Caracterização do acidente de trânsito e gravidade do trauma: um estudo em vítimas de um hospital de emergência de Natal

Descriptive exploratory study, with quantitative approach and prospective data performed on the Monsenhor Walfredo Gurgel Hospital Complex (MWGH), in Natal/RN, aiming to classify the type of motor vehicle involved in the accident, the public roadway s user quality and the more frequent injuries; to evaluate the severity of trauma in traffic accident victims; characterized the severity of the injuries and the trauma, and the type of motor vehicle involved. The population comprises 605 traffic accident victims, with data collected between October and December 2007. We used as a support for the evaluation of severity of injuries and trauma the Glasgow Coma Scale (GCSl), the Condensed Abbreviated Injury Scale (CAIS) and the Injury Severity Score (ISS). The results show that 82.8% of the victims were male; 78.4% were aged 18 to 38; the victims originating from the State s Countryside prevailed (43.1%); 24.3% of the population had completed middle-level instruction; 23.1% worked on commerce and auxiliary activities; most (79.4%) was catholic; 48.8% were married/consensual union; 76.2% earned up to two monthly minimum wages; Sunday was the day with the most accidents (25.1%); 47.4% were attended to in under an hour after the event; the motorcycle on its own was responsible for 53.2% of the accidents; 42.3% were attended to by the SAMU; 61.8% were victims of crashes; over half (53.4%) used individual protection equipment (IPE); 49.4% were helmets and 4.0% the seatbelt; 61.3% were motorcycle drivers; 43.3% of the accidents took place in the afternoon shift; from 395 drivers, 55.2% were licensed, and 50.7% among those had been licensed for 1 to 5 years; 90.7% of the victims had GCS1 between 13 and 15 points at the time of evaluation; the body area most affected was the external surface (35.9%); 38.8% of the injuries were light or moderate (AIS=1 and AIS=2); 83.2% had light trauma (ISS between 1 and 15 points). In face of the results, we can conclude that there is a risk for the elevation of injury severity and trauma resulting from traffic accidents, when these events are related to certain variables such as gender, age, weekday, the interval between the accident and the first care, ingestion of drugs, type of accident, the public roadway s user quality, the use of IPE, day shift, body regions and the type of motor vehicle involved in the accident

Conhecimento dos enfermeiros do Hospital Universitário Onofre Lopes sobre sistematização da assistência de Enfermagem

Nursing as a profession goes in search on quality of their care through new frameworks, trying to break postures of the health care system so fragmented in the care. To change professional practices, it is necessary to build their own knowledge grounded on Nursing Care System. The aim of this study was to analyze the influence of nurses' knowledge on care systematization in nursing in the University Hospital Natal-RN. It is an analytical descriptive study carried out at the Onofre Lopes University Hospital (HUOL), Natal-RN, 2010, the sample was composed of 40 active nurses working in hospitalization units of the hospital, the inclusion criteria were being in the monthly scale sector and agree to participate in the study. The non-participant observation and another interview were used for collecting data, statistical analysis was descriptive and inferential with reliability test, Pearson test, chi-square and Fischer, the variables that correlated were analyzed in a model Multiple logistic , calculating odds ratio. The results were: predominance of female professionals (90%), predominantly in the age range 39-46 years (37.5%), nurses who have the undergraduate degree at the Federal University of Rio Grande do Norte (80%), and who have expertise training as a minimal degree (62.5%). Among the surveyed, the knowledge showed significance with the graduation time (p = 0.018) and time working in HUOL (p = 0.036). The majority of the professionals surveyed do not know which organ is responsible for the SAE legislation (52.5%), aware of the steps needed to build the nursing diagnosis (92.5%), understand the characteristics of nursing planning (90% ). However the same professionals do not perform physical examination in patients (50.0%) did not classify the clinical findings (68.4%), and identify the problems encountered as a classification (13.2%). The planning of nursing care is carried out by verbal order of nurses (82.5%), 41% of the professionals assess only the intervention stage, in other words, the actions taken. Regarding the practical application of nursing records 53% of nurses do not realize records, 30.8% is incomplete, the other held notes (p = 0.003). The nurses know the nursing process (90% of appropriate responses), despite the actions defined by the theory are not applied in practice. Investigators believe the condition of the hospital teacher (22.5%) could positively affect the implementation of the SAE associated with the interest of professionals (20%). Of the respondents, 17.5% accept as truth the lack of facilities to assist the SAE implementation in the hospital. It was concluded that nurses know the theory that underlies the SAE and the nursing process, but do not develop the service know as well, there is need for action to boost the SAE implementation as practice of nurses in the hospital investigated

