This study proposes an intelligent scheduling system for high-altitude photovoltaic power generation, utilizing a hybrid optimization approach that combines the Long-nosed Raccoon optimizationalgorithm (COA) and the ...
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This study proposes an intelligent scheduling system for high-altitude photovoltaic power generation, utilizing a hybrid optimization approach that combines the Long-nosed Raccoon optimizationalgorithm (COA) and the black-wingedkiteoptimizationalgorithm (BKA) (COA-BKA). The goal is to enhance scheduling accuracy, stability, and response speed under the unique environmental conditions of high-altitude regions, such as fluctuating light intensity, extreme temperatures, and dynamic load demands. In experimental comparisons with traditional algorithms like Particle Swarm optimization (PSO) and Genetic algorithm (GA), COA-BKA achieved a scheduling accuracy of 0.98, outperforming PSO (0.92) and GA (0.90). COA-BKA also demonstrated superior convergence speed, reaching the optimal solution by the 50th iteration, while PSO and GA required more iterations (80 and 100, respectively). Additionally, COA-BKA completed scheduling in just 4.5 s, significantly faster than PSO (6.3 s) and GA (7.2 s). The system effectively handled fluctuating light intensity and load demand changes, showcasing its robust adaptability. These results suggest that COA-BKA provides a highly efficient and stable solution for intelligent scheduling in high-altitude photovoltaic power systems, improving operational efficiency and reducing costs, while offering significant advancements for real-time optimization in smart grids.
The extensive grid connection of new energy and nonlinear power electronic devices has made power quality disturbance (PQD) problems more frequent, seriously affecting the stable operation of the power grid system. In...
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The extensive grid connection of new energy and nonlinear power electronic devices has made power quality disturbance (PQD) problems more frequent, seriously affecting the stable operation of the power grid system. In response to the real-time response requirements of the research model of this problem, this study proposed an improved Shapelet method and applied it to the classification of PQDs. First, the concept of subsequence blocks was proposed, and the diversity of Shapelet was enhanced by multiple subsequences of multiple length ranges. In order to solve the problem of high time complexity of searching subsequence blocks, the length range of subsequence blocks was determined by the multi-scale extreme point peak distance method. This method uses the blackkitealgorithm (BKA) to optimize the parameters of the Variable Mode Decomposition (VMD), decomposes the PQD signal into multiple modal components, and then screens out the disturbance components through permutation and combination entropy and calculates the average peak distance of the extreme points;secondly, a multiple loss function is used to optimize the quality of the selected subsequence blocks through the similarity loss and distance loss between subsequence blocks;finally, the K-means weight initialization method is used to accelerate the convergence of the model. Experimental results show that this method has an accuracy rate of 98.63 % in identifying PQDs in 16 simulated environments, with an average time consumption of 0.141 ms for per data sample. On the measured real data, the recognition accuracy rate is 98.20 % with a time consumption of 0.08 ms for per data sample. This method can provide a good choice for real-time PQD analysis of power grid systems.
Background: Quadruplet therapy has become standard frontline therapy in transplant eligible NDMM patients. Using data from the MASTER and GRIFFIN trials, Chhabra et al. reported that Dara-Len containing quadruplet the...
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Background: Quadruplet therapy has become standard frontline therapy in transplant eligible NDMM patients. Using data from the MASTER and GRIFFIN trials, Chhabra et al. reported that Dara-Len containing quadruplet therapies had minimal impact on stem cell harvesting and engraftment. It is unclear if this remains true in a real-world setting where heterogeneity exists among patients and in institutional practices. Herein, we describe our experience of stem cell mobilization and collection in NDMM patients receiving DRVd at Levine Cancer Institute (LCI) and Emory Winship Cancer Institute. Methods: In this multi-center retrospective analysis, NDMM patients were eligible if they received DRVd and pursued stem cell collection between September, 2019 and January, 2024 at LCI and January, 2019 and July, 2022 at Emory. Patients either received 10 mcg/kg of growth colony-stimulating factor (G-CSF) daily (LCI) or 7.5 mcg/kg twice daily (Emory) for 4 days prior to collection and 1 dose on the morning of apheresis. Plerixafor was provided on day -1 of apheresis as a preemptive mobilization strategy at LCI and on an as needed basis at Emory. Patients with a suboptimal stem cell yield on day 1 received additional doses of G-CSF with or without rescue plerixafor at both sites followed by a second day of stem cell collection. Stem cell yield failure was defined as the inability to achieve a minimal goal dose of 2.0 × 106 cells/kg. Categorical outcomes were summarized with frequencies and proportions while numerical outcomes were summarized with descriptive statistics. Select data elements were only available in the LCI cohort. Results: A total of 423 patients were analyzed. The median patient age was 62 years (range, 23-79), and 38.1% of the cohort was African American. Thirteen percent of the cohort had high risk cytogenetics and 19.1% had ISS stage III disease. At LCI, patients received a median of 4 (range, 1-14) cycles of induction therapy before stem cell collection
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