Large-scale interventions in science education: The road to utopia?

2012 ◽  
Vol 49 (3) ◽  
pp. 420-427 ◽  
Author(s):  
Ronald W. Marx
Keyword(s):  
Science Education ◽  
Large Scale ◽  
The Road

Soil/Tire Interaction Analysis Using FEM and FVM

2005 ◽  
Vol 33 (1) ◽  
pp. 38-62 ◽  
Author(s):  
S. Oida ◽  
E. Seta ◽  
H. Heguri ◽  
K. Kato
Keyword(s):  
Large Scale ◽  
Interaction Analysis ◽  
Yield Criterion ◽  
Surrounding Medium ◽  
Flow Analysis ◽  
Measurement Data ◽  
Traction Force ◽  
Elastoplastic Material ◽  
The Road ◽  
Soil Flow

Abstract Vehicles, such as an agricultural tractor, construction vehicle, mobile machinery, and 4-wheel drive vehicle, are often operated on unpaved ground. In many cases, the ground is deformable; therefore, the deformation should be taken into consideration in order to assess the off-the-road performance of a tire. Recent progress in computational mechanics enabled us to simulate the large scale coupling problem, in which the deformation of tire structure and of surrounding medium can be interactively considered. Using this technology, hydroplaning phenomena and tire traction on snow have been predicted. In this paper, the simulation methodology of tire/soil coupling problems is developed for pneumatic tires of arbitrary tread patterns. The Finite Element Method (FEM) and the Finite Volume Method (FVM) are used for structural and for soil-flow analysis, respectively. The soil is modeled as an elastoplastic material with a specified yield criterion and a nonlinear elasticity. The material constants are referred to measurement data, so that the cone penetration resistance and the shear resistance are represented. Finally, the traction force of the tire in a cultivated field is predicted, and a good correlation with experiments is obtained.

scholarly journals Genetic and Transcriptomic Biomarkers in Neurodegenerative Diseases: Current Situation and the Road Ahead

Cells ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 1030
Author(s):  
Julie Lake ◽  
Catherine S. Storm ◽  
Mary B. Makarious ◽  
Sara Bandres-Ciga
Keyword(s):  
Neurodegenerative Diseases ◽  
Large Scale ◽  
Disease Diagnosis ◽  
Open Science ◽  
Future Directions ◽  
The Road ◽  
Current State ◽  
Molecular Complexity ◽  
Science Framework ◽  
Lateral Sclerosis

Neurodegenerative diseases are etiologically and clinically heterogeneous conditions, often reflecting a spectrum of disease rather than well-defined disorders. The underlying molecular complexity of these diseases has made the discovery and validation of useful biomarkers challenging. The search of characteristic genetic and transcriptomic indicators for preclinical disease diagnosis, prognosis, or subtyping is an area of ongoing effort and interest. The next generation of biomarker studies holds promise by implementing meaningful longitudinal and multi-modal approaches in large scale biobank and healthcare system scale datasets. This work will only be possible in an open science framework. This review summarizes the current state of genetic and transcriptomic biomarkers in Parkinson’s disease, Alzheimer’s disease, and amyotrophic lateral sclerosis, providing a comprehensive landscape of recent literature and future directions.

scholarly journals Providing large-scale disk storage at CERN

2019 ◽  
Vol 214 ◽  
pp. 04033
Author(s):  
Hervé Rousseau ◽  
Belinda Chan Kwok Cheong ◽  
Cristian Contescu ◽  
Xavier Espinal Curull ◽  
Jan Iven ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Distributed Storage ◽  
Storage System ◽  
Primary Data ◽  
Disk Storage ◽  
The Road ◽  
Ongoing Work ◽  
Software Distribution ◽  
Microsoft Office

The CERN IT Storage group operates multiple distributed storage systems and is responsible for the support of the infrastructure to accommodate all CERN storage requirements, from the physics data generated by LHC and non-LHC experiments to the personnel users' files. EOS is now the key component of the CERN Storage strategy. It allows to operate at high incoming throughput for experiment data-taking while running concurrent complex production work-loads. This high-performance distributed storage provides now more than 250PB of raw disks and it is the key component behind the success of CERNBox, the CERN cloud synchronisation service which allows syncing and sharing files on all major mobile and desktop platforms to provide offline availability to any data stored in the EOS infrastructure. CERNBox recorded an exponential growth in the last couple of year in terms of files and data stored thanks to its increasing popularity inside CERN users community and thanks to its integration with a multitude of other CERN services (Batch, SWAN, Microsoft Office). In parallel CASTOR is being simplified and transitioning from an HSM into an archival system, focusing mainly in the long-term data recording of the primary data from the detectors, preparing the road to the next-generation tape archival system, CTA. The storage services at CERN cover as well the needs of the rest of our community: Ceph as data back-end for the CERN OpenStack infrastructure, NFS services and S3 functionality; AFS for legacy home directory filesystem services and its ongoing phase-out and CVMFS for software distribution. In this paper we will summarise our experience in supporting all our distributed storage system and the ongoing work in evolving our infrastructure, testing very-dense storage building block (nodes with more than 1PB of raw space) for the challenges waiting ahead.

scholarly journals A Parallel-Computing Approach for Vector Road-Network Matching Using GPU Architecture

2018 ◽  
Vol 7 (12) ◽  
pp. 472 ◽  
Author(s):  
Bo Wan ◽  
Lin Yang ◽  
Shunping Zhou ◽  
Run Wang ◽  
Dezhi Wang ◽  
...  
Keyword(s):  
Road Network ◽  
Large Scale ◽  
Graphics Processing Unit ◽  
Road Networks ◽  
Processing Unit ◽  
Data Partition ◽  
Matching Method ◽  
The Road ◽  
Central Processing ◽  
Relaxation Matching

