Save Our Souls: Study of Twitter Use during India’s COVID-19 Pandemic

Twitter is a commonly used social platform for communication during disasters. Tweets by citizens during disasters to share information, seek, and offer help create a body of spontaneous, decentralized, emergent social media communication. Users’ exploit Twitter’s reach-enabling technological functionalities (hashtags (#), mentions (@), and ‘reply-to’) to draw attention to the messages. Set in context of the second wave of COVID-19 in India, that saw a surge in citizen-driven tweets seeking healthcare resources from fellow citizens and officials (i.e., SOS tweets), our paper empirically analyses the effects of Twitter’s reach-enabling functionalities on online responses (i.e., retweets and replies) to these SOS tweets. We investigate the effects of inclusion of hashtags, mentions, and ‘reply to’ SOS tweets. We also examine the moderating effect of how the effects of the reach-enabling functionalities change as the social platform gets crowded with SOS tweets. The study offers various academic and practical implications

Magnonic spin-transfer torque MRAM with low power, high speed, and error-free switching

A new class of spin-transfer torque magnetic random access memory (STT-MRAM) is discussed, in which writing is achieved using thermally initiated magnonic current pulses as an alternative to conventional electric current pulses. The magnonic pulses are used to destabilize the magnetic free layer from its initial direction, and are followed immediately by a bipolar electric current exerting conventional spin-transfer torque on the free layer. The combination of thermal and electric currents greatly reduces switching errors, and simultaneously reduces the electric switching current density by more than an order of magnitude as compared to conventional STT-MRAM. The energy efficiency of several possible electro-thermal circuit designs have been analyzed numerically. As compared to STT-MRAM with perpendicular magnetic anisotropy, magnonic STT-MRAM reduces the overall switching energy by almost 80%. Furthermore, the lower electric current density allows the use of thicker tunnel barriers, which should result in higher tunneling magneto-resistance and improved tunnel barrier reliability. The combination of lower power, improved reliability, higher integration density, and larger read margin make magnonic STT-MRAM a promising choice for future non-volatile storage.Comment: 9 Pages, 11 Figure

Power System Stability Analysis using Neural Network

This work focuses on the design of modern power system controllers for automatic voltage regulators (AVR) and the applications of machine learning (ML) algorithms to correctly classify the stability of the IEEE 14 bus system. The LQG controller performs the best time domain characteristics compared to PID and LQG, while the sensor and amplifier gain is changed in a dynamic passion. After that, the IEEE 14 bus system is modeled, and contingency scenarios are simulated in the System Modelica Dymola environment. Application of the Monte Carlo principle with modified Poissons probability distribution principle is reviewed from the literature that reduces the total contingency from 1000k to 20k. The damping ratio of the contingency is then extracted, pre-processed, and fed to ML algorithms, such as logistic regression, support vector machine, decision trees, random forests, Naive Bayes, and k-nearest neighbor. A neural network (NN) of one, two, three, five, seven, and ten hidden layers with 25%, 50%, 75%, and 100% data size is considered to observe and compare the prediction time, accuracy, precision, and recall value. At lower data size, 25%, in the neural network with two-hidden layers and a single hidden layer, the accuracy becomes 95.70% and 97.38%, respectively. Increasing the hidden layer of NN beyond a second does not increase the overall score and takes a much longer prediction time; thus could be discarded for similar analysis. Moreover, when five, seven, and ten hidden layers are used, the F1 score reduces. However, in practical scenarios, where the data set contains more features and a variety of classes, higher data size is required for NN for proper training. This research will provide more insight into the damping ratio-based system stability prediction with traditional ML algorithms and neural networks.Comment: Masters Thesis Dissertatio

Evaluation of Undrained Shear Strength of Soil, Ultimate Pile Capacity and Pile Set-Up Parameter from Cone Penetration Test (CPT) Using Artificial Neural Network (ANN)

