| 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. |
- Iqbal, Sajid, et al.
(författare)
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Essential oils of four wild plants inhibit the blood seeking behaviour of female Aedes aegytpi
- 2023
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Ingår i: Experimental parasitology. - : Elsevier BV. - 0014-4894 .- 1090-2449. ; 244
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Tidskriftsartikel (refereegranskat)abstract
- Aedes aegypti (Diptera: Culicidae) mosquito is an important vector of many disease-causing pathogens. An effective way to escape from these mosquito-borne diseases is to prevent mosquito bites. In the current study, essential oils of Lepidium pinnatifidum, Mentha longifolia, Origanum vulgare, and Agrimonia eupatoria were evaluated for their repellent potential against Ae. aegypti females. Essential oils were extracted using steam distillation from freshly collected aerial parts of the plants and tested against 4–5 day old females of Ae. aegypti through the human bait technique for repellency and repellent longevity assays. The chemical composition of extracted essential oils was explored by gas chromatography coupled with mass spectrometry (GC-MS). The essential oils of L. pinnatifidum, M. longifolia, O. vulgare, and A. eupatoria at a dose of 33 μg/cm2 showed 100%, 94%, 87%, and 83% mosquito repellent activity, respectively. Furthermore, M. longifolia and O. vulgare essential oils exhibited 100% repellency at a dose of 165 μg/cm2, whereas A. eupatoria essential oil showed 100% repellency only at 330 μg/cm2. In the time-span bioassay, M. longifolia and O. vulgare essential oils showed protection against Ae. aegypti bites for 90 and 75 min, respectively whereas both A. eupatoria and L. pinnatifidum were found active for 45 min. Phenylacetonitrile (94%), piperitone oxide (34%), carvacrol (20%) and α-pinene (62%) were the most abundant compounds in L. pinnatifidum, M. longifolia, O. vulgare and A. eupatoria essential oils, respectively. The current study demonstrates that M. longifolia and O. vulgare essential oils possess the potential to be used as an alternative to synthetic chemicals to protect humans from mosquito bites.
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| 3. |
- Qadri, Syed Furqan, et al.
(författare)
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CT-based automatic spine segmentation using patch-based deep learning
- 2023
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Ingår i: International Journal of Intelligent Systems. - : Hindawi Publishing Corporation. - 0884-8173 .- 1098-111X. ; 2023
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Tidskriftsartikel (refereegranskat)abstract
- CT vertebral segmentation plays an essential role in various clinical applications, such as computer-assisted surgical interventions, assessment of spinal abnormalities, and vertebral compression fractures. Automatic CT vertebral segmentation is challenging due to the overlapping shadows of thoracoabdominal structures such as the lungs, bony structures such as the ribs, and other issues such as ambiguous object borders, complicated spine architecture, patient variability, and fluctuations in image contrast. Deep learning is an emerging technique for disease diagnosis in the medical field. This study proposes a patch-based deep learning approach to extract the discriminative features from unlabeled data using a stacked sparse autoencoder (SSAE). 2D slices from a CT volume are divided into overlapping patches fed into the model for training. A random under sampling (RUS)-module is applied to balance the training data by selecting a subset of the majority class. SSAE uses pixel intensities alone to learn high-level features to recognize distinctive features from image patches. Each image is subjected to a sliding window operation to express image patches using autoencoder high-level features, which are then fed into a sigmoid layer to classify whether each patch is a vertebra or not. We validate our approach on three diverse publicly available datasets: VerSe, CSI-Seg, and the Lumbar CT dataset. Our proposed method outperformed other models after configuration optimization by achieving 89.9% in precision, 90.2% in recall, 98.9% in accuracy, 90.4% in F-score, 82.6% in intersection over union (IoU), and 90.2% in Dice coefficient (DC). The results of this study demonstrate that our model's performance consistency using a variety of validation strategies is flexible, fast, and generalizable, making it suited for clinical application.
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| 4. |
- Khan, Muhammad Azeem, et al.
(författare)
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Value of Information and Timing-aware Scheduling for Federated Learning
- 2023
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Ingår i: 2023 IEEE CONFERENCE ON STANDARDS FOR COMMUNICATIONS AND NETWORKING, CSCN. - : IEEE. - 9798350395389 - 9798350395396 ; , s. 94-99
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Konferensbidrag (refereegranskat)abstract
- Data possesses significant value as it fuels advancements in AI. However, protecting the privacy of the data generated by end-user devices has become crucial. Federated Learning (FL) offers a solution by preserving data privacy during training. FL brings the model directly to User Equipments (UEs) for local training by an access point (AP). The AP periodically aggregates trained parameters from UEs, enhancing the model and sending it back to them. However, due to communication constraints, only a subset of UEs can update parameters during each global aggregation. Consequently, developing innovative scheduling algorithms is vital to enable complete FL implementation and enhance FL convergence. In this paper, we present a scheduling policy combining Age of Update (AoU) concepts and data Shapley metrics. This policy considers the freshness and value of received parameter updates from individual data sources and real-time channel conditions to enhance FL's operational efficiency. The proposed algorithm is simple, and its effectiveness is demonstrated through simulations.
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