Self-similarity principle: the reduced description of randomness

A new general fitting method based on the Self-Similar (SS) organization of random sequences is presented. The proposed analytical function helps to fit the response of many complex systems when their recorded data form a self-similar curve. The verified SS principle opens new possibilities for the fitting of economical, meteorological and other complex data when the mathematical model is absent but the reduced description in terms of some universal set of the fitting parameters is necessary. This fitting function is verified on economical (price of a commodity versus time) and weather (the Earth’s mean temperature surface data versus time) and for these nontrivial cases it becomes possible to receive a very good fit of initial data set. The general conditions of application of this fitting method describing the response of many complex systems and the forecast possibilities are discussed.

Democratic dialogue and collective learning in the context of self-managed teams

This paper provides a longitudinal, empirical view of the multifaceted and reciprocal processes of organizational learning in a context of self-managed teams. Organizational learning is seen as a social construction between people and actions in a work setting. The notion of learning as situated (Brown & Duguid 1989, Lave& Wenger 1991, Gherardi & al. 1998, Easterby-Smith & Araujo 1999, Abma 2003) opens up the possibility for placing the focus of research on learning in the community rather than in individual learning processes. Further, in studying processes in their social context, we cannot avoid taking power relations into consideration (Contu & Willmott 2003). The study is based on an action research with a methodology close to the ‘democratic dialogue’ presented by Gustavsen (2001). This gives a ground for research into how the learning discourse developed in the case study organization over a period of 5 years, during which time the company abandoned a middle management level of hierarchy and the teams had to figure out how to work as self-managed units. This paper discusses the (re)construction of power relations and its role in organizational learning. Power relations are discussed both in vertical and horizontal work relations. A special emphasis is placed on the dialectic between managerial aims and the space for reflection on the side of employees. I argue that learning is crucial in the search for the limits for empowerment and that these limits are negotiated both in actions and speech. This study unfolds a purpose-oriented learning process, constructing an open dialogue, and describes a favourable context for creative, knowledge building communities.

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

Psychological well-being and perception of disease severity in people with multiple sclerosis, who underwent a program of self-regulation to promote physical activity

Rehabilitation is very important for in the results of treatment in individuals with multiple sclerosis. Rehabilitation processes occur through gradual changes. These changes integrate intrinsic and extrinsic mechanisms of the individual, promoting adaptations to the needs and activities of daily living according to individual goals. Recommendations for exercise in multiple sclerosis: these recommendations apply only to patients with EDSS less than 7; moderate intensity aerobic exercise for a total of 20 to 30 minutes, twice or three times for week; the resistance training with low or moderate intensity is well tolerated by patients with MS; associated with these exercises were recommended flexibility exercises of moderate intensity, as well as strengthening exercises. The aim of this study is to examine the implications of the program of self-regulation in the perception of illness and mental health (psychological well-being domain) in multiple sclerosis patients.

Self-reported diabetes in older people: comparison of prevalences and control measures

OBJECTIVE The objective of this study was to analyze the prevalence of diabetes in older people and the adopted control measures.METHODS Data regarding older diabetic individuals who participated in the Health Surveys conducted in the Municipality of Sao Paulo, SP, ISA-Capital, in 2003 and 2008, which were cross-sectional studies, were analyzed. Prevalences and confidence intervals were compared between 2003 and 2008, according to sociodemographic variables. The combination of the databases was performed when the confidence intervals overlapped. The Chi-square (level of significance of 5%) and the Pearson’s Chi-square (Rao-Scott) tests were performed. The variables without overlap between the confidence intervals were not tested.RESULTS The age of the older adults was 60-69 years. The majority were women, Caucasian, with an income of between > 0.5 and 2.5 times the minimum salary and low levels of schooling. The prevalence of diabetes was 17.6% (95%CI 14.9;20.6) in 2003 and 20.1% (95%CI 17.3;23.1) in 2008, which indicates a growth over this period (p at the limit of significance). The most prevalent measure adopted by the older adults to control diabetes was hypoglycemic agents, followed by diet. Physical activity was not frequent, despite the significant differences observed between 2003 and 2008 results. The use of public health services to control diabetes was significantly higher in older individuals with lower income and lower levels of education.CONCLUSIONS Diabetes is a complex and challenging disease for patients and the health systems. Measures that encourage health promotion practices are necessary because they presented a smaller proportion than the use of hypoglycemic agents. Public health policies should be implemented, and aimed mainly at older individuals with low income and schooling levels. These changes are essential to improve the health condition of older diabetic patients.

