Predicting Autism over Large-Scale Child Dataset

Data Analytics and Machine learning in healthcare are one of the most emerging and needed fields in current time. Also, a lot of research has been performed and is still being done in this field. In healthcare, gone are those days when only doctor examines and patient listens. Now doctor has a lot of technologies which can assist him and help in accurately diagnosing the disease with which his patient is suffering. The backbone of such technologies is data analytics and machine learning where we can make out a lot of inferences from tons of patients‟ data already available. This project aims at performing research and implementation of big data and machine learning techniques on the data related to the patients suffering from the disease called Autism. Autism is a neural disorder disease characterized by impaired social communication, verbal and non-verbal interaction, restrictive and repetitive behavior [4]. Autism is majorly noticed in children under or about the age of two years. One very important thing to be observed here is that autism is highly heritable and the cause includes both environmental factors and genetic susceptibility. Hence it is very important to have such data which contains details of patients including their symptoms, lab test data, history, vaccination details etc. which gives specific details of patients and their history. The project ultimately aims at training the data model with the set of training data and then testing and evaluating the data model using the test data. In this way, it should be a research and solution for implementing machine learning to detect and diagnose autism

A Synthesis of the Dibble et al. Controlled Experiments into the Mechanics of Lithic Production

Archaeologists have explored a wide range of topics regarding archaeological stone tools and their connection to past human lifeways through experimentation. Controlled experimentation systematically quantifies the empirical relationships among different flaking variables under a controlled and reproducible setting. This approach offers a platform to generate and test hypotheses about the technological decisions of past knappers from the perspective of basic flaking mechanics. Over the past decade, Harold Dibble and colleagues conducted a set of controlled flaking experiments to better understand flake variability using mechanical flaking apparatuses and standardized cores. Results of their studies underscore the dominant impact of exterior platform angle and platform depth on flake size and shape and have led to the synthesis of a flake formation model, namely the EPA-PD model. However, the results also illustrate the complexity of the flake formation process through the influence of other parameters such as core surface morphology and force application. Here we review the work of Dibble and colleagues on controlled flaking experiments by summarizing their findings to date. Our goal is to synthesize what was learned about flake variability from these controlled experiments to better understand the flake formation process. With this paper, we are including all of the data produced by these prior experiments and an explanation of the data in the Supplementary Information

Microstructural and mechanical characterisation of laser-welded high-carbon and stainless steel

The final publication is available at Springer via http://dx.doi.org/10.1007/s00170-015-7111-5Laser welding is becoming an important joining technique for welding of stainless steel to carbon steel and is extensively used across various sectors, including aerospace, transportation, power plants, electronics and other industries. However, welding of stainless steel to high-carbon steel is still at its early stage, predominantly due to the formation of hard brittle phases, which undermine the mechanical strength of the joint. This study reports a scientific investigation on controlling the brittle phase formation during laser dissimilar welding of high-carbon steel to stainless steel. Attempts have been made to tailor the microstructure and phase composition of the fusion zone through influencing the alloying composition and the cooling rate. Results show that the heat-affected zone (HAZ) within the high-carbon steel has significantly higher hardness than the weld area, which severely undermines the weld quality. To reduce the hardness of the HAZ, a new heat treatment strategy was proposed and evaluated using a finite element analysis-based numerical simulation model. A series of experiments has been performed to verify the developed thermo-metallurgical finite element analysis (FEA) model, and a qualitative agreement of predicted martensitic phase distribution is shown to exist

Optical and Structural Studies of Sol-Gel Deposited Nanostructured CdO Thin Films: Annealing Effect

Nanocrystalline CdO thin films were prepared by sol-gel dip-coating method using a different solution. The as-deposited films were subjected to drying temperature of 120° in air. The prepared films were annealed in different temperatures of 200, 300 and 400°C. The characterization of samples was carried out by X-ray diffraction, scanning electron microscopy and UV-VIS spectroscopy. Results show that the samples are polycrystalline in nature and the crystallinity of the films improves with temperature. The average grain size is in the range of 19-34 nm. It was observed that the optical parameters of the films were affected by annealing temperature

Influential Factors on Member's Participation in Management of Rural Cooperatives in Gorgan Township

This research aimed to identify and analyze the influential factors on member's participation in management of rural co-operatives in Gorgan Township. This descriptive research has been done by survey method.  Populationincluded all the members of rural co-operatives in Gorgan Township. Sample size has been determined by Cochranformula (n=140). A researcher-made questionnaire was used in order to collect data. Face and content validity were established by a panel of experts. Reliability of the questionnaire was confirmed according to calculated Cronbach's alpha (r=0.76). The results of correlation coefficient showed that there was a significant relationship between members’ participation rate in public conventions and membership background as well as their interest to participate in conventions. Factor analysis of the "influential factors on members’ participation in management of rural cooperatives", extracted three factors, named "management potency", "cooperative performance" and "mechanism of participative management", explained 60.63% variances of analyzed variables. The results of logistic regression analysis showed that probability of participation in conventionsincreased through "management potency" and "cooperative performance" while increase in years of experience and member's age decreased their participation in various conventions

Effect of pH on the Optical Properties of Doped CdS (Cu, Fe) Nanoparticles Incorporated in TG as the Capping Agent

Cu and Fe doped cadmium sulfide nanoparticles with controllable and relatively narrow size distribution were prepared using a facile and efficient wet chemical route by mixing the reactants in water solvent. It was found that the mixture pH plays an active role in the growth procedure of nanoparticles. Diverse physical properties of the prepared nanoparticles were investigated using UV-vis absorption, photoluminescence spectroscopy, scanning electron microscopy, transmission electron microscopy and energy dispersive X-ray spectroscopy. The average particle size of samples was estimated to be about 5 nm by using transmission electron microscopy measurement. The pH optimization study implies that $pH ≈7$ would yield an enhancement in photoluminescence intensity. There are two bands in photoluminescence spectra which related to deep and shallow traps