scholarly journals Julia sets and Mandelbrot-like sets associated with higher order Schröder rational iteration functions: a computer assisted study

1986 ◽  
Vol 46 (173) ◽  
pp. 151-151
Author(s):  
Edward R. Vrscay
Keyword(s):  
Julia Sets ◽  
Higher Order ◽  
Computer Assisted ◽  
Iteration Functions

scholarly journals Higher-order theorem proving and its applications

2019 ◽  
Vol 61 (4) ◽  
pp. 187-191
Author(s):  
Alexander Steen
Keyword(s):  
Theorem Proving ◽  
Formal Analysis ◽  
Logical Consequence ◽  
Effective Means ◽  
Industrial Applications ◽  
Higher Order ◽  
Hardware Verification ◽  
Theorem Prover ◽  
Computer Assisted ◽  
Higher Order Logic

Abstract Automated theorem proving systems validate or refute whether a conjecture is a logical consequence of a given set of assumptions. Higher-order provers have been successfully applied in academic and industrial applications, such as planning, software and hardware verification, or knowledge-based systems. Recent studies moreover suggest that automation of higher-order logic, in particular, yields effective means for reasoning within expressive non-classical logics, enabling a whole new range of applications, including computer-assisted formal analysis of arguments in metaphysics. My work focuses on the theoretical foundations, effective implementation and practical application of higher-order theorem proving systems. This article briefly introduces higher-order reasoning in general and presents an overview of the design and implementation of the higher-order theorem prover Leo-III. In the second part, some example applications of Leo-III are discussed.

scholarly journals Creating a Modified Monopoly Game for Promoting Students’ Higher-Order Thinking Skills and Knowledge Retention

2021 ◽  
Author(s):  
Tan Ming Kuang ◽  
Ralph William Adler ◽  
Rakesh Pandey
Keyword(s):  
Higher Order Thinking ◽  
Knowledge Retention ◽  
Thinking Skills ◽  
Higher Order ◽  
Accounting Students ◽  
Simulation Game ◽  
Computer Assisted ◽  
Business Simulation ◽  
Higher Order Thinking Skills ◽  
Learning And Teaching

This study modifies a popular business simulation game, Monopoly, to assess its effectiveness as a learning and teaching tool for helping high school accounting students acquire and apply foundational accounting concepts. The study compares an accounting-focused, Modified Monopoly with two other instructional methods. Using a quasi-experimental approach involving three learning groups, with random assignment of treatments based on school/class, a sample of 144 accounting students was obtained. This study found students using Modified Monopoly showed significantly greater improvement between their pre-test and the post-test scores than students in Computer-assisted instruction (CAI) but significantly less improvement than a paper-based extended accounting problem (EAP). However, students using Modified Monopoly, similar to the students in CAI, did not suffer the same significant decay in knowledge as students in EAP. These results offer evidence for the significant and more enduring learning benefits Modified Monopoly can produce in students’ higher order thinking skills.

Some higher-order iteration functions for solving nonlinear models

2018 ◽  
Vol 334 ◽  
pp. 80-93 ◽  
Author(s):  
Abdullah Khamis Hassan Alzahrani ◽  
Ramandeep Behl ◽  
Ali Saleh Alshomrani
Keyword(s):  
Nonlinear Models ◽  
Higher Order ◽  
Iteration Functions

Improving computer-assisted instruction in teaching higher-order skills

2004 ◽  
Vol 42 (2) ◽  
pp. 169-180 ◽  
Author(s):  
Kelsey J Sinclair ◽  
Carl E Renshaw ◽  
Holly A Taylor
Keyword(s):  
Higher Order ◽  
Computer Assisted Instruction ◽  
Computer Assisted ◽  
Assisted Instruction

scholarly journals 3D Kidney Segmentation from Abdominal Images Using Spatial-Appearance Models

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Fahmi Khalifa ◽  
Ahmed Soliman ◽  
Adel Elmaghraby ◽  
Georgy Gimel’farb ◽  
Ayman El-Baz
Keyword(s):  
Spatial Model ◽  
Diagnostic System ◽  
Ct Images ◽  
Higher Order ◽  
Computer Assisted ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Kidney Segmentation ◽  
Ct Data

Kidney segmentation is an essential step in developing any noninvasive computer-assisted diagnostic system for renal function assessment. This paper introduces an automated framework for 3D kidney segmentation from dynamic computed tomography (CT) images that integrates discriminative features from the current and prior CT appearances into a random forest classification approach. To account for CT images’ inhomogeneities, we employ discriminate features that are extracted from a higher-order spatial model and an adaptive shape model in addition to the first-order CT appearance. To model the interactions between CT data voxels, we employed a higher-order spatial model, which adds the triple and quad clique families to the traditional pairwise clique family. The kidney shape prior model is built using a set of training CT data and is updated during segmentation using not only region labels but also voxels’ appearances in neighboring spatial voxel locations. Our framework performance has been evaluated on in vivo dynamic CT data collected from 20 subjects and comprises multiple 3D scans acquired before and after contrast medium administration. Quantitative evaluation between manually and automatically segmented kidney contours using Dice similarity, percentage volume differences, and 95th-percentile bidirectional Hausdorff distances confirms the high accuracy of our approach.

Sign in / Sign up

Export Citation Format

Share Document