| 1. |
- Azeem, Muhammad, et al.
(författare)
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Combined Economic Emission Dispatch in Presence of Renewable Energy Resources Using CISSA in a Smart Grid Environment
- 2023
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Ingår i: Electronics. - : MDPI. - 2079-9292. ; 12:3
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Tidskriftsartikel (refereegranskat)abstract
- The geographically spatial and controlled distribution of fossil fuel resources, catastrophic global warming, and depletion of fossil fuel resources have forced us to integrate zero- or low-emissions energy resources, such as wind and solar, in the generation mix. These renewable energy resources are unexhausted, available around the globe, and free of cost. The advancement in wind and solar technologies has caused an appreciable decrease in installed the and global levelized costs of electricity via these sources. Therefore, the penetration of renewable energy resources in the generation mix can provide a promising solution to the above-mentioned problems. The aim of simultaneously reducing fuel consumption in terms of “Fuel Cost” and “Emission” in thermal power plants is called a combined economic emission dispatch problem. It is a combinatorial and multi-objective optimization problem. The solution of this problem is to allocate the load demand and losses on the committed units in such way that the overall costs of the generation and emission of thermal units are reduced, while the legal bounds (constraints) are met. It is a highly non-linear and complex optimization problem. The valve-point loading effect makes this problem non-convex. The addition of renewable energy resources (RERs) adds more complexities to this problem because they are intermittent. In this work, chaotic salp swarm algorithms (CISSA) are used to solve the combined economic emission dispatch problem. Chaos is used as an alternative to randomization for the tuning of the control variable to improve the trait of obtaining global extrema. Different test cases having different combinations of thermal, solar, and wind units are solved using the proposed algorithm. The results show the superiority of this study in comparison to the existent research results in terms of the cost of generation and emissions.
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| 2. |
- Javed, Haseeb, et al.
(författare)
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Ethical Frameworks for Machine Learning in Sensitive Healthcare Applications
- 2024
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Ingår i: IEEE Access. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2169-3536. ; 12, s. 16233-16254
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Tidskriftsartikel (refereegranskat)abstract
- The application of Machine Learning (ML) in healthcare has opened unprecedented avenues for predictive analytics, diagnostics, and personalized medicine. However, the sensitivity of healthcare data and the ethical dilemmas associated with automated decision-making necessitate a rigorous ethical framework. This review paper aims to provide a comprehensive overview of the existing ethical frameworks that guide ML in healthcare and evaluates their adequacy in ad-dressing ethical challenges. Specifically, this article offers an in-depth examination of prevailing ethical constructs that oversee healthcare ML, spotlighting pivotal concerns: data protection, in-formed assent, equity, and patient autonomy. Various analytical approaches including quantitative metrics, statistical methods for bias detection, and qualitative thematic analyses are applied to address these challenges. Insights are further enriched through case studies of Clinical Decision Support Systems, Remote Patient Monitoring, and Telemedicine Applications. Each case is evaluated against existing ethical frameworks to identify limitations and gaps. Based on our com-prehensive review and evaluation, we propose actionable recommendations for evolving ethical guidelines. The paper concludes by summarizing key findings and underscoring the urgent need for robust ethical frameworks to guide ML applications in sensitive healthcare environments. Future work should focus on the development and empirical validation of new ethical frameworks that can adapt to emerging technologies and ethical dilemmas in healthcare ML.
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| 3. |
- Reddy, C. Nagendranatha, et al.
(författare)
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Review of microplastic degradation : Understanding metagenomic approaches for microplastic degrading organisms
- 2023
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Ingår i: Polymer testing. - : Elsevier BV. - 0142-9418 .- 1873-2348. ; 128, s. 108223-
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Forskningsöversikt (refereegranskat)abstract
- Environmental problems caused by plastic pollution are among the most pressing issues of our time. In recent years, metagenomics has become a powerful tool for understanding the microbial communities responsible for plastic biodegradation. In this review, recent developments and trends in metagenomics are discussed, and a comprehensive overview of the metagenomic methodology, analysis, and comparison of plastic-degrading bacteria is provided. In addition, the environmental consequences of plastic degradation are discussed, such as the impact on soil, water, and air quality, as well as the potential health risks posed by ingesting and inhaling microplastics. Possible solutions to the plastic degradation problem, such as using biodegradable materials and implementing recycling programs, are also explained. This review highlights the potential impact of metagenomics on the development of sustainable solutions to plastic pollution.
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