Desenvolvimento de indicadores enzimáticos inteligentes para monitoramento da qualidade de alimentos

A dificuldade em conhecer o histórico de temperatura de um alimento, desde sua produção até o consumo, torna difícil prever sua verdadeira vida-útil. O uso de indicadores de tempo e temperatura (ITT) pode ser uma alternativa inovadora empregada para garantir a validade de produtos de forma dinâmica. Assim, este trabalho visa desenvolver novos indicadores enzimáticos de tempo e temperatura para monitorar a qualidade de alimentos perecíveis durante o seu processamento e armazenamento, baseados na reação de complexação entre o amido e o iodo (azul), e na posterior atuação de uma enzima amilase sobre esse complexo, para causar uma redução da intensidade da cor azul a uma taxa dependente do tempo e da temperatura de armazenagem. Os sistemas inteligentes propostos possuem versatilidade de atuação em função do tipo e quantidade de amilase empregada. Desta forma, foi utilizada uma amilase termoestável para a formulação de um indicador inteligente de processamento, utilizado para o controle de tratamentos térmicos industriais (pasteurização);e uma amilase termosensível na formulação de um indicador de armazenamento, empregada para o controle das condições de temperatura durante a cadeia de frio de produtos perecíveis. Na elaboração dos ITT de processamento foram realizadas simulações em laboratório e testes em planta fabril, os quais avaliaram diferentes concentrações de amilase termoestável nos protótipos de ITT quando submetidos as condições de tempo e temperatura de pasteurização. Os resultados evidenciaram que a resposta de cor dos indicadores foi visualmente interpretada, como adaptável à medição usando equipamentos, apresentando boa reprodutibilidade em todas as condições estudadas. O ITT contendo 6,5 % de amilase termoestável (penzima/pamido) foi aquele cujo resultado melhor se adequou à utilização na validação de cozimento de presunto. Nesta condição, o protótipo anexado à embalagem primária do produto indicou o processo de pasteurização de forma fácil, precisa e não destrutiva. Já durante o desenvolvimento do ITT de armazenamento foram realizadas simulações em laboratório, testes em planta fabril e ponto de venda, os quais avaliaram o uso de diferentes concentrações de amilase termosensível nos protótipos de ITT quando submetidos a diversas condições de cadeia de frio. Os resultados evidenciaram que devido à possibilidade de definir a vida-útil destes protótipos variando as concentrações de enzima termosensível, os indicadores podem ser facilmente adaptados para controlar as condições de temperatura durante a cadeia de diversos alimentos perecíveis. O protótipo contendo 60 % de amilase termosensível (penzima/pamido) foi aquele cujo resultado melhor se adequou à utilização no controle da cadeia avícola. Assim, o ITT indicou visualmente o histórico de tempo e temperatura de produtos à base de frango de forma fácil e precisa. Os resultados obtidos na avaliação das percepções dos consumidores frente ao emprego de indicadores inteligentes em embalagens alimentícias mostraram que o uso de ITT é uma inovação receptiva, com consequente aceitação e intenção de compra elevada pela população brasileira. Assim, com este trabalho espera-se contribuir efetivamente para que o conceito de embalagens inteligentes possa ser aceito comercialmente e que sejam estabelecidas no Brasil normas que regulamentem seu uso, conferindo benefícios à conservação de grande variedade de alimentos.

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A Soft Computing Approach to Quality Evaluation of General Chemistry Learning in Higher Education

In contemporary societies higher education must shape individuals able to solve problems in a workable and simpler manner and, therefore, a multidisciplinary view of the problems, with insights in disciplines like psychology, mathematics or computer science becomes mandatory. Undeniably, the great challenge for teachers is to provide a comprehensive training in General Chemistry with high standards of quality, and aiming not only at the promotion of the student’s academic success, but also at the understanding of the competences/skills required to their future doings. Thus, this work will be focused on the development of an intelligent system to assess the Quality-of-General-Chemistry-Learning, based on factors related with subject, teachers and students.

An AAL Collaborative System: The AAL4ALL and a Mobile Assistant Case Study

Part 20: Health and Care Networks

Reconfiguração dinâmica de estratégias distribuídas em dispositivos foundation fieldbus para a otimização de processos na indústria do petróleo

The petrochemical industry has as objective obtain, from crude oil, some products with a higher commercial value and a bigger industrial utility for energy purposes. These industrial processes are complex, commonly operating with large production volume and in restricted operation conditions. The operation control in optimized and stable conditions is important to keep obtained products quality and the industrial plant safety. Currently, industrial network has been attained evidence when there is a need to make the process control in a distributed way. The Foundation Fieldbus protocol for industrial network, for its interoperability feature and its user interface organized in simple configuration blocks, has great notoriety among industrial automation network group. This present work puts together some benefits brought by industrial network technology to petrochemical industrial processes inherent complexity. For this, a dynamic reconfiguration system for intelligent strategies (artificial neural networks, for example) based on the protocol user application layer is proposed which might allow different applications use in a particular process, without operators intervention and with necessary guarantees for the proper plant functioning