Dimensions of self-rated health in older adults

OBJECTIVE To analyze the association between negative self-rated health and indicators of health, wellbeing and sociodemographic variables in older adults. METHODS Cross-sectional study that used data from a population-based health survey with a probability cluster sample that was carried out in Campinas, SP, Southeastern Brazil,, in 2008 and 2009. The participants were older adults (≥ 60 years) and the dependent variable was self-rated health, categorized as: excellent, very good, good, bad and very bad. The adjusted prevalence ratios were estimated by means of Poisson multiple regression. RESULTS The highest prevalences of bad/very bad self-rated health were observed in the individuals who never attended school, in those with lower level of schooling, with monthly per capita family income lower than one minimum salary. Individuals who scored five or more in the physical health indicator also had bad self-rated health, as well as those who scored five or more in the Self-Reporting Questionnaire 20 and those who did not refer feeling happiness all the time. CONCLUSIONS The independent effects of material life conditions, physical and mental health and subjective wellbeing, observed in self-rated health, suggest that older adults can benefit by health policies supported by a global and integrative view of old age.

Self-adaptive combination of global tabu search and local search for nonlinear equations

Solving systems of nonlinear equations is a very important task since the problems emerge mostly through the mathematical modelling of real problems that arise naturally in many branches of engineering and in the physical sciences. The problem can be naturally reformulated as a global optimization problem. In this paper, we show that a self-adaptive combination of a metaheuristic with a classical local search method is able to converge to some difficult problems that are not solved by Newton-type methods.

Modeling of the lung impedance using a fractional order ladder network with constant phase elements

The self similar branching arrangement of the airways makes the respiratory system an ideal candidate for the application of fractional calculus theory. The fractal geometry is typically characterized by a recurrent structure. This study investigates the identification of a model for the respiratory tree by means of its electrical equivalent based on intrinsic morphology. Measurements were obtained from seven volunteers, in terms of their respiratory impedance by means of its complex representation for frequencies below 5 Hz. A parametric modeling is then applied to the complex valued data points. Since at low-frequency range the inertance is negligible, each airway branch is modeled by using gamma cell resistance and capacitance, the latter having a fractional-order constant phase element (CPE), which is identified from measurements. In addition, the complex impedance is also approximated by means of a model consisting of a lumped series resistance and a lumped fractional-order capacitance. The results reveal that both models characterize the data well, whereas the averaged CPE values are supraunitary and subunitary for the ladder network and the lumped model, respectively.

Self-Scheduling for energy and spinning reserve of wind/CSP plants by a MILP approach

This paper is on the self-scheduling for a power producer taking part in day-ahead joint energy and spinning reserve markets and aiming at a short-term coordination of wind power plants with concentrated solar power plants having thermal energy storage. The short-term coordination is formulated as a mixed-integer linear programming problem given as the maximization of profit subjected to technical operation constraints, including the ones related to a transmission line. Probability density functions are used to model the variability of the hourly wind speed and the solar irradiation in regard to a negative correlation. Case studies based on an Iberian Peninsula wind and concentrated solar power plants are presented, providing the optimal energy and spinning reserve for the short-term self-scheduling in order to unveil the coordination benefits and synergies between wind and solar resources. Results and sensitivity analysis are in favour of the coordination, showing an increase on profit, allowing for spinning reserve, reducing the need for curtailment, increasing the transmission line capacity factor. (C) 2014 Elsevier Ltd. All rights reserved.

Scheduling a cutting and treatment stainless steel sheet line with self-management capabilities

With advancement in computer science and information technology, computing systems are becoming increasingly more complex with an increasing number of heterogeneous components. They are thus becoming more difficult to monitor, manage, and maintain. This process has been well known as labor intensive and error prone. In addition, traditional approaches for system management are difficult to keep up with the rapidly changing environments. There is a need for automatic and efficient approaches to monitor and manage complex computing systems. In this paper, we propose an innovative framework for scheduling system management by combining Autonomic Computing (AC) paradigm, Multi-Agent Systems (MAS) and Nature Inspired Optimization Techniques (NIT). Additionally, we consider the resolution of realistic problems. The scheduling of a Cutting and Treatment Stainless Steel Sheet Line will be evaluated. Results show that proposed approach has advantages when compared with other scheduling systems

Screening for frailty in older adults using a self-reported instrument

OBJECTIVE To validate a screening instrument using self-reported assessment of frailty syndrome in older adults.METHODS This cross-sectional study used data from the Saúde, Bem-estar e Envelhecimento study conducted in Sao Paulo, SP, Southeastern Brazil. The sample consisted of 433 older adult individuals (≥ 75 years) assessed in 2009. The self-reported instrument can be applied to older adults or their proxy respondents and consists of dichotomous questions directly related to each component of the frailty phenotype, which is considered the gold standard model: unintentional weight loss, fatigue, low physical activity, decreased physical strength, and decreased walking speed. The same classification proposed in the phenotype was utilized: not frail (no component identified); pre-frail (presence of one or two components), and frail (presence of three or more components). Because this is a screening instrument, “process of frailty” was included as a category (pre-frail and frail). Cronbach’s α was used in psychometric analysis to evaluate the reliability and validity of the criterion, the sensitivity, the specificity, as well as positive and negative predictive values. Factor analysis was used to assess the suitability of the proposed number of components.RESULTS Decreased walking speed and decreased physical strength showed good internal consistency (α = 0.77 and 0.72, respectively); however, low physical activity was less satisfactory (α = 0.63). The sensitivity and specificity for identifying pre-frail individuals were 89.7% and 24.3%, respectively, while those for identifying frail individuals were 63.2% and 71.6%, respectively. In addition, 89.7% of the individuals from both the evaluations were identified in the “process of frailty” category.CONCLUSIONS The self-reported assessment of frailty can identify the syndrome among older adults and can be used as a screening tool. Its advantages include simplicity, rapidity, low cost, and ability to be used by different professionals.

