In this paper, we propose a hybrid metallo-dielectric core-shell nanorod for the Kerker-type effect at two different frequencies. The effect arises from the interference of the scattering waves of the nanorod, which are generated by the magnetic dipole moment (MD) of the high-index hollow particle and the electric dipole moment (ED) induced in both metallic and dielectric particles. Interestingly, we find that such kind of unidirectional radiation properties, (i.e., zero back scattering occurring at dual frequencies) can be sustained with a single nanorod, which usually being equivalent to a local electric dipole source. The effect of substrate is also considered to investigate the typical experimental realization for the dual-frequency unidirectionalities of the nanoantenna. Furthermore, the unidirectionality can be further improved by the design of one-dimensional array of the hybrid nanoantenna. Our results could provide an additional degree of freedom for light scattering manipulation, and widen the versatile applications in nanoantennas, optical sensor, light emitters, as well as photovoltaic devices.

During the past few years, a lot of efforts have been devoted in studying optical analog computing with artificial structures. Up to now, much of them are primarily focused on classical mathematical operations. How to use artificial structures to simulate quantum algorithm is still to be explored. In this work, an all-dielectric metamaterial-based model is proposed and realized to demonstrate the quantum Deutsch-Jozsa algorithm. The model is comprised of two cascaded functional metamaterial subblocks. The oracle subblock encodes the detecting functions (constant or balanced), onto the phase distribution of the incident wave. Then, the original Hadamard transformation is performed with a graded-index subblock. Both the numerical and experimental results indicate that the proposed metamaterials are able to simulate the Deutsch-Jozsa problem with one round operation and a single measurement of the output eletric field, where the zero (maximum) intensity at the central position results from the destructive (constructive) interference accompanying with the balance (constant) function marked by the oracle subblock. The proposed computational metamaterial is miniaturized and easy-integration for potential applications in communication, wave-based analog computing, and signal processing systems.

Interface engineering can be used to tune the properties of heterostructure materials at an atomic level, yielding exceptional final physical properties. In this work, we synthesized a heterostructure of a p-type semiconductor (NiO) and an n-type semiconductor (CeO2) for solid oxide fuel cell electrolytes. The CeO2-NiO heterostructure exhibited high ionic conductivity of 0.2 S cm−1 at 530 °C, which was further improved to 0.29 S cm−1 by the introduction of Na+ ions. When it was applied in the fuel cell, an excellent power density of 571 mW cm−1 was obtained, indicating that the CeO2-NiO heterostructure can provide favorable electrolyte functionality. The prepared CeO2-NiO heterostructures possessed both proton and oxygen ionic conductivities, with oxygen ionic conductivity dominating the fuel cell reaction. Further investigations in terms of electrical conductivity and electrode polarization, a proton and oxygen ionic co-conducting mechanism, and a mechanism for blocking electron transport showed that the reconstruction of the energy band at the interfaces was responsible for the enhanced ionic conductivity and cell power output. This work presents a new methodology and scientific understanding of semiconductor-based heterostructures for advanced ceramic fuel cells.

Phononic crystals (PC) consisting of periodic materials with different acoustic properties have potential applications in functional devices. To realize more smart functions, it is desirable to actively control the properties of PC on-demand, ideally within the same fabricated system. Here, we report a tunable PC made of Ba 0.7 Sr 0.3 TiO 3 (BST) ceramics, wherein a 20 K temperature change near room temperature results in 20% frequency shift in transmission spectra induced by ferroelectric phase transition. The tunability phenomenon is attributed to the structureinduced resonant excitation of A 0 and A 1 Lamb modes that exist intrinsically in the uniform BST plate, while these Lamb modes are sensitive to the elastic properties of plate and can be modulated by temperature in BST plate around Curie temperature. The study finds new opportunities for creating tunable PC and enables smart temperature-tuned devices such as Lamb wave filter or sensor.

