Strategies for the use of PIMEX and other video exposure monitoring methods. : Final report from sub-project “implementation strategies” within the HERIVIS project

Video exposure monitoring (VEM) is a group of methods used for occupational hygiene studies. The method is based on a combined use of video recordings with measurements taken with real-time monitoring instruments. A commonly used name for VEM is PIMEX. Since PIMEX initially was invented in the mid 1980’s have the method been implemented and developed in a number of countries. With the aim to give an updated picture of how VEM methods are used and to investigate needs for further development have a number of workshops been organised in Finland, UK, the Netherlands, Germany and Austria. Field studies have also been made with the aim to study to what extent the PIMEX method can improve workers motivation to actively take part in actions aimed at workplace improvements.The results from the workshops illustrates clearly that there is an impressive amount of experiences and ideas for the use of VEM within the network of the groups participating in the workshops. The sharing of these experiences between the groups, as well as dissemination of it to wider groups is, however, limited. The field studies made together with a number of welders indicate that their motivation to take part in workplace improvements is improved after the PIMEX intervention. The results are however not totally conclusive and further studies focusing on motivation are called for.It is recommended that strategies for VEM, for interventions in single workplaces, as well as for exposure categorisation and production of training material are further developed. It is also recommended to conduct a research project with the intention of evaluating the effects of the use of VEM as well as to disseminate knowledge about the potential of VEM to occupational hygiene experts and others who may benefit from its use.

Does road network density matter in optimally locating facilities?

Optimal location on the transport infrastructure is the preferable requirement for many decision making processes. Most studies have focused on evaluating performances of optimally locate p facilities by minimizing their distances to a geographically distributed demand (n) when p and n vary. The optimal locations are also sensitive to geographical context such as road network, especially when they are asymmetrically distributed in the plane. The influence of alternating road network density is however not a very well-studied problem especially when it is applied in a real world context. This paper aims to investigate how the density level of the road network affects finding optimal location by solving the specific case of p-median location problem. A denser network is found needed when a higher number of facilities are to locate. The best solution will not always be obtained in the most detailed network but in a middle density level. The solutions do not further improve or improve insignificantly as the density exceeds 12,000 nodes, some solutions even deteriorate. The hierarchy of the different densities of network can be used according to location and transportation purposes and increase the efficiency of heuristic methods. The method in this study can be applied to other location-allocation problem in transportation analysis where the road network density can be differentiated. 

Predictive models for chronic renal disease using decision trees, naïve bayes and case-based methods

Data mining can be used in healthcare industry to “mine” clinical data to discover hidden information for intelligent and affective decision making. Discovery of hidden patterns and relationships often goes intact, yet advanced data mining techniques can be helpful as remedy to this scenario. This thesis mainly deals with Intelligent Prediction of Chronic Renal Disease (IPCRD). Data covers blood, urine test, and external symptoms applied to predict chronic renal disease. Data from the database is initially transformed to Weka (3.6) and Chi-Square method is used for features section. After normalizing data, three classifiers were applied and efficiency of output is evaluated. Mainly, three classifiers are analyzed: Decision Tree, Naïve Bayes, K-Nearest Neighbour algorithm. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. Efficiency of Decision Tree and KNN was almost same but Naïve Bayes proved a comparative edge over others. Further sensitivity and specificity tests are used as statistical measures to examine the performance of a binary classification. Sensitivity (also called recall rate in some fields) measures the proportion of actual positives which are correctly identified while Specificity measures the proportion of negatives which are correctly identified. CRISP-DM methodology is applied to build the mining models. It consists of six major phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.

Decision Tree Classification Model In Water Supply Network

With the service life of water supply network (WSN) growth, the growing phenomenon of aging pipe network has become exceedingly serious. As urban water supply network is hidden underground asset, it is difficult for monitoring staff to make a direct classification towards the faults of pipe network by means of the modern detecting technology. In this paper, based on the basic property data (e.g. diameter, material, pressure, distance to pump, distance to tank, load, etc.) of water supply network, decision tree algorithm (C4.5) has been carried out to classify the specific situation of water supply pipeline. Part of the historical data was used to establish a decision tree classification model, and the remaining historical data was used to validate this established model. Adopting statistical methods were used to access the decision tree model including basic statistical method, Receiver Operating Characteristic (ROC) and Recall-Precision Curves (RPC). These methods has been successfully used to assess the accuracy of this established classification model of water pipe network. The purpose of classification model was to classify the specific condition of water pipe network. It is important to maintain the pipeline according to the classification results including asset unserviceable (AU), near perfect condition (NPC) and serious deterioration (SD). Finally, this research focused on pipe classification which plays a significant role in maintaining water supply networks in the future.