The road-network matching method is an effective tool for map integration, fusion, and update. Due to the complexity of road networks in the real world, matching methods often contain a series of complicated processes to identify homonymous roads and deal with their intricate relationship. However, traditional road-network matching algorithms, which are mainly central processing unit (CPU)-based approaches, may have performance bottleneck problems when facing big data. We developed a particle-swarm optimization (PSO)-based parallel road-network matching method on graphics-processing unit (GPU). Based on the characteristics of the two main stages (similarity computation and matching-relationship identification), data-partition and task-partition strategies were utilized, respectively, to fully use GPU threads. Experiments were conducted on datasets with 14 different scales. Results indicate that the parallel PSO-based matching algorithm (PSOM) could correctly identify most matching relationships with an average accuracy of 84.44%, which was at the same level as the accuracy of a benchmark—the probability-relaxation-matching (PRM) method. The PSOM approach significantly reduced the road-network matching time in dealing with large amounts of data in comparison with the PRM method. This paper provides a common parallel algorithm framework for road-network matching algorithms and contributes to integration and update of large-scale road-networks.

Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network

2022 ◽  
Vol 13 (2) ◽  
pp. 1-25
Author(s):  
Bin Lu ◽  
Xiaoying Gan ◽  
Haiming Jin ◽  
Luoyi Fu ◽  
Xinbing Wang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Large Scale ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Propagation Mechanism ◽  
Urban Road ◽  
The Road ◽  
Traffic Flow Forecasting ◽  
Gating Mechanism

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. Due to the complexity and uncertainty of urban road conditions, how to capture the dynamic spatiotemporal correlation and make accurate predictions is very challenging. In most of existing works, urban road network is often modeled as a fixed graph based on local proximity. However, such modeling is not sufficient to describe the dynamics of the road network and capture the global contextual information. In this paper, we consider constructing the road network as a dynamic weighted graph through attention mechanism. Furthermore, we propose to seek both spatial neighbors and semantic neighbors to make more connections between road nodes. We propose a novel Spatiotemporal Adaptive Gated Graph Convolution Network ( STAG-GCN ) to predict traffic conditions for several time steps ahead. STAG-GCN mainly consists of two major components: (1) multivariate self-attention Temporal Convolution Network ( TCN ) is utilized to capture local and long-range temporal dependencies across recent, daily-periodic and weekly-periodic observations; (2) mix-hop AG-GCN extracts selective spatial and semantic dependencies within multi-layer stacking through adaptive graph gating mechanism and mix-hop propagation mechanism. The output of different components are weighted fused to generate the final prediction results. Extensive experiments on two real-world large scale urban traffic dataset have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines.

scholarly journals Measuring the value of a digital supplemental resource

2021 ◽  
Vol 45 (4) ◽  
pp. 685-693
Author(s):  
Yvonne M. Baptiste ◽  
Samuel Abramovich ◽  
Cherylea J. Browne
Keyword(s):  
Factor Analysis ◽  
Science Education ◽  
Construct Validity ◽  
Student Perceptions ◽  
Large Scale ◽  
Quantitative Methods ◽  
Student Perception ◽  
Digital Learning ◽  
Internal Reliability ◽  
Traditional Component

Supplemental resources in science education are made available to students based on the belief that they will improve course-based student learning. This belief is ubiquitous, with supplemental resources being a traditional component of physiology education. In addition, the recent large-scale transition to remote learning caused by the Covid-19 pandemic suggests an increased relevance and necessity of digital versions of supplemental resources. However, the use of a supplemental resource is entirely dependent on whether students view it as beneficial. If students in a specific course do not perceive a supplemental resource as useful, there is little reason to believe the resources will be used and are worthy of investment. Consequently, measurement of student perception regarding the effectiveness of any digital learning tool is essential for educators and institutions in order to prioritize resources and make meaningful recommendations to students. In this study, a survey was used to determine student perceptions of a digital, supplemental resource. Quantitative methods, including exploratory factor analysis, were performed on data collected from the survey to examine the dimensionality and functionality of this survey. The findings from this study were used to devise an improved, standardized (i.e., reliable and valid) survey that can be used and adapted by physi3ology researchers and educators to determine student perception of a digital supplemental resource. The survey, with known construct validity and internal reliability, can provide useful information for administrators, instructors, and designers of digital supplemental resources.

scholarly journals Deep-Learning-Based Road Crack Detection Frameworks for Dashcam-captured Images under Different Illumination Conditions

2021 ◽  
Author(s):  
Da-Ren Chen ◽  
Wei-Min Chiu
Keyword(s):  
Object Detection ◽  
Large Scale ◽  
Crack Detection ◽  
State Of The Art ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
The Road ◽  
Art Object

Abstract Machine learning techniques have been used to increase detection accuracy of cracks in road surfaces. Most studies failed to consider variable illumination conditions on the target of interest (ToI), and only focus on detecting the presence or absence of road cracks. This paper proposes a new road crack detection method, IlumiCrack, which integrates Gaussian mixture models (GMM) and object detection CNN models. This work provides the following contributions: 1) For the first time, a large-scale road crack image dataset with a range of illumination conditions (e.g., day and night) is prepared using a dashcam. 2) Based on GMM, experimental evaluations on 2 to 4 levels of brightness are conducted for optimal classification. 3) the IlumiCrack framework is used to integrate state-of-the-art object detecting methods with CNN to classify the road crack images into eight types with high accuracy. Experimental results show that IlumiCrack outperforms the state-of-the-art R-CNN object detection frameworks.

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