Avaliação da eficiência de projetos de reabilitação ambiental de áreas mineiras abandonadas: o caso da área mineira de Covas

Dissertação de mestrado em Geociências (área de especialização em Valorização de Recursos Geológicos)

Beeswax organogels: Influence of gelator concentration and oil type in the gelation process

This work focused on how different types of oil phase, MCT (medium chain triglycerides) and LCT (long chain triglycerides), exert influence on the gelation process of beeswax and thus properties of the organogel produced thereof. Organogels were produced at different temperatures and qualitative phase diagrams were constructed to identify and classify the type of structure formed at various compositions. The microstructure of gelator crystals was studied by polarized light microscopy. Melting and crystallization were characterized by differential scanning calorimetry and rheology (flow and small amplitude oscillatory measurements) to understand organogels' behaviour under different mechanical and thermal conditions. FTIR analysis was employed for a further understanding of oil-gelator chemical interactions. Results showed that the increase of beeswax concentration led to higher values of storage and loss moduli (G, G) and complex modulus (G*) of organogels, which is associated to the strong network formed between the crystalline gelator structure and the oil phase. Crystallization occurred in two steps (well evidenced for higher concentrations of gelator) during temperature decreasing. Thermal analysis showed the occurrence of hysteresis between melting and crystallization. Small angle X-ray scattering (SAXS) analysis allowed a better understanding in terms of how crystal conformations were disposed for each type of organogel. The structuring process supported by medium or long-chain triglycerides oils was an important exploit to apprehend the impact of different carbon chain-size on the gelation process and on gels' properties.

Pervasive patient timeline for intensive care units

This research work explores a new way of presenting and representing information about patients in critical care, which is the use of a timeline to display information. This is accomplished with the development of an interactive Pervasive Patient Timeline able to give to the intensivists an access in real-time to an environment containing patients clinical information from the moment in which the patients are admitted in the Intensive Care Unit (ICU) until their discharge This solution allows the intensivists to analyse data regarding vital signs, medication, exams, data mining predictions, among others. Due to the pervasive features, intensivists can have access to the timeline anywhere and anytime, allowing them to make decisions when they need to be made. This platform is patient-centred and is prepared to support the decision process allowing the intensivists to provide better care to patients due the inclusion of clinical forecasts.

Pervasive decision support to predict football corners and goals by means of data mining

Football is considered nowadays one of the most popular sports. In the betting world, it has acquired an outstanding position, which moves millions of euros during the period of a single football match. The lack of profitability of football betting users has been stressed as a problem. This lack gave origin to this research proposal, which it is going to analyse the possibility of existing a way to support the users to increase their profits on their bets. Data mining models were induced with the purpose of supporting the gamblers to increase their profits in the medium/long term. Being conscience that the models can fail, the results achieved by four of the seven targets in the models are encouraging and suggest that the system can help to increase the profits. All defined targets have two possible classes to predict, for example, if there are more or less than 7.5 corners in a single game. The data mining models of the targets, more or less than 7.5 corners, 8.5 corners, 1.5 goals and 3.5 goals achieved the pre-defined thresholds. The models were implemented in a prototype, which it is a pervasive decision support system. This system was developed with the purpose to be an interface for any user, both for an expert user as to a user who has no knowledge in football games.

Towards a pervasive data mining engine: architecture overview

Current data mining engines are difficult to use, requiring optimizations by data mining experts in order to provide optimal results. To solve this problem a new concept was devised, by maintaining the functionality of current data mining tools and adding pervasive characteristics such as invisibility and ubiquity which focus on their users, providing better ease of use and usefulness, by providing autonomous and intelligent data mining processes. This article introduces an architecture to implement a data mining engine, composed by four major components: database; Middleware (control); Middleware (processing); and interface. These components are interlinked but provide independent scaling, allowing for a system that adapts to the user’s needs. A prototype has been developed in order to test the architecture. The results are very promising and showed their functionality and the need for further improvements.

