Decision Support System for Investment Analysis

The purpose of this thesis lies on selecting and automating a set of Fundamental Analysis indicators and studying related software tools that can help investors understanding market behaviour. The several distinct data-sources, tools and methods will be evaluated using a Decision Making process for Financial Markets. Sometimes there’s not enough data in which we can base the investment decision upon, other times the data lacks quality, while other times, despite having the right data, the problem lies on the process of analyzing the data and then turning that analysis into a concrete decision. Also, since the human decision making process is not well systemized, there are times when both the data and the analysis are well performed, but the results may vary even when confronted with similar data patterns more than once. This is particularly crucial when dealing with fast-paced environments like the Financial Markets. This thesis will therefore study tools for systemizing a Decision Making process based on fundamental analysis indicators over financial markets and will evaluate how such tools help to avoid uncertainty in human decision and to complement lack of data and poor data quality. There are two essential building blocks of such a system: the data set and the model that analyses the data and ultimately, provides information that facilitates the decision making process about a particular investment. Both blocks will be made available in the framework of the research project at GoBusiness Finance.O propósito desta dissertação reside na selecção e sistematização de um conjunto de indicadores financeiros para Análise Fundamental, assim como, o estudo de ferramentas que possam ajudar investidores a terem um melhor entendimento do segmento das acções dos Mercados Financeiros. Por vezes, não existe informação suficiente sobre a qual possamos basear as decisões de investimento, por outrem, existem vezes em que a informação existe, mas a qualidade da mesma não pode ser comprovada. Também acontecem casos em que, apesar de possuirmos a informação adequada, o problema recai no processo de análise da informação e na subsequente tomada de decisão. Para além das questões relacionadas com informação, existe também o facto de o processo de decisão desempenhado pelos humanos não ser bem sistematizado. Assim, podem surgir ocasiões em que as decisões resultantes são distintas, mesmo quando confrontados com padrões de informação e resultados de análise semelhantes. Isto é particularmente importante quando lidamos com ambientes em que as decisões são tomadas de forma tremendamente rápida, como é o exemplo dos mercados financeiros. Com isto, esta tese irá estudar ferramentas para sistematizar o processo de tomar decisões relativas a investimentos nos mercados, com base em princípios análise fundamental. Existem duas componentes essenciais para a construção de um sistema de apoio à decisão: o data set e os modelos de análise ao mesmo. Ambas as componentes serão estudadas e disponibilizadas em âmbito empresarial na Gobusiness Finance

Simultaneous prediction of four ATP-binding cassette transporters' substrates using multi-label QSAR

Efflux by the ATP-binding cassette (ABC) transporters affects the pharmacokinetic profile of drugs and it has been implicated in drug-drug interactions as well as its major role in multi-drug resistance in cancer. It is therefore important for the pharmaceutical industry to be able to understand what phenomena rule ABC substrate recognition. Considering a high degree of substrate overlap between various members of ABC transporter family, it is advantageous to employ a multi-label classification approach where predictions made for one transporter can be used for modeling of the other ABC transporters. Here, we present decision tree-based QSAR classification models able to simultaneously predict substrates and non-substrates for BCRP1, P-gp/MDR1 and MRP1 and MRP2, using a dataset of 1493 compounds. To this end, two multi-label classification QSAR modelling approaches were adopted: Binary Relevance (BR) and Classifier Chain (CC). Even though both multi-label models yielded similar predictive performances in terms of overall accuracies (close to 70), the CC model overcame the problem of skewed performance towards identifying substrates compared with non-substrates, which is a common problem in the literature. The models were thoroughly validated by using external testing, applicability domain and activity cliffs characterization. In conclusion, a multi-label classification approach is an appropriate alternative for the prediction of ABC efflux. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Assessment of spatial disparities in India: A contribution to advancing urban research methods in rapid growth contexts

