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Research Article
Automation of a Pharmaceutical Batch Mixer Using Programmable Logic Controller (PLC) and Supervisory Control and Data Acquisition System (SCADA)
Augustine Kweku Mbeah*
,
Erwin Normanyo
Issue:
Volume 12, Issue 4, December 2024
Pages:
71-107
Received:
9 August 2024
Accepted:
5 November 2024
Published:
26 November 2024
Abstract: This research results from the need for an effective, reliable, and highly flexible method of control of processes within a small pharmaceutical environment and will be programmed using Ladder Logic programming Language, Siemens Step 7, which entails integrating hardware and software to provide dependable and effective operations. The mixer is controlled through a Supervisory Control and Data Acquisition (SCADA) system and programmed using Ladder Logic in Siemens Step 7 PLC Programming Software. Temperature control, agitation control, and ingredient addition sequences were all integrated by the automated system. The performance of the modeled system is validated through computer simulations. The Batch Mixer is controlled using a Variable Speed Drive (VSD), enabling precise execution of the required processes throughout the entire mixing cycle. Each phase of the process is carefully controlled and monitored, with detailed simulation results demonstrating enhanced performance and efficiency in the proposed batch mixer system, as well as offering a mathematical model of the Batch Mixer and outlining the performance variables that the PLC systems use in detail. As a backup validation tool, Rockwell Automation's RSLogix 5000 PLC is used to verify the validation. The study demonstrates the effectiveness of using Siemens STEP 7 PLC for automation and validation, and with RSLogix 5000 PLC for confirmation of validation, ensuring reliable and efficient pharmaceutical batch mixing operations.
Abstract: This research results from the need for an effective, reliable, and highly flexible method of control of processes within a small pharmaceutical environment and will be programmed using Ladder Logic programming Language, Siemens Step 7, which entails integrating hardware and software to provide dependable and effective operations. The mixer is cont...
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Research Article
How to Improve the Security of Password Checkers
Tri Minh Ngo*
Issue:
Volume 12, Issue 4, December 2024
Pages:
108-113
Received:
18 October 2024
Accepted:
2 December 2024
Published:
18 December 2024
Abstract: After years of the attempt to replace password with other alternatives such as biometrics and smart cards, password is still the most pervasive user authentication mechanism. The password checking authentication is widely used for financial services, online social networks, and many other applications. This paper aims to analyze the security of a password checker qualitatively and quantitatively, and show how to improve it. Qualitative security analysis, in which it does not allow any information flow from secret date to public data, considers that the password checker is not a secure process. Therefore, an alternative analysis for the password checker is to analyze quantitatively, i.e., quantifying its information flow and determining how much secret information has been leaked. This method can be used to decide whether we can tolerate small leakages. A quantitative security analysis can be seen as a generalization of a qualitative one. To improve the security of the password checker, we propose a noisy-output policy, i.e., a situation where a system operator is able to add noise to the output: instead of always producing the exact outcomes, the system sometimes reports noisy outcomes. The noisy outcomes reduce the correlation between the output and the input, and thus reduce the leakage.
Abstract: After years of the attempt to replace password with other alternatives such as biometrics and smart cards, password is still the most pervasive user authentication mechanism. The password checking authentication is widely used for financial services, online social networks, and many other applications. This paper aims to analyze the security of a p...
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Research Article
Heart Health Detector GPT Based on GPT-4o Model
Neha Bansal,
Bhawna Singla*
Issue:
Volume 12, Issue 4, December 2024
Pages:
114-124
Received:
2 December 2024
Accepted:
12 December 2024
Published:
30 December 2024
DOI:
10.11648/j.acis.20241204.13
Downloads:
Views:
Abstract: The Heart Health Detector paper leverages advancements in artificial intelligence and machine learning to provide an accessible and user-friendly tool for monitoring and managing heart health. This innovative technology has the potential to revolutionize preventive healthcare by empowering individuals to take control of their cardiovascular well-being. By making heart health monitoring more accessible and user-friendly, the Heart Health Detector could lead to earlier detection of cardiac issues and improved patient outcomes. Furthermore, this tool may help reduce the burden on healthcare systems by promoting proactive heart health management and potentially decreasing the incidence of severe cardiac events. Cardiovascular diseases are a leading cause of death globally, and their early detection and management can significantly reduce risks and improve outcomes. This study bridges the gap between complex medical data and user-friendly health monitoring through a web application that offers personalized health insights, proactive heart health management, and simplified user experiences. It ensures data privacy and security, and encourages preventive health measures. The application uses GPT-4 to analyze user-provided health data using data analysis on two data files: a) hospital file and b) heart file, delivering personalized recommendations. It empowers users to take proactive steps toward optimizing their cardiovascular well-being, democratizing access to heart health information, and contributing to the prevention and management of heart diseases on a broader scale. The development involved meticulous conceptualization, data acquisition, model training, and validation, resulting in a sophisticated yet user-friendly platform that integrates advanced AI algorithms to analyze health metrics and provide actionable insights and recommendations. While artificial intelligence and machine learning offer promising opportunities for developing user-friendly heart health monitoring tools, they also present significant challenges in terms of data privacy, security, and the effective integration of complex medical information into accessible applications. The Heart Health Detector aims to bridge this gap by providing personalized insights and recommendations, yet it must carefully balance sophisticated AI algorithms with user-friendliness to ensure widespread adoption and impact. Although such tools have the potential to democratize access to heart health information and promote preventive measures, their limitations and challenges must be carefully considered to maximize their effectiveness and reliability in real-world healthcare settings.
Abstract: The Heart Health Detector paper leverages advancements in artificial intelligence and machine learning to provide an accessible and user-friendly tool for monitoring and managing heart health. This innovative technology has the potential to revolutionize preventive healthcare by empowering individuals to take control of their cardiovascular well-be...
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