Research on Asymmetric and Negative Offset Mechanism of Large-Scale Steel Plate Rolling Shear

According to revised Cailikefu’s rolling shear force formula, motion path equation of spatial seven-bar path is built, and mechanical model, with such new structural features as asymmetric and negative offset, is thus successfully established for 2800 mm heavy shear of some Iron&Steel Company. Shear force and bar force of steel plate, before and after adoption of asymmetric and negative offset structure, are analyzed, as well as horizontal force component of mechanism that influences pure rolling shear and back-wall push force that keeps blade clearance. The discovery is that back-wall push force could be kept large enough at rolling start-up (i.e. the time that the maximum rolling shear produces), meanwhile, back-wall push force is the most approximate to side forces with adoption of 60mm~100mm offset. Theoretical results and on-site shear quality both indicate that new structural features such as asymmetric and negative offset plays an important role in ensuring pure rolling shear and keeping blade clearance constant, which provide an effective means to improve quality of steel plate.

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Industrial Test on Shearing Force of Rolling Shear

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.145.462 ◽

2010 ◽

Vol 145 ◽

pp. 462-466

Author(s):

Li Feng Ma ◽

Jian Mei Wang ◽

Qing Xue Huang

Keyword(s):

Shear Force ◽

Optimization Design ◽

Large Scale ◽

Steel Plate ◽

Industrial Test ◽

Test Results ◽

Shearing Force ◽

Rolling Shear ◽

Structure Strength ◽

Selection Of

The accuracy of the max shear force of rolling shear is of vital importance to the proper selection of motor power and the optimization design of structure strength. The test on shear force during the shearing process of stainless steel was done on 3000 mm cut-to-length rolling shear of some Large-scale Iron & Steel Co., Ltd. The test results have shown that the weight of upper-blade carrier can bring about the fluctuation of measuring signals of shearing force at the start and end instants. Meanwhile, the shear force during the shearing process will gradually increase with the increment relative cut-in depth, and the process from the bite-in of upper blade into steel plate to the fracture of steel plate is very short, thus the link force on the side of firstly-fallen-off blade will initially increase and then will decrease by large increment amplitude. The shear force at the cut-in stage will dramatically increase. During the cut-in stage, the shear force is basically a constant due to the constant shear angle. In summary, the above test results have important referential value to the calculation of shear force and the design of structure strength.

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Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining

10.26434/chemrxiv.7670903.v1 ◽

2019 ◽

Author(s):

Mohammad Atif Faiz Afzal ◽

Mojtaba Haghighatlari ◽

Sai Prasad Ganesh ◽

Chong Cheng ◽

Johannes Hachmann

Keyword(s):

Data Mining ◽

Refractive Index ◽

High Throughput ◽

First Principles ◽

High Throughput Screening ◽

Large Scale ◽

Computational Study ◽

High Refractive Index ◽

Structural Features ◽

Learning Program

<div>We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. This rapid and efficient approach yields a number of highly promising leads compounds. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches.</div>

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mRNA-Based Vaccines

Vaccines ◽

10.3390/vaccines9040390 ◽

2021 ◽

Vol 9 (4) ◽

pp. 390

Author(s):

Frank Kowalzik ◽

Daniel Schreiner ◽

Christian Jensen ◽

Daniel Teschner ◽

Stephan Gehring ◽

...

Keyword(s):

Large Scale ◽

New Technologies ◽

Vaccine Development ◽

Effective Means ◽

Cost Effective ◽

Clinical Applications ◽

Cell Mediated Immunity ◽

Healthy Individuals ◽

Emerging Pathogens ◽

Cell Genome

Increases in the world’s population and population density promote the spread of emerging pathogens. Vaccines are the most cost-effective means of preventing this spread. Traditional methods used to identify and produce new vaccines are not adequate, in most instances, to ensure global protection. New technologies are urgently needed to expedite large scale vaccine development. mRNA-based vaccines promise to meet this need. mRNA-based vaccines exhibit a number of potential advantages relative to conventional vaccines, namely they (1) involve neither infectious elements nor a risk of stable integration into the host cell genome; (2) generate humoral and cell-mediated immunity; (3) are well-tolerated by healthy individuals; and (4) are less expensive and produced more rapidly by processes that are readily standardized and scaled-up, improving responsiveness to large emerging outbreaks. Multiple mRNA vaccine platforms have demonstrated efficacy in preventing infectious diseases and treating several types of cancers in humans as well as animal models. This review describes the factors that contribute to maximizing the production of effective mRNA vaccine transcripts and delivery systems, and the clinical applications are discussed in detail.

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Why Do People Stay Poor?