Sistema inteligente para apoio à decisão na operação de uma malha de escoamento de petróleo

The petroleum production pipeline networks are inherently complex, usually decentralized systems. Strict operational constraints are applied in order to prevent serious problems like environmental disasters or production losses. This paper describes an intelligent system to support decisions in the operation of these networks, proposing a staggering for the pumps of transfer stations that compose them. The intelligent system is formed by blocks which interconnect to process the information and generate the suggestions to the operator. The main block of the system uses fuzzy logic to provide a control based on rules, which incorporate knowledge from experts. Tests performed in the simulation environment provided good results, indicating the applicability of the system in a real oil production environment. The use of the stagger proposed by the system allows a prioritization of the transfer in the network and a flow programming

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.

Bringing forward semantic relations: Issues and proposals

Semantic relations are an important element in the construction of ontologies and models of problem domains. Nevertheless, they remain fuzzy or under-specified. This is a pervasive problem in software engineering and artificial intelligence. Thus, we find semantic links that can have multiple interpretations in wide-coverage ontologies, semantic data models with abstractions that are not enough to capture the relation richness of problem domains, and improperly structured taxonomies. However, if relations are provided with precise semantics, some of these problems can be avoided, and meaningful operations can be performed on them. In this paper we present some insightful issues about the modeling, representation and usage of relations including the available taxonomy structuring methodologies as well as the initiatives aiming to provide relations with precise semantics. Moreover, we explain and propose the control of relations as a key issue for the coherent construction of ontologies.

Screening a Case Base for Stroke Disease Detection

Stroke stands for one of the most frequent causes of death, without distinguishing age or genders. Despite representing an expressive mortality fig-ure, the disease also causes long-term disabilities with a huge recovery time, which goes in parallel with costs. However, stroke and health diseases may also be prevented considering illness evidence. Therefore, the present work will start with the development of a decision support system to assess stroke risk, centered on a formal framework based on Logic Programming for knowledge rep-resentation and reasoning, complemented with a Case Based Reasoning (CBR) approach to computing. Indeed, and in order to target practically the CBR cycle, a normalization and an optimization phases were introduced, and clustering methods were used, then reducing the search space and enhancing the cases re-trieval one. On the other hand, and aiming at an improvement of the CBR theo-retical basis, the predicates` attributes were normalized to the interval 0…1, and the extensions of the predicates that match the universe of discourse were re-written, and set not only in terms of an evaluation of its Quality-of-Information (QoI), but also in terms of an assessment of a Degree-of-Confidence (DoC), a measure of one`s confidence that they fit into a given interval, taking into account their domains, i.e., each predicate attribute will be given in terms of a pair (QoI, DoC), a simple and elegant way to represent data or knowledge of the type incomplete, self-contradictory, or even unknown.

A Case Based approach to assess Waiting Time Prediction at an Intensive Care Unity

Waiting time at an intensive care unity stands for a key feature in the assessment of healthcare quality. Nevertheless, its estimation is a difficult task, not only due to the different factors with intricate relations among them, but also with respect to the available data, which may be incomplete, self-contradictory or even unknown. However, its prediction not only improves the patients’ satisfaction but also enhance the quality of the healthcare being provided. To fulfill this goal, this work aims at the development of a decision support system that allows one to predict how long a patient should remain at an emergency unit, having into consideration all the remarks that were just stated above. It is built on top of a Logic Programming approach to knowledge representation and reasoning, complemented with a Case Base approach to computing.

Chest Breadths to Predict Individuals' Age - A Case Based View

It is well known that rib cage dimensions depend on the gender and vary with the age of the individual. Under this setting it is therefore possible to assume that a computational approach to the problem may be thought out and, consequently, this work will focus on the development of an Artificial Intelligence grounded decision support system to predict individual’s age, based on such measurements. On the one hand, using some basic image processing techniques it were extracted such descriptions from chest X-rays (i.e., its maximum width and height). On the other hand, the computational framework was built on top of a Logic Programming Case Base approach to knowledge representation and reasoning, which caters for the handling of incomplete, unknown, or even contradictory information. Furthermore, clustering methods based on similarity analysis among cases were used to distinguish and aggregate collections of historical data in order to reduce the search space, therefore enhancing the cases retrieval and the overall computational process. The accuracy of the proposed model is satisfactory, close to 90%.

A Case Based Methodology for Problem Solving aiming at Knee Osteoarthritis Detection

Knee osteoarthritis is the most common type of arthritis and a major cause of impaired mobility and disability for the ageing populations. Therefore, due to the increasing prevalence of the malady, it is expected that clinical and scientific practices had to be set in order to detect the problem in its early stages. Thus, this work will be focused on the improvement of methodologies for problem solving aiming at the development of Artificial Intelligence based decision support system to detect knee osteoarthritis. The framework is built on top of a Logic Programming approach to Knowledge Representation and Reasoning, complemented with a Case Based approach to computing that caters for the handling of incomplete, unknown, or even self-contradictory information.