The 21st century urbanization under neoliberalisation unfolding in the countries of the Global South is characterized by unprecedented increase in population and infrastructure demand, and by dramatic spatial and institutional transformation, which has escalated disparities at multiple scales. As per United Nations World Urbanization Prospects: The 2018 Revision, urbanization level is set to raise to almost 70% by 2050 with majority of the countries in the Global South doubling their population. India is predicted to surpass China and become the most populous country by 2050. Despite the adoption of spatial distribution initiatives such as development of Delhi-Mumbai Industrial Corridor and spatial decentralisation by the national government, Indian urban landscape is marred by disparities. Under the prevalent disparities, accommodating population growth will be an enormous challenge for India, given its limited institutional capacity to manage growth and provide infrastructure. This research takes the states in India through which the Delhi-Mumbai Industrial Corridor will pass as a study area and aims to develop an analytical framework hinged on a theoretical foundation for reducing disparities by integrating infrastructure provision with settlement structure. This framework using mixed methods and multiscale approach enables discerning and explaining spatial disparities across space and time. The development of such a framework makes two novel contributions to urban research: first, it underscores the relevance of classic urban theories and models for investigating and interpreting the spatial disparities in the regions of the Global South. Second, given data scarcity in these regions, the employment of mixed methods for understanding spatial disparities can be used as a proactive planning tool by policy makers to formulate evidence-based policies for reducing disparities by integrating growth with infrastructure provision. This research applied classical urban theories and models at multiple scales to describe the manifestation of spatial disparities in India. It established the relevance of these theories and models to understand the settlement system as well as to establish important gaps in infrastructure provision while predicting future growth. The papers presented here provide ample evidence that the mixed methods approach can be usefully applied to explain the context-specific peculiarities of spatial disparities. A further contribution of this research is to show that the development of a dataset well synchronised with spatial information on socioeconomic and infrastructure variables is essential for empirically establishing spatial disparities. This research explained the manifestation of spatial disparities at multiple scales. It applied several indicators such as accessibility, connectivity and commuting patterns to establish the weakness of spatial links at multiple scales (such as metropolitan, regional and inter-state). This can be considered an important contribution since improved transport links and access to employment and public services reduces spatial barriers to development. Variables on social and physical infrastructure were examined to determine a lack of adequate services in small and intermediate cities predicted to grow. This is also a crucial finding, as sufficient infrastructure and other amenities have long been considered essential to reduce spatial disparities. This research provides evidence-based policy reforms at multiple scale for curtailing spatial disparities. It argues for the introduction of spatial planning at the national level and its integration with economic plans. This integration needs to be promoted at lower tiers of government. At regional scale, the findings recommend an empowered regional authority to develop and implement a regional spatial strategy, which is not only integrated with plans of the respective state governments but is also legally binding for the states. At the lowest scale, it is crucial to enhance the potential of small and intermediate settlements, and to move towards an integrated rural-urban governance that treats these areas as one unit for the planning and implementation of infrastructure. This research recommends implementing the 73rd and 74th Constitutional Amendment Act to facilitate integrated planning and governance at multiple scales

Machine Learning for Modelling Tissue Distribution of Drugs and the Impact of Transporters

The ability to predict human pharmacokinetics in early stages of drug development is of paramount importance to prevent late stage attrition as well as in managing toxicity. This thesis explores the machine learning modelling of one of the main pharmacokinetics parameters that determines the therapeutic success of a drug - volume of distribution. In order to do so, a variety of physiological phenomena with known mechanisms of impact on drug distribution were considered as input features during the modelling of volume of distribution namely, Solute Carriers-mediated uptake and ATP-binding Cassette-mediated efflux, drug-induced phospholipidosis and plasma protein binding. These were paired with molecular descriptors to provide both chemical and biological information to the building of the predictive models. Since biological data used as input is limited, prior to modelling volume of distribution, the various types of physiological descriptors were also modelled. Here, a focus was placed on harnessing the information contained in correlations within the two transporter families, which was done by using multi-label classification. The application of such approach to transporter data is very recent and its use to model Solute Carriers data, for example, is reported here for the first time. On both transporter families, there was evidence that accounting for correlations between transporters offers useful information that is not portrayed by molecular descriptors. This effort also allowed uncovering new potential links between members of the Solute Carriers family, which are not obvious from a purely physiological standpoint. The models created for the different physiological parameters were then used to predict these parameters and fill in the gaps in the available experimental data, and the resulting merging of experimental and predicted data was used to model volume of distribution. This exercise improved the accuracy of volume of distribution models, and the generated models incorporated a wide variety of the different physiological descriptors supplied along with molecular features. The use of most of these physiological descriptors in the modelling of distribution is unprecedented, which is one of the main novelty points of this thesis. Additionally, as a parallel complementary work, a new method to characterize the predictive reliability of machine learning classification model was proposed, and an in depth analysis of mispredictions, their trends and causes was carried out, using one of the transporter models as example. This is an important complement to the main body of work in this thesis, as predictive performance is necessarily tied to prediction reliability

