Lasso regression, known for its efficacy in high-dimensional data analysis and feature selection, stands as a cornerstone in the realm of supervised learning for regression estimation. However, hyperparameter tuning f...
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Lasso regression, known for its efficacy in high-dimensional data analysis and feature selection, stands as a cornerstone in the realm of supervised learning for regression estimation. However, hyperparameter tuning for Lasso regression is often time-consuming and susceptible to noisy data in big data scenarios. In this paper we introduce anew additive Lasso regression without Hyperparameter Tuning (ALR-HT) by integrating Markov resampling with additive models. We estimate the generalization bounds of the proposed ALR-HT and establish the fast learning rate. The experimental results for benchmark datasets confirm that the proposed ALR-HT algorithm has better performance in terms of sampling and training total time, mean squared error (MSE) compared to other algorithms. We present some discussions on the ALR-HT algorithm and apply it to Ridge regression, to show its versatility and effectiveness in regularized regression scenarios.
A common issue associated with gas bearing-rotor systems is the tendency to generate self-excited vibrations, leading to instability. To address this problem, the fluid-structure coupling model of an aerostatic bearin...
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A common issue associated with gas bearing-rotor systems is the tendency to generate self-excited vibrations, leading to instability. To address this problem, the fluid-structure coupling model of an aerostatic bearing-rotor system is established in this paper. Then, a hybrid method combining the finite difference method (FDM) and direct integration method is employed to solve the bearing lubrication equation and rotor motion equation simultaneously. Furthermore, based on the orbits of rotor center, frequency spectrum diagrams, Poincare maps, waterfall diagrams, and bifurcation diagrams, the effects of rotational speed, rotor mass, orifice diameter, and nominal clearance on the nonlinear dynamic behaviors of the bearing-rotor system are investigated. The results indicate that the system exhibits rich nonlinear behaviors with increasing rotational speed and rotor mass, including the occurrence of typical half-speed whirl. However, the nonlinear vibrations of the system can be restricted by selecting appropriate bearing structural parameters, providing theoretical guidance for the design of aerostatic bearing-rotor systems.
BiVO4 is extensively studied in photoelectrochemical water splitting systems. Its relatively large bandgap and low charge carrier mobility severely limit the solar-driven oxygen evolution reaction on BiVO4. A gradient...
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BiVO4 is extensively studied in photoelectrochemical water splitting systems. Its relatively large bandgap and low charge carrier mobility severely limit the solar-driven oxygen evolution reaction on BiVO4. A gradient distribution of oxygen vacancies can modulate the structural and electrical properties of BiVO4. However, the formation of oxygen vacancies remains a daunting challenge. Here, we propose a simple hydrothermal post-treatment method to deposit a thin amorphous BiO x layer (similar to 8 nm) with gradient oxygen vacancies on the surface of BiVO4 and enable directional hole migration. The resultant BiVO4/BiO x /NiFeO x photoanode achieves a high photocurrent density of 6.34 mA cm-2 at 1.23 V (vs RHE) in 1.0 M potassium borate solution. A solar-to-hydrogen conversion efficiency of 0.89% is obtained in a device using a BiVO4/BiO x /NiFeO x photoanode and a CuBi2O4 photocathode at zero bias. This work highlights a novel strategy for modulating the oxygen vacancy gradient and provides insights into the design of unbiased solar water splitting systems.
The flow stress behavior and microstructure evolution of the Mg-11Gd-1Zn-0.5Sn-0.5Zr alloy under thermal compression have been studied in detail. The results show that the flow stress of the alloy decreases with incre...
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The flow stress behavior and microstructure evolution of the Mg-11Gd-1Zn-0.5Sn-0.5Zr alloy under thermal compression have been studied in detail. The results show that the flow stress of the alloy decreases with increase in temperature and decrease in strain rate. The activation energy of the alloy was found to be as high as 272.48 kJ/mol by constructing the constitutive equation, which was attributed to the dislocation hindrance of the dense LPSO phase and its kink bands. As the temperature increases, the deformation mechanism gradually shifts from prismatic slip to pyramid slip, and 450 degrees C is a watershed. Twins and kinks also play an important role in the plastic deformation process. In addition to the particle stimulated nucleation (PSN) mechanism of fragmentary precipitates, dynamic recrystallization (DRX) nucleation can also be stimulated by twins and kink bands due to high stress concentration. However, the large-size and dense lamellar LPSO phase inhibits the nucleation and growth of DRX. When the strain temperature reaches 450 degrees C or even 500 degrees C, the LPSO phase in the alloy gradually dissolves into fragments, and the particle stimulated nucleation mechanism promotes the formation of DRX, while reducing the resistance of grain boundary movement to facilitate the growth of DRX.
