Absence of chemopreventive influence of propolis on the rat liver altered foci development

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Using artificial intelligence for the control of medium voltage substations

This paper introduces a method for the supervision and control of devices in electric substations using fuzzy logic and artificial neural networks. An automatic knowledge acquisition process is included which allows the on-line processing of operator actions and the extraction of control rules to replace gradually the human operator. Some experimental results obtained by the application of the implemented software in a simulated environment with random signal generators are presented.

Economic design of Shewhart control charts for monitoring autocorrelated data with skip sampling strategies

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Changes in plant metabolism induced by dioxygenase inhibitors and their effect on the epiphytic microbial community and fire blight (Erwinia amylovora) control

Fire blight, caused by the gram negative bacterium Erwinia amylovora, is one of the most destructive bacterial diseases of Pomaceous plants. Therefore, the development of reliable methods to control this disease is desperately needed. This research investigated the possibility to interfere, by altering plant metabolism, on the interactions occurring between Erwinia amylovora, the host plant and the epiphytic microbial community in order to obtain a more effective control of fire blight. Prohexadione-calcium and trinexapac-ethyl, two dioxygenase inhibitors, were chosen as a chemical tool to influence plant metabolism. These compounds inhibit the 2-oxoglutarate-dependent dioxygenases and, therefore, they greatly influence plant metabolism. Moreover, dioxygenase inhibitors were found to enhance plant resistance to a wide range of pathogens. In particular, dioxygenase inhibitors application seems a promising method to control fire blight. From cited literature, it is assumed that these compounds increase plant defence mainly by a transient alteration of flavonoids metabolism. We tried to demonstrate, that the reduction of susceptibility to disease could be partially due to an indirect influence on the microbial community established on plant surface. The possibility to influence the interactions occurring in the epiphytic microbial community is particularly interesting, in fact, the relationships among different bacterial populations on plant surface is a key factor for a more effective biological control of plant diseases. Furthermore, we evaluated the possibility to combine the application of dioxygenase inhibitors with biological control in order to develop an integrate strategy for control of fire blight. The first step for this study was the isolation of a pathogenic strain of E. amylovora. In addition, we isolated different epiphytic bacteria, which respond to general requirements for biological control agents. Successively, the effect of dioxygenase inhibitors treatment on microbial community was investigated on different plant organs (stigmas, nectaries and leaves). An increase in epiphytic microbial population was found. Further experiments were performed with aim to explain this effect. In particular, changes in sugar content of nectar were observed. These changes, decreasing the osmotic potential of nectar, might allow a more consistent growth of epiphytic bacteria on blossoms. On leaves were found similar differences as well. As far as the interactions between E. amylovora and host plant, they were deeply investigated by advanced microscopical analysis. The influence of dioxygenase inhibitors and SAR inducers application on the infection process and migration of pathogen inside different plant tissues was studied. These microscopical techniques, combined with the use of gpf-labelled E. amylovora, allowed the development of a bioassay method for resistance inducers efficacy screening. The final part of the work demonstrated that the reduction of disease susceptibility observed in plants treated with prohexadione-calcium is mainly due to the accumulation of a novel phytoalexins: luteoforol. This 3-deoxyflavonoid was proven to have a strong antimicrobial activity.

Applicability of Process Mining Techniques in Business Environments

This thesis analyses problems related to the applicability, in business environments, of Process Mining tools and techniques. The first contribution is a presentation of the state of the art of Process Mining and a characterization of companies, in terms of their "process awareness". The work continues identifying circumstance where problems can emerge: data preparation; actual mining; and results interpretation. Other problems are the configuration of parameters by not-expert users and computational complexity. We concentrate on two possible scenarios: "batch" and "on-line" Process Mining. Concerning the batch Process Mining, we first investigated the data preparation problem and we proposed a solution for the identification of the "case-ids" whenever this field is not explicitly indicated. After that, we concentrated on problems at mining time and we propose the generalization of a well-known control-flow discovery algorithm in order to exploit non instantaneous events. The usage of interval-based recording leads to an important improvement of performance. Later on, we report our work on the parameters configuration for not-expert users. We present two approaches to select the "best" parameters configuration: one is completely autonomous; the other requires human interaction to navigate a hierarchy of candidate models. Concerning the data interpretation and results evaluation, we propose two metrics: a model-to-model and a model-to-log. Finally, we present an automatic approach for the extension of a control-flow model with social information, in order to simplify the analysis of these perspectives. The second part of this thesis deals with control-flow discovery algorithms in on-line settings. We propose a formal definition of the problem, and two baseline approaches. The actual mining algorithms proposed are two: the first is the adaptation, to the control-flow discovery problem, of a frequency counting algorithm; the second constitutes a framework of models which can be used for different kinds of streams (stationary versus evolving).

