Ammonia is an essential chemical raw material and energy carrier, with its synthesis process being of significant importance to global agriculture and industrial production. Protonic ceramic electrolysis cells (PCECs)...
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Cardiovascular diseases currently pose the greatest threat to human health and future predicament is uncertain. Since most heart-related problems are reflected by the small variations in the heart's sounds, qualit...
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This study introduces an innovative approach to enhancing e-commerce product listings through subject-driven text-to-image generation, leveraging advanced AI technologies. Focused on transforming consumer first impres...
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This study aims to significantly improve existing quantitative structure-property relationship（QSPR） models for predicting the octanol-water partition coefficient(KOW). This is because accurate predictions of KOWare...
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This study aims to significantly improve existing quantitative structure-property relationship（QSPR） models for predicting the octanol-water partition coefficient(KOW). This is because accurate predictions of KOWare crucial for assessing the environmental behavior and bioaccumulation potential of chemicals. Previous models have reported determination coefficient（R2） values between 0.9451 and 0.9681, and this research seeks to exceed these benchmarks. Three machine learning（ML） models are explored, r.e., feed-forward neural networks（FNN）,extreme gradient boosting（XGBoost）, and random forest（RF）. Using a dataset of 14,610 solvents（14,580 after data cleaning） and 21 molecular descriptors derived from SMILES representations, we rigorously evaluate these models based on R2, mean absolute error（MAE）, root mean squared error（RMSE）, and mean relative error（MRE）.Notably, the best model developed, the XGBoost-based QSPR, demonstrated exceptional performance, exhibiting an impressive R2value of 0.9772, surpassing benchmarks set by prior research models. Additionally, shapley additive explanation（SHAP） analysis is also employed for model interpretation, and it is revealed that the top five influential input features include SMR＿VSA8, SMR＿VSA3, Kappa2, Heavy Atom Count, and fr＿furan. This study not only sets a new benchmark for KOWprediction accuracy but also enhances the interpretability of QSPR models.
Images are used widely nowadays. Images are used in many fields such as medicine to terrain mapping. There is a need to compress the images and represent them in shorter form for effective transmission. Several techni...
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With the rapid development of artificial intelligence technology, the application of deep learning in the field of education is gradually increasing. This article proposes a method for analysing college students’ aca...
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The traditional Bidirectional Wireless Power Transfer (BWPT) system typically utilizes a Power Factor Correction (PFC) converter cascaded with an inverter on the transmitter side. Due to its complex structure and the ...
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As the demand for ultrahigh-speed bearings grows, hybrid gas-magnetic bearings (HGMBs) have emerged as a research focus due to the ability to integrate the merits of active magnetic bearings (AMBs) and gas bearings (G...
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This paper introduces a lightweight remote sensing image dehazing network called multidimensional weight regulation network(MDWR-Net), which addresses the high computational cost of existing methods. Previous works, o...
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This paper introduces a lightweight remote sensing image dehazing network called multidimensional weight regulation network(MDWR-Net), which addresses the high computational cost of existing methods. Previous works, often based on the encoder-decoder structure and utilizing multiple upsampling and downsampling layers, are computationally expensive. To improve efficiency, the paper proposes two modules: the efficient spatial resolution recovery module(ESRR) for upsampling and the efficient depth information augmentation module(EDIA) for *** modules not only reduce model complexity but also enhance performance. Additionally, the partial feature weight learning module(PFWL) is introduced to reduce the computational burden by applying weight learning across partial dimensions, rather than using full-channel *** overcome the limitations of convolutional neural networks(CNN)-based networks, the haze distribution index transformer(HDIT) is integrated into the decoder. We also propose the physicalbased non-adjacent feature fusion module(PNFF), which leverages the atmospheric scattering model to improve generalization of our MDWR-Net. The MDWR-Net achieves superior dehazing performance with a computational cost of just 2.98×10^(9) multiply-accumulate operations(MACs),which is less than one-tenth of previous methods. Experimental results validate its effectiveness in balancing performance and computational efficiency.
Background: Centrifugal pump is widely used in industrial production as a key fluid conveying equipment. The transient characteristics during its start-up process may lead to vibration, noise and efficiency loss. Ther...
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