Fuzzy Logic

The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems

An analysis of changing dietary trends and the implications for global health.

Worldwide, obesity has reached epidemic proportions and has almost tripled between 1975 and 2016. Acknowledging that weight gain is a complex and multifactorial condition involving changes in both dietary and physical activity patterns, this research focuses on the dietary origin of obesity. Given the dearth of empirical literature on food consumption at the global level, the aim of this research is to explore dietary trends and dietary types around the world linked with the indications that they imply for obesity and global health. To this aim, annual food availability data collated by the Food and Agriculture Organisation of the United Nations (FAO) covering the period from 1961 to 2013 for 118 countries are scrutinised by econometric convergence tests, clustering techniques and spatial analysis. Results indicate that countries with lower levels of initial calories tend to exhibit higher growth rates of calorie consumption. However, this process is not homogeneous across countries. Low-income countries have converged at the fastest pace and the convergence rate reduces as income rises. In addition, the dietary convergence is conditioned by a range of structural indicators including agroecological, demographic and socio-economic variables. Evidence suggests that economic factors have become a more important determinant of the dietary convergence since the Millennium. Applying innovative fuzzy clustering algorithms which allow multiple diets to coexist within a single country, several dietary trends and dietary types are detected. While the identified clusters are all associated with relentlessly increasing calories and deteriorating dietary healthiness over the past half a century, the most calorific cluster has shown signs of stabilising calorie consumption. A notable contribution of this research is the examination of spatial patterns of global food consumption using both traditional and non-traditional measures of spatial proximity. Differing from the earlier literature emphasising the role of geographical closeness, this research utilises an economic indicator for proximity and finds that countries with similar income levels tend to have similar diets. Spatial convergence analysis reveals a convergence process that is about three times as fast as the non-spatial model; thus, ignoring the spatial relationship leads to biased results. Incorporating the spatial dimension in cluster analysis also affects the clustering results dramatically. Cluster profiling shows that only the segment of more educated and health-aware populations exhibits the behavioural changes towards better diets, hence underlining the importance of improved education and access to knowledge. The finding that dietary evolutions are ‘spatially’ dependent provides a basis for the development of group-specific interventions that target populations at risk of worsening diets. While these policy measures are often place-based, this research lays foundations for the implementation of coherent food policies beyond geographical boundaries. Overall, this research highlights that healthier diets are possible, but we need to act now. As we are living longer but not necessarily healthier, current attempts to improve diets are obviously inadequate and existing efforts need to be redoubled. This is an urgent message for policymakers considering the sobering fact that no country has been able to significantly reverse the rise in obesity

Techniques Based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for Estimating and Evaluating Physical Demands at Work Using Heart Rate

