The hierarchical hollow framework involving interconnected highly conductive N-doped carbon nanotube networks and CoS2 particles were successfully prepared by metal-organic framework (MOF) derived ...method. After the two pyrolysis process in the atmosphere of reducing gas and inert gases, numerous carbon nanotubes interlaced on the surface of framework and CoS2 nanoparticles also attached on the surface. The electromagnetic parameters of CoS2/NCNTs composites can be well controlled by regulating the loadings of sample in sample-paraffin mixture. The results demonstrate that CoS2/NCNTs with 50% loadings show superior electromagnetic wave absorption properties in the wide frequency range, almost covering the whole X bands (8–12 GHz) only with a relative thin thickness of 1.6 mm. Hierarchical hollow structure and better impedance matching performance between N-doped carbon nanocube and CoS2 nanoparticles contribute to the enhancement of microwave absorption ability. Our work confirms that hollow framework CoS2/NCNTs composites can provide a novel idea for designing high-absorbability microwave absorbers.
•Porous NiCo2O4/NiO/Co3O4 nanoflowers (NCNs) are prepared by adding oxalic acid.•NCNs-0.1 possess optimal distribution of micro-mesopores.•NCNs-0.1 electrodes deliver an enhanced specific capacitance ...of 1693F g−1.•88% capacitance can be retained after 6000 cycles.•Excellent energy densities of 43.02 Wh kg−1 and power densities of 820.29 W kg−1.
The rational design of micro-mesopores is a hugely challenging for porous metal-based nanomaterials. Here oxalic acid (H2C2O4) as control agent is proposed for the first time to prepare 3D optimal micro-mesoporous NiCo2O4/NiO/Co3O4 nanoflowers (NCNs). Theoretical and experimental analyses demonstrate NCNs-0.1 which are prepared by adding 0.1 g H2C2O4 possess optimal distribution of micro-mesopores. The optimal structure creates abundant active sites and fluent ionic channels. Beneficially, NCNs-0.1 electrodes deliver an enhanced specific capacitance of 1693F g−1 at 1 A g−1 and outstanding cyclic stability (88% capacitance retention after 6000 cycles). Further, the assembled NCNs-0.1//AC capacitor achieves excellent energy densities of 43.02 Wh kg−1 at power densities of 820.29 W kg−1. The current NCNs-0.1 confirms a practicable method to optimize the electrochemical performances of supercapacitors by utilizing H2C2O4 to construct 3D optimal micro-mesoporous nanoflower architectures.
Reservoirs are fundamental human‐built infrastructures that collect, store, and deliver fresh surface water in a timely manner for many purposes. Efficient reservoir operation requires policy makers ...and operators to understand how reservoir inflows are changing under different hydrological and climatic conditions to enable forecast‐informed operations. Over the last decade, the uses of Artificial Intelligence and Data Mining AI & DM techniques in assisting reservoir streamflow subseasonal to seasonal forecasts have been increasing. In this study, Random Forest RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR) are employed and compared with respect to their capabilities for predicting 1 month‐ahead reservoir inflows for two headwater reservoirs in USA and China. Both current and lagged hydrological information and 17 known climate phenomenon indices, i.e., PDO and ENSO, etc., are selected as predictors for simulating reservoir inflows. Results show (1) three methods are capable of providing monthly reservoir inflows with satisfactory statistics; (2) the results obtained by Random Forest have the best statistical performances compared with the other two methods; (3) another advantage of Random Forest algorithm is its capability of interpreting raw model inputs; (4) climate phenomenon indices are useful in assisting monthly or seasonal forecasts of reservoir inflow; and (5) different climate conditions are autocorrelated with up to several months, and the climatic information and their lags are cross correlated with local hydrological conditions in our case studies.
