TH-AB-BRB-02: Enabling Web-Based Treatment Planning Using a State-Of-The-Art Convex Optimization Solver

2015 ◽  
Vol 42 (6Part41) ◽  
pp. 3704-3704 ◽  
Author(s):  
B Ungun ◽  
M Folkerts ◽  
K Bush ◽  
S Boyd ◽  
L Xing
Keyword(s):  
Convex Optimization ◽  
Treatment Planning ◽  
State Of The Art ◽  
Web Based

scholarly journals Social and Solidarity Economy Web Information Systems

2014 ◽  
Vol 12 (1) ◽  
pp. 35-52 ◽  
Author(s):  
Mariana Curado Malta ◽  
Ana Alice Baptista ◽  
Cristina Parente
Keyword(s):  
Information Systems ◽  
State Of The Art ◽  
Bibliographic Databases ◽  
Sources Of Information ◽  
Solidarity Economy ◽  
Web Based ◽  
Web Information ◽  
The Social ◽  
Application Profile ◽  
Made In

This paper presents the state of the art on interoperability developments for the social and solidarity economy (SSE) community web based information systems (WIS); it also presents a framework of interoperability for the SSE' WIS and the developments made in a research-in-progress PhD project in the last 3 years. A search on the bibliographic databases showed that so far there are no papers on interoperability initiatives on the SSE, so it was necessary to have other sources of information: a preliminary analysis of the WIS that support SSE activities; and interviews with the representatives of some of the world's most important SSE organisations. The study showed that the WIS are still not interoperable yet. In order to become interoperable a group of the SSE community has been developing a Dublin Corre Application Profile to be used by the SSE community as reference and binding to describe their resources. This paper also describes this on-going process.

scholarly journals Manufacturing systematics and cladistics: state of the art and generic classification

2017 ◽  
Vol 28 (5) ◽  
pp. 655-685 ◽  
Author(s):  
Christen Rose-Anderssen ◽  
James Baldwin ◽  
Keith Ridgway
Keyword(s):  
Expert System ◽  
Observational Data ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
State Of The Art ◽  
Theory Building ◽  
Theory Testing ◽  
Web Based ◽  
Content Type ◽  
Discrete Manufacturing

Purpose The purpose of this paper is to critically evaluate the state of the art of applications of organisational systematics and manufacturing cladistics in terms of strengths and weaknesses and introduce new generic cladistic and hierarchical classifications of discrete manufacturing systems. These classifications are the basis for a practical web-based expert system and diagnostic benchmarking tool. Design/methodology/approach There were two stages for the research methods, with eight re-iterative steps: one for theory building, using secondary and observational data, producing conceptual classifications; the second stage for theory testing and theory development, using quantitative data from 153 companies and 510 manufacturing systems, producing the final factual cladogram. Evolutionary relationships between 53 candidate manufacturing systems, using 13 characters with 84 states, are hypothesised and presented diagrammatically. The manufacturing systems are also organised in a hierarchical classification with 13 genera, 6 families and 3 orders under one class of discrete manufacturing. Findings This work addressed several weaknesses of current manufacturing cladistic classifications which include the lack of an explicit out-group comparison, limited conceptual cladogram development, limited use of characters and that previous classifications are specific to sectors. In order to correct these limitations, the paper first expands on previous work by producing a more generic manufacturing system classification. Second, it describes a novel web-based expert system for the practical application of the discrete manufacturing system. Practical implications The classifications form the basis for a practical web-based expert system and diagnostic benchmarking tool, but also have a novel use in an educational context as it simplifies and relationally organises extant manufacturing system knowledge. Originality/value The research employed a novel re-iterative methodology for both theory building, using observational data, producing the conceptual classification, and through theory testing developing the final factual cladogram that forms the basis for the practical web-based expert system and diagnostic tool.

scholarly journals Current state of the art brachytherapy treatment planning dosimetry algorithms

2014 ◽  
Vol 87 (1041) ◽  
pp. 20140163 ◽  
Author(s):  
P Papagiannis ◽  
E Pantelis ◽  
P Karaiskos
Keyword(s):  
Treatment Planning ◽  
State Of The Art ◽  
Current State