Desenvolvimento de sistemas embarcados para redes de sensores e atuadores sem fio aplicadas em unidades de elevação de petróleo do tipo Plunger-Lift

The Wireless Sensor Networks (WSN) methods applied to the lifting of oil present as an area with growing demand technical and scientific in view of the optimizations that can be carried forward with existing processes. This dissertation has as main objective to present the development of embedded systems dedicated to a wireless sensor network based on IEEE 802.15.4, which applies the ZigBee protocol, between sensors, actuators and the PLC (Programmable Logic Controller), aiming to solve the present problems in the deployment and maintenance of the physical communication of current elevation oil units based on the method Plunger-Lift. Embedded systems developed for this application will be responsible for acquiring information from sensors and control actuators of the devices present at the well, and also, using the Modbus protocol to make this network becomes transparent to the PLC responsible for controlling the production and delivery information for supervisory SISAL

Atmospheric Models Applied to DGPS and RTK Network in Brazil: Preliminary Results

Several positioning techniques have been developed to explore the GPS capability to provide precise coordinates in real time. However, a significant problem to all techniques is the ionosphere effect and the troposphere refraction. Recent researches in Brazil, at São Paulo State University (UNESP), have been trying to tackle these problems. In relation to the ionosphere effects it has been developed a model named Mod_Ion. Concerning tropospheric refraction, a model of Numerical Weather Prediction(NWP) has been used to compute the zenithal tropospheric delay (ZTD). These two models have been integrated with two positioning methods: DGPS (Differential GPS) and network RTK (Real Time Kinematic). These two positioning techniques are being investigated at São Paulo State University (UNESP), Brazil. The in-house DGPS software was already finalized and has provided very good results. The network RTK software is still under development. Therefore, only preliminary results from this method using the VRS (Virtual Reference Station) concept are presented.

Neural network based estimation of torque in induction motors for real-time applications

Induction motors are largely used in several industry sectors. The selection of an induction motor has still been inaccurate because in most of the cases the load behavior in its shaft is completely unknown. The proposal of this article is to use artificial neural networks for torque estimation with the purpose of best selecting the induction motors rather than conventional methods, which use classical identification techniques and mechanical load modeling. Since proposed approach estimates the torque behavior from the transient to the steady state, one of its main contributions is the potential to also be implemented in control schemes for real-time applications. Simulation results are also presented to validate the proposed approach.

A Beam Search Method to Solve the Problem of Assignment Cells to Switches in a Cellular Mobile Network

Assigning cells to switches in a cellular mobile network is known as an NP-hard optimization problem. This means that the alternative for the solution of this type of problem is the use of heuristic methods, because they allow the discovery of a good solution in a very satisfactory computational time. This paper proposes a Beam Search method to solve the problem of assignment cell in cellular mobile networks. Some modifications in this algorithm are also presented, which allows its parallel application. Computational results obtained from several tests confirm the effectiveness of this approach and provide good solutions for large scale problems.

Electric load forecasting using a fuzzy ART&ARTMAP neural network

This work presents a neural network based on the ART architecture ( adaptive resonance theory), named fuzzy ART& ARTMAP neural network, applied to the electric load-forecasting problem. The neural networks based on the ARTarchitecture have two fundamental characteristics that are extremely important for the network performance ( stability and plasticity), which allow the implementation of continuous training. The fuzzy ART& ARTMAP neural network aims to reduce the imprecision of the forecasting results by a mechanism that separate the analog and binary data, processing them separately. Therefore, this represents a reduction on the processing time and improved quality of the results, when compared to the Back-Propagation neural network, and better to the classical forecasting techniques (ARIMA of Box and Jenkins methods). Finished the training, the fuzzy ART& ARTMAP neural network is capable to forecast electrical loads 24 h in advance. To validate the methodology, data from a Brazilian electric company is used. (C) 2004 Elsevier B.V. All rights reserved.