The Quarterly Journal of Economics ◽

10.1093/qje/qjab045 ◽

2021 ◽

Cited By ~ 1

Author(s):

Clare Balboni ◽

Oriana Bandiera ◽

Robin Burgess ◽

Maitreesh Ghatak ◽

Anton Heil

Keyword(s):

Occupational Choice ◽

Large Scale ◽

Choice Model ◽

Effective Means ◽

Threshold Level ◽

Poverty Traps ◽

Asset Transfer ◽

Small Livestock ◽

Program Costs ◽

Almost All

Abstract There are two broad views as to why people stay poor. One emphasizes differences in fundamentals, such as ability, talent, or motivation. The other, the poverty traps view, emphasizes differences in opportunities which stem from access to wealth. To test between these two views, we exploit a large-scale, randomized asset transfer and an 11-year panel of 6,000 households who begin in extreme poverty. The setting is rural Bangladesh and the assets are cows. The data supports the poverty traps view—we identify a threshold level of initial assets above which households accumulate assets, take on better occupations (from casual labor in agriculture or domestic services to running small livestock businesses), and grow out of poverty. The reverse happens for those below the threshold. Structural estimation of an occupational choice model reveals that almost all beneficiaries are misallocated in the work they do at baseline and that the gains arising from eliminating misallocation would far exceed the program costs. Our findings imply that large transfers which create better jobs for the poor are an effective means of getting people out of poverty traps and reducing global poverty.

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Parallel Motion Simulation of Large-Scale Real-Time Crowd in a Hierarchical Environmental Model

Mathematical Problems in Engineering ◽

10.1155/2012/918497 ◽

2012 ◽

Vol 2012 ◽

pp. 1-15 ◽

Cited By ~ 3

Author(s):

Xin Wang ◽

Jianhua Zhang ◽

Massimo Scalia

Keyword(s):

Real Time ◽

Large Scale ◽

Simulation Method ◽

Crowd Simulation ◽

Motion Path ◽

Motion Simulation ◽

Environmental Model ◽

Parallel Methods ◽

Parallel Motion ◽

Methods Numerical

This paper presents a parallel real-time crowd simulation method based on a hierarchical environmental model. A dynamical model of the complex environment should be constructed to simulate the state transition and propagation of individual motions. By modeling of a virtual environment where virtual crowds reside, we employ different parallel methods on a topological layer, a path layer and a perceptual layer. We propose a parallel motion path matching method based on the path layer and a parallel crowd simulation method based on the perceptual layer. The large-scale real-time crowd simulation becomes possible with these methods. Numerical experiments are carried out to demonstrate the methods and results.

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Mass marking of juvenile Schizothorax wangchiachii (Fang) with alizarin red S and evaluation of stock enhancement in the Jinping area of the Yalong River

PeerJ ◽

10.7717/peerj.4142 ◽

2017 ◽

Vol 5 ◽

pp. e4142 ◽

Cited By ~ 1

Author(s):

Kun Yang ◽

Shu Li ◽

Xiaoshuai Liu ◽

Weixiong Gan ◽

Longjun Deng ◽

...

Keyword(s):

Large Scale ◽

Condition Factor ◽

Effective Means ◽

Stock Enhancement ◽

Alizarin Red S ◽

Small Scale ◽

Instantaneous Growth Rate ◽

Alizarin Red ◽

Total Length ◽

Yalong River

Schizothorax wangchiachii is a key fish species in the stock enhancement program of the Yalong River hydropower project, China. Alizarin red S (ARS) was used to mark large numbers of juvenile S. wangchiachii in the Jinping Hatchery and later used to evaluate stock enhancement in the Jinping area of the Yalong River. In a small-scale pilot study, 7,000 juveniles of the 2014 cohort were successfully marked by immersion in ARS solution, and no mortality was recorded during the marking process. The ARS mark in the fish otoliths remained visible 20 months later. In the large-scale marking study, approximately 600,000 juveniles of the 2015 cohort were successfully marked. Mortalities of both marked and unmarked juveniles were very low and did not differ significantly. Total length, wet mass and condition factor did not differ significantly between unmarked and marked individuals after three months. On 24 July 2015, about 840,000 Jinping Hatchery-produced young S. wangchiachii, including 400,000 marked individuals, were released at two sites in the Jinping area. Recapture surveys showed that (1) marked and unmarked S. wangchiachii did not differ significantly in total length, wet mass and condition factor; (2) stocked individuals became an important part of recruitment of the 2015 cohort; (3) instantaneous growth rate of marked individuals tended to slightly increase; and (4) most stocked individuals were distributed along a 10–15 km stretch near the release sites. These results suggest that the ARS method is a cost-efficient way to mass mark juvenile S. wangchiachii and that releasing juveniles is an effective means of stock recruitment.

