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.

Transcription of alternative oxidase (AOX) and plastid terminal oxidase (PTOX) during stress-regulated root tissue growth in Daucus carota L. - An approach to identify functional marker candidates for breeding on carrot yield stability

A presente tese explora a hipótese de utilização dos genes da oxidase alternativa (AOX) e da oxidase terminal da plastoquinona (PTOX) como genes-alvo para o desenvolvimento de marcadores funcionais (MF) para avaliar a performance do crescimento em cenoura, fator determinante da produtividade. Para avaliar se os referidos genes estão associados com o crescimento da cenoura procedeu—se ao seu isolamento e posterior análise dos seus perfis de transcrição em diversos sistemas biológicos. O sistema in vitro selecionado, denominado sistema de culturas primárias, permitiu avaliar alterações na quantidade de transcritos desses genes durante os processos de reprogramação celular e crescimento. Ao nível da planta foi também estudado o efeito do frio na expressão precoce dos genes AOX. Ambos os genes DcAOX1 e DcAOX2a revelaram uma resposta rápida e um padrão semelhante apos stresse (inoculação in vitro e resposta ao frio). Foi igualmente verificado um incremento na expressão do gene DcPTOX durante a fase inicial do processo de reprogramação celular. Estudos de expressão dos genes AOX durante o desenvolvimento da raiz da cenoura revelaram que o gene DcAOX2a será potencialmente o gene mais envolvido neste processo. De modo a avaliar a hipótese de envolvimento do gene DcPTOX no crescimento da raíz procederam—se a estudos de expressão ao nível do tecido meristemático. Todavia, para um mais completo entendimento da ligação entre DcPTOX e o crescimento secundário e/ou acumulação de carotenos, a expressão do gene DcPTOX foi também avaliada em raízes de cenoura durante o desenvolvimento, utilizando cultivares caracterizadas por distintos conteúdos de carotenos. Os resultados obtidos demonstraram a associação do gene DcPTOX a ambos os processos. O envolvimento da PTOX no crescimento adaptativo da raiz foi analisado com um ensaio que permitiu identificar, no tecido meristemático, uma resposta precoce do gene DcPTOX face a uma diminuição da temperatura. Adicionalmente, foi efetuada a seleção de genes de referência para uma analise precisa da expressão génica por RT-qPCR em diversos sistemas biológicos de cenoura, e a importância do seu estudo ao nível do sistema biológico foi realçada. Os resultados desta tese são encorajadores para prosseguir os estudos de utilização dos genes AOX e PTOX como MF no melhoramento da performance do crescimento adaptativo em cenoura, fator determinante para a produtividade; ABSTRACT: This thesis explores the hypothesis of using the alternative oxidase (AOX) and theplastid terminal oxidase (PTOX) as target genes for functional marker (FM) development for yield-determining growth performance in carrot. To understand if these genes are associated to growth, different AOX gene family members and the single PTOX gene were isolated, and their expression patterns evaluated in diverse carrot plant systems. An in-vitro primary culture system was selected to study AOX and PTOX transcript changes during cell reprogramming and growth performance. At plant level, a putative early response of AOX to chilling was also evaluated. In fact, both DcAOXl and DcAOXZa were early responsive and showed similar patterns under stress conditions (in vitro inoculation and chilling). A role for DcPTOX during earliest events of cell reprogramming was also suggested. Next, the expression profiles of AOX gene family members during carrot tap root development were investigated. DcAOXZa was identified as the most responsive gene to root development. In order to evaluate if DcPTOX is associated with carrot tap root growth performance, DcPTOX transcript levels were measured in the central root meristem. To further understand whether DcPTOX is associated with secondary growth and/or carotenoids accumulation, DcPTOX expression was also studied in deveIOping carrot tap roots in cultivars with different carotenoids contents. The results indicated that DcPTOX associates to both carotenoid biosynthesis and secondary growth during storage root development. To obtain further insights into the involvement of PTOX on adaptive growth, the early effects of temperature decrease were explored in the root meristem, where a short—term early response in DcPTOX was found, probably associated with adaptive growth. Furthermore, a selection of the most suitable reference genes for accurate RT—qPCR analysis in several carrot experimental systems was performed and discussed. The present research provides the necessary toolbox for continuing studies in carrot AOX and PTOX genes as promising resources for FM candidates in order to assist breeding on yield—determining adaptive growth performance.

An Artificial Intelligence Approach to Dyscalculia

Dyscalculia stands for a brain-based condition that makes it hard to make sense of numbers and mathematical concepts. Some adolescents with dyscalculia cannot grasp basic number concepts. They work hard to learn and memorize basic number facts. They may know what to do in mathematical classes but do not understand why they are doing it. In other words, they miss the logic behind it. However, it may be worked out in order to decrease its degree of severity. For example, disMAT, an app developed for android may help children to apply mathematical concepts, without much effort, that is turning in itself, a promising tool to dyscalculia treatment. Thus, this work focuses on the development of an Intelligent System to estimate children evidences of dyscalculia, based on data obtained on-the-fly with disMAT. The computational framework is built on top of a Logic Programming framework to Knowledge Representation and Reasoning, complemented with a Case-Based problem solving approach to computing, that allows for the handling of incomplete, unknown, or even contradictory information.

A Case-Based Approach to Nosocomial Infection Detection

The nosocomial infections are a growing concern because they affect a large number of people and they increase the admission time in healthcare facilities. Additionally, its diagnosis is very tricky, requiring multiple medical exams. So, this work is focused on the development of a clinical decision support system to prevent these events from happening. The proposed solution is unique once it caters for the explicit treatment of incomplete, unknown, or even contradictory information under a logic programming basis, that to our knowledge is something that happens for the first time.

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.

