A data-driven analytics approach in the study of pneumonia’s fatalities

As huge amounts of data become available in organizations and society, specific data analytics skills and techniques are needed to explore this data and extract from it useful patterns, tendencies, models or other useful knowledge, which could be used to support the decision-making process, to define new strategies or to understand what is happening in a specific field. Only with a deep understanding of a phenomenon it is possible to fight it. In this paper, a data-driven analytics approach is used for the analysis of the increasing incidence of fatalities by pneumonia in the Portuguese population, characterizing the disease and its incidence in terms of fatalities, knowledge that can be used to define appropriate strategies that can aim to reduce this phenomenon, which has increased more than 65% in a decade.

Combining data mining and evolutionary computation for multi-criteria optimization of earthworks

Earthworks tasks aim at levelling the ground surface at a target construction area and precede any kind of structural construction (e.g., road and railway construction). It is comprised of sequential tasks, such as excavation, transportation, spreading and compaction, and it is strongly based on heavy mechanical equipment and repetitive processes. Under this context, it is essential to optimize the usage of all available resources under two key criteria: the costs and duration of earthwork projects. In this paper, we present an integrated system that uses two artificial intelligence based techniques: data mining and evolutionary multi-objective optimization. The former is used to build data-driven models capable of providing realistic estimates of resource productivity, while the latter is used to optimize resource allocation considering the two main earthwork objectives (duration and cost). Experiments held using real-world data, from a construction site, have shown that the proposed system is competitive when compared with current manual earthwork design.

Measurement of the top-quark mass in the fully hadronic decay channel from ATLAS data at s√=7 TeV

The mass of the top quark is measured in a data set corresponding to 4.6 fb−1 of proton--proton collisions with centre-of-mass energy s√=7 TeV collected by the ATLAS detector at the LHC. Events consistent with hadronic decays of top--antitop quark pairs with at least six jets in the final state are selected. The substantial background from multijet production is modelled with data-driven methods that utilise the number of identified b-quark jets and the transverse momentum of the sixth leading jet, which have minimal correlation. The top-quark mass is obtained from template fits to the ratio of three-jet to dijet mass. The three-jet mass is calculated from the three jets of a top-quark decay. Using these three jets the dijet mass is obtained from the two jets of the W boson decay. The top-quark mass obtained from this fit is thus less sensitive to the uncertainty in the energy measurement of the jets. A binned likelihood fit yields a top-quark mass of mt = 175.1 ± 1.4 (stat.) ± 1.2 (syst.) GeV.

The Data Mining applied for the prediction of highway roughness due to overloaded trucks

Currently, the quality of the Indonesian national road network is inadequate due to several constraints, including overcapacity and overloaded trucks. The high deterioration rate of the road infrastructure in developing countries along with major budgetary restrictions and high growth in traffic have led to an emerging need for improving the performance of the highway maintenance system. However, the high number of intervening factors and their complex effects require advanced tools to successfully solve this problem. The high learning capabilities of Data Mining (DM) are a powerful solution to this problem. In the past, these tools have been successfully applied to solve complex and multi-dimensional problems in various scientific fields. Therefore, it is expected that DM can be used to analyze the large amount of data regarding the pavement and traffic, identify the relationship between variables, and provide information regarding the prediction of the data. In this paper, we present a new approach to predict the International Roughness Index (IRI) of pavement based on DM techniques. DM was used to analyze the initial IRI data, including age, Equivalent Single Axle Load (ESAL), crack, potholes, rutting, and long cracks. This model was developed and verified using data from an Integrated Indonesia Road Management System (IIRMS) that was measured with the National Association of Australian State Road Authorities (NAASRA) roughness meter. The results of the proposed approach are compared with the IIRMS analytical model adapted to the IRI, and the advantages of the new approach are highlighted. We show that the novel data-driven model is able to learn (with high accuracy) the complex relationships between the IRI and the contributing factors of overloaded trucks

Modelling Z→ττ processes in ATLAS with τ-embedded Z→μμ data

This paper describes the concept, technical realisation and validation of a largely data-driven method to model events with Z→ττ decays. In Z→μμ events selected from proton-proton collision data recorded at s√=8 TeV with the ATLAS experiment at the LHC in 2012, the Z decay muons are replaced by τ leptons from simulated Z→ττ decays at the level of reconstructed tracks and calorimeter cells. The τ lepton kinematics are derived from the kinematics of the original muons. Thus, only the well-understood decays of the Z boson and τ leptons as well as the detector response to the τ decay products are obtained from simulation. All other aspects of the event, such as the Z boson and jet kinematics as well as effects from multiple interactions, are given by the actual data. This so-called τ-embedding method is particularly relevant for Higgs boson searches and analyses in ττ final states, where Z→ττ decays constitute a large irreducible background that cannot be obtained directly from data control samples.

Using customer lifetime value to improve the prediction of bank deposit subscription in telemarketing campaigns

Customer lifetime value (LTV) enables using client characteristics, such as recency, frequency and monetary (RFM) value, to describe the value of a client through time in terms of profitability. We present the concept of LTV applied to telemarketing for improving the return-on-investment, using a recent (from 2008 to 2013) and real case study of bank campaigns to sell long- term deposits. The goal was to benefit from past contacts history to extract additional knowledge. A total of twelve LTV input variables were tested, un- der a forward selection method and using a realistic rolling windows scheme, highlighting the validity of five new LTV features. The results achieved by our LTV data-driven approach using neural networks allowed an improvement up to 4 pp in the Lift cumulative curve for targeting the deposit subscribers when compared with a baseline model (with no history data). Explanatory knowledge was also extracted from the proposed model, revealing two highly relevant LTV features, the last result of the previous campaign to sell the same product and the frequency of past client successes. The obtained results are particularly valuable for contact center companies, which can improve pre- dictive performance without even having to ask for more information to the companies they serve.