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Inductive Transfer Learning for Molecular Activity Prediction: Next-Gen QSAR Models with MolPMoFiT

10.26434/chemrxiv.9978743.v2 ◽

2020 ◽

Author(s):

Xinhao Li ◽

Denis Fourches

Keyword(s):

Transfer Learning ◽

High Throughput Screening ◽

Structure Prediction ◽

Large Scale ◽

High Reliability ◽

Structural Features ◽

Fine Tuning ◽

Qsar Modeling ◽

Chemical Structures ◽

Effective Transfer

Deep neural networks can directly learn from chemical structures without extensive, user-driven selection of descriptors in order to predict molecular properties/activities with high reliability. But these approaches typically require large training sets to learn the endpoint-specific structural features and ensure reasonable prediction accuracy. Even though large datasets are becoming the new normal in drug discovery, especially when it comes to high-throughput screening or metabolomics datasets, one should also consider smaller datasets with challenging endpoints to model and forecast. Thus, it would be highly relevant to better utilize the tremendous compendium of unlabeled compounds from publicly-available datasets for improving the model performances for the user’s particular series of compounds. In this study, we propose the Molecular Prediction Model Fine-Tuning (MolPMoFiT) approach, an effective transfer learning method based on self-supervised pre-training + task-specific fine-tuning for QSPR/QSAR modeling. A large-scale molecular structure prediction model is pre-trained using one million unlabeled molecules from ChEMBL in a self-supervised learning manner, and can then be fine-tuned on various QSPR/QSAR tasks for smaller chemical datasets with specific endpoints. Herein, the method is evaluated on four benchmark datasets (lipophilicity, FreeSolv, HIV, and blood-brain barrier penetration). The results showed the method can achieve strong performances for all four datasets compared to other state-of-the-art machine learning modeling techniques reported in the literature so far.

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Application Problems and Solutions of Intelligent Airport Service Robots

E3S Web of Conferences ◽

10.1051/e3sconf/202019804030 ◽

2020 ◽

Vol 198 ◽

pp. 04030

Author(s):

Dai Yanyan ◽

Chen Meng

Keyword(s):

Artificial Intelligence ◽

Cloud Computing ◽

Big Data ◽

Large Scale ◽

New Technologies ◽

Effective Means ◽

Third Party ◽

Service Robots ◽

Service Robot ◽

Operation Conditions

With the development of new technologies such as artificial intelligence, big data, and cloud computing, the “intelligent airport” is considered to be an effective means to solve or alleviate the current industry problems such as large-scale airport business, the large number of operating entities, and the complicated operation conditions. This paper is about the collaboration between universities and enterprises based on the concept of service design. Relying on big data and cloud computing technology, this paper addresses the problems of airport service robots in inquiries, blind spots of security inspection, and full monomer smart navigation diffluence, combined with the basic technology of service robot artificial intelligence and the third-party interface to design solutions to effectively solve the problems of process.

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Characterising RDF data sets

Journal of Information Science ◽

10.1177/0165551516677945 ◽

2017 ◽

Vol 44 (2) ◽

pp. 203-229 ◽

Cited By ~ 6

Author(s):

Javier D Fernández ◽

Miguel A Martínez-Prieto ◽

Pablo de la Fuente Redondo ◽

Claudio Gutiérrez

Keyword(s):

Data Structures ◽

Large Scale ◽

Open Data ◽

Structural Features ◽

Data Sets ◽

Data Set ◽

Wide Range ◽

Rdf Data ◽

Description Framework ◽

Resource Description

The publication of semantic web data, commonly represented in Resource Description Framework (RDF), has experienced outstanding growth over the last few years. Data from all fields of knowledge are shared publicly and interconnected in active initiatives such as Linked Open Data. However, despite the increasing availability of applications managing large-scale RDF information such as RDF stores and reasoning tools, little attention has been given to the structural features emerging in real-world RDF data. Our work addresses this issue by proposing specific metrics to characterise RDF data. We specifically focus on revealing the redundancy of each data set, as well as common structural patterns. We evaluate the proposed metrics on several data sets, which cover a wide range of designs and models. Our findings provide a basis for more efficient RDF data structures, indexes and compressors.

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Variable Porous Electrode Compression for Redox Flow Battery Systems

Batteries ◽

10.3390/batteries4040053 ◽

2018 ◽

Vol 4 (4) ◽

pp. 53 ◽

Cited By ~ 6

Author(s):

Nicholas Gurieff ◽

Victoria Timchenko ◽

Chris Menictas

Keyword(s):

Large Scale ◽

Effective Means ◽

Porous Electrode ◽

Cost Effective ◽

Electrical Energy ◽

Limiting Current ◽

Great Promise ◽

Full Potential ◽

Uniform Compression ◽

Novel Concept

Vanadium redox flow batteries (VRFBs) offer great promise as a safe, cost effective means of storing electrical energy on a large scale and will certainly have a part to play in the global transition to renewable energy. To unlock the full potential of VRFB systems, however, it is necessary to improve their power density. Unconventional stack design shows encouraging possibilities as a means to that end. Presented here is the novel concept of variable porous electrode compression, which simulations have shown to deliver a one third increase in minimum limiting current density together with a lower pressure drop when compared to standard uniform compression cell designs.

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