Association between social capital and self-perception of health in Brazilian adults

OBJECTIVE To investigate the association between social capital and social capital and self-perception of health based on examining the influence of health-related behaviors as possible mediators of this relationship.METHODS A cross-sectional study was used with 1,081 subjects, which is representative of the population of individuals aged 40 years or more in a medium-sized city in Southern Brazil. The subjects who perceived their health as fine, bad or very bad were considered to have a negative self-perception of their health. The social capital indicators were: number of friends, people from whom they could borrow money from when needed; the extent of trust in community members; whether or not members of the community helped each other; community safety; and extent of participation in community activities. The behaviors were: physical activity during leisure time, fruits and vegetable consumption, tobacco use and alcohol abuse. The odds ratios (OR) and confidence intervals (CI) 95% were calculated by binary logistic regression. The significance of mediation was verified using the Sobel test.RESULTS Following adjustment for demographic and clinical variables, subjects with fewer friends (OR = 1.39, 95%CI 1.08;1.80), those who perceived less frequently help from people in the neighborhood (OR = 1.30, 95%CI 1.01;1.68), who saw the violent neighborhood (OR = 1.33, 95%CI 1.01;1.74) and who had not participated in any community activity (OR = 1.39, 95%CI 1.07;1.80) had more negative self-perception of their health. Physical activity during leisure time was a significant mediator in the relationship between all social capital indicators (except for the borrowed money variable) and self-perceived health. Fruit and vegetable consumption was a significant mediator of the relationship between the extent of participation in community activities and self-perceived health. Tobacco use and alcohol abuse did not seem to have a mediating role in any relationship.CONCLUSIONS Lifestyle seems to only partially explain the relationship between social capital and self-perceived health. Among the investigated behaviors, physical activity during leisure time is what seems to have the most important role as a mediator of this relationship.

Self-organization and complexity theory to support evolvable production systems

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores

Prevalence of self-medication in the adult population of Brazil: a systematic review

OBJECTIVE To evaluate the prevalence of self-medication in Brazil’s adult population.METHODS Systematic review of cross-sectional population-based studies. The following databases were used: Medline, Embase, Scopus, ISI, CINAHL, Cochrane Library, CRD, Lilacs, SciELO, the Banco de teses brasileiras(Brazilian theses database) (Capes) and files from the Portal Domínio Público (Brazilian Public Domain). In addition, the reference lists from relevant studies were examined to identify potentially eligible articles. There were no applied restrictions in terms of the publication date, language or publication status. Data related to publication, population, methods and prevalence of self-medication were extracted by three independent researchers. Methodological quality was assessed following eight criteria related to sampling, measurement and presentation of results. The prevalences were measured from participants who used at least one medication during the recall period of the studies.RESULTS The literature screening identified 2,778 records, from which 12 were included for analysis. Most studies were conducted in the Southeastern region of Brazil, after 2000 and with a 15-day recall period. Only five studies achieved high methodological quality, of which one study had a 7-day recall period, in which the prevalence of self-medication was 22.9% (95%CI 14.6;33.9). The prevalence of self-medication in three studies of high methodological quality with a 15-day recall period was 35.0% (95%CI 29.0;40.0, I2 = 83.9%) in the adult Brazilian population.CONCLUSIONS Despite differences in the methodologies of the included studies, the results of this systematic review indicate that a significant proportion of the adult Brazilian population self-medicates. It is suggested that future research projects that assess self-medication in Brazil standardize their methods.

Meta-heuristics self-parameterization in a multi-agent scheduling system using case-based reasoning

This paper proposes a novel agent-based approach to Meta-Heuristics self-configuration. Meta-heuristics are algorithms with parameters which need to be set up as efficient as possible in order to unsure its performance. A learning module for self-parameterization of Meta-heuristics (MH) in a Multi-Agent System (MAS) for resolution of scheduling problems is proposed in this work. The learning module is based on Case-based Reasoning (CBR) and two different integration approaches are proposed. A computational study is made for comparing the two CBR integration perspectives. Finally, some conclusions are reached and future work outlined.