Key Points
Artificial intelligence and data mining (AI&DM) techniques are powerful regression tools in developing reservoir monthly inflow forecasts
Climate phenomenon indices have a complex relationship with hydrological conditions, and provide useful information for reservoir operations
Different AI & DM techniques have strengths and limitations and are suggested to use with proper parameterization and prior examination
Compared to ground precipitation measurements, satellite-based precipitation estimation products have the advantage of global coverage and high spatiotemporal resolutions. However, the accuracy of ...satellite-based precipitation products is still insufficient to serve many weather, climate, and hydrologic applications at high resolutions. In this paper, the authors develop a state-of-the-art deep learning framework for precipitation estimation using bispectral satellite information, infrared (IR), and water vapor (WV) channels. Specifically, a two-stage framework for precipitation estimation from bispectral information is designed, consisting of an initial rain/no-rain (R/NR) binary classification, followed by a second stage estimating the nonzero precipitation amount. In the first stage, the model aims to eliminate the large fraction of NR pixels and to delineate precipitation regions precisely. In the second stage, the model aims to estimate the pointwise precipitation amount accurately while preserving its heavily skewed distribution. Stacked denoising autoencoders (SDAEs), a commonly used deep learning method, are applied in both stages. Performance is evaluated along a number of common performance measures, including both R/NR and real-valued precipitation accuracy, and compared with an operational product, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS). For R/NR binary classification, the proposed two-stage model outperforms PERSIANN-CCS by 32.56% in the critical success index (CSI). For real-valued precipitation estimation, the two-stage model is 23.40% lower in average bias, is 44.52% lower in average mean squared error, and has a 27.21% higher correlation coefficient. Hence, the two-stage deep learning framework has the potential to serve as a more accurate and more reliable satellite-based precipitation estimation product. The authors also provide some future directions for development of satellite-based precipitation estimation products in both incorporating auxiliary information and improving retrieval algorithms.
Lithium–sulfur (Li–S) batteries are fascinating as next-generation high specific energy density storation devices. Herein, we report the fabrication of a three-dimensional porous hollow core@shell ...structure composed of a carbon aerogel assembled core etched via nano-CaCO3 and a polypyrrole nanoparticle shell as a sulfur scaffold for Li–S batteries. The as-prepared sulfur cathodes exhibit excellent reversible capacity (1031.9 mAh g–1 at 0.1 C), outstanding rate capability (566.5 and 477.2 mAh g–1 at 1 and 2 C, respectively), and superior cycling stability (74.2% capacity retention rate at 1 C). The improved electrochemical performance can be attributed to the extraordinary core–shell structure: the honeycomb-like carbon aerogel core provides fast transportation for the Li+/e–, and even sufficient free space for the volume expansion; the polypyrrole nanoparticle shell acts not only as a physical obstacle but also as a polar material to restrict the shuttling of polysulfides by chemical interaction. These inspiring results specify that such electrodes could empower high performance, fast charging, and flexible Li–S batteries through a tunable molecular self-assembly method to clad strong polar material on carbon materials.
Multi-model ensemble strategy is a means to exploit the diversity of skillful predictions from different models. This paper studies the use of Bayesian model averaging (BMA) scheme to develop more ...skillful and reliable probabilistic hydrologic predictions from multiple competing predictions made by several hydrologic models. BMA is a statistical procedure that infers consensus predictions by weighing individual predictions based on their probabilistic likelihood measures, with the better performing predictions receiving higher weights than the worse performing ones. Furthermore, BMA provides a more reliable description of the total predictive uncertainty than the original ensemble, leading to a sharper and better calibrated probability density function (PDF) for the probabilistic predictions. In this study, a nine-member ensemble of hydrologic predictions was used to test and evaluate the BMA scheme. This ensemble was generated by calibrating three different hydrologic models using three distinct objective functions. These objective functions were chosen in a way that forces the models to capture certain aspects of the hydrograph well (e.g., peaks, mid-flows and low flows). Two sets of numerical experiments were carried out on three test basins in the US to explore the best way of using the BMA scheme. In the first set, a single set of BMA weights was computed to obtain BMA predictions, while the second set employed multiple sets of weights, with distinct sets corresponding to different flow intervals. In both sets, the streamflow values were transformed using Box–Cox transformation to ensure that the probability distribution of the prediction errors is approximately Gaussian. A split sample approach was used to obtain and validate the BMA predictions. The test results showed that BMA scheme has the advantage of generating more skillful and equally reliable probabilistic predictions than original ensemble. The performance of the expected BMA predictions in terms of daily root mean square error (DRMS) and daily absolute mean error (DABS) is generally superior to that of the best individual predictions. Furthermore, the BMA predictions employing multiple sets of weights are generally better than those using single set of weights.
