Mechanism and modeling of the multi-stage bending process for large-scale forgings

The orogenic architecture of the Altaids of Central Asia was created by multiple large-scale slab roll-back and oroclinal bending. However, no regional structural deformation related to roll-back processes has been described. In this paper, we report a structural study of the Beishan orogenic collage in the southernmost Altaids, which is located in the southern wing of the Tuva-Mongol Orocline. Our new field mapping and structural analysis integrated with an electron backscatter diffraction study, paleontology, U-Pb dating, 39Ar-40Ar dating, together with published isotopic ages enables us to construct a detailed deformation-time sequence: During D1 times many thrusts were propagated northwards. In D2 there was ductile sinistral shearing at 336−326 Ma. In D3 times there was top-to-W/WNW ductile thrusting at 303−289 Ma. Two phases of folding were defined as D4 and D5. Three stages of extensional events (E1−E3) separately occurred during D1−D5. Two switches of the regional stress field were identified in the Carboniferous to Early Permian (D1-E1-D2-D3-E2) and Late Permian to Early Triassic (D4-E3-D5). These two switches in the stress field were associated with formation of bimodal volcanic rocks, and an extensional interarc basin with deposition of Permian-Triassic sediments, which can be related to two stages of roll-back of the subduction zone on the Paleo-Asian oceanic margin. We demonstrate for the first time that two key stress field switches were responses to the formation of the Tuva-Mongol Orocline.

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Analysis of Large-Scale Multi-Stage All-Optical Packet Switching Routers

Journal of Optical Communications and Networking ◽

10.1364/jocn.4.000412 ◽

2012 ◽

Vol 4 (5) ◽

pp. 412 ◽

Cited By ~ 11

Author(s):

Q. Xu ◽

H. Rastegarfar ◽

Y. Ben M’Sallem ◽

A. Leon-Garcia ◽

S. LaRochelle ◽

...

Keyword(s):

Large Scale ◽

Packet Switching ◽

Optical Packet Switching ◽

Multi Stage ◽

All Optical

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Multi-Stage Large Scale Hydraulic Fracturing in Horizontal Well, a First in India

10.2118/166985-ms ◽

2013 ◽

Author(s):

Andy Sookprasong ◽

Sergey Mikhalovich Stolyarov ◽

Mark Sargon

Keyword(s):

Hydraulic Fracturing ◽

Large Scale ◽

Horizontal Well ◽

Multi Stage

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Reconstruction and visualization of large-scale volumetric models of neocortical circuits for physically-plausible in silico optical studies

10.1101/164483 ◽

2017 ◽

Author(s):

Marwan Abdellah ◽

Juan Hernando ◽

Nicolas Antille ◽

Stefan Eilemann ◽

Henry Markram ◽

...

Keyword(s):

In Silico ◽

Large Scale ◽

Circuit Model ◽

Optical Studies ◽

Domain Experts ◽

Multi Stage ◽

Volumetric Models ◽

Imaging Experiment ◽

Cortical Circuit ◽

Tissue Models

AbstractBackground We present a software workflow capable of building large scale, highly detailed and realistic volumetric models of neocortical circuits from the morphological skeletons of their digitally reconstructed neurons. The limitations of the existing approaches for creating those models are explained, and then, a multi-stage pipeline is discussed to overcome those limitations. Starting from the neuronal morphologies, we create smooth piecewise watertight polygonal models that can be efficiently utilized to synthesize continuous and plausible volumetric models of the neurons with solid voxelization. The somata of the neurons are reconstructed on a physically-plausible basis relying on the physics engine in Blender.Results Our pipeline is applied to create 55 exemplar neurons representing the various morphological types that are reconstructed from the somatsensory cortex of a juvenile rat. The pipeline is then used to reconstruct a volumetric slice of a cortical circuit model that contains ∼210,000 neurons. The applicability of our pipeline to create highly realistic volumetric models of neocortical circuits is demonstrated with an in silico imaging experiment that simulates tissue visualization with brightfield microscopy. The results were evaluated with a group of domain experts to address their demands and also to extend the workflow based on their feedback.Conclusion A systematic workflow is presented to create large scale synthetic tissue models of the neocortical circuitry. This workflow is fundamental to enlarge the scale of in silico neuroscientific optical experiments from several tens of cubic micrometers to a few cubic millimeters.

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Eat or Throw Away? Factors Differentiating High Food Wasters from Low Food Wasters

Sustainability ◽

10.3390/su131910741 ◽

2021 ◽

Vol 13 (19) ◽

pp. 10741

Author(s):

Ovidija Eičaitė ◽

Gitana Alenčikienė ◽

Ingrida Pauliukaitytė ◽

Alvija Šalaševičienė

Keyword(s):

Food Waste ◽

Large Scale ◽

Food Poisoning ◽

Binary Logistic Regression ◽

Impulse Buying ◽

High Food ◽

Household Level ◽

Face To Face ◽

Multi Stage ◽

Sample Data

More than half of food waste is generated at the household level, and therefore, it is important to tackle and attempt to solve the problem of consumer food waste. This study aimed to identify factors differentiating high food wasters from low food wasters. A large-scale survey was conducted in Lithuania. A total of 1001 respondents had participated in this survey and were selected using a multi-stage probability sample. Data were collected through face-to-face interviews using a structured questionnaire. Binary logistic regression modelling was used to analyse the effect of socio-demographics, food-related behaviours, attitudes towards food waste, and knowledge of date labelling on levels of food waste. Impulse buying, inappropriate food preparation practices, non-consumption of leftovers, lack of concern about food waste, and worry about food poisoning were related to higher food waste. On the other hand, correct planning practices and knowledge of date labelling were related to lower food waste. The findings of this study have practical implications for designing interventions aimed at reducing consumer food waste.

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YASS: Yet Another Spike Sorter

10.1101/151928 ◽

2017 ◽

Cited By ~ 14

Author(s):

JinHyung Lee ◽

David Carlson ◽

Hooshmand Shokri ◽

Weichi Yao ◽

Georges Goetz ◽

...

Keyword(s):

Dirichlet Process ◽

Large Scale ◽

Matching Pursuit ◽

Spike Sorting ◽

Dirichlet Process Mixture ◽

Electrode Arrays ◽

Neural Signals ◽

Model Framework ◽

Multi Stage ◽

Computational Bottleneck

AbstractSpike sorting is a critical first step in extracting neural signals from large-scale electrophysiological data. This manuscript describes an efficient, reliable pipeline for spike sorting on dense multi-electrode arrays (MEAs), where neural signals appear across many electrodes and spike sorting currently represents a major computational bottleneck. We present several new techniques that make dense MEA spike sorting more robust and scalable. Our pipeline is based on an efficient multi-stage “triage-then-cluster-then-pursuit” approach that initially extracts only clean, high-quality waveforms from the electrophysiological time series by temporarily skipping noisy or “collided” events (representing two neurons firing synchronously). This is accomplished by developing a neural network detection method followed by efficient outlier triaging. The clean waveforms are then used to infer the set of neural spike waveform templates through nonparametric Bayesian clustering. Our clustering approach adapts a “coreset” approach for data reduction and uses efficient inference methods in a Dirichlet process mixture model framework to dramatically improve the scalability and reliability of the entire pipeline. The “triaged” waveforms are then finally recovered with matching-pursuit deconvolution techniques. The proposed methods improve on the state-of-the-art in terms of accuracy and stability on both real and biophysically-realistic simulated MEA data. Furthermore, the proposed pipeline is efficient, learning templates and clustering much faster than real-time for a ≃ 500-electrode dataset, using primarily a single CPU core.

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