Grand Canyon riverbed sediment changes, experimental release of September 2000 - a sample data set

2003 ◽  
Author(s):  
Florence L. Wong ◽  
Roberto J. Anima ◽  
Peter Galanis ◽  
Jennifer Codianne ◽  
Yu Xia ◽  
...  
Keyword(s):  
Grand Canyon ◽  
Data Set ◽  
Sample Data ◽  
Riverbed Sediment ◽  
Experimental Release

Investigation of Regression-Based Effect Size Methods Developed in Single-Subject Studies

2021 ◽  
pp. 014544552110540
Author(s):  
Nihal Sen
Keyword(s):  
Effect Size ◽  
Experimental Studies ◽  
Single Subject ◽  
Single Subject Design ◽  
Data Set ◽  
Design Studies ◽  
Sample Data ◽  
The Difference ◽  
D Values ◽  
Effect Size Calculation

The purpose of this study is to provide a brief introduction to effect size calculation in single-subject design studies, including a description of nonparametric and regression-based effect sizes. We then focus the rest of the tutorial on common regression-based methods used to calculate effect size in single-subject experimental studies. We start by first describing the difference between five regression-based methods (Gorsuch, White et al., Center et al., Allison and Gorman, Huitema and McKean). This is followed by an example using the five regression-based effect size methods and a demonstration how these methods can be applied using a sample data set. In this way, the question of how the values obtained from different effect size methods differ was answered. The specific regression models used in these five regression-based methods and how these models can be obtained from the SPSS program were shown. R2 values obtained from these five methods were converted to Cohen’s d value and compared in this study. The d values obtained from the same data set were estimated as 0.003, 0.357, 2.180, 3.470, and 2.108 for the Allison and Gorman, Gorsuch, White et al., Center et al., as well as for Huitema and McKean methods, respectively. A brief description of selected statistical programs available to conduct regression-based methods was given.

A HYBRID SCHEME FOR HANDPRINTED NUMERAL RECOGNITION BASED ON A SELF-ORGANIZING NETWORK AND MLP ClASSIFIERS

2002 ◽  
Vol 16 (07) ◽  
pp. 845-864 ◽  
Author(s):  
UJJWAL BHATTACHARYA ◽  
TANMOY KANTI DAS ◽  
AMITAVA DATTA ◽  
SWAPAN KUMAR PARUI ◽  
BIDYUT BARAN CHAUDHURI
Keyword(s):  
Recognition Rate ◽  
Structural Features ◽  
Data Set ◽  
Novel Approach ◽  
Tree Classifier ◽  
Sample Data ◽  
Validation Set ◽  
Numeral Recognition ◽  
Correct Recognition Rate ◽  
Self Organizing

This paper proposes a novel approach to automatic recognition of handprinted Bangla (an Indian script) numerals. A modified Topology Adaptive Self-Organizing Neural Network is proposed to extract a vector skeleton from a binary numeral image. Simple heuristics are considered to prune artifacts, if any, in such a skeletal shape. Certain topological and structural features like loops, junctions, positions of terminal nodes, etc. are used along with a hierarchical tree classifier to classify handwritten numerals into smaller subgroups. Multilayer perceptron (MLP) networks are then employed to uniquely classify the numerals belonging to each subgroup. The system is trained using a sample data set of 1800 numerals and we have obtained 93.26% correct recognition rate and 1.71% rejection on a separate test set of another 7760 samples. In addition, a validation set consisting of 1440 samples has been used to determine the termination of the training algorithm of the MLP networks. The proposed scheme is sufficiently robust with respect to considerable object noise.

Continuity and Change in the Contemporary Canadian Feminist Movement

2017 ◽  
Vol 50 (2) ◽  
pp. 443-459 ◽  
Author(s):  
Brenda O'Neill
Keyword(s):  
Social Movement ◽  
Collective Identity ◽  
Feminist Movement ◽  
Changing Environment ◽  
Data Set ◽  
Large Sample ◽  
Continuity And Change ◽  
Political Generations ◽  
Sample Data ◽  
Formative Experiences

AbstractThis article examines how the changing environment faced by and context within the Canadian feminist movement is reflected in the beliefs and strategies of recruits to the movement at a given point in time. The framework for the investigation is Whittier's generational approach (1997) that posits that different political generations—defined as cohorts of recruits who join a social movement during distinctive periods of protest—introduce change to its collective identity given the formative experiences faced by each generation. Using an original large sample data set, I provide evidence that the changes experienced by the Canadian feminist movement from the 1980s onwards are reflected in noticeable shifts in the collective identity and activist strategies of subsequent waves of feminist recruits. The findings suggest that further research into cohort recruitment and replacement is essential for understanding the forces at play in shaping the contemporary Canadian feminist movement.

