response to these changes, since the beginning of the twenty-first century, most Western countries have adopted a ‘capabilities-based approach’ to developing and operating their armed forces. More responsive and versatile military capabilities must be developed to meet the contemporary challenges. The systems approach is seen as a beneficial means of overcoming traps in resolving complex real -world issues by conventional thinking. The main objectives of this dissertation are to explore and assess the means to enhance the development of military capabilities both in concept development and experimentation (CD&E) and in national defence materiel collaboration issues. This research provides a unique perspective, a systems approach, to the development areas of concern in resolving complex real-world issues. This dissertation seeks to increase the understanding of the military capability concept both as a whole and with in its life cycle. The dissertation follows the generic functionalist systems methodology by Jackson. The methodology applies a comprehensive set of constitutive rules to examine the research objectives. This dissertation makes contribution to current studies about military capability. It presents two interdepen dent conceptual capability models: the comprehensive capability meta-model (CCMM) and the holistic capability life cycle model (HCLCM). These models holistically and systematically complement the existing, but still evolving, understanding of military capability and its life cycle. In addition, this dissertation contributes to the scientific discussion of defence procurement in its broad meaning by introducing the holistic model about the national defence materiel collaboration between the defence forces, defence industry and academia. The model connects the key collaborative mechanisms, which currently work in isolation from each other, and take into consideration the unique needs of each partner. This dissertation contributes empirical evidence regarding the benefits of enterprise architectures (EA) to CD&E. The EA approach may add value to traditional concept development by increasing the clarity, consistency and completeness of the concept. The most important use considered for EA in CD&E is that it enables further utilisation of the concept created in the case project.

Decision support system for the diagnosis of schizophrenia disorders

Clinical decision support systems are useful tools for assisting physicians to diagnose complex illnesses. Schizophrenia is a complex, heterogeneous and incapacitating mental disorder that should be detected as early as possible to avoid a most serious outcome. These artificial intelligence systems might be useful in the early detection of schizophrenia disorder. The objective of the present study was to describe the development of such a clinical decision support system for the diagnosis of schizophrenia spectrum disorders (SADDESQ). The development of this system is described in four stages: knowledge acquisition, knowledge organization, the development of a computer-assisted model, and the evaluation of the system's performance. The knowledge was extracted from an expert through open interviews. These interviews aimed to explore the expert's diagnostic decision-making process for the diagnosis of schizophrenia. A graph methodology was employed to identify the elements involved in the reasoning process. Knowledge was first organized and modeled by means of algorithms and then transferred to a computational model created by the covering approach. The performance assessment involved the comparison of the diagnoses of 38 clinical vignettes between an expert and the SADDESQ. The results showed a relatively low rate of misclassification (18-34%) and a good performance by SADDESQ in the diagnosis of schizophrenia, with an accuracy of 66-82%. The accuracy was higher when schizophreniform disorder was considered as the presence of schizophrenia disorder. Although these results are preliminary, the SADDESQ has exhibited a satisfactory performance, which needs to be further evaluated within a clinical setting.

Os fatores de influência na decisão de escolha entre softwares de business intelligence

O cenário empresarial atual leva as empresas a terem atuações cada vez mais dinâmicas, buscando utilizar as informações disponíveis de modo a melhorar seu processo de decisão. Com esse objetivo, diversas organizações têm adquirido sistemas de business intelligence. O processo de seleção de sistemas é difícil, diferente do utilizado em outras aquisições empresariais e sofre influência de diversos aspectos intangíveis, o que impossibilita o uso das técnicas de análise financeira normalmente utilizadas pelas companhias para apoiar decisões de investimento. Dessa forma, pode-se dizer que a decisão de escolha de um software de business intelligence é baseada em um conjunto de fatores tanto tangíveis quanto intangíveis. Este trabalho teve como objetivo principal identificar e estabelecer um ranking dos principais fatores que influenciam a decisão de escolha entre sistemas de business intelligence, tendo como foco empresas do setor de incorporação imobiliária atuantes na grande São Paulo e como objetivo secundário procurar identificar a possível existência de aspectos determinantes para a decisão de escolha entre a lista de fatores apurados. Essa pesquisa foi realizada através de doze entrevistas com pessoas que participaram de processos de decisão de escolha de sistemas de business intelligence, sendo algumas da área de TI e outras de área de negócio, atuantes em sete empresas incorporadoras da grande São Paulo. Essa avaliação teve como resultado a identificação dos fatores mais importantes e a sua classificação hierárquica, possibilitando a apuração de um ranking composto pelos catorze fatores mais influentes na decisão de escolha e statisticamente válido segundo o coeficiente de concordância de Kendall. Desse total, apenas três puderam ser classificados como determinantes ou não determinantes; o restante não apresentou padrões de resposta estatisticamente válidos para permitir conclusões sobre esse aspecto. Por fim, após a análise dos processos de seleção utilizados pelas sete empresas dessa pesquisa, foram observadas duas fases, as quais sofrem influência de distintos fatores. Posteriormente, estudando-se essas fases em conjunto com os fatores identificados no ranking, pôde-se propor um processo de seleção visando uma possível redução de tempo e custo para a realização dessa atividade. A contribuição teórica deste trabalho está no fato de complementar as pesquisas que identificam os fatores de influência no processo de decisão de escolha de sistemas, mais especificamente de business intelligence, ao estabelecer um ranking de importância para os itens identificados e também o relacionamento de fatores de importância a fases específicas do processo de seleção identificadas neste trabalho.

Methodology for the comparison of military expenditures

Includes bibliography

Neural models for predicting hole diameters in drilling processes

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Neural models for predicting hole diameters in drilling processes

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Bio-inspired enhancement of reputation systems for intelligent environments

Providing security to the emerging field of ambient intelligence will be difficult if we rely only on existing techniques, given their dynamic and heterogeneous nature. Moreover, security demands of these systems are expected to grow, as many applications will require accurate context modeling. In this work we propose an enhancement to the reputation systems traditionally deployed for securing these systems. Different anomaly detectors are combined using the immunological paradigm to optimize reputation system performance in response to evolving security requirements. As an example, the experiments show how a combination of detectors based on unsupervised techniques (self-organizing maps and genetic algorithms) can help to significantly reduce the global response time of the reputation system. The proposed solution offers many benefits: scalability, fast response to adversarial activities, ability to detect unknown attacks, high adaptability, and high ability in detecting and confining attacks. For these reasons, we believe that our solution is capable of coping with the dynamism of ambient intelligence systems and the growing requirements of security demands.