STATE-OF-THE-ART, INTEGRATED INFORMATION ACCESS SYSTEM BRINGS WEB-BASED CONTROL TO THE CONEY ISLAND WPCP

2001 ◽  
Vol 2001 (8) ◽  
pp. 343-352
Author(s):  
John Petito ◽  
Howard Fessel ◽  
David Harris ◽  
Richard Cardazone ◽  
Ertan Akbas ◽  
...  
Keyword(s):  
State Of The Art ◽  
Information Access ◽  
Web Based ◽  
Access System ◽  
Coney Island ◽  
Information Access System ◽  
Integrated Information

Unsupervised acquisition of entailment relations from the Web

2013 ◽  
Vol 21 (1) ◽  
pp. 3-47 ◽  
Author(s):  
IDAN SZPEKTOR ◽  
HRISTO TANEV ◽  
IDO DAGAN ◽  
BONAVENTURA COPPOLA ◽  
MILEN KOUYLEKOV
Keyword(s):  
Large Scale ◽  
Learning Algorithm ◽  
State Of The Art ◽  
High Coverage ◽  
Web Based ◽  
Rule Acquisition ◽  
Learning Rules ◽  
Entailment Relation ◽  
Entailment Relations ◽  
The Web

AbstractEntailment recognition is a primary generic task in natural language inference, whose focus is to detect whether the meaning of one expression can be inferred from the meaning of the other. Accordingly, many NLP applications would benefit from high coverage knowledgebases of paraphrases and entailment rules. To this end, learning such knowledgebases from the Web is especially appealing due to its huge size as well as its highly heterogeneous content, allowing for a more scalable rule extraction of various domains. However, the scalability of state-of-the-art entailment rule acquisition approaches from the Web is still limited. We present a fully unsupervised learning algorithm for Web-based extraction of entailment relations. We focus on increased scalability and generality with respect to prior work, with the potential of a large-scale Web-based knowledgebase. Our algorithm takes as its input a lexical–syntactic template and searches the Web for syntactic templates that participate in an entailment relation with the input template. Experiments show promising results, achieving performance similar to a state-of-the-art unsupervised algorithm, operating over an offline corpus, but with the benefit of learning rules for different domains with no additional effort.

scholarly journals Integrasi Sistem Single Sign On Pada Sistem Informasi Akademik, Web Information System Dan Learning Management System Berbasis Central Authentication Service

2018 ◽  
Vol 1 (1) ◽  
pp. 48-54
Author(s):  
I Putu Agus Eka Darma Udayana
Keyword(s):  
Management System ◽  
State Of The Art ◽  
Learning System ◽  
Access Protocol ◽  
Web Based ◽  
Single Sign On ◽  
Student Management ◽  
User Management ◽  
Web Information System ◽  
E Learning

Elearning and web based information systems is a means to communicate and exchange information for academic purposes.  Nowadays lightweight directory access protocol (LDAP) is a state of the art method of choice. With LDAP technologies user only need one username and password to access to multiple web based application, The problem is if the user wanted to do autentification said user had to input their credentials over and over again for each application. To solve that problem single sign on mechanism (SSO) is invented. With SSO user only need login once and they got all the same credentials with them to all intergrated application wthin the campus. To implement the SSO we use Central authentication service (CAS) as a authentifiation central within LDAP structure as a user management. In this reseach we see that single sign on (SSO) system that intergrated into student management system, E-Learning system and  Internal blog system both use of database based system or even LDAP based system.

scholarly journals Web-Based Tool for Algebraic Modeling and Mathematical Optimization

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2751
Author(s):  
Vaidas Jusevičius ◽  
Remigijus Paulavičius
Keyword(s):  
State Of The Art ◽  
Mathematical Optimization ◽  
Modeling Languages ◽  
Problem Type ◽  
Web Based ◽  
Complex Syntax ◽  
Algebraic Modeling Languages ◽  
Cross Compatibility ◽  
Language Knowledge ◽  
Compatibility Complex

