Using Dr. Scratch as a Formative Feedback Tool to Assess Computational Thinking

This study investigated if using Dr. Scratch as a formative feedback tool would accelerate students' Computational Thinking (CT). Forty-one 4th-6th grade students participated in a 1-hour/week Scratch workshop for nine weeks. We measured pre- and posttest results of the computational thinking test (CTt) between control (n = 18) and treatment groups (n = 23) using three methods: propensity score matching (treatment = .575; control = .607; p = .696), information maximum likelihood technique (treatment effect = -.09; p = .006), and multiple linear regression. Both groups demonstrated significantly increased posttest scores over their pretest (treatment = +8.31%; control = +5.43%), showing that learning to code can increase computational thinking over a 2-month period. In this chapter, we discuss the implications of using Dr. Scratch as a formative feedback tool the possibilities of further research on the use of automatic feedback tools in teaching elementary computational thinking.

Assessment of LULC Changes for Hesaraghatta Watershed Using GIS Tools and Remote Sensed Data

Hesaraghatta watershed is one of the most vital and environmentally substantial watersheds in the Arkavathi basin. It has a freshwater lake created in the year 1894 across the Arkavathi River to quench the drinking water requirements of Bengaluru city. The watershed is facing significant stresses due to rapid urbanization and other developmental activities. For this study, an attempt is made to assess the distribution of various land use land cover classes and their temporal changes over 18 years using remote sensed data and GIS tools. The watershed is categorized into four land use land cover classifications: settlement, waterbody, vegetation, and bare soil. The maximum likelihood technique is utilized for the image classification and accuracy assessment is carried out to evaluate the accuracy of image classification. The outcome of the study revealed that there is a substantial change in land use land cover classes in the Hesaraghatta watershed.

An Operational Earthquake Forecasting Experiment for Israel: Preliminary Results

Operational Earthquake Forecasting (OEF) aims to deliver timely and reliable forecasts that may help to mitigate seismic risk during earthquake sequences. In this paper, we build the first OEF system for the State of Israel, and we evaluate its reliability. This first version of the OEF system is composed of one forecasting model, which is based on a stochastic clustering Epidemic Type Earthquake Sequence (ETES) model. For every day of the forecasting time period, January 1, 2016 - November 15, 2020, the OEF-Israel system produces a weekly forecast for target earthquakes with local magnitudes greater than 4.0 and 5.5 in the entire State of Israel. Specifically, it provides space-time-dependent seismic maps of the weekly probabilities, obtained by using a fixed set of the model’s parameters, which are estimated through the maximum likelihood technique based on a learning period of about 32 years (1983–2015). According to the guidance proposed by the Collaboratory for the Study of Earthquake Predictability (CSEP), we also perform the N- and S-statistical tests to verify the reliability of the forecasts. Results show that the OEF system forecasts a number of events comparable to the observed one, and also captures quite well the spatial distribution of the real catalog with the exception of two target events that occurred in low seismicity regions.

Fitting Wind Speed to a 3-Parameter Distribution Using Maximum Likelihood Technique

Kenya is one of the countries in the world with a good quantity of wind. This makes the country to work ontechnologies that can help in harnessing the wind with a vision of achieving a total capacity of 2GW of wind energy by 2030.The objective of this research is to find the best three-parameter wind speed distribution for examining wind speed using the maximum likelihood fitting technique. To achieve the objective, the study used hourly wind speed data collected for a period of three years (2016 – 2018) from five sites within Narok County. The study examines the best distributions that the data fits and then conducted a suitability test of the distributions using the Kolmogorov-Smirnov test. The distribution parameters were fitted using maximum likelihood technique and model comparison test conducted using Akaike’s Information Criterion (AIC) and the Bayesian Information Criterion (BIC) values with the decision rule that the best distribution relies on the distribution with the smaller AIC and BIC values. The research showed that the best distribution is the gamma distribution with the shape parameter of 2.071773, scale parameter of 1.120855, and threshold parameter of 0.1174. A conclusion that gamma distribution is the best three-parameter distribution for examining the Narok country wind speed data

A NEW FOUR-PARAMETER WEIBULL DISTRIBUTION WITH APPLICATION TO FAILURE TIME DATA

A lifetime model called Transmuted Exponential-Weibull Distribution was proposed in this research. Several statistical properties were derived and presented in an explicit form. Maximum likelihood technique is employed for the estimation of model parameters, and a simulation study was performed to examine the behavior of various estimates under different sample sizes and initial parameter values. Through using real-life datasets, it was empirically shown that the new model provides sufficient fits relative to other existing models.

Investigation of the Stochastic Choice under Risk using Experimental Data

This paper extends the analysis of the data from the experiment undertaken by Pradiptyo et al. (2015), to help explain the subjects’ behaviour when making decisions under risk. This study specifically investigates the relative empirical performance of the two general models of the stochastic choice: the random utility model (RUM) and the random preference model (RPM) where this paper specifies these models using two preference functionals, expected utility (EU) and rank-dependent expected utility (RDEU). The parameters are estimated in each model using a maximum likelihood technique and run a horse-race using the goodness-of-fit between the models. The results show that the RUM better explains the subjects’ behaviour in the experiment. Additionally, the RDEU fits better than the EU for modelling the stochastic choice.

Automatic disturbance identification for linear quadratic Gaussian control in adaptive optics

ABSTRACT Linear quadratic Gaussian (LQG) control is an appealing control strategy to mitigate disturbances in adaptive optics (AO) systems. The key of this method is to quickly and consecutively build an accurate dynamical model to track time-varying disturbances such as turbulence, wind load and vibrations. In order to address this problem, we propose an automatic identification method consisting mainly of an improved spectrum separation procedure and a parameter optimization process based on the particle swarm optimization (PSO) algorithm. The improved spectrum separation can pick out perturbation peaks more accurately, especially when some peaks are very close together. Moreover, compared with the Levenberg–Marquardt method and the maximum-likelihood technique based on grids, the PSO algorithm has a faster convergence speed and lower computational burden, and thus is easier to implement. The entire identification process can run automatically online without human intervention. This identification method is verified with a synthetic disturbance profile in a simulation. Furthermore, the performance of the method is evaluated with consecutive measurement data recorded by the 1-m New Vacuum Solar Telescope at the Fuxian Solar Observatory.

Phasor Estimation for Grid Power Monitoring: Least Square vs. Linear Kalman Filter

This paper deals with a comparative study of two phasor estimators based on the least square (LS) and the linear Kalman filter (KF) methods, while assuming that the fundamental frequency is unknown. To solve this issue, the maximum likelihood technique is used with an iterative Newton–Raphson-based algorithm that allows minimizing the likelihood function. Both least square (LSE) and Kalman filter estimators (KFE) are evaluated using simulated and real power system events data. The obtained results clearly show that the LS-based technique yields the highest statistical performance and has a lower computation complexity.