An insulin infusion advisory system based on autotuning nonlinear model-predictive control

This paper aims at the development and evaluation of a personalized insulin infusion advisory system (IIAS), able to provide real-time estimations of the appropriate insulin infusion rate for type 1 diabetes mellitus (T1DM) patients using continuous glucose monitors and insulin pumps. The system is based on a nonlinear model-predictive controller (NMPC) that uses a personalized glucose-insulin metabolism model, consisting of two compartmental models and a recurrent neural network. The model takes as input patient's information regarding meal intake, glucose measurements, and insulin infusion rates, and provides glucose predictions. The predictions are fed to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. An algorithm based on fuzzy logic has been developed for the on-line adaptation of the NMPC control parameters. The IIAS has been in silico evaluated using an appropriate simulation environment (UVa T1DM simulator). The IIAS was able to handle various meal profiles, fasting conditions, interpatient variability, intraday variation in physiological parameters, and errors in meal amount estimations.

Experiences on using closed-loop control systems for smoke control

The main objective of ventilation systems in case of fire is the reduction of the possible consequences by achieving the best possible conditions for the evacuation of the users and the intervention of the emergency services. In the last years, the required quick response of the ventilation system, from normal to emergency mode, has been improved by the use of automatic and semi-automatic control systems, what reduces the response times through the support to the operators decision taking, and the use of pre-defined strategies. A further step consists on the use of closedloop algorithms, which takes into account not only the initial conditions but their development (air velocity, traffic situation, etc), optimizing the quality of the smoke control process

Point-wise confidence interval estimation by neural networks: A comparative study based on automotive engine calibration.

In developing neural network techniques for real world applications it is still very rare to see estimates of confidence placed on the neural network predictions. This is a major deficiency, especially in safety-critical systems. In this paper we explore three distinct methods of producing point-wise confidence intervals using neural networks. We compare and contrast Bayesian, Gaussian Process and Predictive error bars evaluated on real data. The problem domain is concerned with the calibration of a real automotive engine management system for both air-fuel ratio determination and on-line ignition timing. This problem requires real-time control and is a good candidate for exploring the use of confidence predictions due to its safety-critical nature.

Examining a key aspect of agency-to-business relationships:the effects of regulatory control on the satisfaction of regulated firms

This paper studies an overlooked, but highly important relationship, the relationship that exists between regulatory agencies (e.g., the EPA, OSHA, and the FDA) and the for-profit businesses they attempt to govern. Drawing on business-to-business control and satisfaction research, a framework is developed to understand how regulatory control influences the satisfaction levels of customer firms. Regulatory control is disaggregated into four distinct facets: the controlling agency, the rules and regulations of control, the processes used by the agency to apply the regulations, and sanctions. Each facet is hypothesized to have an effect on satisfaction. A regulator's administration of state food safety regulations provides the empirical context for testing the hypotheses. Results from a survey of 173 restaurants provide empirical support for the conceptual model. Most importantly, the study finds that the informal control process increases customer satisfaction, while the formal control process decreases customer satisfaction. We discuss how these and other findings may contribute to more effective agency-to-business relationships and ongoing research.

Process Monitoring and Defect Detection of Additive Manufacturing Using Inline Coherent Imaging

With applications ranging from aerospace to biomedicine, additive manufacturing (AM) has been revolutionizing the manufacturing industry. The ability of additive techniques, such as selective laser melting (SLM), to create fully functional, geometrically complex, and unique parts out of high strength materials is of great interest. Unfortunately, despite numerous advantages afforded by this technology, its widespread adoption is hindered by a lack of on-line, real time feedback control and quality assurance techniques. In this thesis, inline coherent imaging (ICI), a broadband, spatially coherent imaging technique, is used to observe the SLM process in 15 - 45 $\mu m$ 316L stainless steel. Imaging of both single and multilayer builds is performed at a rate of 200 $kHz$, with a resolution of tens of microns, and a high dynamic range rendering it impervious to blinding from the process beam. This allows imaging before, during, and after laser processing to observe changes in the morphology and stability of the melt. Galvanometer-based scanning of the imaging beam relative to the process beam during the creation of single tracks is used to gain a unique perspective of the SLM process that has been so far unobservable by other monitoring techniques. Single track processing is also used to investigate the possibility of a preliminary feedback control parameter based on the process beam power, through imaging with both coaxial and 100 $\mu m$ offset alignment with respect to the process beam. The 100 $\mu m$ offset improved imaging by increasing the number of bright A-lines (i.e. with signal greater than the 10 $dB$ noise floor) by 300\%. The overlap between adjacent tracks in a single layer is imaged to detect characteristic fault signatures. Full multilayer builds are carried out and the resultant ICI images are used to detect defects in the finished part and improve upon the initial design of the build system. Damage to the recoater blade is assessed using powder layer scans acquired during a 3D build. The ability of ICI to monitor SLM processes at such high rates with high resolution offers extraordinary potential for future advances in on-line feedback control of additive manufacturing.