RÉSUMÉ : Malgré l'évolution rapide de la mécanisation dans les industries lourdes, les emplois physiquement exigeants qui nécessitent un effort humain excessif représentent encore une part importante dans de nombreuses industries (foresterie, construction, mines, etc.). Des études ont montré que les charges de travail excessives imposées aux travailleurs sont la principale cause de fatigue physique, ce qui a des effets négatifs sur les travailleurs, leur performance et la qualité du travail. Par conséquent, les chercheurs ont souligné l'importance de la conception optimale des tâches (à l'intérieur des compétences des travailleurs) afin de maintenir la sécurité, la santé et la productivité des travailleurs. Toutefois, cela ne peut être atteint sans comprendre (c'est-à-dire mesurer et évaluer) les exigences physiologiques du travail. À cet égard, les trois études comprises dans cette thèse présentent des approches pratiques pour estimer et évaluer la dépense énergétique (DE), exprimée en termes de consommation d'oxygène (VO2), au cours du travail réel. La première étude présente de nouvelles approches basées sur le système d'inférence neuro-flou adaptatif (ANFIS) pour l'estimation de la VO2 à partir des mesures de la fréquence cardiaque (FC). Cette étude comprend deux étapes auxquelles ont participé 35 individus en bonne santé. Dans un premier temps, deux modèles novateurs individuels ont été développés en se basant sur l’ANFIS et les méthodes analytiques. Ces modèles s'attaquent au problème de l'incertitude et de la non-linéarité entre la FC et la VO2. Dans un deuxième temps, un modèle général ANFIS qui ne requiert pas d'étalonnage individuel a été développé. Les trois modèles ont été testés en laboratoire et sur le terrain. La performance de chaque modèle a été évaluée et comparée aux VO2 mesurées et à deux méthodes d'estimation individuelles et traditionnelles de VO2 (étalonnage linéaire et Flex-HR). Les résultats ont indiqué la précision supérieure obtenue avec la modélisation ANFIS individualisée (EMQ = 1,0 à 2,8 ml/kg.min en laboratoire et sur le terrain, respectivement). Le modèle analytique a surpassé l'étalonnage linéaire traditionnel et les méthodes Flex-HR avec des données terrain. Les estimations du modèle général ANFIS de la VO2 ne différaient pas significativement des mesures réelles terrain VO2 (EMQ = 3,5 ml/kg.min). Avec sa facilité d'utilisation et son faible coût de mise en œuvre, le modèle général ANFIS montre du potentiel pour remplacer n'importe laquelle des méthodes traditionnelles individualisées pour l’estimation de la VO2 à partir de données recueillies sur le terrain. La deuxième étude présente un modèle de prédiction de la VO2 basé sur ANFIS qui est inspiré de la méthode Flex-HR. Des études ont montré que la méthode Flex-HR est une des méthodes les plus précises pour l'estimation de la VO2. Toutefois, cette méthode est basée sur quatre paramètres qui sont déterminés individuellement et par conséquent ceci est considéré comme coûteux, chronophage et souvent peu pratique, surtout lorsque le nombre de travailleurs augmente. Le modèle prédictif proposé se compose de trois modules ANFIS pour estimer les paramètres de Flex-HR. Pour chaque module ANFIS, la sélection de variables d'entrée et le modèle d'évaluation ont été simultanément réalisés à l'aide de la combinaison de la technique de division des données en trois parties et la technique de validation croisée. La performance de chaque module ANFIS a été testée et comparée avec les paramètres observés ainsi qu'avec les modèles de Rennie et coll. (2001) à l'aide de données de test indépendant. En outre, les performances du modèle global de prédiction ANFIS dans l'estimation de la VO2 a été testé et comparé avec les valeurs mesurées de la VO2, la méthode de Flex-HR standard ainsi qu'avec les autres modèles généraux (c.-à-d., les modèles de Rennie et coll. (2001) et de Keytel et coll. (2005)). Les résultats n'ont indiqué aucune différence significative entre les paramètres observés et estimés de Flex-HR et entre la VO2 mesurée et estimée dans la plage de fréquence cardiaque globale et séparément dans différentes gammes de FC. Le modèle de prédiction ANFIS (EMA = 3 ml/kg.min) a montré de meilleures performances que les modèles de Rennie et coll. (EMA = 7 ml/kg.min) et les modèles de Keytel et coll. (EMA = 6 ml/kg.min) et des performances comparables avec la méthode standard de Flex-HR (EMA = 2,3 ml/kg.min) tout au long de la plage de fréquence cardiaque. Le modèle ANFIS fournit ainsi aux praticiens une méthode pratique, économique et rapide pour l'estimation de la VO2 sans besoin d'étalonnage individuel. La troisième étude présente une nouvelle approche basée sur l'ANFIS pour classer les travaux en quatre classes d'intensité (c'est-à-dire, très léger, léger, modéré et lourd) à l'aide du monitorage du rythme cardiaque. La variabilité intra-individuelle (différences physiologiques et physiques) a été examinée. Vingt-huit participants ont effectué le test de la montée des marches Meyer et Flenghi (1995) et le test maximal sur le tapis roulant pendant lesquels la fréquence cardiaque et la consommation d'oxygène ont été mesurées. Les résultats ont indiqué que le monitorage du rythme cardiaque (FC, FC max et FC repos) et du poids corporel sont des variables significatives pour classer le rythme de travail. Le classificateur ANFIS a montré une sensibilité, une spécificité et une exactitude supérieures par rapport à la pratique courante à l'aide de catégories de rythme de travail basées sur le pourcentage de fréquence cardiaque de réserve (% FCR), avec une différence globale de 29,6 % dans la précision de classification entre les deux méthodes et un bon équilibre entre la sensibilité (90,7 %, en moyenne) et la spécificité (95,2 %, en moyenne). Avec sa facilité de mise en œuvre et sa mesure variable, le classificateur ANFIS montre un potentiel pour une utilisation généralisée par les praticiens pour évaluation du rythme de travail.