An assessment of chronic kidney diseases

Kidney renal failure means that one’s kidney have unexpectedlystoppedfunctioning,i.e.,oncechronicdiseaseis exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient’s history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapiddeteriorationoftherenalfunction,butisoftenreversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis. The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow onetoconsiderincomplete,unknown,and evencontradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9–94.2 %, respectively.

An evaluation of parchments' degradation a hybrid approach

Parchment stands for a multifaceted material made from animal skin, which has been used for centuries as a writing support or as bookbinding. Due to the historic value of objects made of parchment, understanding their degradation and their condition is of utmost importance to archives, libraries and museums, i.e., the assessment of parchment degradation is mandatory, although it is hard to do with traditional methodologies and tools for problem solving. Hence, in this work we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures, under a formal framework based on Logic Programming, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate Parchment Degradation and the respective Degree-of-Confidence that one has on such a happening.

Thrombophilia screening: An artificial neural network approach

Thrombotic disorders have severe consequences for the patients and for the society in general, being one of the main causes of death. These facts reveal that it is extremely important to be preventive; being aware of how probable is to have that kind of syndrome. Indeed, this work will focus on the development of a decision support system that will cater for an individual risk evaluation with respect to the surge of thrombotic complaints. The Knowledge Representation and Reasoning procedures used will be based on an extension to the Logic Programming language, allowing the handling of incomplete and/or default data. The computational framework in place will be centered on Artificial Neural Networks.

Artificial neural networks in diagnosis of liver diseases

Liver diseases have severe patients’ consequences, being one of the main causes of premature death. These facts reveal the centrality of one`s daily habits, and how important it is the early diagnosis of these kind of illnesses, not only to the patients themselves, but also to the society in general. Therefore, this work will focus on the development of a diagnosis support system to these kind of maladies, built under a formal framework based on Logic Programming, in terms of its knowledge representation and reasoning procedures, complemented with an approach to computing grounded on Artificial Neural Networks.

Spousal support and satisfaction with healthcare services as moderators between psychological morbidity and adherence to diet in Type 2 Diabetes patients

Objectives: This study analyzed the moderating role of partners’ support and satisfaction with healthcare services in the relationship between psychological morbidity and adherence to diet in patients with type 2 diabetes (T2DM). Methods: Participants were 387 recently diagnosed T2DM patients that answered the following instruments: Revised Summary of Diabetes Self- Care Activities Measure, Hospital Anxiety and Depression Scales, Multidimensional Diabetes Questionnaire and Patient Satisfaction Questionnaire. Results: Partners’ positive and negative support moderated the relationship between psychological morbidity and adherence to diet. Satisfaction with healthcare services also moderated the relationship between psychological morbidity and adherence to diet. Conclusions: Intervention programs to promote adherence to diet in patients with type 2 diabetes should focus on partners’ support and patient satisfaction with healthcare services.

Repository power: how repositories can support Open Access mandates

Many funding agencies have Open Access mandates in place, but how often are scientific publications as outputs linked to funding details? The benefits of linking funding information to publications as part of the deposit workflow can assist in adhering to Open Access mandates. This paper examines how OpenAIRE – Open Access Infrastructure for Research in Europe – can ease monitoring Open Access and reporting processes for funders, and presents some results and opportunities. It also outlines how it relies on cleaned and curated repository content, a vital cog in the ever turning wheel of the global scholarly landscape, and the benefits it brings.

Deteção de colisões com guardas de segurança

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e Computadores

Towards the design and implementation of aspect-oriented programming for spreadsheets

A spreadsheet usually starts as a simple and singleuser software artifact, but, as frequent as in other software systems, quickly evolves into a complex system developed by many actors. Often, different users work on different aspects of the same spreadsheet: while a secretary may be only involved in adding plain data to the spreadsheet, an accountant may define new business rules, while an engineer may need to adapt the spreadsheet content so it can be used by other software systems.Unfortunately,spreadsheetsystemsdonotoffermodular mechanisms, and as a consequence, some of the previous tasks may be defined by adding intrusive “code” to the spreadsheet. In this paper we go through the design and implementation of an aspect-oriented language for spreadsheets so that users can work on different aspects of a spreadsheet in a modular way. For example, aspects can be defined in order to introduce new business rules to an existing spreadsheet, or to manipulate the spreadsheet data to be ported to another system. Aspects are defined as aspect-oriented program specifications that are dynamically woven into the underlying spreadsheet by an aspect weaver. In this aspect-oriented style of spreadsheet development, differentusers develop,orreuse,aspects withoutaddingintrusive code to the original spreadsheet. Such code is added/executed by the spreadsheet weaving mechanism proposed in this paper.

Metaphorisms in programming

This paper introduces the metaphorism pattern of relational specification and addresses how specification following this pattern can be refined into recursive programs. Metaphorisms express input-output relationships which preserve relevant information while at the same time some intended optimization takes place. Text processing, sorting, representation changers, etc., are examples of metaphorisms. The kind of metaphorism refinement proposed in this paper is a strategy known as change of virtual data structure. It gives sufficient conditions for such implementations to be calculated using relation algebra and illustrates the strategy with the derivation of quicksort as example.

Interação entre arquiteto e engenheiro de estruturas em fases preliminares da conceção estrutural: oportunidades e desafios das metodologias BIM

Dissertação de mestrado integrado em Engenharia Civil

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.

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%.