Sistema de classificação fuzzy para o risco de infestação por plantas daninhas considerando a sua variabilidade espacial

Este artigo trata do problema de classificação do risco de infestação por plantas daninhas usando técnicas geoestatísticas, análise de imagens e modelos de classificação fuzzy. Os principais atributos utilizados para descrever a infestação incluem a densidade de sementes, bem como a sua extensão, a cobertura foliar e a agressividade das plantas daninhas em cada região. A densidade de sementes reflete a produção de sementes por unidade de área, e a sua extensão, a influência das sementes vizinhas; a cobertura foliar indica a extensão dos agrupamentos das plantas daninhas emergentes; e a agressividade descreve a porcentagem de ocupação de espécies com alta capacidade de produção de sementes. Os dados da densidade de sementes, da cobertura foliar e da agressividade para as diferentes regiões são obtidos a partir de simulação com modelos matemáticos de populações. Neste artigo propõe-se um sistema de classificação fuzzy utilizando os atributos descritos para inferir os riscos de infestação de regiões da cultura por plantas daninhas. Resultados de simulação são apresentados para ilustrar o uso desse sistema na aplicação localizada de herbicida.

Fuzzy ontology for knowledge mobilisation and decision support

A growing concern for organisations is how they should deal with increasing amounts of collected data. With fierce competition and smaller margins, organisations that are able to fully realize the potential in the data they collect can gain an advantage over the competitors. It is almost impossible to avoid imprecision when processing large amounts of data. Still, many of the available information systems are not capable of handling imprecise data, even though it can offer various advantages. Expert knowledge stored as linguistic expressions is a good example of imprecise but valuable data, i.e. data that is hard to exactly pinpoint to a definitive value. There is an obvious concern among organisations on how this problem should be handled; finding new methods for processing and storing imprecise data are therefore a key issue. Additionally, it is equally important to show that tacit knowledge and imprecise data can be used with success, which encourages organisations to analyse their imprecise data. The objective of the research conducted was therefore to explore how fuzzy ontologies could facilitate the exploitation and mobilisation of tacit knowledge and imprecise data in organisational and operational decision making processes. The thesis introduces both practical and theoretical advances on how fuzzy logic, ontologies (fuzzy ontologies) and OWA operators can be utilized for different decision making problems. It is demonstrated how a fuzzy ontology can model tacit knowledge which was collected from wine connoisseurs. The approach can be generalised and applied also to other practically important problems, such as intrusion detection. Additionally, a fuzzy ontology is applied in a novel consensus model for group decision making. By combining the fuzzy ontology with Semantic Web affiliated techniques novel applications have been designed. These applications show how the mobilisation of knowledge can successfully utilize also imprecise data. An important part of decision making processes is undeniably aggregation, which in combination with a fuzzy ontology provides a promising basis for demonstrating the benefits that one can retrieve from handling imprecise data. The new aggregation operators defined in the thesis often provide new possibilities to handle imprecision and expert opinions. This is demonstrated through both theoretical examples and practical implementations. This thesis shows the benefits of utilizing all the available data one possess, including imprecise data. By combining the concept of fuzzy ontology with the Semantic Web movement, it aspires to show the corporate world and industry the benefits of embracing fuzzy ontologies and imprecision.

Overcoming crossing barriers between cassava, Manihot esculenta Crantz and a wild relative, M. pohlii Warwa

The use of mentor pollen has enabled successful hybridization between cassava, Manihot esculenta Crantz, and the wild species M. pohlii Warwa. Killed pollen of a cross compatible type produced by freeze-thawing was mixed with incompatible pollen and the mixes were dusted on stigmas. This treatment resulted in production of seed in 4.9% of the total pollinations, compared to 0% in the case of untreated pollinations. The use of a bridge species, M. neusana Nassar, through the hybrid M. pohlii and M. neusana also proved successful in overcoming interspecific barriers between cassava and M. pohlii.