The development of an immobilized enzyme reactor containing glyceraldehyde-3-phosphate dehydrogenase from Trypanosoma cruzi: the effect of species' specific differences on the immobilization

Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) plays an important role in the life cycle of the Trypanosoma cruzi, and an immobilized enzyme reactor (IMER) has been developed for use in the on-line screening for GAPDH inhibitors. An IMER containing human GAPDH has been previously reported; however, these conditions produced a T. cruzi GAPDH-IMER with poor activity and stability. The factors affecting the stability of the human and T. cruzi GAPDHs in the immobilization process and the influence of pH and buffer type on the stability and activity of the IMERs have been investigated. The resulting T. cruzi GAPDH-IMER was coupled to an analytical octyl column, which was used to achieve chromatographic separation of NAD+ from NADH. The production of NADH stimulated by D-glyceraldehyde-3-phosphate was used to investigate the activity and kinetic parameters of the immobilized T. cruzi GAPDH. The Michaelis-Menten constant (K-m) values determined for D-glyceraldehyde-3-phosphate and NAD(+) were K-m = 0.5 +/- 0.05 mM and 0.648 +/- 0.08 mM, respectively, which were consistent with the values obtained using the non-immobilized enzyme.

Effect of Laser (670 nm) on Healing of Wounds Covered with Occlusive Dressing: A Histologic and Biomechanical Analysis

Objectives: To analyze the effects of low-level laser therapy (LLLT), 670 nm, with doses of 4 and 7 J/cm(2), on the repair of surgical wounds covered by occlusive dressings. Background Data: The effect of LLLT on the healing process of covered wounds is not well defined. Materials and Methods: For the histologic analysis with HE staining, 50 male Wistar rats were submitted to surgical incisions and divided into 10 groups (n=5): control; stimulated with 4 and 7 J/cm(2) daily, for 7 and 14 days, with or without occlusion. Reepithelization and the number of leukocytes, fibroblasts, and fibrocytes were obtained with an image processor. For the biomechanical analysis, 25 rats were submitted to a surgical incision and divided into five groups (n=5): treated for 14 days with and without occlusive dressing, and the sham group. Samples of the lesions were collected and submitted to the tensile test. One-way analysis of variance was performed, followed by post hoc analysis. A Tukey test was used on the biomechanical data, and the Tamhane test on the histologic data. A significance level of 5% was chosen (p <= 0.05). Results: The 4 and 7J/cm(2) laser with and without occlusive dressing did not alter significantly the reepithelization rate of the wounds. The 7 J/cm(2) laser reduced the number of leukocytes significantly. The number of fibroblasts was higher in the groups treated with laser for 7 days, and was significant in the covered 4 J/cm(2) laser group. Conclusions: Greater interference of the laser-treatment procedure was noted with 7 days of stimulation, and the occlusive dressing did not alter its biostimulatory effects.

Gait Pattern Adaptation for an Active Lower-Limb Orthosis Based on Neural Networks

This work deals with neural network (NN)-based gait pattern adaptation algorithms for an active lower-limb orthosis. Stable trajectories with different walking speeds are generated during an optimization process considering the zero-moment point (ZMP) criterion and the inverse dynamic of the orthosis-patient model. Additionally, a set of NNs is used to decrease the time-consuming analytical computation of the model and ZMP. The first NN approximates the inverse dynamics including the ZMP computation, while the second NN works in the optimization procedure, giving an adapted desired trajectory according to orthosis-patient interaction. This trajectory adaptation is added directly to the trajectory generator, also reproduced by a set of NNs. With this strategy, it is possible to adapt the trajectory during the walking cycle in an on-line procedure, instead of changing the trajectory parameter after each step. The dynamic model of the actual exoskeleton, with interaction forces included, is used to generate simulation results. Also, an experimental test is performed with an active ankle-foot orthosis, where the dynamic variables of this joint are replaced in the simulator by actual values provided by the device. It is shown that the final adapted trajectory follows the patient intention of increasing the walking speed, so changing the gait pattern. (C) Koninklijke Brill NV, Leiden, 2011

A novel multiobjective optimization algorithm based on bacterial chemotaxis

In this article a novel algorithm based on the chemotaxis process of Echerichia coil is developed to solve multiobjective optimization problems. The algorithm uses fast nondominated sorting procedure, communication between the colony members and a simple chemotactical strategy to change the bacterial positions in order to explore the search space to find several optimal solutions. The proposed algorithm is validated using 11 benchmark problems and implementing three different performance measures to compare its performance with the NSGA-II genetic algorithm and with the particle swarm-based algorithm NSPSO. (C) 2009 Elsevier Ltd. All rights reserved.

Economic design of an X chart for short-run production

The aim of this paper is to present an economical design of an X chart for a short-run production. The process mean starts equal to mu(0) (in-control, State I) and in a random time it shifts to mu(1) > mu(0) (out-of-control, State II). The monitoring procedure consists of inspecting a single item at every m produced ones. If the measurement of the quality characteristic does not meet the control limits, the process is stopped, adjusted, and additional (r - 1) items are inspected retrospectively. The probabilistic model was developed considering only shifts in the process mean. A direct search technique is applied to find the optimum parameters which minimizes the expected cost function. Numerical examples illustrate the proposed procedure. (C) 2009 Elsevier B.V. All rights reserved.