Investigation of type and density of bio-aerosols in air samples from educational hospital wards of Kerman city, 2014

Background: Bio-aerosols in the air of hospital wards have an important role in the development of infections. It is important to make quantitative and qualitative estimations of microorganisms in the air of these wards as an index for environmental hygiene applicable to different hospital wards. The aim of the study was to investigate degrees of diversity and density of bio-aerosols in the education hospitals of Kerman city. Methods: This study applied a descriptive-cross-sectional methodology in the second half of 2014 in the education hospitals of Kerman city, with bed capacity of over 300. As many as 200 samples were collected from the air in different wards of each hospital using the standard method of the National Occupational Health and Safety Institute. Following collection, samples were placed in an incubator for 48 hours and then bio-aerosol detections were made for and resulting data reported as colonies/m3. Results: Results indicated that maximum and minimum degrees of bacterial density were observed in operation rooms and in the intensive care unit (ICU) of Shafa hospital. Furthermore, comparison showed that the operating room at Afzalipour hospital had the lowest level of fungal contamination, while ICU at Bahonar hospital had the highest level of fungal contamination. The emitted fungi of Aspergillus and Penicillium along with the bacteria, staphylococci and Acinetobacter had greater frequencies. The means of bacterial density and fungal density were not equal across the studied hospitals and significant statistical, difference was observed between means of bacterial and fungal density (P ≤ 0.001). Conclusion: Amounts of bacterial and fungal density were greater than those proposed in the American Industrial Health State Conference in 73.3% of the wards in the educational hospitals of Kerman city sampled in this study. Therefore it is suggested that implementation of some, necessary measures for continuous monitoring, promotion of hygienic disinfection standards, and ventilation systems are taken more seriously by stipulating regulations to control this important issue in the country’s hospitals. Keywords: Bacteria, Fungal, Bio-aerosol, Hospital, Kerma

RF Self-Powered Sensor to Design Fully Autonomous IoT Devices

University of Technology Sydney. Faculty of Engineering and Information Technology.The need for continuous, accurate and autonomous sensors has increased significantly given the rapid growth of the Internet of Things (IoT). Sensors collect data for a specific measurable phenomenon. Data may then be transmitted to a controller or cloud service for processing. Alternatively, data may be (pre)processed on the sensor device prior to transmission. Sensors are a necessary element of IoT devices. Sensors are designed to detect specific phenomenon and covert this into digital data that can be leveraged for analytics and machine learning to determine future actions. The energy consumed by a sensor has a direct impact upon the IoT device and its application and requirements. Depending upon the phenomenon being sensed, supporting continuous operation may be critical as discontinuities in sensing may result in the sensing of a phenomenon missing vital data points thereby limiting accuracy. Further continuous sensing increases the overall energy consumption of the IoT device, reducing overall lifetime of operation of the IoT device or the need for frequent battery replacements. Self-powered sensors provide a promising solution to produce autonomous sensors that can operate both indefinitely and free from energy source limitations. Self-powered sensors can acquire energy using varying types of ambient energy. Recently, various ambient energy sources have been used to implement self-powered sensors. However, these structures require specific requirement to provide electricity. Alternatively, ambient electromagnetic (EM) waves in the environment can be used as a new power source due to the ubiquitous wide spread modern use of wireless communication. However, the available energy levels of ambient EM signals is low. Therefore, to harness EM signals, a highly efficient receiver is required. The use of a rectenna is a common solution to convert EM signals to electricity, however there is still need for EM energy harvesting to be significantly improved. Metamaterials are a promising solution to address this problem. Metamaterials are well-known artificial structures with exciting features such as negative permittivity, and negative permeability. Metamaterial perfect absorbers (MPAs) are able to absorb incident EM signals with near 100% efficiency. This capability makes MPAs advantageous for both sensing and EM energy harvesting. Small changes in metamaterial structures cause significant variation in absorption characteristics - a desirable feature to design highly accurate sensors. Furthermore, MPAs are able to absorb extremely low-power ambient EM waves. This thesis focuses upon the dual use of MPAs for highly efficient EM harvesting and passive sensing of phenomenon

Modeling the emission and calculation of the risk of steelmaking contaminants using the AERMOD model