Compositional nutrient diagnosis of corn using the Mahalanobis distance as nutrient imbalance index

Parent, L. E., Natale, W. and Ziadi, N. 2009. Compositional nutrient diagnosis of corn using the Mahalanobis distance as nutrient imbalance index. Can. J. Soil Sci. 89: 383-390. Compositional nutrient diagnosis (CND) provides a plant nutrient imbalance index (CND - r(2)) with assumed chi(2) distribution. The Mahalanobis distance D(2), which detects outliers in compositional data sets, also has a chi(2) distribution. The objective of this paper was to compare D(2) and CND - r(2) nutrient imbalance indexes in corn (Zea mays L.). We measured grain yield as well as N, P, K, Ca, Mg, Cu, Fe, Mn, and Zn concentrations in the ear leaf at silk stage for 210 calibration sites in the St. Lawrence Lowlands [2300-2700 corn thermal units (CTU)] as well as 30 phosphorus (2300-2700 CTU; 10 sites) and 10 nitrogen (1900-2100 CTU; one site) replicated fertilizer treatments for validation. We derived CND norms as mean, standard deviation, and the inverse covariance matrix of centred log ratios (clr) for high yielding specimens (>= 9.0 Mg grain ha(-1) at 150 g H(2)O kg(-1) moisture content) in the 2300-2700 CTU zone. Using chi(2) = 17 (P < 0.05) with nine degrees of freedom (i.e., nine nutrients) as a rejection criterion for outliers and a yield threshold of 8.6 Mg ha(-1) after Cate-Nelson partitioning between low- and high-yielders in the P validation data set, D(2) misclassified two specimens compared with nine for CND -r(2). The D(2) classification was not significantly different from a chi(2) classification (P > 0.05), but the CND - r(2) classification differed significantly from chi(2) or D(2) (P < 0.001). A threshold value for nutrient imbalance could thus be derived probabilistically for conducting D(2) diagnosis, while the CND - r(2) nutrient imbalance threshold must be calibrated using fertilizer trials. In the proposed CND - D(2) procedure, D(2) is first computed to classify the specimen as possible outlier. Thereafter, nutrient indices are ranked in their order of limitation. The D(2) norms appeared less effective in the 1900-2100 CTU zone.

Importância do gerenciamento com base em dados estudo de caso : farmácias de manipulação do Estado do Rio Grande do Norte

Last century Six Sigma Strategy has been the focus of study for many scientists, between the discoveries we have the importance of data process for the free of error product manufactory. So, this work focuses on data quality importance in an enterprise. For this, a descriptive-exploratory study of seventeen pharmacies of manipulations from Rio Grande do Norte was undertaken with the objective to be able to create a base structure model to classify enterprises according to their data bases. Therefore, statistical methods such as cluster and discriminant analyses were used applied to a questionnaire built for this specific study. Data collection identified four group showing strong and weak characteristics for each group and that are differentiated from each other

Análise comparativa dos métodos de fatores ponderados na seleção de áreas para aterros sanitários

This study presents a comparative analysis of methodologies about weighted factors considered in the selection of areas for deployment of Sanitary Landfills, applying the methodologies of classification criteria with scoring bands Gomes, Coelho, Erba & Veronez (2000); Waquil et al, 2000. That means, we have the Scoring System used by Union of Municipalities of Bahia and the Quality Index Landfill Waste (IQR) which are applyed for this study in Massaranduba Sanitary Landfill located in the municipality of Ceará Mirim /RN, northeastern of Brazil. The study was conducted in order to classify the methodologies and to give support for future studies on environmental management segment, with main goal to propose suited methodologies which allow safety and rigor during the selection, deployment and management of sanitary landfill, in the Brazilian municipalities, in order to help them in the process to extinction of their dumps, in according of Brazilian Nacional Plan of Solid Waste. During this investigation we have studied the characteristics of the site as morphological, hydrogeological, environmental and socio-economic to permit the installation. We consider important to mention the need of deployment from Rio Grande do Norte State Secretary of Environment and Water (SEMARH), Institute of Sustainable Development and Environment of RN (IDEMA), as well, from Federal and Municipal Governments a public policies for the integrated management of urban solid waste that address environmental preservation and improvement of health conditions of the population of the Rio Grande do Norte

Análise e classificação de imagens de lesões da pele por atributos de cor, forma e textura utilizando máquina de vetor de suporte

