KDD, SEMMA and CRISP-DM: a parallel overview

In the last years there has been a huge growth and consolidation of the Data Mining field. Some efforts are being done that seek the establishment of standards in the area. Included on these efforts there can be enumerated SEMMA and CRISP-DM. Both grow as industrial standards and define a set of sequential steps that pretends to guide the implementation of data mining applications. The question of the existence of substantial differences between them and the traditional KDD process arose. In this paper, is pretended to establish a parallel between these and the KDD process as well as an understanding of the similarities between them.

Knowledge extraction from medium voltage load diagrams to support the definition of electrical tariffs

With the electricity market liberalization, distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity customers. In this environment all consumers are free to choose their electricity supplier. A fair insight on the customer´s behaviour will permit the definition of specific contract aspects based on the different consumption patterns. In this paper Data Mining (DM) techniques are applied to electricity consumption data from a utility client’s database. To form the different customer´s classes, and find a set of representative consumption patterns, we have used the Two-Step algorithm which is a hierarchical clustering algorithm. Each consumer class will be represented by its load profile resulting from the clustering operation. Next, to characterize each consumer class a classification model will be constructed with the C5.0 classification algorithm.

Data mining techniques to support the classification of MV electricity customers

This paper describes a methodology that was developed for the classification of Medium Voltage (MV) electricity customers. Starting from a sample of data bases, resulting from a monitoring campaign, Data Mining (DM) techniques are used in order to discover a set of a MV consumer typical load profile and, therefore, to extract knowledge regarding to the electric energy consumption patterns. In first stage, it was applied several hierarchical clustering algorithms and compared the clustering performance among them using adequacy measures. In second stage, a classification model was developed in order to allow classifying new consumers in one of the obtained clusters that had resulted from the previously process. Finally, the interpretation of the discovered knowledge are presented and discussed.

Development of polyaniline microarray electrodes for cadmium analysis

Disposable screen-printed electrodes (SPCE) were modified using a cosmetic product to partially block the electrode surface in order to obtain a microelectrode array. The microarrays formed were electropolymerized with aniline. Scanning electron microscopy was used to evaluate the modified and polymerized electrode surface. Electrochemical characteristics of the constructed sensor for cadmium analysis were evaluated by cyclic and square-wave voltammetry. Optimized stripping procedure in which the preconcentration of cadmium was achieved by depositing at –1.20 V (vs. Ag/AgCl) resulted in a well defined anodic peak at approximately –0.7 V at pH 4.6. The achieved limit of detection was 4 × 10−9 mol dm−3. Spray modified and polymerized microarray electrodes were successfully applied to quantify cadmium in fish sample digests.

OsRMC, a negative regulator of salt stress response in rice, is regulated by two AP2/ERF transcription factors

High salinity causes remarkable losses in rice productivity worldwide mainly because it inhibits growth and reduces grain yield. To cope with environmental changes, plants evolved several adaptive mechanisms, which involve the regulation of many stress-responsive genes. Among these, we have chosen OsRMC to study its transcriptional regulation in rice seedlings subjected to high salinity. Its transcription was highly induced by salt treatment and showed a stress-dose-dependent pattern. OsRMC encodes a receptor-like kinase described as a negative regulator of salt stress responses in rice. To investigate how OsRMC is regulated in response to high salinity, a salt-induced rice cDNA expression library was constructed and subsequently screened using the yeast one-hybrid system and the OsRMC promoter as bait. Thereby, two transcription factors (TFs), OsEREBP1 and OsEREBP2, belonging to the AP2/ERF family were identified. Both TFs were shown to bind to the same GCC-like DNA motif in OsRMC promoter and to negatively regulate its gene expression. The identified TFs were characterized regarding their gene expression under different abiotic stress conditions. This study revealed that OsEREBP1 transcript level is not significantly affected by salt, ABA or severe cold (5 °C) and is only slightly regulated by drought and moderate cold. On the other hand, the OsEREBP2 transcript level increased after cold, ABA, drought and high salinity treatments, indicating that OsEREBP2 may play a central role mediating the response to different abiotic stresses. Gene expression analysis in rice varieties with contrasting salt tolerance further suggests that OsEREBP2 is involved in salt stress response in rice.

