The impact of recycled coarse aggregates obtained from waste concretes on lightweight pervious concrete properties

Environmental Science and Pollution Research - Significant environmental damage can result from the use of natural resources such as cement, aggregate, and water in concrete production. Thus, more...     

Application of artificial neural networks to predict the heavy metal contamination in the Bartin River

Environmental Science and Pollution Research - In this study, copper (Cu), iron (Fe), zinc (Zn), manganese (Mn), nickel (Ni), and lead (Pb) analyses were performed, and the results were modelled by...     

An online air pollution forecasting system using neural networks

In this work, an online air pollution forecasting system for Greater Istanbul Area is developed. The system predicts three air pollution indicator (SO(2), PM(10) and CO) levels for the next three days (+1, +2, and +3 days) using neural networks. AirPolTool, a user-friendly website (http://airpol.fatih.edu.tr), publishes +1, +2, and +3 days predictions of air pollutants updated twice a day. Experiments presented in this paper show that quite accurate predictions of air pollutant indicator levels are possible with a simple neural network. It is shown that further optimizations of the model can be achieved using different input parameters and different experimental setups. Firstly, +1, +2, and +3 days' pollution levels are predicted independently using same training data, then +2 and +3 days are predicted cumulatively using previously days predicted values. Better prediction results are obtained in the cumulative method. Secondly, the size of training data base used in the model is optimized. The best modeling performance with minimum error rate is achieved using 3-15 past days in the training data set. Finally, the effect of the day of week as an input parameter is investigated. Better forecasts with higher accuracy are observed using the day of week as an input parameter.     

Sharps injury prevention for hospital workers

Objectives. The purpose of this study was to collect data on self-reported sharps injuries to develop best practices to reduce them. Methods. Data on sharps injuries were collected for the period of January-October 2008 using Adverse Event Notification Forms already in use at Sema Hospital. Results. On average, 0.2% of all self-reported injuries were sharps injuries averaging one injury per month. Housekeeping staff sustained 64% of such injuries, nurses sustained 36% (5 incidents). Outpatient clinics experienced the most injuries at 28%, followed by the Internal Diseases Inpatient Unit with 21% and the Medical Waste Room with 14%. Injuries often occurred during contact with medical waste bags (28%) and while replacing full sharp-boxes (14%). Conclusion. In summary, reducing needle stick injuries is an important component of the occupational and patient safety program at Sema Hospital. The research described in this study allowed the hospital to provide targeted interventions to increase awareness of the risks of needle stick injuries and reduce such injuries. The steps used in the study can be used in any health care organization in the world to design a customized improvement plan to reduce risk and injury.