Lignin has emerged as a promising asphalt binder modifier due to its sustainable and renewable nature, with the potential to improve flexible pavement performance. This study investigates the use of Soda Lignin Powder (SLP), derived from Pinus wood sawdust via alkaline treatment, as an asphalt modifier to enhance mixture durability. SLP was characterized using Fourier Transformation Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD), and Scanning Electron Microscopy with Energy Dispersive X-ray Analysis (SEM/EDX), revealing significant changes in its chemical structure post-extraction. These analyses showed the presence of phenolic units, including hydroxyphenyl propane, syringyl, and guaiacyl units. The morphology of SLP was identified as irregular and spherical particles consisting of carbon, oxygen, nitrogen, and sulfur. Experimental evaluations involved three SLP dosages (2%, 4%, and 6% by weight of asphalt binder), with tests for penetration, softening point, ductility and rotational viscosity. Additionally, the asphalt mixtures were tested for their performance in terms of moisture susceptibility, resilient modulus, permanent deformation, and fatigue resistance. Results indicated that SLP effectively reduces the temperature susceptibility of asphalt by increasing its stiffness and rotational viscosity. Furthermore, mixtures with 6% SLP showed enhanced moisture resistance, with a Tensile Strength Ratio (TSR) of 86.98%, a 74.1% reduction in accumulated permanent deformation at 10,000 cycles, and a 38.1% increase in the Cracking Tolerance Index (CT index) compared to the control mix (0% SLP content). These findings confirm that SLP has the potential to be an effective additive in the design of asphalt mixture. Moreover, it allows producing endurable mixtures with higher resistance to distress.
The interests toward developing accurate automatic face emotion recognition methodologies are growing vastly, and it is still one of an ever growing research field in the region of computer vision, artificial intelligent and automation. However, there is a challenge to build an automated system which equals human ability to recognize facial emotion because of the lack of an effective facial feature descriptor and the difficulty of choosing proper classification method. In this paper, a geometric based feature vector has been proposed. For the classification purpose, three different types of classification methods are tested: statistical, artificial neural network (NN) and Support Vector Machine (SVM). A modified K-Means clustering algorithm
... Show MoreThe research aims to apply a modified SERVQUAL model to evaluate the quality of the educational services via conducting exploratory research for students from the College of Administration and Economics- Department of Business Administration- Evening studies at the University of Baghdad. Questionnaire of two parts was distributed to a sample of (72) students out of (720) students of the 2nd.,3rd. and 4th. year in the beginning of the second semester of the year 2008-2009 to measure the expectations and perceptions to the quality of the educational services. Five major dimensions were analyzed to see the gaps for (22) variables. The study concluded that there were (13) variables confirmed that the
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In 2020 one of the researchers in this paper, in his first research, tried to find out the Modified Weighted Pareto Distribution of Type I by using the Azzalini method for weighted distributions, which contain three parameters, two of them for scale while the third for shape.This research compared the distribution with two other distributions from the same family; the Standard Pareto Distribution of Type I and the Generalized Pareto Distribution by using the Maximum likelihood estimator which was derived by the researchers for Modified Weighted Pareto Distribution of Type I, then the Mont Carlo method was used–that is one of the simulation manners for generating random samples data in different sizes ( n= 10,30,50), and in di
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