scholarly journals Non-Symmetric Gyroscope Skewed Pyramids

Aerospace ◽  
2019 ◽  
Vol 6 (9) ◽  
pp. 98 ◽  
Author(s):  
Zachary Lewis ◽  
Joshua Ten Eyck ◽  
Kyle Baker ◽  
Eryn Culton ◽  
Jonathan Lang ◽  
...  
Keyword(s):  
Recent Literature ◽  
State Of The Art ◽  
Physical Space ◽  
The Novel ◽  
Figures Of Merit ◽  
Control Moment ◽  
Current State ◽  
Control Moment Gyroscopes ◽  
Continuous Surface ◽  
Significant Attention

The novel contribution in this manuscript is an expansion of the current state-of-the-art in the geometric installation of control moment gyroscopes beyond the benchmark symmetric skewed arrays and the four asymmetric arrays presented in recent literature. The benchmark pyramid symmetrically skewed at 54.73 degrees mandates significant attention to singularity avoidance, escape, and penetration, while the most recent four asymmetric arrays are strictly useful in instances where space is available to mount at least one gyro orthogonal to the others. Skewed arrays of gyros and the research-benchmark are introduced, followed by the present-day box-90 and “roof” configurations, where the roof configuration is the first prevalently used asymmetric geometry. Six other asymmetric options in the most recent literature are introduced, where four of the six options are obviously quite useful. From this inspiration, several dozen discrete options for asymmetric installations are critically evaluated using two figures of merit: maximum momentum (saturation) and maximum singularity-free momentum. Furthermore, continuous surface plots are presented to provide readers with countless (infinite) options for geometric installations. The manuscript firmly establishes many useful options for engineers who learn that the physical space on their spacecraft is insufficient to permit standard installations.

scholarly journals Hydrothermal Carbonization of Organic Waste and Biomass: A Review on Process, Reactor, and Plant Modeling

2020 ◽  
Author(s):  
Giulia Ischia ◽  
Luca Fiori
Keyword(s):  
Organic Waste ◽  
Review Paper ◽  
Recent Literature ◽  
State Of The Art ◽  
Hydrothermal Carbonization ◽  
Residual Biomass ◽  
Current State ◽  
Potential Applications ◽  
Intrinsic Complexity ◽  
Context Models

Abstract Hydrothermal carbonization (HTC) is an emerging path to give a new life to organic waste and residual biomass. Fulfilling the principles of the circular economy, through HTC “unpleasant” organics can be transformed into useful materials and possibly energy carriers. The potential applications of HTC are tremendous and the recent literature is full of investigations. In this context, models capable to predict, simulate and optimize the HTC process, reactors, and plants are engineering tools that can significantly shift HTC research towards innovation by boosting the development of novel enterprises based on HTC technology. This review paper addresses such key-issue: where do we stand regarding the development of these tools? The literature presents many and simplified models to describe the reaction kinetics, some dealing with the process simulation, while few focused on the heart of an HTC system, the reactor. Statistical investigations and some life cycle assessment analyses also appear in the current state of the art. This work examines and analyzes these predicting tools, highlighting their potentialities and limits. Overall, the current models suffer from many aspects, from the lack of data to the intrinsic complexity of HTC reactions and HTC systems. Therefore, the emphasis is given to what is still necessary to make the HTC process duly simulated and therefore implementable on an industrial scale with sufficient predictive margins. Graphic Abstract

scholarly journals Exemplification Modeling: Can You Give Me an Example, Please?

2021 ◽  
Author(s):  
Edoardo Barba ◽  
Luigi Procopio ◽  
Caterina Lacerra ◽  
Tommaso Pasini ◽  
Roberto Navigli
Keyword(s):  
Gold Standard ◽  
State Of The Art ◽  
Word Sense Disambiguation ◽  
Full Range ◽  
Training Data ◽  
Training Procedure ◽  
Word Sense ◽  
The Novel ◽  
Current State ◽  
Sense Disambiguation

Recently, generative approaches have been used effectively to provide definitions of words in their context. However, the opposite, i.e., generating a usage example given one or more words along with their definitions, has not yet been investigated. In this work, we introduce the novel task of Exemplification Modeling (ExMod), along with a sequence-to-sequence architecture and a training procedure for it. Starting from a set of (word, definition) pairs, our approach is capable of automatically generating high-quality sentences which express the requested semantics. As a result, we can drive the creation of sense-tagged data which cover the full range of meanings in any inventory of interest, and their interactions within sentences. Human annotators agree that the sentences generated are as fluent and semantically-coherent with the input definitions as the sentences in manually-annotated corpora. Indeed, when employed as training data for Word Sense Disambiguation, our examples enable the current state of the art to be outperformed, and higher results to be achieved than when using gold-standard datasets only. We release the pretrained model, the dataset and the software at https://github.com/SapienzaNLP/exmod.

