An Intelligent Tutoring System for Teaching Grammar English Tenses

The evolution of Intelligent Tutoring System (ITS) is the result of the amount of research in the field of education and artificial intelligence in recent years. English is the third most common languages in the world and also is the internationally dominant in the telecommunications, science and trade, aviation, entertainment, radio and diplomatic language as most of the areas of work now taught in English. Therefore, the demand for learning English has increased. In this paper, we describe the design of an Intelligent Tutoring System for teaching English language grammar to help students learn English grammar easily and smoothly. The system provides all topics of English grammar and generates a series of questions automatically for each topic for the students to solve. The system adapts with all the individual differences of students and begins gradually with students from easier to harder level. The intelligent tutoring system was given to a group of students of all age groups to try it and to see the impact of the system on students. The results showed a good satisfaction of the students toward the system

Arsitektur Titik Balik: Participatory Design Dan Memori Kolektif

Kenangan adalah ingatan yang akan menjadi cerminan manusia dalam menghadapi keadaan kedepannya. Sehingga, momen akan kenangan itu sendiri harus dibangkitkan. Arsitektur sebagai media membangkitkan momen tidak hanya sebatas intrusi ruang semata. Namun, juga mengajak pengguna dan penghuni untuk berpartisipasi dalam membangkitkan momen tersebut. Karena ruang bukanlah sesuatu yang statis melalui material yang disajikannya. Selama ini, kita tidak pernah menyadari bahwa arsitektur yang kita alami sehari-hari selalu menjadi bagian dari kenangan hidup kita. Dari permasalahan ini penulis menyadari perlu adanya sesuatu dari rancangan yang membuat user menyadari bahwa mereka sedang merakit kenangan mereka sendiri. Rancangan yang dapat disempurnakan oleh penggunanya, seperti baju jemuran yang menjadi elemen estetika, sirkulasi yang diberi pekerasan sendiri oleh penggunanya, dan material yang bersifat temporer yang diganti secara berkala, akan memberi kesadaran secara penuh kepada penggunanya bahwa mereka sedang merajut kenangan mereka terhadap tempat tinggal mereka

Enhancement of the Kondo effect through Rashba spin-orbit interactions

We analyze the physics of a one-orbital Anderson impurity model in a two-dimensional electron gas in the presence of Rashba spin-orbit (RSO) interactions in the Kondo regime. The spin SU(2) symmetry breaking results in an effective two-band electron gas coupled to the impurity. The Kondo regime is obtained by a Schrieffer-Wolff transformation revealing the existence of a parity breaking term with the form of the Dzyaloshinsky-Moriya (DM) interaction. The DM term vanishes at the particle-hole symmetric point of the system, but it has important effects otherwise. Performing a renormalization group (RG) analysis we find that the model describes a two-channel Kondo system with ferro- and anti-ferromagnetic couplings. Furthermore, the DM term renormalizes the antiferromagnetic Kondo coupling producing an exponential enhancement of the Kondo temperature. We suggest that these effects can be observed in semiconducting systems, as well as in graphene and topological insulators.Comment: 4 pages, 1 figure. Final published versio

Super-Resolution Radar

In this paper we study the identification of a time-varying linear system from its response to a known input signal. More specifically, we consider systems whose response to the input signal is given by a weighted superposition of delayed and Doppler shifted versions of the input. This problem arises in a multitude of applications such as wireless communications and radar imaging. Due to practical constraints, the input signal has finite bandwidth B, and the received signal is observed over a finite time interval of length T only. This gives rise to a delay and Doppler resolution of 1/B and 1/T. We show that this resolution limit can be overcome, i.e., we can exactly recover the continuous delay-Doppler pairs and the corresponding attenuation factors, by solving a convex optimization problem. This result holds provided that the distance between the delay-Doppler pairs is at least 2.37/B in time or 2.37/T in frequency. Furthermore, this result allows the total number of delay-Doppler pairs to be linear up to a log-factor in BT, the dimensionality of the response of the system, and thereby the limit for identifiability. Stated differently, we show that we can estimate the time-frequency components of a signal that is S-sparse in the continuous dictionary of time-frequency shifts of a random window function, from a number of measurements, that is linear up to a log-factor in S.Comment: Revised versio

