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Renewable energy forecasting using optimized quantum temporal model based on Ninja optimization algorithm
Abstract Artificial intelligence allows improvements in renewable energy systems by increasing efficiency while enhancing reliability and reducing costs. Renewable energy forecasting receives substantial improvement by applying deep learning methods as one of its promising approaches. The research utilizes QTM with NiOA optimization for achieving maximum forecasting performance. NiOA functions through critical optimization processes when enhancing deep learning models with high accuracy for large complex datasets by selecting the most appropriate features. Fundamental data preparation steps, including normalization scaling, and gap handling, play a vital role before using input data for reliable renewable energy forecasting operations. Using the Ninja binary optimization engine produces superior results than all tested binary algorithms, including SBO, bSCA, bFA, bGA, bFEP, bGSA, bDE, bTSH and bBA, resulting in enhanced classification accuracy. The superior capability of bNinja to choose optimal features establishes its usefulness for renewable energy forecasting applications. Experimental implementation revealed that incorporating the Ninja Optimization Algorithm with the QTM model delivered the best R 2 performance at 95.15% with an exceptional RMSE value of 0.00003, thus establishing its ability to optimize renewable energy forecasting accuracy.
Measurement of eco-efficiency in the horse industry, spatiotemporal evolution and convergence analysis
Scale validation and prediction of environmental health literacy in Brazil
Effects of increasing the dietary contents of metabolizable energy and protein during the peripartum period on mammary gland development in Sistani goats
Sweet pepper yield modeling via deep learning and selection of superior genotypes using GBLUP and MGIDI
Intratumoral and peritumoral radiomics signature based on DCE-MRI can distinguish between luminal and non-luminal breast cancer molecular subtypes
Research on the impact of borehole parameters on the instability and precursor characteristics of large diameter borehole in coal seam
Assessing the contribution of wind and water erosion in the agro-pastoral ecotone of Northern China with 137Cs tracer technology
Abstract This study addresses the critical ecological challenges of soil wind and water erosion in the agro-pastoral ecotone of northern China, both of which significantly contribute to soil degradation. Understanding the relative contributions of these erosion types is essential for developing effective control measures. Using the 136 Cs tracer method, we quantified the ratio of soil wind erosion to water erosion under varying topographic and geomorphic conditions. The results revealed that cropland has experienced the most severe erosion in recent decades. Specifically, on gentle slopes (6°–8°), the rate of water erosion exceeded wind erosion by approximately eightfold. On steeper slopes (10°–15°), this trend was even more pronounced, with water erosion surpassing wind erosion by a factor of approximately 27. These findings were corroborated by measured data from a previous study area. Overall, water erosion is the dominant process in the agro-pastoral ecotone of northern China, with wind erosion playing a secondary role. Future erosion prevention strategies should prioritize hydraulic erosion control measures, particularly on sloping cropland. Furthermore, advancing research on the compound mechanisms of wind and water erosion is imperative for developing integrated mitigation strategies, ultimately supporting the sustainable development of the region’s ecological environment.
Study on the bending deformation properties of microcracked R-ECC road–bridge link slabs
Long-term systemic androgen deprivation partially modulates neuroinflammation in male AppNL−G−F/NL−G−F mice
Valosin-containing protein modulator KUS121 protects retinal neurons in a rat model of anterior ischemic optic neuropathy
Investigation of the properties of polyester blended knitted fabric dyed with luminescent dyes as a highly visible fabric
Abstract Recently, many challenges have emerged in the dyes and textile fields to keep pace with the needs of the modern era while achieving safety and comfort during usage. Where the fluorescent dye caused a major boom in the field of dyes and was used to paint many surfaces for exciting and to attract attention purpose. They were utilized on textiles, particularly those composed of synthetic materials employed in hazardous areas such as traffic roads, where they become more visible upon exposure to light. Therefore, this research aims to investigate the color fastness, physio-mechanical properties, and UV resistance of fabrics produced by knitting techniques from polyester blended with cotton or bamboo and dyed with fluorescence dyes as dispersing yellow and red dyes. The evaluation results of dyed fabrics pointed to the dispersed red dyes improved the physio-mechanical, comfortable and colorfastness properties of polyester/cotton samples compared to polyester/bamboo samples except bursting resistance, while the dispersed yellow dyes considerably enrich the ultraviolet protections of polyester/bamboo samples compared to polyester/cotton.