Identification of lubricant contamination by biodiesel using vibration analysis and neural network

Purpose - The purpose of this paper is to provide information on lubricant contamination by biodiesel using vibration and neural network.Design/methodology/approach - The possible contamination of lubricants is verified by analyzing the vibration and neural network of a bench test under determinated conditions.Findings - Results have shown that classical signal analysis methods could not reveal any correlation between the signal and the presence of contamination, or contamination grade. on other hand, the use of probabilistic neural network (PNN) was very successful in the identification and classification of contamination and its grade.Research limitations/implications - This study was done for some specific kinds of biodiesel. Other types of biodiesel could be analyzed.Practical implications Contamination information is presented in the vibration signal, even if it is not evident by classical vibration analysis. In addition, the use of PNN gives a relatively simple and easy-to-use detection tool with good confidence. The training process is fast, and allows implementation of an adaptive training algorithm.Originality/value - This research could be extended to an internal combustion engine in order to verify a possible contamination by biodiesel.

A total laparoscopic technique for endovascular thoracic stent graft deployment

Background: Limitations of endovascular thoracic aneurym treatment include small, tortuous, or severely calcified iliac Back, arteries. We present our experience with a total laparoscopic access to deploy thoracic endografts.Methods. A total laparoscopic left retrocolic approach was used in all cases. A Dacron conduit was laparoscopically sutured to either the iliac artery or to the aorta directly. The endograft was inserted through this conduit. After graft deployment, the Dacron prosthesis was tunneled to the groin and anastomosed with the femoral artery.Results. The laparoscopic procedure could successfully be performed in 11 patients. In six cases, the aorta was used as all access and in five patients, the iliac arteries were preferred. In one of these cases, the right iliac artery, was used for deployment of the endograft. After successful aorto- or ileo-femoral bypass grafting, all patients had an improvement of their ankle brachial index postoperatively. The mean operative time was almost four hours, including laparoscopy, laparoscopic anastomosis, endograft deployment, and femoral artery anastomosis or profundaplasty.Conclusion: Totally laparoscopic assisted graft implantation in aorta or iliac arteries provides a safe and effective access for the endovascular delivery system. However, further evaluation and long follow-up are necessary to ensure the potential advantages of this technique. It is a less invasive option to overcome access-related problems with thoracic endograft deployment, giving the patient the advantage of a totally minimal invasive procedure. (J Vasc Surg 2010;51:504-8.)

Perinatal factors associated with early deaths of preterm infants born in Brazilian Network on Neonatal Research centers

Objective: To evaluate perinatal factors associated with early neonatal death in preterm infants with birth weights (BW) of 400-1,500 g.Methods: A multicenter prospective cohort study of all infants with BW of 400-1,500 g and 23-33 weeks of gestational age (GA), without malformations, who were born alive at eight public university tertiary hospitals in Brazil between June of 2004 and May of 2005. Infants who died within their first 6 days of life were compared with those who did not regarding maternal and neonatal characteristics and morbidity during the first 72 hours of life. Variables associated with the early deaths were identified by stepwise logistic regression.Results: A total of 579 live births met the inclusion criteria. Early deaths occurred in 92 (16%) cases, varying between centers from 5 to 31%, and these differences persisted after controlling for newborn illness severity and mortality risk score (SNAPPE-II). According to the multivariate analysis, the following factors were associated with early intrahospital neonatal deaths: gestational age of 23-27 weeks (odds ratio - OR = 5.0; 95%CI 2.7-9.4), absence of maternal hypertension (OR = 1.9; 95%CI 1.0-3.7), 5th minute Apgar 0-6 (OR = 2.8; 95%CI 1.4-5.4), presence of respiratory distress syndrome (OR = 3.1; 95%CI 1.4-6.6), and network center of birth.Conclusion: Important perinatal factors that are associated with early neonatal deaths in very low birth weight preterm infants can be modified by interventions such as improving fetal vitality at birth and reducing the incidence and severity of respiratory distress syndrome. The heterogeneity of early neonatal rates across the different centers studied indicates that best clinical practices should be identified and disseminated throughout the country.