Relative influence of age, resting heart rate and sedentary life style in short-term analysis of heart rate variability

In order to assess the relative influence of age, resting heart rate (HR) and sedentary life style, heart rate variability (HRV) was studied in two different groups. The young group (YG) consisted of 9 sedentary subjects aged 15 to 20 years (YG-S) and of 9 nonsedentary volunteers (YG-NS) also aged 15 to 20. The elderly sedentary group (ESG) consisted of 16 sedentary subjects aged 39 to 82 years. HRV was assessed using a short-term procedure (5 min). R-R variability was calculated in the time-domain by means of the root mean square successive differences. Frequency-domain HRV was evaluated by power spectrum analysis considering high frequency and low frequency bands. In the YG the effort tolerance was ranked in a bicycle stress test. HR was similar for both groups while ESG showed a reduced HRV compared with YG. Within each group, HRV displayed a negative correlation with HR. Although YG-NS had better effort tolerance than YG-S, their HR and HRV were not significantly different. We conclude that HRV is reduced with increasing HR or age, regardless of life style. The results obtained in our short-term study agree with others of longer duration by showing that age and HR are the main determinants of HRV. Our results do not support the idea that changes in HRV are related to regular physical activity.

Fuzzy tolerance/equivalence relations from intersection, union and complement operators and their application to a clustering method

This master thesis work introduces the fuzzy tolerance/equivalence relation and its application in cluster analysis. The work presents about the construction of fuzzy equivalence relations using increasing generators. Here, we investigate and research on the role of increasing generators for the creation of intersection, union and complement operators. The objective is to develop different varieties of fuzzy tolerance/equivalence relations using different varieties of increasing generators. At last, we perform a comparative study with these developed varieties of fuzzy tolerance/equivalence relations in their application to a clustering method.

Semantic and fuzzy modelling for human behaviour recognition in smart spaces : a case study on ambient assisted living.

Human activity recognition in everyday environments is a critical, but challenging task in Ambient Intelligence applications to achieve proper Ambient Assisted Living, and key challenges still remain to be dealt with to realize robust methods. One of the major limitations of the Ambient Intelligence systems today is the lack of semantic models of those activities on the environment, so that the system can recognize the speci c activity being performed by the user(s) and act accordingly. In this context, this thesis addresses the general problem of knowledge representation in Smart Spaces. The main objective is to develop knowledge-based models, equipped with semantics to learn, infer and monitor human behaviours in Smart Spaces. Moreover, it is easy to recognize that some aspects of this problem have a high degree of uncertainty, and therefore, the developed models must be equipped with mechanisms to manage this type of information. A fuzzy ontology and a semantic hybrid system are presented to allow modelling and recognition of a set of complex real-life scenarios where vagueness and uncertainty are inherent to the human nature of the users that perform it. The handling of uncertain, incomplete and vague data (i.e., missing sensor readings and activity execution variations, since human behaviour is non-deterministic) is approached for the rst time through a fuzzy ontology validated on real-time settings within a hybrid data-driven and knowledgebased architecture. The semantics of activities, sub-activities and real-time object interaction are taken into consideration. The proposed framework consists of two main modules: the low-level sub-activity recognizer and the high-level activity recognizer. The rst module detects sub-activities (i.e., actions or basic activities) that take input data directly from a depth sensor (Kinect). The main contribution of this thesis tackles the second component of the hybrid system, which lays on top of the previous one, in a superior level of abstraction, and acquires the input data from the rst module's output, and executes ontological inference to provide users, activities and their in uence in the environment, with semantics. This component is thus knowledge-based, and a fuzzy ontology was designed to model the high-level activities. Since activity recognition requires context-awareness and the ability to discriminate among activities in di erent environments, the semantic framework allows for modelling common-sense knowledge in the form of a rule-based system that supports expressions close to natural language in the form of fuzzy linguistic labels. The framework advantages have been evaluated with a challenging and new public dataset, CAD-120, achieving an accuracy of 90.1% and 91.1% respectively for low and high-level activities. This entails an improvement over both, entirely data-driven approaches, and merely ontology-based approaches. As an added value, for the system to be su ciently simple and exible to be managed by non-expert users, and thus, facilitate the transfer of research to industry, a development framework composed by a programming toolbox, a hybrid crisp and fuzzy architecture, and graphical models to represent and con gure human behaviour in Smart Spaces, were developed in order to provide the framework with more usability in the nal application. As a result, human behaviour recognition can help assisting people with special needs such as in healthcare, independent elderly living, in remote rehabilitation monitoring, industrial process guideline control, and many other cases. This thesis shows use cases in these areas.