Conceptual-physical bridging – From BPMN models to physical implementations on kettle

Developing and implementing data-oriented workflows for data migration processes are complex tasks involving several problems related to the integration of data coming from different schemas. Usually, they involve very specific requirements - every process is almost unique. Having a way to abstract their representation will help us to better understand and validate them with business users, which is a crucial step for requirements validation. In this demo we present an approach that provides a way to enrich incrementally conceptual models in order to support an automatic way for producing their correspondent physical implementation. In this demo we will show how B2K (Business to Kettle) system works transforming BPMN 2.0 conceptual models into Kettle data-integration executable processes, approaching the most relevant aspects related to model design and enrichment, model to system transformation, and system execution.

Search for massive supersymmetric particles decaying to many jets using the ATLAS detector in pp collisions at s√=8TeV

Results of a search for decays of massive particles to fully hadronic final states are presented. This search uses 20.3 fb−1 of data collected by the ATLAS detector in s√=8TeV proton--proton collisions at the LHC. Signatures based on high jet multiplicities without requirements on the missing transverse momentum are used to search for R-parity-violating supersymmetric gluino pair production with subsequent decays to quarks. The analysis is performed using a requirement on the number of jets, in combination with separate requirements on the number of b-tagged jets, as well as a topological observable formed from the scalar sum of the mass values of large-radius jets in the event. Results are interpreted in the context of all possible branching ratios of direct gluino decays to various quark flavors. No significant deviation is observed from the expected Standard Model backgrounds estimated using jet-counting as well as data-driven templates of the total-jet-mass spectra. Gluino pair decays to ten or more quarks via intermediate neutralinos are excluded for a gluino with mass mg~<1TeV for a neutralino mass mχ~01=500GeV. Direct gluino decays to six quarks are excluded for mg~<917GeV for light-flavor final states, and results for various flavor hypotheses are presented.

Caso de demonstração de uma framework para automatização do teste de APIs de aplicações SaaS

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

Systems biology approaches for the design of novel Saccharomyces cerevisiae winemaking strains for enhanced flavour compounds synthesis

Tese de Doutoramento em Biologia Ambiental e Molecular

Hazard assessment of storm events for the battery, New York

The environmental and socio-economic importance of coastal areas is widely recognized, but at present these areas face severe weaknesses and high-risk situations. The increased demand and growing human occupation of coastal zones have greatly contributed to exacerbating such weaknesses. Today, throughout the world, in all countries with coastal regions, episodes of waves overtopping and coastal flooding are frequent. These episodes are usually responsible for property losses and often put human lives at risk. The floods are caused by coastal storms primarily due to the action of very strong winds. The propagation of these storms towards the coast induces high water levels. It is expected that climate change phenomena will contribute to the intensification of coastal storms. In this context, an estimation of coastal flooding hazards is of paramount importance for the planning and management of coastal zones. Consequently, carrying out a series of storm scenarios and analyzing their impacts through numerical modeling is of prime interest to coastal decision-makers. Firstly, throughout this work, historical storm tracks and intensities are characterized for the northeastern region of United States coast, in terms of probability of occurrence. Secondly, several storm events with high potential of occurrence are generated using a specific tool of DelftDashboard interface for Delft3D software. Hydrodynamic models are then used to generate ensemble simulations to assess storms' effects on coastal water levels. For the United States’ northeastern coast, a highly refined regional domain is considered surrounding the area of The Battery, New York, situated in New York Harbor. Based on statistical data of numerical modeling results, a review of the impact of coastal storms to different locations within the study area is performed.

Mother’s anxiety and depression and associated risk factors during early pregnancy: effects on fetal growth and activity at 20–22 weeks of gestation

To examine effects of mother's anxiety and depression and associated risk factors during early pregnancy on fetal growth and activity. Repeated measures of mother's anxiety (State-Anxiety Inventory (STAI-S)) and depression (Edinburgh Postnatal Depression Scale (EPDS)) and related socio demographics and substance consumption were obtained at the 1st and 2nd pregnancy trimesters, and fetus' (N = 147) biometric data and behavior was recorded during ultrasound examination at 20-22 weeks of gestation. Higher anxiety symptoms were associated to both lower fetal growth and higher fetal activity. While lower education, primiparity, adolescent motherhood, and tobacco consumption predicted lower fetal growth, coffee intake predicted lower fetal activity. Vulnerability of fetal development to mother's psychological symptoms as well as to other sociodemographic and substance consumption risk factors during early and mid pregnancy is suggested.

Automatic visual detection of human behavior: a review from 2000 to 2014

Due to advances in information technology (e.g., digital video cameras, ubiquitous sensors), the automatic detection of human behaviors from video is a very recent research topic. In this paper, we perform a systematic and recent literature review on this topic, from 2000 to 2014, covering a selection of 193 papers that were searched from six major scientific publishers. The selected papers were classified into three main subjects: detection techniques, datasets and applications. The detection techniques were divided into four categories (initialization, tracking, pose estimation and recognition). The list of datasets includes eight examples (e.g., Hollywood action). Finally, several application areas were identified, including human detection, abnormal activity detection, action recognition, player modeling and pedestrian detection. Our analysis provides a road map to guide future research for designing automatic visual human behavior detection systems.

Modeling microbes: New methods for integrated metabolic and regulatory network reconstruction

PhD Thesis in Bioengineering

High strain rate constitutive modeling forhistorical structures subjected to blast loading

Doctoral Thesis Civil Engineering