To estimate the prevalence of computer vision syndrome (CVS) in healthcare workers and its relationship to video display terminal (VDT) exposure, sociodemographic, optical correction and work ...characteristics, and to analyse whether there are differences among occupational groups.
Cross-sectional study.
A sample of 1179 physicians and surgeons, nurses, and nursing assistants from two hospitals in Spain between January 2017 and February 2018 were invited to participate in this study. Of these, 622 workers from both hospitals were finally included. CVS was measured using a questionnaire, the CVS-Q
. Logistic regression was used to identify the factors associated with CVS. All the results were stratified by occupational group.
The prevalence of CVS was 56.75% with nurses being the most affected occupational group (61.75%). It was associated significantly with female sex (aOR = 2.57; 95% CI 1.36-4.88) and morning shifts plus on-call (aOR = 2.33; 95% CI 1.11-4.88) in the physicians and surgeons group. Among the nurses, it was associated with female sex (aOR = 2.35; 95% CI 1.03-5.37), seniority between 10 and 20 years (aOR = 2.17; 95% CI 1.03-4.59), VDT exposure at work of 2-4 h/day (aOR = 6.14; 95% CI 1.08-35.02), VDT exposure at work >4 h/day (aOR = 7.14; 95% CI 1.29-39.62) and self-perception that using the software application was not easy (aOR = 2.49; 95% CI 1.23-5.01).
A high prevalence of CVS among healthcare workers was observed. The risk factors that increased the likelihood of suffering from this syndrome depended on the occupation.
The findings may be used as a reference for occupational health services to implement specific preventive measures to reduce CVS for each occupational group. Such measures should consider both individual factors and the working conditions.
Battery State-of-Charge Estimator Using the MARS Technique Álvarez Antón, Juan Carlos; García Nieto, Paulino José; de Cos Juez, Francisco Javier ...
IEEE transactions on power electronics,
08/2013, Letnik:
28, Številka:
8
Journal Article
Recenzirano
State of charge (SOC) is the equivalent of a fuel gauge for a battery pack in an electric vehicle. Determining the state of charge is thus particularly important for electric vehicles (EVs), hybrid ...EVs, or portable devices. The aim of this innovative study is to estimate the SOC of a high-capacity lithium iron phosphate (LiFePO 4 ) battery cell from an experimental dataset obtained in the University of Oviedo Battery Laboratory using the multivariate adaptive regression splines (MARS) technique. An accurate predictive model able to forecast the SOC in the short term is obtained and it is a first step using the MARS technique to estimate the SOC of batteries. The agreement of the MARS model with the experimental dataset confirmed the goodness of fit for a limited range of SOC (25-90% SOC) and for a simple dynamic data profile constant-current (CC) constant-voltage charge-CC discharge.
Global solar broadband irradiance on a planar surface is measured at weather stations by pyranometers. In the case of the present research, solar radiation values from nine meteorological stations of ...the MeteoGalicia real-time observational network, captured and stored every ten minutes, are considered. In this kind of record, the lack of data and/or the presence of wrong values adversely affects any time series study. Consequently, when this occurs, a data imputation process must be performed in order to replace missing data with estimated values. This paper aims to evaluate the multivariate imputation of ten-minute scale data by means of the chained equations method (MICE). This method allows the network itself to impute the missing or wrong data of a solar radiation sensor, by using either all or just a group of the measurements of the remaining sensors. Very good results have been obtained with the MICE method in comparison with other methods employed in this field such as Inverse Distance Weighting (IDW) and Multiple Linear Regression (MLR). The average RMSE value of the predictions for the MICE algorithm was 13.37% while that for the MLR it was 28.19%, and 31.68% for the IDW.
Implant design evolved alongside the development of implant therapy. The purpose of this finite element analysis (FEA) study was to analyze the influence of different implant designs on the stress ...and strain distribution to the implants and surrounding bone. Three implant designs with the same length and diameter were used. The three-dimensional geometry of the bone was simulated with a cortical bone of three different thicknesses and two medullar bone densities: low density (150 Hounsfield units) and high density (850 Hounsfield units). A 30° oblique load of 150 N was applied to the implant restoration. Displacement and stress (von Mises) results were obtained for bone and dental implants. The strain and stress distributions to the bone were higher for the tissue-level implant for all types of bone. The maximum principal strain and stress decreased with an increase in cortical bone thickness for both cancellous bone densities. The distribution of the load was concentrated at the coronal portion of the bone and implants. All implants showed a good distribution of forces for non-axial loads, with higher forces concentrated at the crestal region of the bone–implant interface. Decrease in medullar bone density negatively affects the strain and stress produced by the implants.