Background and Aims: Nowadays, air pollution has become a major challenge especially in large cities. Considering the paramount importance of air pollutants impact of human health, the study of pollutants emission models to estimate their dispersion and consequent impacts on human health is very important.Materials and Methods: In this research, CO, NO2 and SO2 emissions from steel complexes, which are the most important pollutants of this industry, is discussed. For this purpose, AERMOD models have been designed to investigate the dispersion of pollutants and then BREEZE AERMOD model to study the risk of emission of pollutants. Finally, the risk of pollutants inhalation was estimated using the RAIS model. The data were collectedstatistically. All stages of this research were conducted ethically and relevant permits were obtained.Results: According to the calculations made in our study, the risk factor for non-cancerous inhalation of air pollutants in the steel complex was 3.7 for employees, 4.8 for workers and 7.7 for office workers, 3.7 for over-threshold workers. These individuals were at risk caused by contaminants, especially carbon monoxide and nitrogen dioxide.Residents in the neighbor regions demonstrating a risk index of 0.2% are at a lower risk.Conclusion: Considering the risk assessments made, emissions from steel complexes pose a serious health risk specially for workers. Indeed, due to the close proximity of the surrounding villages in the southern areas of the site to the steel complex flares, they are exposed to the large amounts of contaminants.Keywords: steel complex, air pollution modeling, breeze aermod, aermod, rias method

Multiple Crack Detection using Wavelet Transforms and Energy Signal Techniques

Wavelet transforms are efficient tools for structural health monitoring (SHM) and damage detection. However, these methods are encountered with some limitations in practice. Thus, signal energy analysis is used as an alternative technique for damage detection. In this paper, discrete wavelet transforms (DWT) and Teager energy operator (TEO) is applied to the curvature of the mode shapes of the beams, and the locations of the damages are identified. The results show that in comparison with the discrete wavelet transform, the signal energy operator has better performance. This superiority in detecting the damages, especially near the supports of the beam, is obvious and has enough sensitivities in low damage intensities. Additionally, the damage detection in the cases that the response data are noisy is investigated. For this purpose, by adding low-intensity noises to the curvature of the mode shapes, the abilities of the mentioned methods are evaluated. The results indicate that each method is not individually efficient in the detection of damages in noisy conditions, but the combination of them under noisy conditions is more reliabl

Efficiency Evaluation of the Graphene Oxide in Adsorption of Malathion Toxin from Aqueous Media

The introduction of pesticides into drinking water sources has harmful effects on human health and the environment. In recent years, several methods for the removal of organophosphate pesticide water sources have been emerged. The aim of the present study was to determine the modified nanographene oxide efficiency in the removal of malathion from aqueous media. Optimization of effective parameters in the adsorption process including pH and conact time was performed. Measuring the absorbance rate of malathion was carried out using atomic adsorption spectrometer. The results of pH experiments showed that the optimal pH for malathion adsorption was 7. The removal percentage at this pH was 99.8. The study of the contact time of adsorbent with metal ion showed that the highest adsorption rate obtained within less than 2 min

A Rule Based Classification Model to Predict Colon Cancer Survival

Introduction: Colon cancer is the second most common cancer in the world and fourth most common cancer in both sexes in Iran, whose % 8.12 of all cancers in the covers. Predict the outcome of cancer and basic clinical data about it is very important. Data mining techniques can be used to predict cancer outcome. In our country, data mining studies on colon cancer, not covered as lung or breast cancers. It seems can be with identify factors influencing on survival and modify them, increased survival of colon cancer patients. Then according to high rates of colon cancer and the benefits of data mining to predict survival, in this study examined factors influencing on the survival of these patients. Materials and Methods: We use a dataset with four attributes that include the records of 570 patients in which 327 Patients (57.4%) and 243 (42.6%) patients were males and females respectively. Trees Random Forest (TRF), AdaBoost (AD), RBF Network (RBFN), and Multilayer Perceptron (MLP) machine learning techniques with 10-cross fold technique were used with the proposed model for the prediction of colon cancer survival. The performance of machine learning techniques were evaluated with accuracy, precision, sensitivity, specificity, and area under ROC curve. Results: Out of 570 patients, 338 patients and 232 patients were alive and dead respectively. In this Study, at first sight it seems that among this techniques, Trees Random Forest (TRF) technique showed better results in comparison to other techniques (AD, RBFN and MLP). The accuracy, sensitivity, specificity and the area under ROC curve of TRF are 0.76, 0.808, 0.70 and 0.83, respectively. Conclusions: In this study seems that Trees Random Forest model (TRF) which is a rule based classification model was the best model with the highest level of accuracy. Therefore, this model is recommended as a useful tool for colon cancer survival prediction as well as medical decision making