Over the years, numerous design methods were developed to evaluate the undrained shear strength, Su, ultimate pile capacity and pile set-up parameter, A. In recent decades, the emphasis was given to the in-situ cone and piezocone penetration tests (CPT, PCPT) to estimate these parameters since CPT/PCPT has been proven to be fast, reliable and cost-effective soil investigation method. However, because of the paucity of a vivid comprehension of the physical problem, some of the developed methods incorporate correlation assumptions which might compromise the consistent accuracy. In this study, the Artificial Neural Network (ANN) was exerted using CPT data and soil properties to generate a better and unswerving interpretation of Su, ultimate pile capacity and ‘A’ parameter. In this regard, a data set was prepared consisting of CPT/PCPT data as well as relevant soil properties from 70 sites in Louisiana for the evaluation of Su. For ultimate pile capacity, a database of 80 pile load tests was prepared. Lastly, data was collected from 12 instrumented pile load tests for the interpretation of the ‘A’ parameter. Corresponding CPTs along with the soil borings were also collected. Presenting these data to ANN, models were trained through trial and error using different feed-forward network techniques, e.g. Back Propagation method. Different models of ANN were explored with cone sleeve friction, fs, and tip resistance, qt, as well as plasticity index, PI, effective overburden pressure, σ’vo, etc. as input data and were compared to the conventional methods. It was found that the ANN model with qt, fs, and σ’vo as inputs performed satisfactorily and was found to be better than the conventional empirical method of evaluation of Su. On the other hand, ANN models with pile embedment length, pile width, qt, and fs as inputs, outperformed the best-performed direct pile-CPT methods in the interpretation of ultimate pile capacity. Similarly, the ‘A’ parameter predicted by the ANN models (PI, OCR, and Su as inputs) was also in good agreement with the actual one. These findings, hence, fortifies the applicability of ANN for estimating the undrained shear strength, ultimate pile capacity and ‘A’ parameter from CPT data and soil properties

Study of Uniaxial Tensile Properties of Hexagonal Boron Nitride Nanoribbons

Uniaxial tensile properties of hexagonal boron nitride nanoribbons and dependence of these properties on temperature, strain rate, and the inclusion of vacancy defects have been explored with molecular dynamics simulations using Tersoff potential. The ultimate tensile strength of pristine hexagonal boron nitride nanoribbon of 26 nm x 5 nm with armchair chirality is found to be 100.5 GPa. The ultimate tensile strength and strain have been found decreasing with increasing the temperature while an opposite trend has been observed for increasing the strain rate. Furthermore, the vacancy defects reduce ultimate tensile strength and strain where the effect of bi-vacancy is clearly dominating over point vacancy

Food of the cat-fish, Tachysurus thalassinus (Ruppell)

The food habits of the cat-fish, Tachysurus thalassinus, v/eit studied for a period of three years from April 1964 to March 1967, based mainly on specimens from the fishing areas oif Waltair, and also some from further north in the Bay of Bengal. From the pooled data of all zones it is observed that 67% of the food consists of crabs, prawns, Squilla spp., and crustacean remains, 22% of teleosts and 4% of molluscs. The-fluctuations in the intensity of feeding (points) and the volume of stomach contents (ml) generally showed similar trends both in the small (less than 36 cm total length) and large (more than 36 cm total length) cat-fish; A rough correlation between the stomach contents and the availability of food items in the environment is observed

Studies on oceanographic conditions of the surface and bottom waters of the Bay of Bengal off Visakhapatnam during 1968-1972

The inshore hydrographic data in the region off Waltair for the period from October 1968 to March 1972 were observed in order to study the seasonal variation of temperature, salinity, dissolved oxygen content, phosphate and silicate. Salinity showed lesser variation at bottom than at surface. Changes of salinity from maximum to minimum and vice versa were quite rapid with a minimum in October and a steady maintenance in the maximum for a long period from April to July. Temperature variation gave rise to double maxima and double minima in the annual trend. Bottom waters showed consistantly higher values than surface with a maximum during September-October. Phosphates indicated two maxima, one during onset of monsoon and another during winter. Silicates showed a steady increase from January to December during 1971