Clinical signs of pneumonia in children: association with and prediction of diagnosis by fuzzy sets theory

The present study compares the performance of stochastic and fuzzy models for the analysis of the relationship between clinical signs and diagnosis. Data obtained for 153 children concerning diagnosis (pneumonia, other non-pneumonia diseases, absence of disease) and seven clinical signs were divided into two samples, one for analysis and other for validation. The former was used to derive relations by multi-discriminant analysis (MDA) and by fuzzy max-min compositions (fuzzy), and the latter was used to assess the predictions drawn from each type of relation. MDA and fuzzy were closely similar in terms of prediction, with correct allocation of 75.7 to 78.3% of patients in the validation sample, and displaying only a single instance of disagreement: a patient with low level of toxemia was mistaken as not diseased by MDA and correctly taken as somehow ill by fuzzy. Concerning relations, each method provided different information, each revealing different aspects of the relations between clinical signs and diagnoses. Both methods agreed on pointing X-ray, dyspnea, and auscultation as better related with pneumonia, but only fuzzy was able to detect relations of heart rate, body temperature, toxemia and respiratory rate with pneumonia. Moreover, only fuzzy was able to detect a relationship between heart rate and absence of disease, which allowed the detection of six malnourished children whose diagnoses as healthy are, indeed, disputable. The conclusion is that even though fuzzy sets theory might not improve prediction, it certainly does enhance clinical knowledge since it detects relationships not visible to stochastic models.

Fuzzy expert system in the prediction of neonatal resuscitation

In view of the importance of anticipating the occurrence of critical situations in medicine, we propose the use of a fuzzy expert system to predict the need for advanced neonatal resuscitation efforts in the delivery room. This system relates the maternal medical, obstetric and neonatal characteristics to the clinical conditions of the newborn, providing a risk measurement of need of advanced neonatal resuscitation measures. It is structured as a fuzzy composition developed on the basis of the subjective perception of danger of nine neonatologists facing 61 antenatal and intrapartum clinical situations which provide a degree of association with the risk of occurrence of perinatal asphyxia. The resulting relational matrix describes the association between clinical factors and risk of perinatal asphyxia. Analyzing the inputs of the presence or absence of all 61 clinical factors, the system returns the rate of risk of perinatal asphyxia as output. A prospectively collected series of 304 cases of perinatal care was analyzed to ascertain system performance. The fuzzy expert system presented a sensitivity of 76.5% and specificity of 94.8% in the identification of the need for advanced neonatal resuscitation measures, considering a cut-off value of 5 on a scale ranging from 0 to 10. The area under the receiver operating characteristic curve was 0.93. The identification of risk situations plays an important role in the planning of health care. These preliminary results encourage us to develop further studies and to refine this model, which is intended to implement an auxiliary system able to help health care staff to make decisions in perinatal care.

Relative contribution of expectancy and immediate arousal to the facilitatory effect of an auditory accessory stimulus

An auditory stimulus speeds up a digital response to a subsequent visual stimulus. This facilitatory effect has been related to the expectancy and the immediate arousal that would be caused by the accessory stimulus. The present study examined the relative contribution of these two influences. In a first and a third experiment a simple reaction time task was used. In a second and fourth experiment a go/no-go reaction time task was used. In each of these experiments, the accessory stimulus preceded the target stimulus by 200 ms for one group of male and female volunteers (G Fix). For another group of similar volunteers (G Var) the accessory stimulus preceded the target stimulus by 200 ms in 25% of the trials, by 1000 ms in 25% of the trials and was not followed by the target stimulus in 50% of the trials (Experiments 1a and 1b) or preceded the target stimulus by 200 ms in 6% of the trials and by 1000 ms in 94% of the trials (Experiments 2a and 2b). There was a facilitatory effect of the accessory stimulus for G Fix in the four experiments. There was also a facilitatory effect of the accessory stimulus at the 200-ms stimulus onset asynchrony for G Var in Experiments 1a and 1b but not in Experiments 2a and 2b. The facilitatory effects observed were larger in the go/no-go task than in the simple task. Taken together, these results suggest that expectancy is much more important than immediate arousal for the improvement of performance caused by an accessory stimulus.