Hydrological simulation plays a very important role in understanding the hydrological processes and is of great significance to flood forecasting and optimal allocation of water resources in the watershed. The development of deep learning techniques has brought new opportunities and methods for long-term hydrological simulation research at the watershed scale. Different from traditional hydrological models, the application of deep learning techniques in the hydrological field has greatly promoted the development trend of runoff prediction and provides a new paradigm for hydrological simulation. In this study, a CNN–LSTM model based on the convolutional neural network (CNN) and long short-term memory (LSTM) network, and a CNN–GRU model based on CNN and gated recurrent unit (GRN) are constructed to study the watershed hydrological processes. To compare the performance of deep learning techniques and the hydrological model, we also constructed the distributed hydrological model: Soil and Water Assessment Tool (SWAT) model based on remote sensing data. These models were applied to the Xixian Basin, and the promising results had been achieved, which verified the rationality of the method, with the majority of percent bias error (PBE) values ranging between 3.17 and 13.48, Nash–Sutcliffe efficiency (NSE) values ranging between 0.63 and 0.91, and Kling–Gupta efficiency (KGE) values ranging between 0.70 and 0.90 on a monthly scale. The results demonstrated their strong ability to learn complex hydrological processes. The results also indicated that the proposed deep learning models could provide the certain decision support for the water environment management at the watershed scale, which was of great significance to improve the hydrological disaster prediction ability and was conducive to the sustainable development of water resources.

Farm dams may exert various pressures on the flow network depending on the position and scale, which may influence the magnitude, timing, and duration of the flow in the basin. Considering the cumulative effects of farm dams is important for understanding their spatial impacts on the rainfall-runoff process. However, a few studies have been able to reckon the temporal and spatial variation in the flow. In this study, we developed an integrated approach based on remote sensing and hydrologic–hydrodynamic modeling to simulate the rainfall-runoff process in a farm dam-dominated basin. Compared with the classical Xinanjiang model (XAJ), the developed coupled hydrological–hydrodynamic model (coupled-XAJ) shows improved performance in the simulation of the no-linear confluence process in terms of flood flow and peak appearance time. It demonstrates that water retention of multiple farm dams is eminent and that the developed model is effective and feasible in farm dam-dominated basins. Furthermore, the integrated approach enables to control and utilize the rain and flood resources with the safety of arm dams guaranteed. This study provides an innovative method for the scientific management of water resources under the influence of human activities and environmental changes.

The study of runoff under the influence of human activities is a research hot spot in the field of water science. Land-use change is one of the main forms of human activities and it is also the major driver of changes to the runoff process. As for the relationship between land use and the runoff process, runoff yield theories pointed out that the runoff yield capacity is spatially heterogeneous. The present work hypothesizes that the distribution of the runoff yield can be divided by land use, which is, areas with the same land-use type are similar in runoff yield, while areas of different land uses are significantly different. To prove it, we proposed a land-use-based framework for runoff yield calculations based on a conceptual rainfall–runoff model, the Xin’anjiang (XAJ) model. Based on the framework, the modified land-use-based Xin’anjiang (L-XAJ) model was constructed by replacing the yielding area (f/F) in the water storage capacity curve of the XAJ model with the area ratio of different land-use types (L/F; L is the area of specific land-use types, F is the whole basin area). The L-XAJ model was then applied to the typical cultivated–urban binary land-use-type basin (Taipingchi basin) to evaluate its performance. Results showed great success of the L-XAJ model, which demonstrated the area ratio of different land-use types can represent the corresponding yielding area in the XAJ model. The L-XAJ model enhanced the physical meaning of the runoff generation in the XAJ model and was expected to be used in the sustainable development of basin water resources.

Floods are one of the main natural disaster threats to the safety of people’s lives and property. Flood hazards intensify as the global risk of flooding increases. The control of flood disasters on the basin scale has always been an urgent problem to be solved that is firmly associated with the sustainable development of water resources. As important nonengineering measures for flood simulation and flood control, the hydrological and hydraulic models have been widely applied in recent decades. In our study, on the basis of sufficient remote-sensing and hydrological data, a hydrological (Xin’anjiang (XAJ)) and a two-dimensional hydraulic (2D) model were constructed to simulate flood events and provide support for basin flood management. In the Chengcun basin, the two models were applied, and the model parameters were calibrated by the parameter estimation (PEST) automatic calibration algorithm in combination with the measured data of 10 typical flood events from 1990 to 1996. Results show that the two models performed well in the Chengcun basin. The average Nash–Sutcliffe efficiency (NSE), percentage error of peak discharge (PE), and percentage error of flood volume (RE) were 0.79, 16.55%, and 18.27%, respectively, for the XAJ model, and those values were 0.76, 12.83%, and 11.03% for 2D model. These results indicate that the models had high accuracy, and hydrological and hydraulic models both had good application performance in the Chengcun basin. The study can a provide decision-making basis and theoretical support for flood simulation, and the formulation of flood control and disaster mitigation measures in the basin.