Predicting triage waiting time in maternity emergency care by means of data mining

Healthcare organizations often benefit from information technologies as well as embedded decision support systems, which improve the quality of services and help preventing complications and adverse events. In Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto (CHP), an intelligent pre-triage system is implemented, aiming to prioritize patients in need of gynaecology and obstetrics care in two classes: urgent and consultation. The system is designed to evade emergency problems such as incorrect triage outcomes and extensive triage waiting times. The current study intends to improve the triage system, and therefore, optimize the patient workflow through the emergency room, by predicting the triage waiting time comprised between the patient triage and their medical admission. For this purpose, data mining (DM) techniques are induced in selected information provided by the information technologies implemented in CMIN. The DM models achieved accuracy values of approximately 94% with a five range target distribution, which not only allow obtaining confident prediction models, but also identify the variables that stand as direct inducers to the triage waiting times.

Real-Time models to predict the use of vasopressors in monitored patients

The needs of reducing human error has been growing in every field of study, and medicine is one of those. Through the implementation of technologies is possible to help in the decision making process of clinics, therefore to reduce the difficulties that are typically faced. This study focuses on easing some of those difficulties by presenting real-time data mining models capable of predicting if a monitored patient, typically admitted in intensive care, will need to take vasopressors. Data Mining models were induced using clinical variables such as vital signs, laboratory analysis, among others. The best model presented a sensitivity of 94.94%. With this model it is possible reducing the misuse of vasopressors acting as prevention. At same time it is offered a better care to patients by anticipating their treatment with vasopressors.

Predicting pre-triage waiting time in a maternity emergency room through data mining

An unsuitable patient flow as well as prolonged waiting lists in the emergency room of a maternity unit, regarding gynecology and obstetrics care, can affect the mother and child’s health, leading to adverse events and consequences regarding their safety and satisfaction. Predicting the patients’ waiting time in the emergency room is a means to avoid this problem. This study aims to predict the pre-triage waiting time in the emergency care of gynecology and obstetrics of Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto, situated in the north of Portugal. Data mining techniques were induced using information collected from the information systems and technologies available in CMIN. The models developed presented good results reaching accuracy and specificity values of approximately 74% and 94%, respectively. Additionally, the number of patients and triage professionals working in the emergency room, as well as some temporal variables were identified as direct enhancers to the pre-triage waiting time. The imp lementation of the attained knowledge in the decision support system and business intelligence platform, deployed in CMIN, leads to the optimization of the patient flow through the emergency room and improving the quality of services.

Real-Time decision support using data mining to predict blood pressure critical events in intensive medicine patients

Patient blood pressure is an important vital signal to the physicians take a decision and to better understand the patient condition. In Intensive Care Units is possible monitoring the blood pressure due the fact of the patient being in continuous monitoring through bedside monitors and the use of sensors. The intensivist only have access to vital signs values when they look to the monitor or consult the values hourly collected. Most important is the sequence of the values collected, i.e., a set of highest or lowest values can signify a critical event and bring future complications to a patient as is Hypotension or Hypertension. This complications can leverage a set of dangerous diseases and side-effects. The main goal of this work is to predict the probability of a patient has a blood pressure critical event in the next hours by combining a set of patient data collected in real-time and using Data Mining classification techniques. As output the models indicate the probability (%) of a patient has a Blood Pressure Critical Event in the next hour. The achieved results showed to be very promising, presenting sensitivity around of 95%.

Automating the testing process of image processing algorithms

As digital imaging processing techniques become increasingly used in a broad range of consumer applications, the critical need to evaluate algorithm performance has become recognised by developers as an area of vital importance. With digital image processing algorithms now playing a greater role in security and protection applications, it is of crucial importance that we are able to empirically study their performance. Apart from the field of biometrics little emphasis has been put on algorithm performance evaluation until now and where evaluation has taken place, it has been carried out in a somewhat cumbersome and unsystematic fashion, without any standardised approach. This paper presents a comprehensive testing methodology and framework aimed towards automating the evaluation of image processing algorithms. Ultimately, the test framework aims to shorten the algorithm development life cycle by helping to identify algorithm performance problems quickly and more efficiently.

