4 resultados para linear functional state bounding

em Universidade Federal do Rio Grande do Norte(UFRN)


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In this dissertation, the theoretical principles governing the molecular modeling were applied for electronic characterization of oligopeptide α3 and its variants (5Q, 7Q)-α3, as well as in the quantum description of the interaction of the aminoglycoside hygromycin B and the 30S subunit of bacterial ribosome. In the first study, the linear and neutral dipeptides which make up the mentioned oligopeptides were modeled and then optimized for a structure of lower potential energy and appropriate dihedral angles. In this case, three subsequent geometric optimization processes, based on classical Newtonian theory, the semi-empirical and density functional theory (DFT), explore the energy landscape of each dipeptide during the search of ideal minimum energy structures. Finally, great conformers were described about its electrostatic potential, ionization energy (amino acids), and frontier molecular orbitals and hopping term. From the hopping terms described in this study, it was possible in subsequent studies to characterize the charge transport propertie of these peptides models. It envisioned a new biosensor technology capable of diagnosing amyloid diseases, related to an accumulation of misshapen proteins, based on the conductivity displayed by proteins of the patient. In a second step of this dissertation, a study carried out by quantum molecular modeling of the interaction energy of an antibiotic ribosomal aminoglicosídico on your receiver. It is known that the hygromycin B (hygB) is an aminoglycoside antibiotic that affects ribosomal translocation by direct interaction with the small subunit of the bacterial ribosome (30S), specifically with nucleotides in helix 44 of the 16S ribosomal RNA (16S rRNA). Due to strong electrostatic character of this connection, it was proposed an energetic investigation of the binding mechanism of this complex using different values of dielectric constants (ε = 0, 4, 10, 20 and 40), which have been widely used to study the electrostatic properties of biomolecules. For this, increasing radii centered on the hygB centroid were measured from the 30S-hygB crystal structure (1HNZ.pdb), and only the individual interaction energy of each enclosed nucleotide was determined for quantum calculations using molecular fractionation with conjugate caps (MFCC) strategy. It was noticed that the dielectric constants underestimated the energies of individual interactions, allowing the convergence state is achieved quickly. But only for ε = 40, the total binding energy of drug-receptor interaction is stabilized at r = 18A, which provided an appropriate binding pocket because it encompassed the main residues that interact more strongly with the hygB - C1403, C1404, G1405, A1493, G1494, U1495, U1498 and C1496. Thus, the dielectric constant ≈ 40 is ideal for the treatment of systems with many electrical charges. By comparing the individual binding energies of 16S rRNA nucleotides with the experimental tests that determine the minimum inhibitory concentration (MIC) of hygB, it is believed that those residues with high binding values generated bacterial resistance to the drug when mutated. With the same reasoning, since those with low interaction energy do not influence effectively the affinity of the hygB in its binding site, there is no loss of effectiveness if they were replaced.

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This work presents a modelling and identification method for a wheeled mobile robot, including the actuator dynamics. Instead of the classic modelling approach, where the robot position coordinates (x,y) are utilized as state variables (resulting in a non linear model), the proposed discrete model is based on the travelled distance increment Delta_l. Thus, the resulting model is linear and time invariant and it can be identified through classical methods such as Recursive Least Mean Squares. This approach has a problem: Delta_l can not be directly measured. In this paper, this problem is solved using an estimate of Delta_l based on a second order polynomial approximation. Experimental data were colected and the proposed method was used to identify the model of a real robot

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ABSTRACT Introduction: The cerebrovascular accident (CVA) is an important cause of neurological impairment. Few data about the factors associated with morbidity of cerebrovascular accident are found in Brazil. Objectives: Evaluate sociodemographic characteristics, sleep habits, cognitive and functional status of patients with cerebrovascular accident. Methods: The patients evaluated through questionnaire Step 1 to survey the sociodemographic characteristics and Modified Rankin Scale for functional assessment. The neurological degree was evaluated by the National Institutes of Health Stroke Scale (NIHSS), the sleep Habits questionnaire for sleep and cognitive status by the Mini-Examination of the Mental State (MEMS). The data were analyzed using the chi-square test to determine differences in proportions of variables and linear regression analysis. Results: 305 patients were evaluated and the larger number of subjects was between 50 and 69 years (40%), most patients had no formal education (40.3%) and had ischemic type of cerebrovascular accident (72.5%). In the analysis of the functionality it was found that most patients had moderate impairment (55.1%). The results of the sleep habits showed that 63,6% of patients had one more person in the bedroom,12,3% complained about too much noise in the 11 room and 35% of too much light. From these patients 5,8% were smokers, 7,8% and 70,1% drank coffee drinkers, 28,6% had difficulty in initiate to sleep and woke up 37,6% in the middle of the night. Were showed complaints about nightmares (11%), feeling of suffocation (37,7%) and 35% felt very sleepy during the day. In addition, 95% were unemployed, 80,5% did not perform physical activities and 95,4% did not perform mental activities. The cognitive screening conducted a determined association of cognitive status with age and education level and neurological status. Conclusion: The study showed a high frequency of cases of cerebrovascular accident with functional dependence in a moderate degree, identified that many patients do not follow hygienic measures of sleep and found that the assessment of cognitive deficits must take into consideration the age, educational level and degree of neurological patients. We suggest the need for programs of assistance to victims of cerebrovascular accident patients, with a multidimensional approach including the rehabilitation team, the role of sleep medicine and Neuropsychology, so that patients have access to a more appropriate functional rehabilitation, develop a lifestyle that ensures a good sleep quality and are evaluated and rehabilitated with regard to cognitive impairment

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The separation methods are reduced applications as a result of the operational costs, the low output and the long time to separate the uids. But, these treatment methods are important because of the need for extraction of unwanted contaminants in the oil production. The water and the concentration of oil in water should be minimal (around 40 to 20 ppm) in order to take it to the sea. Because of the need of primary treatment, the objective of this project is to study and implement algorithms for identification of polynomial NARX (Nonlinear Auto-Regressive with Exogenous Input) models in closed loop, implement a structural identification, and compare strategies using PI control and updated on-line NARX predictive models on a combination of three-phase separator in series with three hydro cyclones batteries. The main goal of this project is to: obtain an optimized process of phase separation that will regulate the system, even in the presence of oil gushes; Show that it is possible to get optimized tunings for controllers analyzing the mesh as a whole, and evaluate and compare the strategies of PI and predictive control applied to the process. To accomplish these goals a simulator was used to represent the three phase separator and hydro cyclones. Algorithms were developed for system identification (NARX) using RLS(Recursive Least Square), along with methods for structure models detection. Predictive Control Algorithms were also implemented with NARX model updated on-line, and optimization algorithms using PSO (Particle Swarm Optimization). This project ends with a comparison of results obtained from the use of PI and predictive controllers (both with optimal state through the algorithm of cloud particles) in the simulated system. Thus, concluding that the performed optimizations make the system less sensitive to external perturbations and when optimized, the two controllers show similar results with the assessment of predictive control somewhat less sensitive to disturbances