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3 results for “Product Lifecycle”
Unlocking the power of computer modelling and simulation across the life sciences product lifecycle
<p><strong>Unlocking the Power of Computer Modelling and Simulation Across the Life Sciences Product Lifecycle</strong></p> <p>In an era where technology continuously reshapes the boundaries of research and development, the field of life sciences stands at the cusp of a transformative shift. The potent combination of computer modelling and simulation has begun to unlock unprecedented opportunities across the product lifecycle in life sciences, promising to revolutionize everything from medicinal product development to clinical research. Let's delve into how these technological advancements are paving the way for groundbreaking progress in medicine and healthcare.</p> <p><strong>The Fusion of Technology and Life Sciences</strong></p> <p><em>In Silico Methods: A New Frontier in Medicine</em></p> <p>The term 'in silico' refers to computer simulations used in the study of biological and chemical processes. The video highlights the growing importance of in silico methods in the life sciences sector, particularly in the United Kingdom. These methods allow for the virtual testing of new medicinal products, significantly reducing the need for costly and time-consuming physical trials.</p> <p><em>Bridging the Gap with Computational Modeling</em></p> <p>Computational modeling is another key aspect discussed in the presentation. It involves the use of computer algorithms and mathematical models to simulate real-world medical data. This approach enables researchers to predict how medicinal products will behave in various scenarios, including their interaction with different types of patient data. As a result, computational modeling is instrumental in enhancing the precision of clinical research and improving medical equitability by considering a broader range of patient profiles.</p> <p><strong>The Impact on Clinical Research and Patient Care</strong></p> <p><em>Enhancing Precision and Efficiency</em></p> <p>One of the most notable benefits of integrating computer modelling and simulation into the life sciences is the enhanced precision and efficiency it brings to clinical research. By leveraging real-world medical data, researchers can obtain more accurate predictions about the efficacy and safety of new medicinal products. This not only accelerates the development process but also ensures that treatments are more tailored to individual patient needs.</p> <p><em>Promoting Medical Equitability</em></p> <p>The video underscores the role of these technologies in promoting medical equitability. Through the use of patient data simulations, it becomes possible to account for a wider array of genetic, environmental, and lifestyle factors that influence health outcomes. This inclusive approach ensures that the benefits of medical advancements are accessible to a diverse population, addressing disparities in healthcare access and treatment efficacy.</p> <p><strong>Conclusion: The Future is Now</strong></p> <p>The integration of computer modelling and simulation in the life sciences heralds a new era of medical research and patient care. As we continue to explore the potential of these technologies, it's clear that they hold the key to unlocking more efficient, precise, and equitable healthcare solutions. The journey towards fully realizing this potential is just beginning, but the promise it holds is immense. As we stand on the brink of this technological revolution, one thing is certain: the future of medicine and healthcare is being shaped here and now, and it's brighter than ever.</p>
Digital Twin or Digital Model: An Analysis of Definitions along the Product Lifecycle - Research data
<p>This research data contains the statements of the authors Grieves, Stark and Tao with regard to selected characteristics of Digital Twins. According to these statements different case studies along the product life cycle are classified as Digital Twin or Digital Model.</p> <p>Version 2 added a change in characteristic 2.</p>
Raw data set for Negative Emissions in the Chemical Sector: Lifecycle CO2 Accounting for Biomass and CCS Integration into Ethanol, Ammonia, Urea, and Hydrogen Production.
<p>This repository contains the raw data and code used to generate the results in the paper:</p> <p>Tanzer S.E., Blok K., Ramirez Ramirez A. Negative Emissions in the Chemical Sector: Lifecycle CO2 Accounting for Biomass and CCS Integration into Ethanol, Ammonia, Urea, and Hydrogen Production. 15th International Conference on Greenhouse Gas Control Technologies, GHGT-15. 2021. doi: 10.2139/ssrn.3819778.</p> <p>also published as chapter 4 in the PhD dissertation ”Negative Emissions in the Industrial Sector”. The PhD was the department of Engineering Systems and Services, Faculty of Technology Policy, Management at the Delft University of Technology, between 2017-2022. </p> <p>This is intended to be a record of the exact data and code used to generate the results and graphics used in this publication. It is not necessarily designed for user-friendliness or tested to work on other machines and may contain extraneous data and files.</p> <p>To make use of the python black box modelling library for your own work, please check out the most recent public release, which can be found at https://zenodo.org/record/5800104#.YjUTnC8w30o</p>
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