Thermal-hydraulic analysis is crucial in reactor engineering. High-fidelity simulations, utilizing advanced computing techniques and supercomputing resources, are highly regarded. High-quality fluid mesh models are es...
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Thermal-hydraulic analysis is crucial in reactor engineering. High-fidelity simulations, utilizing advanced computing techniques and supercomputing resources, are highly regarded. High-quality fluid mesh models are essential for complex reactors' high-fidelity simulations. Using existing tools for model construction has limitations in quality control, performance, user dependency, file generation, and visualization. Estimating time and memory consumption for full-core meshing is also not possible. A R-IMG approach is designed, it effortlessly creates mesh models for intricate flow field, demonstrating exceptional modeling performance, robustness, scalability, and reduced user dependency, while its flexible file manner effectively addresses challenges in generating and visualizing large-scale mesh files. Extensive testing validates R-IMG's effectiveness and reliability in meshing the reactor's flow field. It efficiently generates high-quality meshes for the complex flow field in the entire fuel region of CEFR, completing the process within 7 hand 10GB of memory. The resulting model has around 14 billion cells and an average quality of 0.7. R-IMG achieves a maximum parallel scale of 3200 processes for file generation, with approximately 90% parallel efficiency. These results demonstrate that R-IMG outperforms existing tools in core meshing and shows significant potential for full-core meshing. Successful visualization of models and benchmark tests provide evidence for models' correctness.
With the growing penetration of renewable energy in power systems, its stochastic nature poses significant challenges to the reliable operation of integrated transmission-distribution systems (ITDSs). However, most ex...
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With the growing penetration of renewable energy in power systems, its stochastic nature poses significant challenges to the reliable operation of integrated transmission-distribution systems (ITDSs). However, most existing approaches for handling uncertainty overlook the statistical relationship between the wind power forecast value and its associated forecast error, with the latter being a conditional distribution under the given prediction. Approaches that disregard the structural information of the true distribution often lead to overly conservative solutions and exhibit poor out-of-sample performance. This paper closes this gap by proposing a conditional distributionally robust optimization (DRO) method for ITDSs. Specifically, a novel ambiguity set is built by exploiting the dependence of the wind power forecast error on its forecast value, which differs from most of the existing ones. Consequently, a conditional DRO framework is developed for the ITDS dispatch problem under uncertainty. To ensure tractability, the model is reformulated into a linear programming problem through the duality theory-based transformation and Conditional Value-at-Risk (CVaR) approximation. Furthermore, a self-adaptive alternating direction method of multipliers (ADMM) is applied to enable a distributed solution to improve computational efficiency and preserve privacy. Case studies demonstrate that the proposed approach outperforms regular DRO in minimizing dispatch costs and satisfying reliability requirements due to the utilization of amore informed ambiguity set. Also, the distributed algorithm reduces solution time and scales well under various ITDSs.
Metal organic frameworks (MOFs) are a class of potential superhydrophobic-oleophilic materials. The organic ligands in superhydrophobic MOFs usually contain long alkyl chains, fluorine groups or aromatic rings with la...
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Metal organic frameworks (MOFs) are a class of potential superhydrophobic-oleophilic materials. The organic ligands in superhydrophobic MOFs usually contain long alkyl chains, fluorine groups or aromatic rings with large pi conjugation, the preparation of which suffers from high cost, complex operation and so on. In addition, the topological structure of MOFs plays an important role in the hydrophobicity, which may be ignored previously. Here we report a superhydrophobic-oleophilic MOF (BiPPA2) obtained by a facile and fast method, which not only displays a large water contact angle of up to 163 degrees and a small sliding angle of nearly equal to 0 degrees, but also exhibits high sorption capacity for multiple oils and organic solvents, well reusability and high oil retention. In addition, BiPPA2 is stable in a wide pH range (0.5-11.0). Finally, the single crystal structure of BiPPA2 is resolved to reveal the intrinsic reason for the super-hydrophobicity. This work may inspire the further design of pristine superhydrophobic MOFs based on a simple method, which enriches the family of superhydrophobic MOFs and has great significance for practical applications. A novel two-dimensional layered MOF (BiPPA2) is obtained by a facile and fast method, which displays a large water contact angle of up to 163 degrees, a small sliding angle of nearly equal to 0 degrees and favorable sorption capacity for various oils and organic solvents, due to its unique structure of two-terminal hydrophobic-oleophilic benzene ring layers. image
One-dimensional nano-grating standard (ODNGS) is widely recognized as a crucial nanometric standard for metrological technology. However, achieving the ultratiny size of ODNGS with high consistent uniformity and low r...