----------ABSTRACT : Despite the rapid evolution of mechanization in heavy industries, physically demanding jobs that require excessive human effort still represent a significant part of many industries (e.g., forestry, construction, mining etc.). Studies have shown that excessive workloads placed on workers are the main cause of physical fatigue, which has negative effects on the workers, their performance and quality of work. Therefore, researchers have emphasized on the importance of the optimal job design (within workers’ capacity) in order to maintain workers’ safety, health and productivity. However, this cannot be achieved without understanding (i.e., measuring and evaluating) the physiological demands of work. In this respect, the three studies comprising this dissertation present practical approaches for estimating and evaluating energy expenditure (EE), expressed in terms of oxygen consumption (VO2), during actual work. The first study presents new approaches based on adaptive neuro-fuzzy inference system (ANFIS) for the estimation of VO2 from heart rate (HR) measurements. This study comprises two stages in which 35 healthy individuals participated. In the first stage, two novel individual models were developed based on the ANFIS and the analytical methods. These models tackle the problem of uncertainty and nonlinearity between HR and VO2. In the second stage, a General ANFIS model was developed which does not require individual calibration. The three models were tested under laboratory and field conditions. Performance of each model was evaluated and compared to the measured VO2 and two traditional individual VO2 estimation methods (linear calibration and Flex-HR). Results indicated the superior precision achieved with individualized ANFIS modeling (RMSE= 1.0 and 2.8 ml/kg.min in laboratory and field, respectively). The analytical model outperformed the traditional linear calibration and Flex-HR methods with field data. The General ANFIS model’s estimates of VO2 were not significantly different from actual field VO2 measurements (RMSE= 3.5 ml/kg.min). With its ease of use and low implementation cost, the General ANFIS model shows potential to replace any of the traditional individualized methods for VO2 estimation from HR data collected in the field. The second study presents an ANFIS-based VO2 prediction model that is inspired by the Flex-HR method. Studies have shown that the Flex-HR method is one of the most accurate methods for VO2 estimation. However, this method is based on four parameters that are determined individually and therefore it is considered costly, time consuming and often impractical, especially when the number of workers increases. The proposed prediction model consists of three ANFIS modules for estimating the Flex-HR parameters. For each ANFIS module, input variables selection and model assessment were simultaneously performed using the combination of three-way data split and cross-validation techniques. The performance of each ANFIS module was tested and compared with the observed parameters as well as with Rennie et al.’s (2001) models using independent test data. In addition, the performance of the overall ANFIS prediction model in estimating VO2 was tested and compared with the measured VO2 values, the standard Flex-HR method as well as with other general models (i.e., Rennie et al.’s (2001) and Keytel et al.’s (2005) models). Results indicated no significant difference between observed and estimated Flex-HR parameters and between measured and estimated VO2 in the overall HR range, and separately in different HR ranges. The ANFIS prediction model (MAE = 3 ml/kg.min) demonstrated better performance than Rennie et al.’s (MAE = 7 ml/kg.min) and Keytel et al.’s (MAE = 6 ml/kg.min) models, and comparable performance with the standard Flex-HR method (MAE = 2.3 ml/kg.min) throughout the HR range. The ANFIS model thus provides practitioners with a practical, cost- and time-efficient method for VO2 estimation without the need for individual calibration. The third study presents a new approach based ANFIS for classifying work intensity into four classes (i.e., very light, light, moderate and heavy) by using heart rate monitoring. Intersubject variability (physiological and physical differences) was considered. Twenty-eight participants performed Meyer and Flenghi (1995) step-test and a maximal treadmill test, during which heart rate and oxygen consumption were measured. Results indicated that heart rate monitoring (HR, HRmax, and HRrest) and body weight are significant variables for classifying work rate. The ANFIS classifier showed superior sensitivity, specificity, and accuracy compared to current practice using established work rate categories based on percent heart rate reserve (%HRR), with an overall 29.6% difference in classification accuracy between the two methods, and good balance between sensitivity (90.7%, on average) and specificity (95.2%, on average). With its ease of implementation and variable measurement, the ANFIS classifier shows potential for widespread use by practitioners for work rate assessment