In this article, we present a new open-source tool for algebraic modeling and mathematical optimization. We begin by distilling the main gaps within the existing algebraic modeling languages and tools (varying performance, limited cross-compatibility, complex syntax, and different solver, feature, and problem type support). Later, we propose a state-of-the-art web-based tool (WebAML and Optimization System) for algebraic modeling languages and mathematical optimization. The tool does not require specific algebraic language knowledge, allows solving problems using different solvers, and utilizes the best characteristics of existing algebraic modeling languages. We also provide clear extension points and ideas on how we could further improve such a tool.

scholarly journals PAVOOC: Designing CRISPR sgRNAs using 3D protein structures and functional domain annotation

2018 ◽  
Author(s):  
Moritz Schaefer ◽  
Dr. Djork-Arné Clevert ◽  
Dr. Bertram Weiss ◽  
Dr. Andreas Steffen
Keyword(s):  
Gene Editing ◽  
State Of The Art ◽  
Protein Structures ◽  
Design Tool ◽  
Protein Crystal ◽  
Functional Domain ◽  
Web Based ◽  
Domain Annotation ◽  
Sgrna Design ◽  
Processing Steps

AbstractSummary: sgRNAs targeting the same gene can significantly vary in terms of efficacy and specificity. PAVOOC (Prediction And Visualization of On- and Off-targets for CRISPR) is a web-based CRISPR sgRNA design tool that employs state-of-the art machine learning models to prioritize most effective candidate sgRNAs. In contrast to other tools, it maps sgRNAs to functional domains and protein structures and visualizes cut sites on corresponding protein crystal structures. Furthermore, PAVOOC supports HDR template generation for gene editing experiments and the visualization of the mutated amino acids in 3D.Availability and Implementation: PAVOOC is available under https://pavooc.me and accessible using current browsers (Chrome/Chromium recommended). The source code is hosted at github.com/moritzschaefer/pavooc under the MIT License. The backend, including data processing steps, and the frontend is implemented in Python 3 and ReactJS respectively. All components run in a simple Docker environment.Contact: [email protected]

scholarly journals Transparent exploration of machine learning for biomarker discovery from proteomics and omics data

2021 ◽  
Author(s):  
Furkan M. Torun ◽  
Sebastian Virreira Winter ◽  
Sophia Doll ◽  
Felix M. Riese ◽  
Artem Vorobyev ◽  
...  
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Biomarker Discovery ◽  
Expert Knowledge ◽  
State Of The Art ◽  
Open Science ◽  
Easy Access ◽  
Health State ◽  
List Type ◽  
Web Based

AbstractBiomarkers are of central importance for assessing the health state and to guide medical interventions and their efficacy, but they are lacking for most diseases. Mass spectrometry (MS)-based proteomics is a powerful technology for biomarker discovery, but requires sophisticated bioinformatics to identify robust patterns. Machine learning (ML) has become indispensable for this purpose, however, it is sometimes applied in an opaque manner, generally requires expert knowledge and complex and expensive software. To enable easy access to ML for biomarker discovery without any programming or bioinformatic skills, we developed ‘OmicLearn’ (https://OmicLearn.com), an open-source web-based ML tool using the latest advances in the Python ML ecosystem. We host a web server for the exploration of the researcher’s results that can readily be cloned for internal use. Output tables from proteomics experiments are easily uploaded to the central or a local webserver. OmicLearn enables rapid exploration of the suitability of various ML algorithms for the experimental datasets. It fosters open science via transparent assessment of state-of-the-art algorithms in a standardized format for proteomics and other omics sciences.Graphical AbstractHighlightsOmicLearn is an open-source platform allows researchers to apply machine learning (ML) for biomarker discoveryThe ready-to-use structure of OmicLearn enables accessing state-of-the-art ML algorithms without requiring any prior bioinformatics knowledgeOmicLearn’s web-based interface provides an easy-to-follow platform for classification and gaining insights into the datasetSeveral algorithms and methods for preprocessing, feature selection, classification and cross-validation of omics datasets are integratedAll results, settings and method text can be exported in publication-ready formats

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