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Average Ratings 0 Ratings
Description
AVEVA Engineering facilitates collaboration among multi-disciplinary engineering teams, allowing them to efficiently create and manage detailed specifications for essential components in both plant and marine projects. This software is not only highly adaptable and customizable but also serves as an authoring tool primarily for process and mechanical engineering, while being equally effective for specialized areas such as pipe stress and safety. Each discipline maintains comprehensive control over its own data, while still having the ability to access and refer to information from other fields. When integrated with AVEVA Unified Engineering, the entire project information model is made available for AVEVA’s suite of technologies including process simulation, design, engineering, collaboration, and lifecycle management, all of which can be securely accessed through a unified cloud-based platform known as AVEVA™ Connect. As a result, a transformative phase in AVEVA’s 1D and 2D engineering and design software is emerging, enhancing the efficiency and effectiveness of engineering workflows. This evolution marks a significant step forward in how engineering projects are conceived and executed in the digital age.
Description
Progressing artificial intelligence to remove the need for trial and error in healthcare, our digital twins facilitate swift and assured clinical trials. We focus on areas such as neuroscience, immunology, and metabolic diseases, among others. TwinRCTs expedite full enrollment by requiring fewer participants to provide equivalent statistical power compared to conventional trial methodologies. This approach significantly reduces the time needed for late-stage study enrollment. Additionally, TwinRCTs enhance the ability to detect treatment effects in early-stage studies by bolstering statistical power without necessitating an increase in participant numbers. They enable researchers to make informed decisions based on initial study outcomes and help attract more participants to trials. By utilizing smaller control groups, TwinRCTs also improve participants' odds of receiving the experimental treatment. Our commitment to positioning clinical trials with digital twins for regulatory success is unwavering. Unlearn is at the forefront of transforming the medical field through the innovative application of artificial intelligence, creating and implementing novel generative models that are trained on vast datasets derived from previous patient studies. This evolution in methodology not only streamlines research but also enhances the overall effectiveness of clinical trials.
API Access
Has API
API Access
Has API
Integrations
AVEVA Electrical and Instrumentation
AVEVA Unified Engineering
Integrations
AVEVA Electrical and Instrumentation
AVEVA Unified Engineering
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
AVEVA
Country
United Kingdom
Website
www.aveva.com/en/products/aveva-engineering/
Vendor Details
Company Name
Unlearn
Country
United States
Website
www.unlearn.ai/
Product Features
Engineering
2D Drawing
3D Modeling
Chemical Engineering
Civil Engineering
Collaboration
Design Analysis
Design Export
Document Management
Electrical Engineering
Mechanical Engineering
Mechatronics
Presentation Tools
Structural Engineering
Product Features
Clinical Trial Management
21 CFR Part 11 Compliance
Document Management
Electronic Data Capture
Enrollment Management
HIPAA Compliant
Monitoring
Patient Database
Recruiting Management
Scheduling
Study Planning