USING SEMANTIC MAPS AS A TEACHING STRATEGY FOR VOCABULARY DEVELOPMENT

Semantic maps ensure a high potential to facilitate enhanced quality of understanding words. English as second language learners are often presented with new English vocabulary items that are often pre-organized in sets of semantically related words. However, there is an assumption that word grouping facilitates activities for vocabulary learning and no empirical justification supports employing this teaching technique. This study aims to examine to what extent semantic relatedness influences ESL vocabulary recall and retention for middle school students of Telangana. The current study was conducted with 30 seventh-grade students over six weeks. Learners were divided into two groups to compare the effects of presenting semantic maps (retention, recall) and wordlists (recognition, cued recall) for reading comprehension activities. The results reveal that both teaching strategies positively affect vocabulary recall and retention. Between these two strategies, semantic mapping yields better results on recall. The difference between the groups explains from the perspective of information process theory and memory model. Lastly, significant learning and the effectiveness of semantic maps were found in the experiment group.  Article visualizations

Composite sandwich structure with grid stiffened core

Composite sandwich structures have been widely used in aerospace structures, ship building, infrastructure, etc. due to their light weight and high strength to weight ratio. Traditionally, light-weight core materials such as foam core, truss core, honeycomb core have been used in fabricating sandwich structures with limited success. In this study, a new composite sandwich structure with a hybrid core was proposed, fabricated, tested, and modeled. The hybrid core consists of a fiber reinforced polymer grid skeleton that is filled in by extremely light-weight syntactic foam in the bay areas. The new sandwich structure was manufactured using a new manufacturing process. The behavior of these structures under impact and compression was investigated. Experimental results show that the grid structure with smaller bay area has higher impact resistance compared to grids with larger bay areas. For the hybrid core with a smaller bay area, the damage was localized to a much smaller area as evidenced by ultrasonic inspection. The nodal region was observed to have the highest impact resistance while the bay area was the weakest and had the least impact resistance. Compression testing was done after subjecting the specimens to impact testing. The results obtained show that the residual load carrying capacity decreases as the bay area increases. However, compared to the control group, which was the traditional laminated composite, the sandwich structure with grid stiffened core shows better impact tolerance and higher residual strength, although the fiber volume fraction used was the same. SEM images of impact region were taken and the mechanisms involved in the failure of foam, ribs and nodes were observed. From impact analysis it was found that grid stiffened sandwich structures with a smaller bay area have a higher impact loading capacity. The damage was localized to a smaller area. The residual load carrying capacity of grid structures was found to be higher than that of the laminates. Finite element analysis was conducted on the hybrid structure using ANSYS. A 3-D finite element model was developed and appropriate material properties were given to each component. Boundary conditions similar to those used in compression testing were utilized. The model was firstly validated by the experimental results. Parametric analysis was then performed on the validated model by varying important design parameters like skin thickness, skin modulus, rib width, rib modulus and bay area. Results obtained by changing these parameters were analyzed. The experimental and finite element results were discussed and general conclusions were drawn. From the impact results it was observed that grid structures with small bay area have a higher impact tolerance. The damage caused by impact was confined to a much smaller area compared to laminate composites. The residual load carrying capacity of grid structures with small bay area was found to be higher compared to large bay area specimens. It was found that for higher energy impacts the grids with smaller bay areas were able to retain higher load carrying ability

Data-driven Job Search Engine Using Skills and Company Attribute Filters

According to a report online, more than 200 million unique users search for jobs online every month. This incredibly large and fast growing demand has enticed software giants such as Google and Facebook to enter this space, which was previously dominated by companies such as LinkedIn, Indeed and CareerBuilder. Recently, Google released their "AI-powered Jobs Search Engine", "Google For Jobs" while Facebook released "Facebook Jobs" within their platform. These current job search engines and platforms allow users to search for jobs based on general narrow filters such as job title, date posted, experience level, company and salary. However, they have severely limited filters relating to skill sets such as C++, Python, and Java and company related attributes such as employee size, revenue, technographics and micro-industries. These specialized filters can help applicants and companies connect at a very personalized, relevant and deeper level. In this paper we present a framework that provides an end-to-end "Data-driven Jobs Search Engine". In addition, users can also receive potential contacts of recruiters and senior positions for connection and networking opportunities. The high level implementation of the framework is described as follows: 1) Collect job postings data in the United States, 2) Extract meaningful tokens from the postings data using ETL pipelines, 3) Normalize the data set to link company names to their specific company websites, 4) Extract and ranking the skill sets, 5) Link the company names and websites to their respective company level attributes with the EVERSTRING Company API, 6) Run user-specific search queries on the database to identify relevant job postings and 7) Rank the job search results. This framework offers a highly customizable and highly targeted search experience for end users.Comment: 8 pages, 10 figures, ICDM 201

A Study on Human Face Expressions using Convolutional Neural Networks and Generative Adversarial Networks