Acoustic emission analysis of the effect of a 2D wedge shaped blade on the compact bone cutting process

Surgeons may use a number of cutting instruments such as osteotomes and chisels to cut bone during an operative procedure. The initial loading of cortical bone during the cutting process results in the formation of microcracks in the vicinity of the cutting zone with main crack propagation to failure occuring with continued loading. When a material cracks, energy is emitted in the form of Acoustic Emission (AE) signals that spread in all directions, therefore, AE transducers can be used to monitor the occurrence and development of microcracking and crack propagation in cortical bone. In this research, number of AE signals (hits) and related parameters including amplitude, duration and absolute energy (abs-energy) were recorded during the indentation cutting process by a wedge blade on cortical bone specimens. The cutting force was also measured to correlate between load-displacement curves and the output from the AE sensor. The results from experiments show AE signals increase substantially during the loading just prior to fracture between 90% and 100% of maximum fracture load. Furthermore, an amplitude threshold value of 64dB (with approximate abs-energy of 1500 aJ) was established to saparate AE signals associated with microcracking (41 – 64dB) from fracture related signals (65 – 98dB). The results also demonstrated that the complete fracture event which had the highest duration value can be distinguished from other growing macrocracks which did not lead to catastrophic fracture. It was observed that the main crack initiation may be detected by capturing a high amplitude signal at a mean load value of 87% of maximum load and unsteady crack propagation may occur just prior to final fracture event at a mean load value of 96% of maximum load. The author concludes that the AE method is useful in understanding the crack initiation and fracture during the indentation cutting process.

Application of multivariate statistical process control to fuel cell manufacturing

Univariate statistical control charts, such as the Shewhart chart, do not satisfy the requirements for process monitoring on a high volume automated fuel cell manufacturing line. This is because of the number of variables that require monitoring. The risk of elevated false alarms, due to the nature of the process being high volume, can present problems if univariate methods are used. Multivariate statistical methods are discussed as an alternative for process monitoring and control. The research presented is conducted on a manufacturing line which evaluates the performance of a fuel cell. It has three stages of production assembly that contribute to the final end product performance. The product performance is assessed by power and energy measurements, taken at various time points throughout the discharge testing of the fuel cell. The literature review performed on these multivariate techniques are evaluated using individual and batch observations. Modern techniques using multivariate control charts on Hotellings T2 are compared to other multivariate methods, such as Principal Components Analysis (PCA). The latter, PCA, was identified as the most suitable method. Control charts such as, scores, T2 and DModX charts, are constructed from the PCA model. Diagnostic procedures, using Contribution plots, for out of control points that are detected using these control charts, are also discussed. These plots enable the investigator to perform root cause analysis. Multivariate batch techniques are compared to individual observations typically seen on continuous processes. Recommendations, for the introduction of multivariate techniques that would be appropriate for most high volume processes, are also covered.

Optimisation of a surface mount technology process using a SnAgCu lead-free alloy

The impending introduction of lead-free solder in the manufacture of electrical and electronic products has presented the electronics industry with many challenges. European manufacturers must transfer from a tin-lead process to a lead-free process by July 2006 as a result of the publication of two directives from the European Parliament. Tin-lead solders have been used for mechanical and electrical connections on printed circuit boards for over fifty years and considerable process knowledge has been accumulated. Extensive literature reviews were conducted on the topic and as a result it was found there are many implications to be considered with the introduction of lead-free solder. One particular question that requires answering is; can lead-free solder be used in existing manufacturing processes? The purpose of this research is to conduct a comparative study of a tin-lead solder and a lead-free solder in two key surface mount technology (SMT) processes. The two SMT processes in question were the stencil printing process and the reflow soldering process. Unreplicated fractional factorial experimental designs were used to carry out the studies. The quality of paste deposition in terms of height and volume were the characteristics of interest in the stencil printing process. The quality of solder joints produced in the reflow soldering experiment was assessed using x-ray and cross sectional analysis. This provided qualitative data that was then uniquely scored and weighted using a method developed during the research. Nested experimental design techniques were then used to analyse the resulting quantitative data. Predictive models were developed that allowed for the optimisation of both processes. Results from both experiments show that solder joints of comparable quality to those produced using tin-lead solder can be produced using lead-free solder in current SMT processes.

Data mining with graphical models

Data Mining, Learning from data, graphical models, possibility theory