Owing to the generation of waste during mining and processing, granite stone causes great damage to the environment. In this study, the broken granite waste and the stone powder were used as concrete ...coarse aggregate and “admixture” to make granite waste concrete hollow blocks, which could meet the requirements of compressive strength, durability and economy. The influence of the water-binder ratio, cement dosage, and powder-slag ratio on the compressive strength and freeze-thaw properties of the block were analyzed. Subsequently, the optimal mix ratio was determined based on range analysis. The effect of granite waste on the compressive strength and durability of the blocks was also analyzed. In addition, the economic effects of the granite waste concrete hollow block were analyzed. The results show that granite waste concrete hollow blocks with a water-binder ratio of 0.55, powder slag ratio of 15%, and cement dosages of 340 kg/m3 and 320 kg/m3 can meet the strength requirements of MU10 grade and the requirements of freeze-thaw properties. The strength and frost resistance of the granite concrete hollow blocks were enhanced because the granite powder filled the aggregate gaps. In addition, the price of granite concrete hollow blocks was reduced by approximately 50%. The small hollow block of granite concrete is environmentally friendly and worth promoting.
Despite the fact that the popular particle swarm optimizer (PSO) is currently being extensively applied to many real-world problems that often have high-dimensional and complex fitness landscapes, ...the effects of boundary constraints on PSO have not attracted adequate attention in the literature. However, in accordance with the theoretical analysis in
11, our numerical experiments show that particles tend to fly outside of the boundary in the first few iterations at a very high probability in high-dimensional search spaces. Consequently, the method used to handle boundary violations is critical to the performance of PSO. In this study, we reveal that the widely used random and absorbing bound-handling schemes may paralyze PSO for high-dimensional and complex problems. We also explore in detail the distinct mechanisms responsible for the failures of these two bound-handling schemes. Finally, we suggest that using high-dimensional and complex benchmark functions, such as the composition functions in
19, is a prerequisite to identifying the potential problems in applying PSO to many real-world applications because certain properties of standard benchmark functions make problems inexplicit.
The effect of RE
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(RE = La, Ce) fluxes on penetration and microstructure in 3-mm-thick Ti6Al4V weld joints made by activated flux tungsten inert gas (A-TIG) welding process was investigated. The ...mechanism of penetration increase was discussed. Microstructure characterization in the fusion zone region was also observed under optical microscope. It is shown that RE
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fluxes can effectively increase weld penetration of Ti6Al4V for applying a thin layer paste when welding. The mechanism would comply with changing the Marangoni convection in the weld pool, rather than the arc. A-TIG welding with RE
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fluxes can slightly refine the width of β-Ti grains. Phase constitution shows single α′-Ti in solidification structure in both two welding process. There are no significant changes in weld metal microstructure with and without Re
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fluxes. Microhardness distribution of weld seam indicates that the appearance of RE
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has not affected the metal plastic deformation capacity.
With excellent properties, high-temperature superalloys have become the main application materials for aircraft engines, gas turbines, and many other devices. However, superalloys are typically ...difficult to machine, especially for the thread cutting. In this article, an ultrasonic vibration–assisted turning system is proposed for thread cutting operations in superalloys. A theoretical analysis of ultrasonic vibration–assisted thread cutting is carried out. An ultrasonic vibration–assisted system was integrated into a standard lathe to demonstrate thread turning in Inconel 718 superalloy. The influence of ultrasonic vibration–assisted machining on workpiece surface quality, chip shape, and tool wear was analyzed. The relationship between machining parameters and ultrasonic vibration–assisted processing performance was also explored. By analyzing the motion relationship between tool path and workpiece surface, the reasons for improved workpiece surface quality by ultrasonic vibration–assisted machining were explained.
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