Selection of patients for myocardial perfusion scintigraphy based on fuzzy sets theory applied to clinical-epidemiological data and treadmill test results

Coronary artery disease (CAD) is a worldwide leading cause of death. The standard method for evaluating critical partial occlusions is coronary arteriography, a catheterization technique which is invasive, time consuming, and costly. There are noninvasive approaches for the early detection of CAD. The basis for the noninvasive diagnosis of CAD has been laid in a sequential analysis of the risk factors, and the results of the treadmill test and myocardial perfusion scintigraphy (MPS). Many investigators have demonstrated that the diagnostic applications of MPS are appropriate for patients who have an intermediate likelihood of disease. Although this information is useful, it is only partially utilized in clinical practice due to the difficulty to properly classify the patients. Since the seminal work of Lotfi Zadeh, fuzzy logic has been applied in numerous areas. In the present study, we proposed and tested a model to select patients for MPS based on fuzzy sets theory. A group of 1053 patients was used to develop the model and another group of 1045 patients was used to test it. Receiver operating characteristic curves were used to compare the performance of the fuzzy model against expert physician opinions, and showed that the performance of the fuzzy model was equal or superior to that of the physicians. Therefore, we conclude that the fuzzy model could be a useful tool to assist the general practitioner in the selection of patients for MPS.

Relative frequency, clinical, neuroimaging, and postsurgical features of pediatric temporal lobe epilepsy

We describe the relative frequency, clinical features, neuroimaging and pathological results, and outcome after pharmacological or surgical intervention for a series of pediatric patients with temporal lobe epilepsy (TLE) from an epilepsy center in Brazil. The medical records of children younger than 12 years with features strongly suggestive of TLE were reviewed from January 1999 to June 1999. Selected children were evaluated regarding clinical, EEG, and magnetic resonance imaging (MRI) investigation and divided into three groups according to MRI: group 1 (G1, N = 9), patients with hippocampal atrophy; group 2 (G2, N = 10), patients with normal MRI, and group 3 (G3, N = 12), patients with other specific temporal lesions. A review of 1732 records of children with epilepsy revealed 31 cases with TLE (relative frequency of 1.79%). However, when the investigation was narrowed to cases with intractable seizures that needed video-EEG monitoring (N = 68) or epilepsy surgery (N = 32), the relative frequency of TLE increased to 19.11 (13/68) and 31.25% (10/32), respectively. At the beginning of the study, 25 of 31 patients had a high seizure frequency (80.6%), which declined to 11 of 31 (35.5%) at the conclusion of the study, as a consequence of pharmacological and/or surgical therapy. This improvement in seizure control was significant in G1 (P < 0.05) and G3 (P < 0.01) mainly due to good postsurgical outcome, and was not significant in G2 (P > 0.1, McNemar's test). These results indicate that the relative frequency of TLE in children was low, but increased considerably among cases with pharmacoresistant seizures. Patients with specific lesions were likely to undergo surgery, with good postoperative outcomes.

Fuzzy real option analysis in patent related decision making and patent valuation

The shift towards a knowledge-based economy has inevitably prompted the evolution of patent exploitation. Nowadays, patent is more than just a prevention tool for a company to block its competitors from developing rival technologies, but lies at the very heart of its strategy for value creation and is therefore strategically exploited for economic pro t and competitive advantage. Along with the evolution of patent exploitation, the demand for reliable and systematic patent valuation has also reached an unprecedented level. However, most of the quantitative approaches in use to assess patent could arguably fall into four categories and they are based solely on the conventional discounted cash flow analysis, whose usability and reliability in the context of patent valuation are greatly limited by five practical issues: the market illiquidity, the poor data availability, discriminatory cash-flow estimations, and its incapability to account for changing risk and managerial flexibility. This dissertation attempts to overcome these impeding barriers by rationalizing the use of two techniques, namely fuzzy set theory (aiming at the first three issues) and real option analysis (aiming at the last two). It commences with an investigation into the nature of the uncertainties inherent in patent cash flow estimation and claims that two levels of uncertainties must be properly accounted for. Further investigation reveals that both levels of uncertainties fall under the categorization of subjective uncertainty, which differs from objective uncertainty originating from inherent randomness in that uncertainties labelled as subjective are highly related to the behavioural aspects of decision making and are usually witnessed whenever human judgement, evaluation or reasoning is crucial to the system under consideration and there exists a lack of complete knowledge on its variables. Having clarified their nature, the application of fuzzy set theory in modelling patent-related uncertain quantities is effortlessly justified. The application of real option analysis to patent valuation is prompted by the fact that both patent application process and the subsequent patent exploitation (or commercialization) are subject to a wide range of decisions at multiple successive stages. In other words, both patent applicants and patentees are faced with a large variety of courses of action as to how their patent applications and granted patents can be managed. Since they have the right to run their projects actively, this flexibility has value and thus must be properly accounted for. Accordingly, an explicit identification of the types of managerial flexibility inherent in patent-related decision making problems and in patent valuation, and a discussion on how they could be interpreted in terms of real options are provided in this dissertation. Additionally, the use of the proposed techniques in practical applications is demonstrated by three fuzzy real option analysis based models. In particular, the pay-of method and the extended fuzzy Black-Scholes model are employed to investigate the profitability of a patent application project for a new process for the preparation of a gypsum-fibre composite and to justify the subsequent patent commercialization decision, respectively; a fuzzy binomial model is designed to reveal the economic potential of a patent licensing opportunity.