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One-dimensional nano-grating standard (ODNGS) is widely recognized as a crucial nanometric standard for metrological technology. However, achieving the ultratiny size of ODNGS with high consistent uniformity and low roughness by conventional processes such as the inductively coupled plasma (ICP) etching method presents a significant challenge in obtaining accurate calibration values. In this work, a 50-nm ODNGS with a conformal buffer layer (Al2O3) is successfully obtained, indicating outstanding stability and abrasion resistance. Remarkably, the introduction of hydrogen silsesquioxane (HSQ) and amorphous Al2O3 simultaneously guarantees an incredibly small expanded uncertainty (0.5 nm) and repeatability of the standard uniformity (less than 0.3 nm) in the grating dimensions. The I-V curves of ODNGS with an Al2O3 buffer layer at room temperature (RT) and 200 degrees C are depicted respectively to showcase the sustained favorable insulation properties. Notably, the nanostructure fluctuation, line edge roughness (LER) and line width roughness (LWR) of the standard can be decreased obviously by 64.1%, 63% and 70%, respectively. Our results suggest that the ODNGS with Al2O3 exhibits exceptional precision and robust calibration reliability for calibrating nanoscale measuring instruments. It holds tremendous potential for manufacturing high-precision nanostructures and grating arrays with precisely controllable dimensions, which will play a pivotal role in the fabrication of microfluidics chips, metasurface and photodetectors in the future.
The paper reports on the atomic investigation aboutβphase in Mg_(96)Gd_(2)Y_(1)Ni_(1) alloy by using the first-principles study and the high-angle annular dark-field scanning transmission electron microscope(HAADF-ST...
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The paper reports on the atomic investigation aboutβphase in Mg_(96)Gd_(2)Y_(1)Ni_(1) alloy by using the first-principles study and the high-angle annular dark-field scanning transmission electron microscope(HAADF-STEM)corrected by atomic *** using HAADF-STEM,the rectangularβphases were observed in the underage and peak aging stages in Mg_(96)Gd_(2)Y_(1)Ni_(1) ***βphase could be precipitated from the previously precipitatedβphase,and theβphase grew in steps when it was precipitated.A special transition structure of three atomic layer thicknesses was first observed at the edge of theβphase and the structure of this interface is probably as theβ/Mg_(1) interface for the analysis of thermodynamic characterization and electronic ***β'phase and theβ_(H) structure were precipitated only at the edge of the length directions of theβ***β'phase continues to grow into aβphase directly without the formation ofβ_(1) phase,resulting in an increase in the length of theβphase,which is discovered for the first time.
Based on the principles of thermodynamics, we elucidate the fundamental reasons behind the hysteresis of spontaneous polarization in ferroelectric materials during heating and cooling processes. By utilizing the effec...
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Based on the principles of thermodynamics, we elucidate the fundamental reasons behind the hysteresis of spontaneous polarization in ferroelectric materials during heating and cooling processes. By utilizing the effective Hamiltonian method in conjuction with the phase-field model, we have successfully reproduced the thermal hysteresis observed in ferroelectric materials during phase transitions. The computational results regarding the electrocaloric effect from these two different computational scales closely align with experimental measurements. Furthermore, we analyze how the first-order ferroelectric phase transition gradually diminishes with an increasing applied electric field, exhibiting characteristics of second-order-like phase transition. By employing the characteristic parameters of thermal hysteresis, we have established a pathway for calculations across different computational scales, thereby providing theoretical support for further investigations into the properties of ferroelectric materials through concurrent multiscale simulations.
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