Towards Sustainable Global Food Systems

environment; food; agriculture; policy; global food system

Public Health

Public health can be thought of as a series of complex systems. Many things that individual living in high income countries take for granted like the control of infectious disease, clean, potable water, low infant mortality rates require a high functioning systems comprised of numerous actors, locations and interactions to work. Many people only notice public health when that system fails. This book explores several systems in public health including aspects of the food system, health care system and emerging issues including waste minimization in nanosilver. Several chapters address global health concerns including non-communicable disease prevention, poverty and health-longevity medicine. The book also presents several novel methodologies for better modeling and assessment of essential public health issues

Computational Methods for the Analysis of Genomic Data and Biological Processes

In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality

Ontology-based personalized performance evaluation and dietary recommendation for weightlifting.

Studies in weightlifting have been characterized by unclear results and information paucity, mainly due to the lack of information sharing between athletes, coaches, biomechanists, physiologists and nutritionists. Becoming successful in weightlifting performance requires a unique physiological and biomechanics profile based on a distinctive combination of muscular strength, muscular power, flexibility, and lifting technique. An effective training which is carefully designed and monitored, is needed for accomplishment of consistent high performance. While it takes years of dedicated training, diet is also critical as optimal nutrition is essential for peak performance. Nutritional misinformation can do as much harm to ambitious athletes as good nutrition can help. In spite of several studies on nutrition guidelines for weightlifting training and competition as well as on design and implementation of weightlifting training programs, to the best of authors' knowledge, there is no attempt to semantically model the whole "training-diet-competition" cycle by integrating training, biomechanics, and nutrition domains.This study aims to conceive and design an ontology-enriched knowledge model to guide and support the implementation of "Recommender system of workout and nutrition forweightlifters". In doing so, it will propose: (i) understanding the weightlifting training system, from both qualitative and quantitative perspectives, following a modular ontology modeling, (ii) understanding the weightlifting diet following a modular ontology modeling, (iii) semantically integrating weightlifting and nutrition ontologies to mainly promote nutrition and weightlifting snatch exercises interoperability, (iv) extending modular ontology scope by mining rules while analyzing open data from the literature, and (v) devising reasoning capability toward an automated weightlifting "training-diet-competition" cycle supported by previously mined rulesTo support the above claims, two main artefacts were generated such as: (i) a weightliftingnutritional knowledge questionnaire to assess Thai weightlifting coaches' and athletes'knowledge regarding the weightlifting "training-diet-competition" cycle and (ii) a dual ontologyoriented weightlifting-nutrition knowledge model extended with mined rules and designed following a standard ontology development methodology.Studies in weightlifting have been characterized by unclear results and information paucity, mainly due to the lack of information sharing between athletes, coaches, biomechanists, physiologists and nutritionists. Becoming successful in weightlifting performance requires a unique physiological and biomechanics profile based on a distinctive combination of muscular strength, muscular power, flexibility, and lifting technique. An effective training which is carefully designed and monitored, is needed for accomplishment of consistent high performance. While it takes years of dedicated training, diet is also critical as optimal nutrition is essential for peak performance. Nutritional misinformation can do as much harm to ambitious athletes as good nutrition can help. In spite of several studies on nutrition guidelines for weightlifting training and competition as well as on design and implementation of weightlifting training programs, to the best of authors' knowledge, there is no attempt to semantically model the whole "training-diet-competition" cycle by integrating training, biomechanics, and nutrition domains.This study aims to conceive and design an ontology-enriched knowledge model to guide and support the implementation of "Recommender system of workout and nutrition forweightlifters". In doing so, it will propose: (i) understanding the weightlifting training system, from both qualitative and quantitative perspectives, following a modular ontology modeling, (ii) understanding the weightlifting diet following a modular ontology modeling, (iii) semantically integrating weightlifting and nutrition ontologies to mainly promote nutrition and weightlifting snatch exercises interoperability, (iv) extending modular ontology scope by mining rules while analyzing open data from the literature, and (v) devising reasoning capability toward an automated weightlifting "training-diet-competition" cycle supported by previously mined rulesTo support the above claims, two main artefacts were generated such as: (i) a weightliftingnutritional knowledge questionnaire to assess Thai weightlifting coaches' and athletes'knowledge regarding the weightlifting "training-diet-competition" cycle and (ii) a dual ontologyoriented weightlifting-nutrition knowledge model extended with mined rules and designed following a standard ontology development methodology