Human beings express themselves via words, signs, gestures, and facial emotions. Previous research using pre-trained convolutional models had been done by freezing the entire network and running the models without the use of any image processing techniques. In this research, we attempt to enhance the accuracy of many deep CNN architectures like ResNet and Senet, using a variety of different image processing techniques like Image Data Generator, Histogram Equalization, and UnSharpMask. We used FER 2013, which is a dataset containing multiple classes of images. While working on these models, we decided to take things to the next level, and we attempted to make changes to the models themselves to improve their accuracy. While working on this research, we were introduced to another concept in Deep Learning known as Generative Adversarial Networks, which are also known as GANs. They are generative deep learning models which are based on deep CNN models, and they comprise two CNN models - a Generator and a Discriminator. The primary task of the former is to generate random noises in the form of images and passes them to the latter. The Discriminator compares the noise with the input image and accepts/rejects it, based on the similarity. Over the years, there have been various distinguished architectures of GANs namely CycleGAN, StyleGAN, etc. which have allowed us to create sophisticated architectures to not only generate the same image as the original input but also to make changes to them and generate different images. For example, CycleGAN allows us to change the season of scenery from Summer to Winter or change the emotion in the face of a person from happy to sad. Though these sophisticated models are good, we are working with an architecture that has two deep neural networks, which essentially creates problems with hyperparameter tuning and overfitting

A Meander Line-Based Frequency Selective Surface for Strain Sensing

Frequency selective surfaces (FSSs) are periodic arrays of conductive elements that have distinct reflection and transmission responses. In this work, an FSS sensor designed to operate in Ka-band to measure a wide range of uni-directional strain using a meander line-based unit cell is presented. Specifically, the proposed unit cell of the sensor consists of a convoluted meander line geometry designed on a thin dielectric substrate. Strain sensing is achieved by monitoring the change in the resonant frequency of the FSS when under strain that is parallel to an interrogating signal linearly polarized and aligned with the convoluted dimension of the meander line element. Simulation results of strain measurement over two ranges, small-(0%-0.5%) and large-scale (0%-5%), are presented. The simulated results indicate that the sensitivity of the sensor to small-scale strain is 21 MHz/0.1% strain and 230 MHz/1.0% strain for large-scale

Online Library Project

The main objective of this project is to provide the hand free access to the library portal through web interface. This project of “ONLINE LIBRARY” gives us the complete information about the library. We can enter the record of new books and retrieve the details of books available in the library. We can issue the books to the students and maintain their records and can also check how many books are issued and stock available in the library. In this project we can maintain the late fine of students who returns the issued books after the due date. Throughout the project the focus has been on making the students to grab the books of which they are in need with an exact details of the versions and editions of their respected volumes in an easy and intelligible manner. The project is very useful for those who want to know about online Library System

Towards continuous-time MPC: a novel trajectory optimization algorithm

This article introduces a numerical algorithm that serves as a preliminary step toward solving continuous-time model predictive control (MPC) problems directly without explicit time-discretization. The chief ingredients of the underlying optimal control problem (OCP) are a linear time-invariant system, quadratic instantaneous and terminal cost functions, and convex path constraints. The thrust of the method involves finitely parameterizing the admissible space of control trajectories and solving the OCP satisfying the given constraints at every time instant in a tractable manner without explicit time-discretization. The ensuing OCP turns out to be a convex semi-infinite program (SIP), and some recently developed results are employed to obtain an optimal solution to this convex SIP. Numerical illustrations on some benchmark models are included to show the efficacy of the algorithm.Comment: Accepted in IEEE Conference on Decision and Control (CDC), 202

A SIMPLE LIQUID CHROMATOGRAPHIC METHOD FOR SIMULTANEOUS ESTIMATION OF AZITHROMYCIN, FLUCONAZOLE AND ORNIDAZOLE IN BULK AND PHARMACEUTICAL DOSAGE FORMS

Objective: The objective of the study was to develop and validate a new rapid and more sensitive Reverse Phase High-Performance Liquid Chromatography (RP-HPLC) method for the simultaneous estimation of azithromycin, fluconazole and ornidazole in bulk and pharmaceutical dosage forms. Methods: Separation was achieved with a cap cell pack C18 column (4.6 x 250 mm, 5μ) with an isocratic mobile phase containing a mixture of acetonitrile and phosphate buffer pH 4.8 [adjusted with ortho-phosphoric acid] (50:50 % v/v) at the flow rate of 1 ml/min and detection was monitored at 210 nm. Results: The retention time (Rt) of azithromycin, fluconazole and ornidazole were found to be 4.82±0.01, 5.25±0.01 and 6.33±0.01 min respectively. The precision was found with<1.5% of %RSD. The calibration curve was linear over the concentration ranging from 500-1000 µg/ml for azithromycin, 75-150 µg/ml for fluconazole and 375-750 µg/ml for ornidazole with the correlation coefficient (r2) of 0.999. The percentage recovery was found to be within the specified range i.e., 98-102 % for three drugs. Limit of detection (LOD) was found to be 5.810, 1.790 and 4.924 µg/ml, whereas Limit of quantification limits (LOQ) was found to be 9.834, 2.667 and 7.980 µg/ml, respectively. Conclusion: A simple isocratic liquid chromatographic method was developed and validated for simultaneous estimation of azithromycin, fluconazole and ornidazole in their formulations. Due to its simplicity, rapidness and specificity, it can be applied for routine quality control analysis of these drugs