Influence of additives on the charactaristics of stone matrix asphalt

The increase in traffic growth and maintenance expenditures demands the urgent need for building better, long-lasting, and more efficient roads preventing or minimizing bituminous pavement distresses. Many of the principal distresses in pavements initiate or increase in severity due to the presence of water. In Kerala highways, where traditional dense graded mixtures are used for the surface courses, major distress is due to moisture induced damages. The Stone Matrix Asphalt (SMA) mixtures provide a durable surface course. Proven field performance of test track at Delhi recommends Stone Matrix Asphalt as a right choice to sustain severe climatic and heavy traffic conditions. But the concept of SMA in India is not so popularized and its application is very limited mainly due to the lack of proper specifications. This research is an attempt to study the influence of additives on the characteristics of SMA mixtures and to propose an ideal surface course for the pavements. The additives used for this investigation are coir, sisal, banana fibres (natural fibres), waste plastics (waste material) and polypropylene (polymer). A preliminary investigation is conducted to characterize the materials used in this study. Marshall test is conducted for optimizing the SMA mixtures (Control mixture-without additives and Stabilized mixtures with additives). Indirect tensile strength tests, compression strength tests, triaxial strength tests and drain down sensitivity tests are conducted to study the engineering properties of stabilized mixtures. The comparison of the performance of all stabilized mixtures with the control mixture and among themselves are carried out. A statistical analysis (SPSS package Ver.16) is performed to establish the findings of this study

“Adrenergic receptors and monoamines in the brain and pancreatic islets of streptozotoein diabetic rats: Role in insulin secretion as a function of age���

I) To study the changes in the content of brain rrrorroamirres in streptozotocirr-irrduced tliabetes as a lirnction of age and to lirrd the role oliadrenal lrornroncs in diabetic state. 2) To assess the adrenergic receptor function in the brain stem ofstreptozotocin-induced diabetic rats ofdillerent ages. 3) To study the changes in the basal levels of second messenger cAMP in the brain stenr ofstreptozotocin-induced diabetic rats as a function of age. 4) To study the changes occurring in the content ofmorroamines and their metabolites in whole pancreas and isolated pancreatic islets of streptozotocin-diabetic rats as a function ofage and the effect of adrenal hormones. 5) To study the adrenergic receptors and basal levels of cAMP in isolated pancreatic islets in young and old streptozotoein-diabetic rats. 6) The in virro study of CAMP content in pancreatic islets of young and old rats and its ellect on glucose induced insulin secretion. 7) 'lhe in vitro study on the involvement of dopamine and corticosteroids in glucose induced insulin secretion in pancreatic islets as a function of age.

Prediction of Key Symptoms of Learning Disabilities in School-Age Children Using Rough Sets

This paper highlights the prediction of learning disabilities (LD) in school-age children using rough set theory (RST) with an emphasis on application of data mining. In rough sets, data analysis start from a data table called an information system, which contains data about objects of interest, characterized in terms of attributes. These attributes consist of the properties of learning disabilities. By finding the relationship between these attributes, the redundant attributes can be eliminated and core attributes determined. Also, rule mining is performed in rough sets using the algorithm LEM1. The prediction of LD is accurately done by using Rosetta, the rough set tool kit for analysis of data. The result obtained from this study is compared with the output of a similar study conducted by us using Support Vector Machine (SVM) with Sequential Minimal Optimisation (SMO) algorithm. It is found that, using the concepts of reduct and global covering, we can easily predict the learning disabilities in children

Prediction of Learning Disabilities in School Age Children using SVM and Decision Tree

This paper highlights the prediction of Learning Disabilities (LD) in school-age children using two classification methods, Support Vector Machine (SVM) and Decision Tree (DT), with an emphasis on applications of data mining. About 10% of children enrolled in school have a learning disability. Learning disability prediction in school age children is a very complicated task because it tends to be identified in elementary school where there is no one sign to be identified. By using any of the two classification methods, SVM and DT, we can easily and accurately predict LD in any child. Also, we can determine the merits and demerits of these two classifiers and the best one can be selected for the use in the relevant field. In this study, Sequential Minimal Optimization (SMO) algorithm is used in performing SVM and J48 algorithm is used in constructing decision trees.

Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children

Learning Disability (LD) is a classification including several disorders in which a child has difficulty in learning in a typical manner, usually caused by an unknown factor or factors. LD affects about 15% of children enrolled in schools. The prediction of learning disability is a complicated task since the identification of LD from diverse features or signs is a complicated problem. There is no cure for learning disabilities and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. The aim of this paper is to develop a new algorithm for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the performance of fuzzy and neuro fuzzy classifiers with specific emphasis on prediction of learning disabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorithm, viz. the correlation based new algorithm for imputing the missing values and Principal Component Analysis (PCA) for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in prediction system and is capable of improving the performance of a classifier.