Stability of relative oxygen pulse curve during repeated maximal cardiopulmonary testing in professional soccer players

During cardiopulmonary exercise testing (CPET), stroke volume can be indirectly assessed by O2 pulse profile. However, for a valid interpretation, the stability of this variable over time should be known. The objective was to analyze the stability of the O2 pulse curve relative to body mass in elite athletes. VO2, heart rate (HR), and relative O2 pulse were compared at every 10% of the running time in two maximal CPETs, from 2005 to 2010, of 49 soccer players. Maximal values of VO2 (63.4 ± 0.9 vs 63.5 ± 0.9 mL O2•kg-1•min-1), HR (190 ± 1 vs188 ± 1 bpm) and relative O2 pulse (32.9 ± 0.6 vs 32.6 ± 0.6 mL O2•beat-1•kg-1) were similar for the two CPETs (P > 0.05), while the final treadmill velocity increased from 18.5 ± 0.9 to 18.9 ± 1.0 km/h (P < 0.01). Relative O2 pulse increased linearly and similarly in both evaluations (r² = 0.64 and 0.63) up to 90% of the running time. Between 90 and 100% of the running time, the values were less stable, with up to 50% of the players showing a tendency to a plateau in the relative O2 pulse. In young healthy men in good to excellent aerobic condition, the morphology of the relative O2 pulse curve is consistent up to close to the peak effort for a CPET repeated within a 1-year period. No increase in relative O2pulse at peak effort could represent a physiologic stroke volume limitation in these athletes.

Relative performance of the two hands in simple and choice reaction time tasks

There is evidence that the left hemisphere is more competent for motor control than the right hemisphere. This study investigated whether this hemispheric asymmetry is expressed in the latency/duration of sequential responses performed by the left and/or right hands. Thirty-two right-handed young adults (16 males, 16 females; 18-25 years old) were tested in a simple or choice reaction time task. They responded to a left and/or right visual target by moving their left and/or right middle fingers between two keys on each side of the midline. Right hand reaction time did not differ from left hand reaction time. Submovement times were longer for the right hand than the left hand when the response was bilateral. Pause times were shorter for the right hand than the left hand, both when the responses were unilateral or bilateral. Reaction time results indicate that the putatively more efficient response preparation by the left hemisphere motor mechanisms is not expressed behaviorally. Submovement time and pause time results indicate that the putatively more efficient response execution by the left hemisphere motor mechanisms is expressed behaviorally. In the case of the submovements, the less efficient motor control of the left hand would be compensated by a more intense attention to this hand.

Estimating the average length of hospitalization due to pneumonia: a fuzzy approach

Exposure to air pollutants is associated with hospitalizations due to pneumonia in children. We hypothesized the length of hospitalization due to pneumonia may be dependent on air pollutant concentrations. Therefore, we built a computational model using fuzzy logic tools to predict the mean time of hospitalization due to pneumonia in children living in São José dos Campos, SP, Brazil. The model was built with four inputs related to pollutant concentrations and effective temperature, and the output was related to the mean length of hospitalization. Each input had two membership functions and the output had four membership functions, generating 16 rules. The model was validated against real data, and a receiver operating characteristic (ROC) curve was constructed to evaluate model performance. The values predicted by the model were significantly correlated with real data. Sulfur dioxide and particulate matter significantly predicted the mean length of hospitalization in lags 0, 1, and 2. This model can contribute to the care provided to children with pneumonia.