Constrained Target Clustering for Military Targeting Process

2017 ◽  
Vol 67 (5) ◽  
pp. 523 ◽  
Author(s):  
Jungmok Ma
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Support System ◽  
Background Information ◽  
Target Level ◽  
Objective Functions ◽  
Data Set ◽  
Constrained Problems ◽  
Damage Rate ◽  
Sample Data

<p>Constrained target clustering (CTC) is proposed to support the targeting decision-making in the network centric warfare environment. When area targets are detected by sensors, it is required to decide the points at which a missile or bomb is aimed to achieve operational goals. CTC can determine the optimal numbers and positions of aiming points by transforming the targeting problem into clustering-based optimisation problems. The CTC formulations include objective functions and constraints in consideration of area targets, protected objects, target-level background information, lethal radius, and required damage rate. The numerical example shows how to apply the CTC formulation given a sample data set. In order to compare the effects of different constraints, the demonstration explores from an unconstraint problem to constrained problems by adding constraints. The results show that CTC can effectively decide the aiming points with consideration of both targets and capabilities of friendly weapons, and serve as a targeting decision support system in the network centric warfare environment.</p>

A Clustering Method for Pruning Fully Connected Neural Network

2011 ◽  
Vol 204-210 ◽  
pp. 600-603
Author(s):  
Gang Li ◽  
Xing San Qian ◽  
Chun Ming Ye ◽  
Lin Zhao
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Submersible Pump ◽  
Clustering Method ◽  
Backpropagation Neural Network ◽  
Data Set ◽  
Initial Network ◽  
Sample Data ◽  
Hidden Layer ◽  
Fully Connected

This paper focuses mainly on a clustering method for pruning Fully Connected Backpropagation Neural Network (FCBP). The initial neural network is fully connected, after training with sample data, a clustering method is employed to cluster weights between input to hidden layer and from hidden to output layer, and connections that are relatively unnecessary are deleted, thus the initial network becomes a PCBP (Partially Connected Backpropagation) Neural Network. PCBP can be used in prediction or data mining more efficiently than FCBP. At the end of this paper, An experiment is conducted to illustrate the effects of PCBP using the submersible pump repair data set.

scholarly journals Supplemental Material: Stratigraphic correlation chart of Carboniferous–Paleogene rocks of Mexico, adjacent southwestern United States, Central America, and Colombia

2020 ◽  
Author(s):  
Timothy Lawton ◽  
et al.
Keyword(s):  
United States ◽  
Central America ◽  
Detrital Zircon ◽  
Stratigraphic Correlation ◽  
Southwestern United States ◽  
Data Set ◽  
Sample Data

Three-sample data set, Table S1: Detrital Zircon U-Pb Geochronology of Todos Santos Formation, and Plate 1<br>

scholarly journals Borrowing trouble? The impact of a systematic review service on interlibrary loan borrowing in an academic health sciences library

2021 ◽  
Vol 109 (1) ◽  
Author(s):  
Christy Jarvis ◽  
Joan Marcotte Gregory ◽  
Alison Mortensen-Hayes ◽  
Mary McFarland
Keyword(s):  
Systematic Review ◽  
Systematic Reviews ◽  
Full Text ◽  
Health Sciences ◽  
Financial Impact ◽  
Interlibrary Loan ◽  
Data Set ◽  
Academic Health ◽  
Sample Data ◽  
The Impact

Background: With the mandate to review all available literature in the study’s inclusion parameters, systematic review projects are likely to require full-text access to a significant number of articles that are not available in a library’s collection, thereby necessitating ordering content via interlibrary loan (ILL). The aim of this study is to understand what effect a systematic review service has on the copyright royalty fees accompanying ILL requests at an academic health sciences library.Case Presentation: The library created a custom report using ILLiad data to look specifically at 2018 ILL borrowing requests that were known to be part of systematic reviews. This subset of borrowing activity was then analyzed to determine its impact on the library’s copyright royalty expenditures for the year. In 2018, copyright eligible borrowing requests that were known to be part of systematic reviews represented only approximately 5% of total filled requests that involved copyright eligible borrowing. However, these systematic review requests directly or indirectly caused approximately 10% of all the Spencer S. Eccles Library copyright royalty expenditures for 2018 requests.Conclusion: Based on the sample data set, the library’s copyright royalty expenditures did increase, but the overall financial impact was modest.

