A key challenge faced by small and medium-sized business entities is securely managing software updates and changes. Specifically, with rapidly evolving cybersecurity threats, changes/updates/patches to software systems are necessary to stay ahead of emerging threats and are often mandated by regulators or statutory authorities to counter these. However, security patches/updates require stress testing before they can be released in the production system. Stress testing in production environments is risky and poses security threats. Large businesses usually have a non-production environment where such changes can be made and tested before being released into production. Smaller businesses do not have such facilities. In this work, we show how “digital twins”, especially for a mix of IT and IoT environments, can be created on the cloud. These digital twins act as a non-production environment where changes can be applied, and the system can be securely tested before patch release. Additionally, the non-production digital twin can be used to collect system data and run stress tests on the environment, both manually and automatically. In this paper, we show how using a small sample of real data/interactions, Generative Artificial Intelligence (AI) models can be used to generate testing scenarios to check for points of failure.
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Exploiting electricity market dynamics using flexible electrolysis units for retrofitting methanol synthesis
Exploiting the dynamics of electricity markets can make hydrogen production cost-competitive and this can lead to viable alternatives to electrify methanol production.
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- Award ID(s):
- 1748516
- PAR ID:
- 10522367
- Publisher / Repository:
- Energy and Environmental Science
- Date Published:
- Journal Name:
- Energy & Environmental Science
- Volume:
- 16
- Issue:
- 5
- ISSN:
- 1754-5692
- Page Range / eLocation ID:
- 2346 to 2357
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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