A survey of machine learning wall models for large eddy simulation

This survey investigates wall modeling in large eddy simulations (LES) using data-driven machine learning (ML) techniques. To this end, we implement three ML wall models in an open-source code and compare their performances with the equilibrium wall model in LES of half-channel flow at eleven friction Reynolds numbers between $180$ and $10^{10}$. The three models have ''seen'' flows at only a few Reynolds numbers. We test if these ML wall models can extrapolate to unseen Reynolds numbers. Among the three models, two are supervised ML models, and one is a reinforcement learning ML model. The two supervised ML models are trained against direct numerical simulation (DNS) data, whereas the reinforcement learning ML model is trained in the context of a wall-modeled LES with no access to high-fidelity data. The two supervised ML models capture the law of the wall at both seen and unseen Reynolds numbers--although one model requires re-training and predicts a smaller von K\'arm\'an constant. The reinforcement learning model captures the law of the wall reasonably well but has errors at both low ($Re_\tau<10^3$) and high Reynolds numbers ($Re_\tau>10^6$). In addition to documenting the results, we try to ''understand'' why the ML models behave the way they behave. Analysis shows that the errors of the supervised ML model is a result of the network design and the errors in the reinforcement learning model arise due to the present choice of the ''states'' and the mismatch between the neutral line and the line separating the action map. In all, we see promises in data-driven machine learning models

Cellular location and activity of Escherichia coli RecG proteins shed light on the function of its structurally unresolved C-terminus

RecG is a DNA translocase encoded by most species of bacteria. The Escherichia coli protein targets branched DNA substrates and drives the unwinding and rewinding of DNA strands. Its ability to remodel replication forks and to genetically interact with PriA protein have led to the idea that it plays an important role in securing faithful genome duplication. Here we report that RecG co-localises with sites of DNA replication and identify conserved arginine and tryptophan residues near its C-terminus that are needed for this localisation. We establish that the extreme C-terminus, which is not resolved in the crystal structure, is vital for DNA unwinding but not for DNA binding. Substituting an alanine for a highly conserved tyrosine near the very end results in a substantial reduction in the ability to unwind replication fork and Holliday junction structures but has no effect on substrate affinity. Deleting or substituting the terminal alanine causes an even greater reduction in unwinding activity, which is somewhat surprising as this residue is not uniformly present in closely related RecG proteins. More significantly, the extreme C-terminal mutations have little effect on localisation. Mutations that do prevent localisation result in only a slight reduction in the capacity for DNA repair. © 2014 The Author(s)

The Influence of Dy2O3 doping on the Electrical Properties of ZnO-Based Varistor

ZnO is a ceramic material which tends to intrinsically form as an n-type semiconductor material. In this paper, the effect of Dy2O3 doping on the grain size and the electrical properties of ZnO-based varistor has been investigated, where we studied the I-V nonlinear coefficient behavior, the breakdown voltage, the potential gradient, leakage current, voltage per grain boundary before and after doping with Dy2O3 at concentration of 10-3 mol% and sintering temperature of 1050, 1100, and 1150oC. Keywords: ZnO varistor, Dy2O3 doping, electrical properties

System for Visually Disabled through Wearables Utilizing Arduino and Ultrasound

Blindness and other vision impairment is on the rise with more than 2.2 billion people worldwide are affected including children, elder persons, pregnant women, chronically ill and disabled persons who experience difficulties in mobility and being independent. Some of the conventional assistances like usage of white cane or a guide dog lacks the ability to cater all the needs of the blind people. The present research outlines a wearable system with Arduino and ultrasound equipment to improve the walking ability of the persons with vision impairment. From the use of the proposed system, there is the potentiality of detecting obstacles in real time and also determine the location hence minimizing the dependence on other help. The system consists of two wearable components: a glove and a belt which contain ultrasonic sensors, GPS module and GSM module and a vibration motor. The glove senses the objects that are in front of the user while the belt detects stairs or any other raised ground. The method used here was the development and calibration of these components separately then brought together to form a coherent entire system where all the component was precise and reliable. The findings show that the proposed system is successful to identify obstacles on its path before the user comes close to them and gives out alerts through sound and touch. GPS and GSM modules provide an extra layer of security to the kids by allowing a tracking of their location in real time

Combined Economic and Emission Dispatch Incorporating Renewable Energy Sources and Plug-In Hybrid Electric Vehicles

Conventional transportation and electricity industries are considered as two major sources of greenhouse gases (GHGs) emission. Improvement of vehicle’s operational efficiency can be a partial solution but it is necessary to employ Plug-In Hybrid Electric Vehicles (PHEVs) and Renewable Energy Sources (RESs) in the network to slow the increasing rate of the GHGs emission. However, it is crucial to investigate the effectiveness of each solution. In this paper, a combination of generation cost and GHGs emission of the two mentioned industries, as economic and environmental aspects of using PHEVs and RESs will be analyzed. The effectiveness of five different scenarios of utilizing the mentioned elements is studied on a test system. To have a realistic evaluation, an extended cost function model of wind farm is employed in optimal power dispatch calculations. Particle Swarm Optimization (PSO) algorithm is applied to the combined economic and emission dispatch (CEED) non- linear problem