An experimental and computational investigation of rotating flexible shaft system dynamics in rotary drilling assemblies for down hole drilling vibration mitigation

Rotary drilling system vibration has long been associated with damaging the bit, the bottom hole assembly (BHA) and drill string. Vibration has been traditionally measured in the bottom hole assembly, and been closely associated with the resonant behaviors. This research study proposes an improved physical laboratory model to explore the dynamic behaviors associated with vibration. This model includes contact with the borehole wall allowing a range of stabilization geometries while removing bit-formation interaction effects. The results of exercising the model help develop new insights into both vibration measurement diagnostics and mitigation strategy execution. Presented here is a review of other physical bottom hole assembly and drilling concepts, and a new novel model. Experimental investigation using the new model for a range of geometries is presented with recorded conditions, annotated video stills and analysis using regression and response surface methods. The analysis when compared to existing industry mitigation methods allows unique insight to the possible effectiveness of such methods. A numerical simulation of the system was also performed and its results compared to the laboratory tests. Results show that a shaft system alone can generate stick-slip and whirl behaviors. Such behaviors occur in distinct regions. Another conclusion of this work is that a popular method for inferring stick-slip from acceleration measures is not reliable for the system used in this study

Resource provisioning and scheduling algorithms for hybrid workflows in edge cloud computing

In recent years, Internet of Things (IoT) technology has been involved in a wide range of application domains to provide real-time monitoring, tracking and analysis services. The worldwide number of IoT-connected devices is projected to increase to 43 billion by 2023, and IoT technologies are expected to engaged in 25% of business sector. Latency-sensitive applications in scope of intelligent video surveillance, smart home, autonomous vehicle, augmented reality, are all emergent research directions in industry and academia. These applications are required connecting large number of sensing devices to attain the desired level of service quality for decision accuracy in a sensitive timely manner. Moreover, continuous data stream imposes processing large amounts of data, which adds a huge overhead on computing and network resources. Thus, latency-sensitive and resource-intensive applications introduce new challenges for current computing models, i.e, batch and stream. In this thesis, we refer to the integrated application model of stream and batch applications as a hybrid work ow model. The main challenge of the hybrid model is achieving the quality of service (QoS) requirements of the two computation systems. This thesis provides a systemic and detailed modeling for hybrid workflows which describes the internal structure of each application type for purposes of resource estimation, model systems tuning, and cost modeling. For optimizing the execution of hybrid workflows, this thesis proposes algorithms, techniques and frameworks to serve resource provisioning and task scheduling on various computing systems including cloud, edge cloud and cooperative edge cloud. Overall, experimental results provided in this thesis demonstrated strong evidences on the responsibility of proposing different understanding and vision on the applications of integrating stream and batch applications, and how edge computing and other emergent technologies like 5G networks and IoT will contribute on more sophisticated and intelligent solutions in many life disciplines for more safe, secure, healthy, smart and sustainable society

An investigation of stress wave propagation through rock joints and rock masses

Tese de doutoramento. Engenharia Civil. Faculdade de Engenharia. Universidade do Porto, Laboratório Nacional de Engenharia Civil. 201