Waste plastic as a stabilizing additive in Stone Mastic Asphalt

The present study investigates the benefits of stabilizing the stone mastic asphalt (SMA) mixture in flexible pavement with shredded waste plastic. Conventional (without plastic) and the stabilized SMA mixtures were subjected to performance tests including Marshall Stability, tensile strength and compressive strength tests. Triaxial tests were also conducted with varying percentage bitumen by weight of mineral aggregate (6% to 8%) and by varying percentage plastic by weight of mix (6% to 12% with an increment of 1%). Plastic content of 10% by weight of bitumen is recommended for the improvement of the performance of Stone Mastic Asphalt mixtures. 10% plastic content gives an increase in the stability, split tensile strength and compressive strength of about 64%, 18% and 75% respectively compared to the conventional SMA mix. Triaxial test results show a 44% increase in cohesion and 3% decrease in angle of shearing resistance showing an increase in the shear strength. The drain down value decreases with an increase in plastic content and the value is only 0.09 % at 10% plastic content and proves to be an effective stabilizing additive in SMA mixtures

Influence Of Additives On The Drain Down Characteristics Of Stone Matrix Asphalt Mixtures

In Kerala highways, where traditional dense graded mixtures are used for the surface courses, major distress is due to moisture induced damages. Development of stabilized Stone Matrix Asphalt (SMA) mixtures for improved pavement performance has been the focus of research all over the world for the past few decades. Many successful attempts are made to stabilize SMA mixtures with synthetic fibres and polymers. India, being an agricultural economy produces fairly huge quantity of natural fibres such as coconut, sisal, banana, sugar cane, jute etc.. Now- a -days the disposal of waste plastics is a major concern for an eco- friendly sustainable environment. This paper focuses on the influence of additives like coir, sisal, banana fibres (natural fibres), waste plastics (waste material) and polypropylene (polymer) on the drain down characteristics of SMA mixtures. A preliminary investigation is conducted to characterize the materials used in this study. Drain down sensitivity tests are conducted to study the bleeding phenomena and drain down of SMA mixtures. Based on the drain down characteristics of the various stabilized mixtures it is inferred that the optimum fibre content is 0.3% by weight of mixture for all fibre mixtures irrespective of the type of fibre. For waste plastics and polypropylene stabilized SMA mixtures, the optimum additive contents are respectively 7% and 5% by weight of mixture. Due to the absorptive nature of fibres, fibre stabilizers are found to be more effective in reducing the drain down of the SMA mixture. The drain values for the waste plastics mix is within the required specification range. The coir fibre additive is the best among the fibres investigated. Sisal and banana fibre mixtures showed almost the same characteristics on stabilization.

Age and growth of Gerres filamentosus (Cuvier, 1829) from Kodungallur, Azhikode Estuary, Kerala

The age and growth, length – weight relationship and relative condition factor of Gerres filamentosus (Cuvier, 1829) from Kodungallur, Azhikode Estuary were studied by examination of 396 specimens collected between May 2008 to October 2008. Here, length frequency method was used to study age and growth in fishes. L∞, K and t 0 obtained from seasonal and non - seasonal growth curves. Gerres filamentosus showed a low mortality rate (Z) 3.702 y-1. G. filamentosus has moderately low K value and long life span. The relation between the total length and weight of G. filamentosus was described as Log W = 1.321+2.5868 log L for males, Log W = 1.467 + 2.7227 log L for females and Log W = 1.481 + 2.7316 log L for sexes combined. The mean relative condition factor (Kn) values ranged from 0.9 to 1.14 for males, 0.89 to 1.11 for females and 0.73 to 1.08 for sexes combined. The length weight relationship and relative condition factor showed that the wellbeing of G. filamentosus were good. The morphometric measurements of various body parts were recorded. The morphometric measurements were found to be nonlinear and there is no significant difference observed between the two sexes.

Analytical Characterization, Thermal And Ftir Studies Of Urinary Calculi

Urolithiasis is identified to be a major urological disorder affecting people all over the world irrespective of their age, sex and race. Urinary stone samples resected from the urinary bladders of two patients belonging to tropical region, Kollam District of Kerala State, India are investigated by using XRD,SEM, EDAX, TGA, DSC and FTIR to understand its chemical structure. Uric acid shows exothermic peak around 432°C is due to the decomposition with the evolution of CO and cracking of the remaining products. Results of analytical studies reveal that samples under investigation consist mainly in uric acid and hydrated uric acid. Hydrogen bonding exists in hydrated uric acid samples

DWIVEDIYUGIN KAVYA MEN RASHTRIYATA EVAM GANDHIVAD KA SWAROOP (NATIONALISM AND GANDHISM AS REFLECTED IN THE POETRY OF DWIVEDI-AGE)

HINDI

Morningness-eveningness and sleep habits among adolescents : Age and gender differences.

Resumen tomado de la publicaci??n

Evaluación de la psicopatología del preescolar mediante el Early Childhood Inventory-4 (ECI-4): concordancia entre padres y maestros.= Assessment of psychopathology in preschool age children through the Early Childhood Inventory-4 (ECI-4): Agreement among parents and teachers

El objetivo principal de este estudio es conocer la concordancia entre informantes, padres y maestros, en cada una de las dimensiones o categorías diagnósticas del Early Childhood Inventory-4 (ECI-4). Además, se pretende analizar la influencia de la presencia de problemas de salud en los padres en la descripción y valoración de la conducta de una muestra de 204 alumnos de preescolar (3 a 6 años) de perfiles socioeconómicos diferentes. Los resultados indican que los padres tienden a valorar con mayor severidad los síntomas, observándose una mayor concordancia entre informantes en los relativos a los trastornos del desarrollo

Age Continuum essay podcast

Participators Trevor Kettle and Yvonne Middlewick

Age continuum presentation podcast

Participators Trevor Kettle and Yvonne Middlewick