The skin cancer is the most common of all cancers and the increase of its incidence must, in part, caused by the behavior of the people in relation to the exposition to the sun. In Brazil, the non-melanoma skin cancer is the most incident in the majority of the regions. The dermatoscopy and videodermatoscopy are the main types of examinations for the diagnosis of dermatological illnesses of the skin. The field that involves the use of computational tools to help or follow medical diagnosis in dermatological injuries is seen as very recent. Some methods had been proposed for automatic classification of pathology of the skin using images. The present work has the objective to present a new intelligent methodology for analysis and classification of skin cancer images, based on the techniques of digital processing of images for extraction of color characteristics, forms and texture, using Wavelet Packet Transform (WPT) and learning techniques called Support Vector Machine (SVM). The Wavelet Packet Transform is applied for extraction of texture characteristics in the images. The WPT consists of a set of base functions that represents the image in different bands of frequency, each one with distinct resolutions corresponding to each scale. Moreover, the characteristics of color of the injury are also computed that are dependants of a visual context, influenced for the existing colors in its surround, and the attributes of form through the Fourier describers. The Support Vector Machine is used for the classification task, which is based on the minimization principles of the structural risk, coming from the statistical learning theory. The SVM has the objective to construct optimum hyperplanes that represent the separation between classes. The generated hyperplane is determined by a subset of the classes, called support vectors. For the used database in this work, the results had revealed a good performance getting a global rightness of 92,73% for melanoma, and 86% for non-melanoma and benign injuries. The extracted describers and the SVM classifier became a method capable to recognize and to classify the analyzed skin injuries

Classificação de distúrbios na rede elétrica usando redes neurais e wavelets

Post dispatch analysis of signals obtained from digital disturbances registers provide important information to identify and classify disturbances in systems, looking for a more efficient management of the supply. In order to enhance the task of identifying and classifying the disturbances - providing an automatic assessment - techniques of digital signal processing can be helpful. The Wavelet Transform has become a very efficient tool for the analysis of voltage or current signals, obtained immediately after disturbance s occurrences in the network. This work presents a methodology based on the Discrete Wavelet Transform to implement this process. It uses a comparison between distribution curves of signals energy, with and without disturbance. This is done for different resolution levels of its decomposition in order to obtain descriptors that permit its classification, using artificial neural networks

Localização e mapeamento simultâneos de ambientes planos usando visão monocular e representação híbrida do ambiente

The goal of this work is to propose a SLAM (Simultaneous Localization and Mapping) solution based on Extended Kalman Filter (EKF) in order to make possible a robot navigates along the environment using information from odometry and pre-existing lines on the floor. Initially, a segmentation step is necessary to classify parts of the image in floor or non floor . Then the image processing identifies floor lines and the parameters of these lines are mapped to world using a homography matrix. Finally, the identified lines are used in SLAM as landmarks in order to build a feature map. In parallel, using the corrected robot pose, the uncertainty about the pose and also the part non floor of the image, it is possible to build an occupancy grid map and generate a metric map with the obstacle s description. A greater autonomy for the robot is attained by using the two types of obtained map (the metric map and the features map). Thus, it is possible to run path planning tasks in parallel with localization and mapping. Practical results are presented to validate the proposal

Técnicas neuronais de agrupamentos aplicadas à detecção de anomalias em mamogramas digitais

This work proposes the development of an intelligent system for analysis of digital mammograms, capable to detect and to classify masses and microcalcifications. The digital mammograms will be pre-processed through techniques of digital processing of images with the purpose of adapting the image to the detection system and automatic classification of the existent calcifications in the suckles. The model adopted for the detection and classification of the mammograms uses the neural network of Kohonen by the algorithm Self Organization Map - SOM. The algorithm of Vector quantization, Kmeans it is also used with the same purpose of the SOM. An analysis of the performance of the two algorithms in the automatic classification of digital mammograms is developed. The developed system will aid the radiologist in the diagnosis and accompaniment of the development of abnormalities

Identificação remota de sistemas operacionais utilizando análise de processos aleatórios e redes neurais artificiais

A new method to perform TCP/IP fingerprinting is proposed. TCP/IP fingerprinting is the process of identify a remote machine through a TCP/IP based computer network. This method has many applications related to network security. Both intrusion and defence procedures may use this process to achieve their objectives. There are many known methods that perform this process in favorable conditions. However, nowadays there are many adversities that reduce the identification performance. This work aims the creation of a new OS fingerprinting tool that bypass these actual problems. The proposed method is based on the use of attractors reconstruction and neural networks to characterize and classify pseudo-random numbers generators

Reconhecimento de padrões de falhas em motores trifásicos utilizando redes neurais