Non pre-emptive scheduling of messages on SMTV token-passing networks

Fieldbus communication networks aim to interconnect sensors, actuators and controllers within distributed computer-controlled systems. Therefore, they constitute the foundation upon which real-time applications are to be implemented. A specific class of fieldbus communication networks is based on a simplified version of token-passing protocols, where each station may transfer, at most, a single message per token visit (SMTV). In this paper, we establish an analogy between non-preemptive task scheduling in single processors and the scheduling of messages on SMTV token-passing networks. Moreover, we clearly show that concepts such as blocking and interference in non-preemptive task scheduling have their counterparts in the scheduling of messages on SMTV token-passing networks. Based on this task/message scheduling analogy, we provide pre-run-time schedulability conditions for supporting real-time messages with SMTV token-passing networks. We provide both utilisation-based and response time tests to perform the pre-run-time schedulability analysis of real-time messages on SMTV token-passing networks, considering RM/DM (rate monotonic/deadline monotonic) and EDF (earliest deadline first) priority assignment schemes

MnFe2O4@CNT-N as novel electrochemical nanosensor for determination of caffeine, acetaminophen and ascorbic acid

For the first time, a glassy carbon electrode (GCE) modified with novel N-doped carbon nanotubes (CNT-N) functionalized with MnFe2O4 nanoparticles (MnFe2O4@CNT-N) has been prepared and applied for the electrochemical determination of caffeine (CF), acetaminophen (AC) and ascorbic acid (AA). The electrochemical behaviour of CF, AC and AA on the bare GCE, CNT-N/GCE and MnFe2O4@CNT-N/GCE were carefully investigated using cyclic voltammetry (CV) and square-wave voltammetry (SWV). Compared to bare GCE and CNT-N modified electrode, the MnFe2O4@CNT-N modified electrode can remarkably improve the electrocatalytic activity towards the oxidation of CF, AC and AA with an increase in the anodic peak currents of 52%, 50% and 55%, respectively. Also, the SWV anodic peaks of these molecules could be distinguished from each other at the MnFe2O4@CNT-N modified electrode with enhanced oxidation currents. The linear response ranges for the square wave voltammetric determination of CF, AC and AA were 1.0 × 10−6 to 1.1 × 10−3 mol dm−3, 1.0 × 10−6 to 1.0 × 10−3 mol dm−3 and 2.0 × 10−6 to 1.0 × 10−4 mol dm−3 with detection limit (S/N = 3) of 0.83 × 10−6, 0.83 × 10−6 and 1.8 × 10−6 mol dm−3, respectively. The sensitivity values at the MnFe2O4@CNT-N/GCE for the individual determination of AC, AA and CF and in the presence of the other molecules showed that the quantification of AA and CF show no interferences from the other molecules; however, AA and CF interfered in the determination of AC, with the latter molecule showing the strongest interference. Nevertheless, the obtained results show that MnFe2O4@CNT-N composite material acted as an efficient electrochemical sensor towards the selected biomolecules.

Clinical and Bacteriological Survey of Diabetic Foot Infections in Lisbon

AIMS: An epidemiological survey of diabetic foot infections (DFIs) in Lisbon, stratifying the bacterial profile based on patient demographical data, diabetic foot characteristics (PEDIS classification), ulcer duration and antibiotic therapy. METHODS: A transversal observational multicenter study, with clinical data collection using a structured questionnaire and microbiological products (aspirates, biopsies or swabs collected using the Levine method) of clinically infected foot ulcers of patients with diabetes mellitus (DM). RESULTS: Forty-nine hospitalized and ambulatory patients were enrolled in this study, and 147 microbial isolates were cultured. Staphylococcus was the main genus identified, and methicillin-resistant Staphylococcus aureus (MRSA) was present in 24.5% of total cases. In the clinical samples collected from patients undergoing antibiotic therapy, 93% of the antibiotic regimens were considered inadequate based on the antibiotic susceptibility test results. The average duration of an ulcer with any isolated multi-drug resistant (MDR) organism was 29 days, and previous treatment with fluoroquinolones was statistically associated with multi-drug resistance. CONCLUSIONS: Staphylococcus aureus was the most common cause of DFIs in our area. Prevalence and precocity of MDR organisms, namely MRSA, were high and were probably related to previous indiscriminate antibiotic use. Clinicians should avoid fluoroquinolones and more frequently consider the use of empirical anti-MRSA therapy.

