A field study coupling soil fractionation and sonic energy for enhancing the in situ removal of volatile organic compounds in the vadose zone

Remediation of sites contaminated with hazardous wastes could be an expensive endeavor. There is, therefore, the need to explore techniques, which can reduce the remediation time and achieve regulatory specifications, thus reducing the cost involved in a site remediation exercise. In this work, we investigated the use of sonic energy to enhance the in situ removal rate of trichloroethylene and dichloroethylene from a site in Hillsborough Township. New Jersey. The experiments were performed with and without sonic energy and each time the concentration of the trichloroethylene swept out from the site and the flowrate of the effluent gas were measured. The results obtained indicate that when sonic energy is used as an enhancement technique the removal rate of trichloroethylene increases by an average value of about 37.9 % and the concentration of trichloroethylene in the effluent stream increases by an average value of about 20.8 %. These results mean that sonic energy, when used as an enhancement technique, will reduce the remediation time and can help achieve regulatory specifications in a site clean-up exercise after coventional Vapor Extraction methods have reached assymptotic values. It is recommended that further work be done to find the attenuation coefficients of the sonic field and also to determine the decay rate of the sonic intensity at this site

Influence of environmental variations on physiological attributes of sunflower

High degree of adaptability, wide range of climatic conditions, high photosynthetic capacity, maximum stomatal conductance and efficient hydraulic mechanism allow sunflower crop to be productive in broad range of environments. Combined effects of environmental factors not only modify plant phenology but also cause many physiological changes. Field experiments, one each in spring and autumn were conducted at Pir Mehr Ali Shah, Arid Agriculture University Rawalpindi, Pakistan for 2 years (2007 and 2008) to document the effect of environmental variations on the physiological functions of sunflower hybrids. Four sunflower hybrids, Alisson-RM, Parasio-24, MG-2 and S-278 were planted in randomized complete block design with 4 replications. The data on physiological attributes like photosynthetic rate, stomatal conductance and transpiration rate at 10 days interval after complete emergence to 60 days after emergence (DAE) was recorded. Overall higher values of photosynthetic rate, stomatal conductance and transpiration rate were recorded during spring as compared to autumn for both the years. Photosynthates accumulation and utilization was depressed in cold imposing a restriction on biomass production than at warm temperature. Physiological performance of all the hybrids during spring at the start was slower as compared to autumn. Progressive increase in photosynthetic rate, stomatal conductance and transpiration was recorded with the gradual increase in temperature up to a certain level during spring but further increase in temperature caused decline in these attributes. However during autumn, values of all these 3 physiological attributes were higher at the start those declined with gradual decrease in temperature later in the season

An Intelligent Healthcare system for detecting diabetes using machine learning algorithms

The human disease prediction is specifically a struggling piece of work for an accurate and on time treatment. Around the world, diabetes is a hazardous disease. It affects the various essential organs of the human body, for example, nerves, retinas, and eventually heart. By using models of machine learning algorithms, we can recommend and predict diabetes on various healthcare datasets more accurately with the assistance of an intelligent healthcare recommendation system. Not long ago, for the prediction of diabetes, numerous models and methods of machine learning have been introduced. But despite that, enormous multi-featured healthcare datasets cannot be handled by those systems appropriately. By using Machine Learning, an intelligent healthcare recommendation system is introduced for the prediction of diabetes. Ultimately, the model of machine learning is trained to predict this disease along with K-Fold Cross validation testing.  The evaluation of this intelligent and smart recommendation system is depending on datasets of diabetes and its execution is differentiated from the latest development of previous literatures. Our system accomplished 99.0% of efficiency with the shortest time of 12 Milliseconds, which is highly analyzed by the previous existing models of machine learning. Consequently, this recommendation system is superior for the prediction of diabetes than the previous ones. This system enhances the performance of automatic diagnosis of this disease. Code is available at (https://github.com/RaoHassanKaleem/Diebetes-Detection-using-Machine-Learning-Algorithms). &nbsp