A Soft Computing Approach to Acute Coronary Syndrome

Acute Coronary Syndrome (ACS) is transversal to a broad and heterogeneous set of human beings, and assumed as a serious diagnosis and risk stratification problem. Although one may be faced with or had at his disposition different tools as biomarkers for the diagnosis and prognosis of ACS, they have to be previously evaluated and validated in different scenarios and patient cohorts. Besides ensuring that a diagnosis is correct, attention should also be directed to ensure that therapies are either correctly or safely applied. Indeed, this work will focus on the development of a diagnosis decision support system in terms of its knowledge representation and reasoning mechanisms, given here in terms of a formal framework based on Logic Programming, complemented with a problem solving methodology to computing anchored on Artificial Neural Networks. On the one hand it caters for the evaluation of ACS predisposing risk and the respective Degree-of-Confidence that one has on such a happening. On the other hand it may be seen as a major development on the Multi-Value Logics to understand things and ones behavior. Undeniably, the proposed model allows for an improvement of the diagnosis process, classifying properly the patients that presented the pathology (sensitivity ranging from 89.7% to 90.9%) as well as classifying the absence of ACS (specificity ranging from 88.4% to 90.2%).

A Case-base Approach to Workforces’ Satisfaction Assessment

It is well known that human resources play a valuable role in a sustainable organizational development. Indeed, this work will focus on the development of a decision support system to assess workers’ satisfaction based on factors related to human resources management practices. The framework is built on top of a Logic Programming approach to Knowledge Representation and Reasoning, complemented with a Case Based approach to computing. The proposed solution is unique in itself, once it caters for the explicit treatment of incomplete, unknown, or even self-contradictory information, either in terms of a qualitative or quantitative setting. Furthermore, clustering methods based on similarity analysis among cases were used to distinguish and aggregate collections of historical data or knowledge in order to reduce the search space, therefore enhancing the cases retrieval and the overall computational process.

A fuzzy clustering approach to a demand response model

This paper proposes a novel demand response model using a fuzzy subtractive cluster approach. The model development provides support to domestic consumer decisions on controllable loads management, considering consumers’ consumption needs and the appropriate load shape or rescheduling in order to achieve possible economic benefits. The model based on fuzzy subtractive clustering method considers clusters of domestic consumption covering an adequate consumption range. Analysis of different scenarios is presented considering available electric power and electric energy prices. Simulation results are presented and conclusions of the proposed demand response model are discussed.

Age Prediction through Pelvis X-Ray Images - A Case Based approach to Problem Solving

It is well known that the dimensions of the pelvic bones depend on the gender and vary with the age of the individual. Indeed, and as a matter of fact, this work will focus on the development of an intelligent decision support system to predict individual’s age based on pelvis’ dimensions criteria. On the one hand, some basic image processing technics were applied in order to extract the relevant features from pelvic X-rays. On the other hand, the computational framework presented here was built on top of a Logic Programming approach to knowledge representation and reasoning, that caters for the handling of incomplete, unknown, or even self-contradictory information, complemented with a Case Base approach to computing.

An Artificial Intelligence Approach to Individuals' Age Assessment

A link between patterns of pelvic growth and human life history is supported by the finding that, cross-culturally, variation in maturation rates of female pelvis are correlated with variation in ages of menarche and first reproduction, i.e., it is well known that the human dimensions of the pelvic bones depend on the gender and vary with the age. Indeed, one feature in which humans appear to be unique is the prolonged growth of the pelvis after the age of sexual maturity. Both the total superoinferior length and mediolateral breadth of the pelvis continues to grow markedly after puberty, and do not reach adult proportions until the late teens years. This continuation of growth is accomplished by relatively late fusion of the separate centers of ossification that form the bones of the pelvis. Hence, in this work we will focus on the development of an intelligent decision support system to predict individual’s age based on a pelvis' dimensions criteria. Some basic image processing techniques were applied in order to extract the relevant features from pelvic X-rays, being the computational framework built on top of a Logic Programming approach to Knowledge Representation and Reasoning that caters for the handling of incomplete, unknown, or even self-contradictory information, complemented with a Case Base approach to computing.

A fuzzy clustering approach to a demand response model

This paper proposes a novel demand response model using a fuzzy subtractive cluster approach. The model development provides support to domestic consumer decisions on controllable loads management, considering consumers’ consumption needs and the appropriate load shape or rescheduling in order to achieve possible economic benefits. The model based on fuzzy subtractive clustering method considers clusters of domestic consumption covering an adequate consumption range. Analysis of different scenarios is presented considering available electric power and electric energy prices. Simulation results are presented and conclusions of the proposed demand response model are discussed.

An Integrated Soft Computing Approach to Hughes Syndrome Risk Assessment

The AntiPhospholipid Syndrome (APS) is an acquired autoimmune disorder induced by high levels of antiphospholipid antibodies that cause arterial and veins thrombosis, as well as pregnancy-related complications and morbidity, as clinical manifestations. This autoimmune hypercoagulable state, usually known as Hughes syndrome, has severe consequences for the patients, being one of the main causes of thrombotic disorders and death. Therefore, it is required to be preventive; being aware of how probable is to have that kind of syndrome. Despite the updated of antiphospholipid syndrome classification, the diagnosis remains difficult to establish. Additional research on clinically relevant antibodies and standardization of their quantification are required in order to improve the antiphospholipid syndrome risk assessment. Thus, this work will focus on the development of a diagnosis decision support system in terms of a formal agenda built on a Logic Programming approach to knowledge representation and reasoning, complemented with a computational framework based on Artificial Neural Networks. The proposed model allows for improving the diagnosis, classifying properly the patients that really presented this pathology (sensitivity higher than 85%), as well as classifying the absence of APS (specificity close to 95%).