How to increase reach efficiency and effectiveness of MEO’s digital marketing campaigns?

Field lab: Business project

Predicting preterm birth in maternity care by means of data mining

Worldwide, around 9% of the children are born with less than 37 weeks of labour, causing risk to the premature child, whom it is not prepared to develop a number of basic functions that begin soon after the birth. In order to ensure that those risk pregnancies are being properly monitored by the obstetricians in time to avoid those problems, Data Mining (DM) models were induced in this study to predict preterm births in a real environment using data from 3376 patients (women) admitted in the maternal and perinatal care unit of Centro Hospitalar of Oporto. A sensitive metric to predict preterm deliveries was developed, assisting physicians in the decision-making process regarding the patients’ observation. It was possible to obtain promising results, achieving sensitivity and specificity values of 96% and 98%, respectively.

Rockburst laboratory tests database - Application of data mining techniques

Rockburst is characterized by a violent explosion of a block causing a sudden rupture in the rock and is quite common in deep tunnels. It is critical to understand the phenomenon of rockburst, focusing on the patterns of occurrence so these events can be avoided and/or managed saving costs and possibly lives. The failure mechanism of rockburst needs to be better understood. Laboratory experiments are undergoing at the Laboratory for Geomechanics and Deep Underground Engineering (SKLGDUE) of Beijing and the system is described. A large number of rockburst tests were performed and their information collected, stored in a database and analyzed. Data Mining (DM) techniques were applied to the database in order to develop predictive models for the rockburst maximum stress (σRB) and rockburst risk index (IRB) that need the results of such tests to be determined. With the developed models it is possible to predict these parameters with high accuracy levels using data from the rock mass and specific project.

Effect of surface plasmon resonance in TiO2/Au thin films on the fluorescence of self-assembled CdTe QDs structure

The exceptional properties of localised surface plasmons (LSPs), such as local field enhancement and confinement effects, resonant behavior, make them ideal candidates to control the emission of luminescent nanoparticles. In the present work, we investigated the LSP effect on the steady-state and time-resolved emission properties of quantum dots (QDs) by organizing the dots into self-assembled dendrite structures deposited on plasmonic nanostructures. Self-assembled structures consisting of water-soluble CdTe mono-size QDs, were developed on the surface of co-sputtered TiO2 thin films doped with Au nanoparticles (NPs) annealed at different temperatures. Their steady-state fluorescence properties were probed by scanning the spatially resolved emission spectra and the energy transfer processes were investigated by the fluorescence lifetime imaging (FLIM) microscopy. Our results indicate that a resonant coupling between excitons confined in QDs and LSPs in Au NPs located beneath the self-assembled structure indeed takes place and results in (i) a shift of the ground state luminescence towards higher energies and onset of emission from excited states in QDs, and (ii) a decrease of the ground state exciton lifetime (fluorescence quenching).

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

An algorithmic approach to estimating the minimal number of periodic points for smooth self-maps of simply-connected manifolds

For a given self-map f of M, a closed smooth connected and simply-connected manifold of dimension m ≥ 4, we provide an algorithm for estimating the values of the topological invariant Dm r [f], which equals the minimal number of r-periodic points in the smooth homotopy class of f. Our results are based on the combinatorial scheme for computing Dm r [f] introduced by G. Graff and J. Jezierski [J. Fixed Point Theory Appl. 13 (2013), 63–84]. An open-source implementation of the algorithm programmed in C++ is publicly available at http://www.pawelpilarczyk.com/combtop/.

Sistema de apoio à decisão clínica: quality of life system

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