This work presents a diagnosis faults system (rotor, stator, and contamination) of three-phase induction motor through equivalent circuit parameters and using techniques patterns recognition. The technology fault diagnostics in engines are evolving and becoming increasingly important in the field of electrical machinery. The neural networks have the ability to classify non-linear relationships between signals through the patterns identification of signals related. It is carried out induction motor´s simulations through the program Matlab R & Simulink R , and produced some faults from modifications in the equivalent circuit parameters. A system is implemented with multiples classifying neural network two neural networks to receive these results and, after well-trained, to accomplish the identification of fault´s pattern

Sistema inteligente para detecção de vazamentos em dutos de petróleo usando transformada Wavelet e redes neurais

This work consists in the use of techniques of signals processing and artificial neural networks to identify leaks in pipes with multiphase flow. In the traditional methods of leak detection exists a great difficulty to mount a profile, that is adjusted to the found in real conditions of the oil transport. These difficult conditions go since the unevenly soil that cause columns or vacuum throughout pipelines until the presence of multiphases like water, gas and oil; plus other components as sand, which use to produce discontinuous flow off and diverse variations. To attenuate these difficulties, the transform wavelet was used to map the signal pressure in different resolution plan allowing the extraction of descriptors that identify leaks patterns and with then to provide training for the neural network to learning of how to classify this pattern and report whenever this characterize leaks. During the tests were used transient and regime signals and pipelines with punctures with size variations from ½' to 1' of diameter to simulate leaks and between Upanema and Estreito B, of the UN-RNCE of the Petrobras, where it was possible to detect leaks. The results show that the proposed descriptors considered, based in statistical methods applied in domain transform, are sufficient to identify leaks patterns and make it possible to train the neural classifier to indicate the occurrence of pipeline leaks

Sistema para detecção e diagnóstico de falhas em motores elétricos de indução utilizando lógica fuzzy

Induction motors are one of the most important equipment of modern industry. However, in many situations, are subject to inadequate conditions as high temperatures and pressures, load variations and constant vibrations, for example. Such conditions, leaving them more susceptible to failures, either external or internal in nature, unwanted in the industrial process. In this context, predictive maintenance plays an important role, where the detection and diagnosis of faults in a timely manner enables the increase of time of the engine and the possibiity of reducing costs, caused mainly by stopping the production and corrective maintenance the motor itself. In this juncture, this work proposes the design of a system that is able to detect and diagnose faults in induction motors, from the collection of electrical line voltage and current, and also the measurement of engine speed. This information will use as input to a fuzzy inference system based on rules that find and classify a failure from the variation of thess quantities

Medição da densidade óssea através de ondas eletromagnéticas e classificação por MVS

This work holds the purpose of presenting an auxiliary way of bone density measurement through the attenuation of electromagnetic waves. In order to do so, an arrangement of two microstrip antennas with rectangular configuration has been used, operating in a frequency of 2,49 GHz, and fed by a microstrip line on a substrate of fiberglass with permissiveness of 4.4 and height of 0,9 cm. Simulations were done with silica, bone meal, silica and gypsum blocks samples to prove the variation on the attenuation level of different combinations. Because of their good reproduction of the human beings anomaly aspects, samples of bovine bone were used. They were subjected to weighing, measurement and microwave radiation. The samples had their masses altered after mischaracterization and the process was repeated. The obtained data were inserted in a neural network and its training was proceeded with the best results gathered by correct classification on 100% of the samples. It comes to the conclusion that through only one non-ionizing wave in the 2,49 GHz zone it is possible to evaluate the attenuation level in the bone tissue, and that with the appliance of neural network fed with obtained characteristics in the experiment it is possible to classify a sample as having low or high bone density

Construção, calibração e análise de desempenho de um piranômetro de baixo custo, baseado no modelo Kimball-Hobbs

In this work a pyrometer using the classic model of Kimball-Hobbs was developed, tested and calibrated. The solar radiation is verified through the temperature difference between the sensible elements covered by absorbing (black) and reflecting (white) pigmentations of the incoming radiation. The photoacoustic technique was used to optimize the choice of the pigments. Methodologies associated with linearity, thermo-variation, sensibility, response time and distance are also presented. To correctly classify the results, the international standard ISO 9060 as well as indicative parameters of World Meteorological Organization (WMO) are used. In addition a system of data acquisition of two channels with 12 bits, constructed during the this time, was used to measure the global solar radiation on the ground by the pyrometer and also by another pyrometer certified in the case of Keep & zonen. The results statistically show, through the hypothesis test presented here, that both equipments find population average with 95% of correctness