Optimization of adsorptive removal of α-toluic acid by CaO2 nanoparticles using response surface methodology

The present work addresses the optimization of process parameters for adsorptive removal of α-toluic acid by calcium peroxide (CaO2) nanoparticles using response surface methodology (RSM). CaO2 nanoparticles were synthesized by chemical precipitation method and confirmed by Transmission electron microscopy (TEM) and high-resolution TEM (HRTEM) analysis which shows the CaO2 nanoparticles size range of 5-15 nm. A series of batch adsorption experiments were performed using CaO2 nanoparticles to remove α-toluic acid from the aqueous solution. Further, an experimental based central composite design (CCD) was developed to study the interactive effect of CaO2 adsorbent dosage, initial concentration of α-toluic acid, and contact time on α-toluic acid removal efficiency (response) and optimization of the process. Analysis of variance (ANOVA) was performed to determine the significance of the individual and the interactive effects of variables on the response. The model predicted response showed a good agreement with the experimental response, and the coefficient of determination, (R2) was 0.92. Among the variables, the interactive effect of adsorbent dosage and the initial α-toluic acid concentration was found to have more influence on the response than the contact time. Numerical optimization of process by RSM showed the optimal adsorbent dosage, initial concentration of α-toluic acid, and contact time as 0.03 g, 7.06 g/L, and 34 min respectively. The predicted removal efficiency was 99.50%. The experiments performed under these conditions showed α-toluic acid removal efficiency up to 98.05%, which confirmed the adequacy of the model prediction

Forecasting precipitation over Delhi during the south-west monsoon season

The south-west monsoon (June-September) is the major rainy season over India. Information about the occurrence of precipitation and the expected quantity at a specific place is important in many sectors of human activity. In this study, objective methods are developed to forecast the probability of precipitation (POP) and provide the quantity of precipitation forecast (QPF) over Delhi. As the onset of the monsoon at Delhi is around 30 June, the models are developed for the months of July, August and September (JAS) using surface and upper-air data for the period 1985-90 and tested with data from JAS for 1994 and 1995. A multiple linear regression equation is developed to forecast the POP and multiple discriminant analysis is used to produce the QPF in terms of one of four groups (0.1-1.0; 1.1-10.0; 10.1-30.0; and ≥30.1 mm). The QPF model is used only if precipitation is expected to occur (the POP forecast is turned into a categorical forecast). The categorical forecasts based on the POP exhibit positive skill scores consistently with both the development and independent data sets. The model for QPF also performed satisfactorily

Angiogenesis Inhibition in Prostate Cancer: Current Uses and Future Promises

Angiogenesis has been well recognized as a fundamental part of a multistep process in the evolution of cancer progression, invasion, and metastasis. Strategies for inhibiting angiogenesis have been one of the most robust fields of cancer investigation, focusing on the vascular endothelial growth factor (VEGF) family and its receptors. There are numerous regulatory drug approvals to date for the use of these agents in treating a variety of solid tumors. While therapeutic efficacy has been established, challenges remain with regards to overcoming resistance and assessing response to antiangiogenic therapies. Prostate cancer is the most common noncutaneous malignancy among American men and angiogenesis plays a role in disease progression. The use of antiangiogenesis agents in prostate cancer has been promising and is hereby explored

Forecasting minimum temperature during winter and maximum temperature during summer at Delhi

A knowledge of minimum temperature during winter and maximum temperature during summer is a very useful for individuals, as well as for organisations whose workers and machines have to operate in the open, e.g. the armed forces, railways, roadways, tourism, etc. Accurate forecasts of minimum temperature during winter help in the prediction of cold-wave conditions and those of maximum temperature during summer help in the prediction of heat-wave conditions over northern India. Models for forecasting the minimum temperature during December and the maximum temperature during May at Delhi have been developed using surface and upper-air meteorological data from 1984-89. The results of testing the models on independent data from recent years (1994-95) are presented. The results are encouraging and more than 80% of the forecasts are correct within ±2°C. Possible reasons for large deviations are also investigated