In this study, low cost biosorbent ̶inactive biomass (IB) granules (dp=0.433mm) taken from drying beds of Al-Rustomia Wastewater Treatment Plant, Baghdad-Iraq were used for investigating the optimum conditions of Pb(II), Cu(II), and Ni(II) biosorption from aqueous solutions. Various physico-chemical parameters such as initial metal ion concentration (50 to 200 mg/l), equilibrium time (0-180 min), pH (2-9), agitation speed (50-200 rpm), particles size (0.433 mm), and adsorbent dosage (0.05-1 g/100 ml) were studied. Six mathematical models describing the biosorption equilibrium and isotherm constants were tested to find the maximum uptake capacities: Langmuir, Freundlich, Redlich–Peterson, Sips, Khan, and Toth models. The best fit to the P

Conditional logistic regression is often used to study the relationship between event outcomes and specific prognostic factors in order to application of logistic regression and utilizing its predictive capabilities into environmental studies. This research seeks to demonstrate a novel approach of implementing conditional logistic regression in environmental research through inference methods predicated on longitudinal data. Thus, statistical analysis of longitudinal data requires methods that can properly take into account the interdependence within-subjects for the response measurements. If this correlation ignored then inferences such as statistical tests and confidence intervals can be invalid largely.

Background/objectives: To study the motion equation under all perturbations effect for Low Earth Orbit (LEO) satellite. Predicting a satellite’s orbit is an important part of mission exploration. Methodology: Using 4th order Runge–Kutta’s method this equation was integrated numerically. In this study, the accurate perturbed value of orbital elements was calculated by using sub-steps number m during one revolution, also different step numbers nnn during 400 revolutions. The predication algorithm was applied and orbital elements changing were analyzed. The satellite in LEO influences by drag more than other perturbations regardless nnn through semi-major axis and eccentricity reducing. Findings and novelty/improvement: The results demo

The hydroisomerization of n-decane was studied on SAPO-11 catalyst. Catalyst of 0.25wt.%Pt/SAPO-11 was prepared locally and used in the present work. The hydroconversion performed in a continuous fixed-bed laboratory reaction unit. Experiments of n-decane isomerization were performed in a temperature range of 200 to 275°C,LHSV range of 0.5-2 h-1, and hydrogen to decane mole ratio of 2.1-8.2. The results show that the n-decane conversion increases with increasing temperature and  decreasing LHSV , the maximum conversion 56.77 % was achieved at temperature 275°C and LHSV of 0.5 h-1. The kinetic of n-decane isomerization was also studied and the reaction was first order. The kinetic analysis also showed that the activation energy eq

The hydroisomerization of n-decane was studied on SAPO-11 catalyst. Catalyst of 0.25wt.%Pt/SAPO-11 was prepared locally and used in the present work. The hydroconversion performed in a continuous fixed-bed laboratory reaction unit. Experiments of n-decane isomerization were performed in a temperature range of 200 to 275°C,LHSV range of 0.5-2 h^-1, and hydrogen to decane mole ratio of 2.1-8.2. The results show that the n-decane conversion increases with increasing temperature and  decreasing LHSV , the maximum conversion 56.77 % was achieved at temperature 275°C and LHSV of 0.5 h^-1. The kinetic of n-decane isomerization was also studied and the reaction was first order. The kinetic analysis also showed that the

We propose a new method for detecting the abnormality in cerebral tissues present within Magnetic Resonance Images (MRI). Present classifier is comprised of cerebral tissue extraction, image division into angular and distance span vectors, acquirement of four features for each portion and classification to ascertain the abnormality location. The threshold value and region of interest are discerned using operator input and Otsu algorithm. Novel brain slices image division is introduced via angular and distance span vectors of sizes 24˚ with 15 pixels. Rotation invariance of the angular span vector is determined. An automatic image categorization into normal and abnormal brain tissues is performed using Support Vector Machine (SVM). St

     Today in the digital realm, where images constitute the massive resource of the social media base but unfortunately suffer from two issues of size and transmission, compression is the ideal solution. Pixel base techniques are one of the modern spatially optimized modeling techniques of deterministic and probabilistic bases that imply mean, index, and residual. This paper introduces adaptive pixel-based coding techniques for the probabilistic part of a lossy scheme by incorporating the MMSA of the C321 base along with the utilization of the deterministic part losslessly. The tested results achieved higher size reduction performance compared to the traditional pixel-based techniques and the standard JPEG by about 40% and 50%,