scholarly journals On-demand generation of as-built infrastructure information models for mechanised Tunnelling from TBM data: A computational design approach

2021 ◽  
Vol 121 ◽  
pp. 103434
Author(s):  
Vito Getuli ◽  
Pietro Capone ◽  
Alessandro Bruttini ◽  
Farzad Pour Rahimian
Keyword(s):  
Computational Design ◽  
Design Approach ◽  
On Demand ◽  
Information Models ◽  
Mechanised Tunnelling

Incorporating Adjustable Features in the Optimal Design of Polymer Sheet Extrusion Dies

2005 ◽  
Vol 128 (1) ◽  
pp. 11-19 ◽  
Author(s):  
Douglas E. Smith ◽  
Qi Wang
Keyword(s):  
Optimization Procedure ◽  
Computational Design ◽  
Operating Conditions ◽  
Die Design ◽  
Design Approach ◽  
Extrusion Dies ◽  
Design Variables ◽  
Gradient Based ◽  
Multiple Materials ◽  
Sheet Extrusion

It is common for materials processing operations to have adjustable features that may be used to improve the quality of the final product when variability in operating conditions is encountered. This paper considers the polymer sheeting die design problem where variability in operating temperature or material properties, for example, requires that the die be designed to perform well under multiple operating conditions. An optimization procedure is presented where the design variables parametrize both stationary and adjustable model variables. In this approach, adjustable features of the die cavity are modified in an optimal manner consistent with the overall design objectives. The computational design approach incorporates finite element simulations based on the Generalized Hele-Shaw approximation to evaluate the die’s performance measures, and includes a gradient-based optimization algorithm and analytical design sensitivities to update the die’s geometry. Examples are provided to illustrate the design methodology where die cavities are designed to accommodate multiple materials, multiple flow rates, and various temperatures. This paper demonstrates that improved tooling designs may be computed with an optimization-based process design approach that incorporates the effect of adjustable features.

scholarly journals Applicability of a computational design approach for synthetic riboswitches

2016 ◽  
pp. gkw1267 ◽  
Author(s):  
Gesine Domin ◽  
Sven Findeiß ◽  
Manja Wachsmuth ◽  
Sebastian Will ◽  
Peter F. Stadler ◽  
...  
Keyword(s):  
Computational Design ◽  
Design Approach

scholarly journals Human centered AI design for clinical monitoring and data management

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
H S Adnan ◽  
S Matthews ◽  
M Hackl ◽  
P P Das ◽  
M Manaswini ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Data Acquisition ◽  
Data Management ◽  
Design Approach ◽  
Clinical Monitoring ◽  
Sensor Data ◽  
Healthcare Sector ◽  
Clinical Settings ◽  
On Demand

Abstract Background In clinical settings, significant resources are spent on data collection and monitoring patients' health parameters to improve decision-making and provide better care. With increased digitization, the healthcare sector is shifting towards implementing digital technologies for data management and in administration. New technologies offer better treatment opportunities and streamline clinical workflow, but the complexity can cause ineffectiveness, frustration, and errors. To address this, we believe digital solutions alone are not sufficient. Therefore, we take a human-centred design approach for AI development, and apply systems engineering methods to identify system leverage points. We demonstrate how automation enables monitoring clinical parameters, using existing non-intrusive sensor technology, resulting in more resources toward patient care. Furthermore, we provide a framework on digitization of clinical data for integration with data management. Methods Activities of Daily Living (ADLs) are essential parameters, necessary for evaluating patients in mental health wards. Ideally logging the parameters should take place at hourly intervals; however, time constraints and lack of resources restrict the nursing staff to consolidating the overall impression during the day, relying on what they recall. Using design methods, sensors (e.g. infrared, proximity, pressure) are used to automate the acquisition of data for machine learning that correspond to the ADLs, considering privacy and other medical requirements. Results We present a concept of a room with sensors that can be deployed in clinical settings. Sensor data log ADLs, and provide machine learning data. A theoretical framework demonstrates how collected data can be used in electronic/medical health records. Conclusions Data acquisition of the ADLs with automation enable variable specificity and sensitivity on-demand. It further facilitates interoperability and provides data for machine learning. Key messages Our research demonstrates automated data acquisition techniques for clinical monitoring. Human centered AI design approach enables on-demand analysis of ADLs for mental health treatment.

scholarly journals An iterative computational design approach to increase the thermal endurance of a mesophilic enzyme

2018 ◽  
Vol 11 (1) ◽  
Author(s):  
Deanne W. Sammond ◽  
Noah Kastelowitz ◽  
Bryon S. Donohoe ◽  
Markus Alahuhta ◽  
Vladimir V. Lunin ◽  
...  
Keyword(s):  
Computational Design ◽  
Design Approach

scholarly journals Stimulus-responsive Self-Assembly of Protein-Based Fractals by Computational Design

2018 ◽  
Author(s):  
Nancy E. Hernández ◽  
William A. Hansen ◽  
Denzel Zhu ◽  
Maria E. Shea ◽  
Marium Khalid ◽  
...  
Keyword(s):  
Self Assembly ◽  
Electron Tomography ◽  
Computational Design ◽  
Degradation Pathway ◽  
Design Approach ◽  
Length Scales ◽  
Surface Areas ◽  
Multiple Length Scales ◽  
Src Homology ◽  
Self Similar

AbstractFractal topologies, which are statistically self-similar over multiple length scales, are pervasive in nature. The recurrence of patterns at increasing length scales in fractal-shaped branched objects, e.g., trees, lungs, and sponges, results in high effective surface areas, and provides key functional advantages, e.g., for molecular trapping and exchange. Mimicking these topologies in designed protein-based assemblies will provide access to novel classes of functional biomaterials for wide ranging applications. Here we describe a computational design approach for the reversible self-assembly of proteins into tunable supramolecular fractal-like topologies in response to phosphorylation. Computationally-guided atomic-resolution modeling of fusions of symmetric, oligomeric proteins with Src homology 2 (SH2) binding domain and its phosphorylatable ligand peptide was used to design iterative branching leading to assembly formation by two enzymes of the atrazine degradation pathway. Structural characterization using various microscopy techniques and Cryo-electron tomography revealed a variety of dendritic, hyperbranched, and sponge-like topologies which are self-similar over three decades (~10nm-10μm) of length scale, in agreement with models from multi-scale computational simulations. Control over assembly topology and formation dynamics is demonstrated. Owing to their sponge-like structure on the nanoscale, fractal assemblies are capable of efficient and phosphorylation-dependent reversible macromolecular capture. The described design framework should enable the construction of a variety of novel, spatiotemporally responsive biomaterials featuring fractal topologies.One Sentence SummaryWe report a computationally-guided bottom up design approach for constructing fractal-shaped protein assemblies for efficient molecular capture.

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