Estimation of the volumetric oxygen tranfer coefficient (KLa) from the gas balance and using a neural network technique

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

The Brazilian Research Consortium on Obsessive-Compulsive Spectrum Disorders: recruitment, assessment instruments, methods for the development of multicenter collaborative studies and preliminary results

OBJETIVO: Descrever o recrutamento de pacientes, instrumentos de avaliação, métodos para o desenvolvimento de estudos colaborativos multicêntricos e os resultados preliminares do Consórcio Brasileiro de Pesquisa em Transtornos do Espectro Obsessivo-Compulsivo, que inclui sete centros universitários. MÉTODO: Este estudo transversal incluiu entrevistas semi-estruturadas (dados sociodemográficos, histórico médico e psiquiátrico, curso da doença e diagnósticos psiquiátricos comórbidos) e instrumentos que avaliam os sintomas do transtorno obsessivo-compulsivo (Escala para Sintomas Obsessivo-Compulsivos de Yale-Brown e Escala Dimensional para Sintomas Obsessivo-Compulsivos de Yale-Brown), sintomas depressivos (Inventário de Depressão de Beck), sintomas ansiosos (Inventário de Ansiedade de Beck), fenômenos sensoriais (Escala de Fenômenos Sensoriais da Universidade de São Paulo), juízo crítico (Escala de Avaliação de Crenças de Brown), tiques (Escala de Gravidade Global de Tiques de Yale) e qualidade de vida (questionário genérico de avaliação de qualidade de vida, Medical Outcome Quality of Life Scale Short-form-36 e Escala de Avaliação Social). O treinamento dos avaliadores consistiu em assistir cinco entrevistas filmadas e entrevistar cinco pacientes junto com um pesquisador mais experiente, antes de entrevistar pacientes sozinhos. A confiabilidade entre todos os líderes de grupo para os instrumentos mais importantes (Structured Clinical Interview for DSM-IV, Dimensional Yale-Brown Obsessive-Compulsive Scale, Universidade de São Paulo Sensory Phenomena Scale ) foi medida após seis entrevistas completas. RESULTADOS: A confiabilidade entre avaliadores foi de 96%. Até março de 2008, 630 pacientes com transtorno obsessivo-compulsivo tinham sido sistematicamente avaliados. A média de idade (±SE) foi de 34,7 (±0,51), 56,3% eram do sexo feminino e 84,6% caucasianos. Os sintomas obsessivo-compulsivos mais prevalentes foram os de simetria e os de contaminação. As comorbidades psiquiátricas mais comuns foram depressão maior, ansiedade generalizada e transtorno de ansiedade social. O transtorno de controle de impulsos mais comum foi escoriação neurótica. CONCLUSÃO: Este consórcio de pesquisa, pioneiro no Brasil, permitiu delinear o perfil sociodemográfico, clínico e terapêutico do paciente com transtorno obsessivo-compulsivo em uma grande amostra clínica de pacientes. O Consórcio Brasileiro de Pesquisa em Transtornos do Espectro Obsessivo-Compulsivo estabeleceu uma importante rede de colaboração de investigação clínica padronizada sobre o transtorno obsessivo-compulsivo e pode abrir o caminho para projetos semelhantes destinados a integrar outros grupos de pesquisa no Brasil e em todo o mundo.

Short term hydroelectric scheduling combining network flow and interior point approaches

In this paper, short term hydroelectric scheduling is formulated as a network flow optimization model and solved by interior point methods. The primal-dual and predictor-corrector versions of such interior point methods are developed and the resulting matrix structure is explored. This structure leads to very fast iterations since it avoids computation and factorization of impedance matrices. For each time interval, the linear algebra reduces to the solution of two linear systems, either to the number of buses or to the number of independent loops. Either matrix is invariant and can be factored off-line. As a consequence of such matrix manipulations, a linear system which changes at each iteration has to be solved, although its size is reduced to the number of generating units and is not a function of time intervals. These methods were applied to IEEE and Brazilian power systems, and numerical results were obtained using a MATLAB implementation. Both interior point methods proved to be robust and achieved fast convergence for all instances tested. (C) 2004 Elsevier Ltd. All rights reserved.