Surgical management of BRCA pathogenic variant carriers with breast cancer: a recent literature review and current state of the art

2021 ◽  
Author(s):  
Daniela A. TERRIBILE ◽  
Elena J. MASON ◽  
Federica MURANDO ◽  
Alba DI LEONE ◽  
Alejandro M. SANCHEZ ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Literature Review ◽  
Surgical Management ◽  
Recent Literature ◽  
State Of The Art ◽  
Pathogenic Variant ◽  
Current State

scholarly journals No AI Is an Island: The Case for Teaming Intelligence

AI Magazine ◽  
2019 ◽  
Vol 40 (1) ◽  
pp. 16-28 ◽  
Author(s):  
Matthew Johnson ◽  
Alonso Vera
Keyword(s):  
Crucial Role ◽  
Intelligent Systems ◽  
State Of The Art ◽  
General Structure ◽  
Technological System ◽  
Full Potential ◽  
Current State ◽  
The World ◽  
Collaborative Skills ◽  
Significant Attention

The purpose of this article is to draw attention to an aspect of intelligence that has not yet received significant attention from the AI community, but that plays a crucial role in a technology’s effectiveness in the world, namely teaming intelligence. We propose that Al will reach its full potential only if, as part of its intelligence, it also has enough teaming intelligence to work well with people. Although seemingly counterintuitive, the more intelligent the technological system, the greater the need for collaborative skills. This paper will argue why teaming intelligence is important to AI, provide a general structure for AI researchers to use in developing intelligent systems that team well, assess the current state of the art and, in doing so, suggest a path forward for future AI systems. This is not a call to develop a new capability, but rather, an approach to what AI capabilities should be built, and how, so as to imbue intelligent systems with teaming competence.

scholarly journals SNPfiltR: an R package for interactive and reproducible SNP filtering

2021 ◽  
Author(s):  
Devon DeRaad
Keyword(s):  
State Of The Art ◽  
R Package ◽  
Working Environment ◽  
The Novel ◽  
Bioinformatic Pipeline ◽  
Reduced Representation ◽  
Performance Benchmarking ◽  
R Programming Language ◽  
Current State ◽  
R Programming

Here I describe the novel R package SNPfiltR and demonstrate its functionalities as the backbone of a customizable, reproducible SNP filtering pipeline implemented exclusively via the widely adopted R programming language. SNPfiltR extends existing SNP filtering functionalities by automating the visualization of key parameters such as depth, quality, and missing data, then allowing users to set filters based on optimized thresholds, all within a single, cohesive working environment. All SNPfiltR functions require a vcfR object as input, which can be easily generated by reading a SNP dataset stored as a standard vcf file into an R working environment using the function read.vcfR() from the R package vcfR. Performance benchmarking reveals that for moderately sized SNP datasets (up to 50M genotypes with associated quality information), SNPfiltR performs filtering with comparable efficiency to current state of the art command-line-based programs. These benchmarking results indicate that for most reduced-representation genomic datasets, SNPfiltR is an ideal choice for investigating, visualizing, and filtering SNPs as part of a cohesive and easily documentable bioinformatic pipeline. The SNPfiltR package can be downloaded from CRAN with the command [install.packages(“SNPfiltR”)], and a development version is available from GitHub at: (github.com/DevonDeRaad/SNPfiltR). Additionally, thorough documentation for SNPfiltR, including multiple comprehensive vignettes, is available at the website: (devonderaad.github.io/SNPfiltR/).

scholarly journals What Can We Learn from the CloudSat Radiometric Mode Observations of Snowfall over the Ice-Free Ocean?

2020 ◽  
Vol 12 (20) ◽  
pp. 3285
Author(s):  
Alessandro Battaglia ◽  
Giulia Panegrossi
Keyword(s):  
State Of The Art ◽  
Polar Regions ◽  
The Novel ◽  
Snow Density ◽  
Factors Affecting ◽  
Total Attenuation ◽  
Current State ◽  
Crucial Information ◽  
Level 2

