Quality of Life, Pulmonary Spirometry, and Dosage of Steroid in Asthmatic Patients with Polyposis after Endoscopic Sinus Surgery

Introduction: The association between asthma and sinonasal disease has been established for years. As sinonasal disease is one of the factors that exacerbates asthma, effective treatment of this disorder may also improve and stabilize the asthmatic condition. This study examines the outcome of endoscopic sinus surgery (ESS) on asthmatic patients with massive nasal polyposis.   Materials and Methods: Forty-five asthmatic patients were included in the study. All were operated on and analyzed in our department. A questionnaire (SNOT-20) investigating the subjective evaluation of asthma and sinonasal states was presented to the patients, while objective evaluations including nasal rhinoscopy, forced expiratory volume in 1 second (FEV1), and steroid use were conducted with 1–2 years of follow up.   Results: Quality of life (QoL) improved in 97.8% of patients, while clinical symptoms, emotional signs, and social signs improved in 97.7%, 84.4%, and 93.3%, respectively. Medication use for asthma showed a similar improvement, with approximately 80% of patients reducing and 75.6% of patients discontinuing steroid use. A total of 91.1% of patients showed improvement in post-operative FEV1.   Conclusion:  ESS achieved a beneficial effect on sinonasal and asthma symptomatology in patients with nasal polyps and asthma. QoL was also improved in these patients

A satellite-based model for estimating PM2.5 concentration in a sparsely populated environment using soft computing techniques

We applied three soft computing methods including adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and back-propagation artificial neural network (BPANN) algorithms for estimating the ground-level PM2.5 concentration. These models were trained by comprehensive satellite-based, meteorological, and geographical data. A 10-fold cross-validation (CV) technique was used to identify the optimal predictive model. Results showed that ANFIS was the best-performing model for predicting the variations in PM2.5 concentration. Our findings demonstrated that the CV-R2 of the ANFIS (0.81) is greater than that of the SVM (0.67) and BPANN (0.54) model. The results suggested that soft computing methods like ANFIS, in combination with spatiotemporal data from satellites, meteorological data and geographical information improve the estimate of PM2.5 concentration in sparsely populated areas.</p

Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS–SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS–SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65–85% for hybrid PLS–SVM model respectively. Also it was found that the hybrid PLS–SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS–SVM model

Development of a land use regression model for daily NO2 and NOx concentrations in the Brisbane metropolitan area, Australia

Highlights - This study developed a novel land use regression model for predicting daily average NO2 and NOx concentration. - Model development was based on a forward regression method which incorporated monitoring data and predictor variables. - Distance to major road, open area, residential area, and industrial were major predictor variables in the final model. - Model showed reliable predictive ability and its performance was comparable to much more data demanding models. Abstract Land use regression models are an established method for estimating spatial variability in gaseous pollutant levels across urban areas. Existing LUR models have been developed to predict annual average concentrations of airborne pollutants. None of those models have been developed to predict daily average concentrations, which are useful in health studies focused on the acute impacts of air pollution. In this study, we developed LUR models to predict daily NO2 and NOx concentrations during 2009−2012 in the Brisbane Metropolitan Area (BMA), Australia’s third-largest city. The final models explained 64% and 70% of spatial variability in NO2 and NOx, respectively, with leave-one-out-cross-validation R2 of 3−49% and 2−51%. Distance to major road and industrial area were the common predictor variables for both NO2 and NOx, suggesting an important role for road traffic and industrial emissions. The novel modeling approach adopted here can be applied in other urban locations in epidemiological studies

Reliable prediction of carbon monoxide using developed support vector machine

Air pollution modeling is always along with uncertainties which results in improper decision making and affects the health of the people exposed to the pollution. Therefore, the determination of model uncertainty can improve air pollution control strategies especially in critical conditions. This study aims to develop an appropriate methodology for determination of uncertainty in support vector regression (SVR) as a well-known modeling approach in atmospheric science. The methodology is based on running SVR model many times using different calibration datasets. The robustness of the proposed methodology was checked to predict the next day carbon monoxide (CO) concentrations in Tehran metropolitan. Thereafter, a comparison was carried out between the results of the present study and another research on uncertainty determination of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Generally, the results showed that the SVR had less uncertainty in CO prediction than the ANN and ANFIS models. Moreover, repetition of SVR runs with different calibration datasets resulted in different SVR responses. Different SVR responses provided the required information to determine the band of uncertainty for predictions, using specific lower and upper percentiles. Besides, it is found that more than 75% and 78% of SVR predictions are located in the band of uncertainty determined by 2.5th–97.5th and 0.5th–99th percentiles, respectively