Prognostics is an engineering discipline that predicts the future health of a system. In this research work, a data-driven approach for prognostics is proposed. Indeed, the present paper describes a ...data-driven hybrid model for the successful prediction of the remaining useful life of aircraft engines. The approach combines the multivariate adaptive regression splines (MARS) technique with the principal component analysis (PCA), dendrograms and classification and regression trees (CARTs). Elements extracted from sensor signals are used to train this hybrid model, representing different levels of health for aircraft engines. In this way, this hybrid algorithm is used to predict the trends of these elements. Based on this fitting, one can determine the future health state of a system and estimate its remaining useful life (RUL) with accuracy. To evaluate the proposed approach, a test was carried out using aircraft engine signals collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). Simulation results show that the PCA-CART-MARS-based approach can forecast faults long before they occur and can predict the RUL. The proposed hybrid model presents as its main advantage the fact that it does not require information about the previous operation states of the input variables of the engine. The performance of this model was compared with those obtained by other benchmark models (multivariate linear regression and artificial neural networks) also applied in recent years for the modeling of remaining useful life. Therefore, the PCA-CART-MARS-based approach is very promising in the field of prognostics of the RUL for aircraft engines.
The present experimental trial uses two types of dental implants, one made of titanium (Ti
A
V) and the other one of zirconia (ZrO
), but both of identical design, to compare their stability and ...micro-movements values under load. One of each type of implant (
= 42) was placed into 21 cow ribs, recording the insertion torque and the resonance frequency using a specific transducer. Subsequently, a prosthetic crown made of PMMA was screwed onto each of the implants in the sample. They were then subjected to a static compression load on the vestibular cusp of the crown. The resulting micromovements were measured. The zirconia implants obtained a higher mean of both IT and RFA when compared with those of titanium, with statistically significant differences in both cases (
= 0.0483 and
= 0.0296). However, the micromovement values when load was applied were very similar for both types, with the differences between them (
= 0.3867) not found to be statistically significant. The results show that zirconia implants have higher implant stability values than titanium implants. However, the fact that there are no differences in micromobility values implies that caution should be exercised when applying clinical protocols for zirconia based on RFA, which only has evidence for titanium.
In astronomy, the light emitted by an object travels through the vacuum of space and then the turbulent atmosphere before arriving at a ground based telescope. By passing through the atmosphere a ...series of turbulent layers modify the light's wave-front in such a way that Adaptive Optics reconstruction techniques are needed to improve the image quality. A novel reconstruction technique based in Artificial Neural Networks (ANN) is proposed. The network is designed to use the local tilts of the wave-front measured by a Shack Hartmann Wave-front Sensor (SHWFS) as inputs and estimate the turbulence in terms of Zernike coefficients. The ANN used is a Multi-Layer Perceptron (MLP) trained with simulated data with one turbulent layer changing in altitude. The reconstructor was tested using three different atmospheric profiles and compared with two existing reconstruction techniques: Least Squares type Matrix Vector Multiplication (LS) and Learn and Apply (L + A).
One of the major consequences of the digital revolution has been the increase in the use of electronic devices in health services. Despite their remarkable advantages, though, the use of computers ...and other visual display terminals for a prolonged time may have negative effects on vision, leading to a greater risk of Computer Vision Syndrome (CVS) among their users. In this study, the importance of ocular and visual symptoms related to CVS was evaluated, and the factors associated with CVS were studied, with the help of an algorithm based on regression trees and genetic algorithms. The performance of this proposed model was also tested to check its ability to predict how prone a worker is to suffering from CVS. The findings of the present research confirm a high prevalence of CVS in healthcare workers, and associate CVS with a longer duration of occupation and higher daily computer usage.
In this research, a model is proposed for predicting the number of days absent from work due to sick or health-related leave among workers in the industry sector, according to ergonomic, social and ...work-related factors. It employs selected microdata from the Sixth European Working Conditions Survey (EWCS) and combines a genetic algorithm with Multivariate Adaptive Regression Splines (MARS). The most relevant explanatory variables identified by the model can be included in the following categories: ergonomics, psychosocial factors, working conditions and personal data and physiological characteristics. These categories are interrelated, and it is difficult to establish boundaries between them. Any managing program has to act on factors that affect the employees’ general health status, process design, workplace environment, ergonomics and psychosocial working context, among others, to achieve success. This has an extensive field of application in the energy sector.
This paper presents a literature review in which methodologies employed for the forecast of the price of stock companies and raw materials in the fields of electricity, oil, gas and energy are ...studied. This research also makes an analysis of which data variables are employed for training the forecasting models. Three scientific databases were consulted to perform the present research: The Directory of Open Access Journals, the Multidisciplinary Digital Publishing Institute and the Springer Link. After running the same query in the three databases and considering the period from January 2017 to December 2021, a total of 1683 articles were included in the analysis. Of these, only 13 were considered relevant for the topic under study. The results obtained showed that when compared with other areas, few papers focus on the forecasting of the prices of raw materials and stocks of companies in the field under study. Furthermore, most make use of either machine learning methodologies or time series analysis. Finally, it is also remarkable that some not only make use of existing algorithms but also develop and test new methodologies.
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