Comparing Coding Viewing and Recording Methods to Quantify Embedded Instruction Learning Trials

The purpose of this study was to examine the comparability of counts of embedded instruction learning trials when different methods of viewing and recording direct behavioral observations were used. In 13 classrooms, while videotaping embedded instruction implementation for a larger randomized controlled efficacy trial was occurring, teachers’ implementation of trials was coded in situ using pencil-and-paper methods. Videos were later coded using computer-assisted methods. Dependent-samples t tests, Pearson product-moment correlation coefficients, and additional score agreement calculations were conducted. Statistically significant differences were found in the estimates of trial frequency. Correlational analyses showed positive and strong relationships between the coding methods. Coding agreement was higher across the entire observation versus during 10-min continuous event blocks. In situ coding took significantly less time than video coding. Results provide empirical evidence for the advantages and disadvantages of common viewing and recording methods for quantifying behavior as part of systematic observation systems.

Some Properties of the Basins of Attraction of the Newton’s Method for Simple Nonlinear Geodetic Systems

The research presented in this paper concerns the determination of the attraction basins of the Newton’s iterative method which was used to solve the non-linear systems of observational equations associated with the geodetic measurements. The authors considered simple observation systems corresponding to the intersections, or linear and angular resections, used in practice. The main goal was to investigate the properties of the sets of convergent in the initial points of the applied iterative method. An important issue regarding the possibility of automatic and quick selection of such points was also considered. Therefore, the answers to the questions regarding the geometric structure of the basins, their limitations, connectedness or self-similarity were sought. The research also concerned the iterative structures of the basins, i.e. maps of the number of iterations which are necessary to achieve the convergence of the Newton’s method. The determined basins were compared with the areas of convergence that result from theorems on the convergence of the Newton’s method, i.e. the conditions imposed on the eigenvalues and norms of the matrices of the studied iterative systems. One of the essential results of the research is the indication that the obtained basins of attraction contain areas resulting from the theoretical premises and their diameters can be comparable with the sizes of the analyzed geodetic structures. Consequently, in the analyzed cases it is possible to construct methods that enable quick selection of the initial starting points or automation of such selection. The paper also characterizes the global convergence mechanism of the Newton’s method for disconnected basins and, as a consequence, the non-local initial points, i.e. located far from the solution points.

Minimum altitude variation orbits. Analysis of characteristics and stability

The article discusses the regularities of satellite motion in almost circular orbits under the influence of the second zonal harmonic of the geopotential. The aim of the research is to determine the parameters of orbits with a minimum change in radius and to study the properties of these orbits. It is shown that the problem of determining the parameters of orbits with a minimum change in radius is of theoretical and practical interest. These orbits are the closest to Keplerian circular orbits. The practical interest in such orbits is determined by the possibility of using them for scientific research and Earth observation systems. Based on the analysis of the literature, it was concluded that the solution of the problem under consideration is not complete by now: the algorithm for determining the parameters of the orbits are not well founded and unnecessarily complicated; there is no analytical analysis of the stability of the orbits of the minimum change in radius. The efficiency of application of the previously developed theory of describing the motion of satellites in almost circular orbits for determining the parameters of orbits with a minimum change in radius is shown. For this purpose, the solutions of the first approximation of the motion of satellites in almost circular orbits under the influence of the second zonal harmonic of the geopotential have been improved. These solutions make it easy to determine the parameters of the orbits of the minimum change in radius. The averaged equations of the second approximation of the influence of the second zonal harmonic on the satellite motion are constructed and, on their basis, the stability of the orbits with a minimum change in radius is proved. It is shown that the second approximation in small parameters completely describes the main regularities of the long-period satellite motion under the influence of the second zonal harmonic of the geopotential. With the help of numerical studies, the instability of orbits with a minimum change in radius is shown with allowance for the effect of higher order harmonics of the geopotential. Analysis of the area of possible application of orbits with a minimum change in radius showed that such orbits can be of practical importance for very low and ultra low orbits, where the control action on the satellite movement is carried out at least once every two days.

Progress report on addressing meteotsunami risk in the eastern Yellow Sea

On March 31, 2007, strong, tsunami-like waves of 1.0–2.5 m were recorded at most tide gauges along the west coast of Korea. The following year, on May 4, unexpected, abnormal waves in the eastern Yellow Sea reached a maximum height of ~1.3 m. Both events occurred without warning, resulting in severe loss of life and property. Subsequent analysis found that these tsunami-like waves were meteotsunamis generated by air pressure oscillations. Evidence of possible meteotsunamis has been recorded by existing observation systems. However, the lack of a comprehensive, meteotsunami-specific observation system has hindered community preparedness, resulting in severe damage. We utilized existing observation systems (meteorological stations, tide gauges, and radar) during 2018 to develop a real-time meteotsunami monitoring system in the eastern Yellow Sea. This system detects the intensity and propagation of air pressure oscillations to identify potential coastal hazards and prevent damage caused by meteotsunamis. Two air pressure disturbance methods for assessing air pressure oscillation intensity (a range of pressure changes over a 60 min window vs. the rate of pressure change over a 10 min window) were compared, and several test operations were performed during development of the proposed system. The progress and limitations of the current observation and monitoring system were confirmed based on recent monitoring reports of air pressure jumps during the meteotsunamis on April 7, 2019. To address the insufficient lead time of meteotsunami warnings, installation and testing of open-ocean buoys outfitted with pressure sensors commenced in 2019.

