A Study on the Learning Progressions of Understanding the Core Concepts of Kinetic Energy in High School

Learning progressions divide the logical system of a subject into ordered and continuously developing levels that are suitable for the cognitive development level of students,which plays an important role in understanding students’learning process.This paper focuses on the theme of“kinetic energy”in high school physics as the research object.Firstly,the concept map was used to represent the relationship between knowledge,and then five core concepts were selected based on the opinions of high school teachers.Secondly,the test tools were compiled and tested based on the relevant test questions.Finally,the paper analyzed the results based on the Rasch model,clarified students’cognitive development level of“kinetic energy”and constructed the learning progressions of“kinetic energy”based on the logical order of subject knowledge.The research provides theoretical and methodological support for the study of other subjects and learning progressions,and provides a valuable reference for high school teachers to effectively carry out the instruction of“kinetic energy.”

Progress of machine learning in geosciences:Preface

In the past two decades, artificial intelligence （AI） algorithms have proved to be promising tools for solving several tough scientific problems, As a broad subfield of AI, machine learning is concerned with algorithms and techniques that allow computers to ＂learn＂. The machine learning approach covers main domains such as data mining, difficult-to-program applications, and soft- ware applications. It is a collection of a variety of algorithms that can provide multivariate, nonlinear, nonparametric regression or classification. The remarkable simulation capabilities of the ma- chine learning-based methods have resulted in their extensive ap- plications in science and engineering. Recently, the machine learning techniques have found many applications in the geoscien- ces and remote sensing. More specifically, these techniques are proved to be practical for cases where the system＇s deterministic model is computationally expensive or there is no deterministic model to solve the problem （Lary, 2010）.

CT reconstruction from a single X-ray image for a particular patient via progressive learning

Computed tomography(CT)has enjoyed widespread applications,especially in the assistance of clinical diagnosis and treatment.However,fast CT imaging is not available for guiding adaptive precise radiotherapy in the current radiation treatment process because the conventional CT reconstruction requires numerous projections and rich computing resources.This paper mainly studies the challenging task of 3 D CT reconstruction from a single 2 D X-ray image of a particular patient,which enables fast CT imaging during radiotherapy.It is widely known that the transformation from a 2 D projection to a 3 D volumetric CT image is a highly nonlinear mapping problem.In this paper,we propose a progressive learning framework to facilitate 2 D-to-3 D mapping.The proposed network starts training from low resolution and then adds new layers to learn increasing high-resolution details as the training progresses.In addition,by bridging the distribution gap between an X-ray image and a CT image with a novel attention-based 2 D-to-3 D feature transform module and an adaptive instance normalization layer,our network obtains enhanced performance in recovering a 3 D CT volume from a single X-ray image.We demonstrate the effectiveness of our approach on a ten-phase 4 D CT dataset including 20 different patients created from a public medical database and show its outperformance over some baseline methods in image quality and structure preservation,achieving a PSNR value of 22.76±0.708 dB and FSIM value of 0.871±0.012 with the ground truth as a reference.This method may promote the application of CT imaging in adaptive radiotherapy and provide image guidance for interventional surgery.