The quantification of global snowfall by the current observing system remains challenging, with the CloudSat 94 GHz Cloud Profiling Radar (CPR) providing the current state-of-the-art snow climatology, especially at high latitudes. This work explores the potential of the novel Level-2 CloudSat 94 GHz Brightness Temperature Product (2B-TB94), developed in recent years by processing the noise floor data contained in the 1B-CPR product; the focus of the study is on the characterization of snow systems over the ice-free ocean, which has well constrained emissivity and backscattering properties. When used in combination with the path integrated attenuation (PIA), the radiometric mode can provide crucial information on the presence/amount of supercooled layers and on the contribution of the ice to the total attenuation. Radiative transfer simulations show that the location of the supercooled layers and the snow density are important factors affecting the warming caused by supercooled emission and the cooling induced by ice scattering. Over the ice-free ocean, the inclusion of the 2B-TB94 observations to the standard CPR observables (reflectivity profile and PIA) is recommended, should more sophisticated attenuation corrections be implemented in the snow CloudSat product to mitigate its well-known underestimation at large snowfall rates. Similar approaches will also be applicable to the upcoming EarthCARE mission. The findings of this paper are relevant for the design of future missions targeting precipitation in the polar regions.

scholarly journals Object Detection Based on Template Matching through Use of Best-So-Far ABC

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Anan Banharnsakun ◽  
Supannee Tanathong
Keyword(s):  
Object Detection ◽  
Template Matching ◽  
Artificial Bee Colony ◽  
Recent Literature ◽  
State Of The Art ◽  
Target Object ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Current State ◽  
The Difference

Best-so-far ABC is a modified version of the artificial bee colony (ABC) algorithm used for optimization tasks. This algorithm is one of the swarm intelligence (SI) algorithms proposed in recent literature, in which the results demonstrated that the best-so-far ABC can produce higher quality solutions with faster convergence than either the ordinary ABC or the current state-of-the-art ABC-based algorithm. In this work, we aim to apply the best-so-far ABC-based approach for object detection based on template matching by using the difference between the RGB level histograms corresponding to the target object and the template object as the objective function. Results confirm that the proposed method was successful in both detecting objects and optimizing the time used to reach the solution.

scholarly journals EXPRESS: Recent Advancements in Pulmonary Arterial Hypertension and Right Heart Failure Research: Overview of Selected Abstracts from ATS2020 and Emerging COVID-19 Research

2021 ◽  
pp. 204589402110372
Author(s):  
Francois Potus ◽  
Andrea Frump ◽  
Soban Umar ◽  
Rebecca Vanderpool ◽  
Imad Al Ghouleh ◽  
...  
Keyword(s):  
Pulmonary Arterial Hypertension ◽  
Arterial Hypertension ◽  
State Of The Art ◽  
Right Heart Failure ◽  
The Novel ◽  
Preclinical Research ◽  
Right Heart ◽  
Current State ◽  
Pulmonary Arterial ◽  
Rv Function

Each year the American Thoracic Society (ATS) Conference brings together scientists who conduct basic, translational and clinical research to present on the recent advances in the field of respirology. Due to the Coronavirus Disease of 2019 (COVID-19) pandemic, the ATS2020 Conference was held online in a series of virtual meetings. In this review, we focus on the breakthroughs in pulmonary hypertension (PH) research. We have selected ten of the best basic science abstracts which were presented at the ATS2020 Assembly on Pulmonary Circulation mini-symposium “What’s new in Pulmonary Arterial Hypertension (PAH) and Right Ventricular (RV) Signaling: Lessons from the Best Abstracts”, reflecting the current state-of-the-art and associated challenges in PH. Particular emphasis is placed on understanding the mechanisms underlying RV failure, the regulation of inflammation, and the novel therapeutic targets that emerged from preclinical research. The pathologic interactions between PH, RV function and COVID-19 are also discussed.

scholarly journals CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning

2020 ◽  
Vol 34 (05) ◽  
pp. 9507-9514 ◽  
Author(s):  
Daojian Zeng ◽  
Haoran Zhang ◽  
Qianying Liu
Keyword(s):  
Detailed Analysis ◽  
State Of The Art ◽  
Effective Model ◽  
Model Structure ◽  
Entity Extraction ◽  
Learning Framework ◽  
Task Learning ◽  
Current State ◽  
Significant Attention

Joint extraction of entities and relations has received significant attention due to its potential of providing higher performance for both tasks. Among existing methods, CopyRE is effective and novel, which uses a sequence-to-sequence framework and copy mechanism to directly generate the relation triplets. However, it suffers from two fatal problems. The model is extremely weak at differing the head and tail entity, resulting in inaccurate entity extraction. It also cannot predict multi-token entities (e.g. Steven Jobs). To address these problems, we give a detailed analysis of the reasons behind the inaccurate entity extraction problem, and then propose a simple but extremely effective model structure to solve this problem. In addition, we propose a multi-task learning framework equipped with copy mechanism, called CopyMTL, to allow the model to predict multi-token entities. Experiments reveal the problems of CopyRE and show that our model achieves significant improvement over the current state-of-the-art method by 9% in NYT and 16% in WebNLG (F1 score). Our code is available at https://github.com/WindChimeRan/CopyMTL

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