Estimating the spatiotemporal variation of NO2 concentration using an adaptive neuro-fuzzy inference system

Statistical modelling has been successfully used to estimate the variations of NO concentration, but employing new modelling techniques can make these estimations far more accurate. To do so, for the first time in application to spatiotemporal air pollution modelling, we employed a soft computing algorithm called adaptive neuro-fuzzy inference system (ANFIS) to estimate the NO variations. Comprehensive data sets were investigated to determine the most effective predictors for the modelling process, including land use, meteorological, satellite, and traffic variables. We have demonstrated that using selected satellite, traffic, meteorological, and land use predictors in modelling increased the R by 21%, and decreased the root mean square error (RMSE) by 47% compared with the model only trained by land use and meteorological predictors. The ANFIS model found to have better performance and higher accuracy than the multiple regression model. Our best model, captures 91% of the spatiotemporal variability of monthly mean NO concentrations at 1 km spatial resolution (RMSE 1.49 ppb) in a selected area of Australia

Investigations into factors affecting personal exposure to particles in urban microenvironments using low-cost sensors

Epidemiological studies have linked outdoor PM2.5 concentrations to a range of health effects, although people spend most of the time indoors. To better understand how individuals' exposure vary as they move between different indoor and outdoor microenvironments, our study investigated personal PM2.5 exposure and exposure intensity of 14 adult volunteers over one week (five weekdays and one weekend), using low-cost personal monitors, recording PM2.5 concentrations in 5 min intervals. Further, the study evaluated community perception of air pollution exposure during the recruitment and engagement with the volunteers. We found that people with tertiary education across all ages had greater interest in participating, with younger people being interested regardless of the level of education. The derived exposures and exposure intensities differed between weekdays and the weekend due to larger variations in individuals' daily routines. In general, time spent at home and engaged in indoor activities was associated with the highest personal PM2.5 exposure and exposure intensity on both, week and weekend days, implying the significance of both duration of the exposure and the indoor PM2.5 concentrations. The results showed no relationship between personal exposures and indoor characteristics of home (ventilation, building age and cooktop), which are expected to be due to the study's small sample size. The observed PM2.5 > 10 μg m−3 were significantly higher for distances <50 m to the roads for both major and minor roads, and were observed in areas with <16% open space, which were also close to a major road

The Effect of Rocker Shoe on the Ground Reaction Force Parameters in Patients with Rheumatoid Arthritis

Objectives: Foot and ankle problems are common complications in rheumatoid arthritis disease. Gait pattern such as normal foot and ankle rocker is impaired in patients with rheumatoid arthritis. Rocker sole as an external shoe modification is commonly prescribed in this pathology. The aim of this study was to investigate the effect of rocker shoe on vertical ground reaction force parameters during walking in patients with rheumatoid arthritis. Methods: Sixteen female participants with rheumatoid arthritis were recruited in this study. All patients were prepared with a pair of high-top, heel-to-toe rocker shoe and were asked to wear the shoes for one month. Ground reaction force parameters including peak forces and peak force times were evaluated in the first session, and after seven days and thirty days follow up were carried on. Results: first maximal vertical force was significantly increased with rocker shoe compared to barefoot after 7 days follow up. Walking with rocker shoe reduced the minimal vertical force after 7 days. The second maximal vertical force showed to be statistically lower with rocker shoes than barefoot after 7 and 30 days. Furthermore, stance time decreased with rocker shoe after one month. Discussion: Results of this study revealed that vertical ground reaction force parameters changed in rheumatoid arthritis patients with heel-to-toe rocker shoe, both immediately and after one month follow up. This might suggest the effectiveness of rocker shoes in improving gait in rheumatoid arthritis patients