Paleomagnetism of the Chuar Group and evaluation of the late Tonian Laurentian apparent polar wander path with implications for the makeup and breakup of Rodinia

2019 ◽  
Vol 132 (3-4) ◽  
pp. 710-738 ◽  
Author(s):  
Athena Eyster ◽  
Benjamin P. Weiss ◽  
Karl Karlstrom ◽  
Francis A. Macdonald
Keyword(s):  
Grand Canyon ◽  
Quality Criteria ◽  
Paleomagnetic Data ◽  
Geochronological Data ◽  
Data Set ◽  
Polar Wander ◽  
Apparent Polar Wander Path ◽  
Robust Tests ◽  
Laurentian Margin ◽  
Global Data

AbstractPaleogeographic models commonly assume that the supercontinent Rodinia was long-lived, with a static geometry involving Mesoproterozoic links that developed during assembly and persisted until Neoproterozoic rifting. However, Rodinian paleogeography and dynamics of continental separation around its centerpiece, Laurentia, remain poorly constrained. On the western Laurentian margin, geological and geochronological data suggest that breakup did not occur until after 720 Ma. Thus, late Tonian (ca. 780–720 Ma) paleomagnetic data are critical for reconstructing paleogeography prior to dispersal and assessing the proposed stasis of Rodinia. Here, we report new paleomagnetic data from the late Tonian Chuar Group in the Grand Canyon, Arizona. We combined this new data set with reanalyzed existing data to obtain a new paleopole preserved in hematite, the reliability of which is supported by six of the seven (Q1–Q6) Van der Voo reliability quality criteria. In addition, we identified pervasive mid- to high-temperature overprints. This new paleomagnetic pole was incorporated with recent high-precision geochronological data and existing paleomagnetic data to present a new late Tonian Laurentian apparent polar wander path (APWP). Having examined the paleomagnetic data of other cratons, global reconstructions for 775 Ma, 751 Ma, and 716 Ma are presented. These reconstructions are consistent with Australia located near the present southern margin of Laurentia. However, a stringent analysis of the global data set does not support a good match between any major craton and the rifted conjugate margin to western Laurentia. Breakup on the western Laurentian margin may have involved rifting of a continental fragment or a craton with uncertainties in its late Tonian geochronologic and paleomagnetic constraints. Our revised Laurentian APWP will allow for more robust tests of paleogeography and evaluation of the proposed supercontinent Rodinia.

scholarly journals A Real Time Application of Soft Set in Parameterization Reduction for Decision Making Problem

2017 ◽  
Vol 7 (1) ◽  
pp. 324 ◽  
Author(s):  
Janmejay Pant ◽  
Amit Juyal ◽  
Himanshu Pant ◽  
Akhilesh Dwivedi
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Information Science ◽  
Soft Set ◽  
Data Uncertainty ◽  
Data Set ◽  
Decision Making Problem ◽  
Sample Data ◽  
Different Types ◽  
Real Time Application

In each and every field of science and technology Information science plays an important role. Sometimes information science is facing different types of problems to handle the data and information. Data Uncertainty is one of the challenging difficulties to handle. In past, there are several theories like fuzzy set, Rough set, Probability etc.to dealing with uncertainty. Soft set theory is the youngest theory to deal with uncertainty. In this paper we discussed how to find reducts. This paper focuses on how we can transform a sample data set to binary valued information system. We are also going to reduce the dimension of data set by using the binary valued information that results a better decision.

A technique of identifying the contour point to assess the change of abnormal areas on medical imaging

2020 ◽  
Vol 14 (5) ◽  
pp. 51-58
Author(s):  
Thi Kim Nga Le ◽  
◽  
Thi Xuong Doan ◽  
Thi Thu Cuc Doan ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Medical Imaging ◽  
Convolutional Neural Networks ◽  
Liver Tumors ◽  
Research Issue ◽  
Important Research ◽  
Data Set ◽  
Local Structures ◽  
Sample Data ◽  
Contour Point

Assessing the growth or reduction of abnormal areas in medical imaging in general and tomography in particular is a greatly concerned and important research issue in recent years in Vietnam. The article presents a technique of identifying contour points of an abnormal area as a basis for later assessment of the development of abnormal area on medical imaging. The technique is based on the analysis of local structures in the vicinity of abnormal area boundaries combined with the use of convolutional neural networks and has been tested and evaluated based on 3D-IRCADb-01 sample data set with liver tumors.

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