Technique for the imaging crystalline basement according to the DSS data

The method of deep seismic sounding (DSS), the observation systems in which are characterized by an irregular arrangement of both sources and receivers along the profile, a significant step between receivers, as well as maximum source-receiver distances exceeding several hundred kilometers, makes it possible to obtain an image of the crystalline basement using seismic migration fields of reflected/refracted waves. The main part of the existing migration methods, the use of which makes it possible to form an image of the deep structure of the study area in the dynamic characteristics of the recorded wave field, is focused on processing seismic data obtained by the method of reflected waves with multiple overlap observation systems (MOV—CDP). And, as a rule, these migration methods are designed for a smooth change in speed with depth. At the same time, at the boundary of the crystalline basement, the speed changes very sharply, which must be taken into account when processing data using migration. The proposed method for constructing an image of the crystalline basement is based on the use of finite-difference migration of the field of reflected/refracted waves, which was developed at the Institute of Geophysics named after S. I. Subbotin National Academy of Sciences of Ukraine. This migration method is designed to isolate supercritically reflected and refracted waves recorded from the basement in the far zone of the source and takes into account the full trajectory of waves passing through a two-layer medium, at the boundary of which there is a significant jump in velocity. Thus, the migration of the field of reflected/refracted waves makes it possible to obtain a correct image of the structure of the refractive layer of the crystalline basement. The article describes in detail the algorithm of the technique for constructing an image of the crystalline basement using finite-difference migration of the field of reflected/refracted waves and its difference from similar methods of migration. The advantages and disadvantages of the proposed method are shown when solving problems of regional seismic research. Explained and illustrated the features of constructing the image of violations on the border of the foundation. The effectiveness of the technique is demonstrated on a model example and real seismic data observed by the DSS method on the territory of Ukraine.

Classification of Video Observation Data for Volcanic Activity Monitoring Using Computer Vision and Modern Neural NetWorks (on Klyuchevskoy Volcano Example)

Currently, video observation systems are actively used for volcano activity monitoring. Video cameras allow us to remotely assess the state of a dangerous natural object and to detect thermal anomalies if technical capabilities are available. However, continuous use of visible band cameras instead of special tools (for example, thermal cameras), produces large number of images, that require the application of special algorithms both for preliminary filtering out the images with area of interest hidden due to weather or illumination conditions, and for volcano activity detection. Existing algorithms use preselected regions of interest in the frame for analysis. This region could be changed occasionally to observe events in a specific area of the volcano. It is a problem to set it in advance and keep it up to date, especially for an observation network with multiple cameras. The accumulated perennial archives of images with documented eruptions allow us to use modern deep learning technologies for whole frame analysis to solve the specified task. The article presents the development of algorithms to classify volcano images produced by video observation systems. The focus is on developing the algorithms to create a labelled dataset from an unstructured archive using existing and authors proposed techniques. The developed solution was tested using the archive of the video observation system for the volcanoes of Kamchatka, in particular the observation data for the Klyuchevskoy volcano. The tests show the high efficiency of the use of convolutional neural networks in volcano image classification, and the accuracy of classification achieved 91%. The resulting dataset consisting of 15,000 images and labelled in three classes of scenes is the first dataset of this kind of Kamchatka volcanoes. It can be used to develop systems for monitoring other stratovolcanoes that occupy most of the video frame.

Earth Observation Systems and Pasture Modeling: A Bibliometric Trend Analysis

An Earth observation system (EOS) is essential in monitoring and improving our understanding of how natural and managed agricultural landscapes change over time or respond to climate change and overgrazing. Such changes can be quantified using a pasture model (PM), a critical tool for monitoring changes in pastures driven by the growing population demands and climate change-related challenges and thus ensuring a sustainable food production system. This study used the bibliometric method to assess global scientific research trends in EOS and PM studies from 1979 to 2019. This study analyzed 399 published articles from the Scopus indexed database with the search term “Earth observation systems OR pasture model”. The annual growth rate of 19.76% suggests that the global research on EOS and PM has increased over time during the survey period. The average growth per article is n = 74, average total citations (ATC) = 2949 in the USA, is n = 37, ATC = 488, in China and is n = 22, ATC = 544 in Italy). These results show that the field of the study was inconsistent in terms of ATC per article during the study period. Furthermore, these results show three countries (USA, China, and Italy) ranked as the most productive countries by article publications and the Netherlands had the highest average total citations. This may suggest that these countries have strengthened research development on EOS and PM studies. However, developing counties such as Mexico, Thailand, Sri Lanka, and other African countries had a lower number of publications during the study period. Moreover, the results showed that Earth observation is fundamental in understanding PM dynamics to design targeted interventions and ensure food security. In general, the paper highlights various advances in EOS and PM studies and suggests the direction of future studies.

Automatic inspection and analysis of digital waveform images by means of convolutional neural networks

Analyzing seismic data to get information about earthquakes has always been a major task for seismologists and, more in general, for geophysicists. Recently, thanks to the technological development of observation systems, more and more data are available to perform such tasks. However, this data “grow up” makes “human possibility” of data processing more complex in terms of required efforts and time demanding. That is why new technological approaches such as artificial intelligence are becoming very popular and more and more exploited. In this paper, we explore the possibility of interpreting seismic waveform segments by means of pre-trained deep learning. More specifically, we apply convolutional networks to seismological waveforms recorded at local or regional distances without any pre-elaboration or filtering. We show that such an approach can be very successful in determining if an earthquake is “included” in the seismic wave image and in estimating the distance between the earthquake epicenter and the recording station.

Tracers and Timescales: Tools for Distilling and Simplifying Complex Fluid Mechanical Problems

The last several decades have seen significant advances in fluid–mechanical, water-quality, and ecological observation systems, as well as in related scientific computing capabilities [...]