An Efficient Method for GPS Multipath Mitigation Using the Teager-Kaiser-Operator-based MEDLL

An efficient method for GPS multipath mitigation is proposed. The motivation for this proposed method is to integrate the Teager-Kaiser Operator (TKO) with the Multipath Estimating Delay Lock Loop (MEDLL) module to mitigate the GPS multipath efficiently. The general implementation process of the proposed method is that we first utilize the TKO to operate on the received signal’s Auto-Correlation Function (ACF) to get an initial estimate of the multipaths. Then we transfer the initial estimated results to the MEDLL module for a further estimation. Finally, with a few iterations which are less than those of the original MEDLL algorithm, we can get a more accurate estimate of the Line-Of-Sight (LOS) signal, and thus the goal of the GPS multipath mitigation is achieved. The simulation results show that compared to the original MEDLL algorithm, the proposed method can reduce the computation load and the hardware and/or software consumption of the MEDLL module, meanwhile, without decreasing the algorithm accuracy

Enhanced spin-orbit torques in MnAl/Ta films with improving chemical ordering

We report the enhancement of spin-orbit torques in MnAl/Ta films with improving chemical ordering through annealing. The switching current density is increased due to enhanced saturation magnetization MS and effective anisotropy field HK after annealing. Both damplinglike effective field HD and fieldlike effective field HF have been increased in the temperature range of 50 to 300 K. HD varies inversely with MS in both of the films, while the HF becomes liner dependent on 1/MS in the annealed film. We infer that the improved chemical ordering has enhanced the interfacial spin transparency and the transmitting of the spin current in MnAl layer

The effect of asymmetry of the coil block on self-assembly in ABC coil-rod-coil triblock copolymers

Using the self-consistent field approach, the effect of asymmetry of the coil block on the microphase separation is focused in ABC coil-rod-coil triblock copolymers. For different fractions of the rod block $f_{\text B}$, some stable structures are observed, i.e., lamellae, cylinders, gyroid, and core-shell hexagonal lattice, and the phase diagrams are constructed. The calculated results show that the effect of the coil block fraction $f_{\text A}$ is dependent on $f_{\text B}$. When $f_{\text B}=0.2$, the effect of asymmetry of the coil block is similar to that of the ABC flexible triblock copolymers; When $f_{\text B}=0.4$, the self-assembly of ABC coil-rod-coil triblock copolymers behaves like rod-coil diblock copolymers under some condition. When $f_{\text B}$ continues to increase, the effect of asymmetry of the coil block reduces. For $f_{\text B}=0.4$, under the symmetrical and rather asymmetrical conditions, an increase in the interaction parameter between different components leads to different transitions between cylinders and lamellae. The results indicate some remarkable effect of the chain architecture on self-assembly, and can provide the guidance for the design and synthesis of copolymer materials.Comment: 9 pages, 3 figure

Mean-field embedding of the dual fermion approach for correlated electron systems

To reduce the rapidly growing computational cost of the dual fermion lattice calculation with increasing system size, we introduce two embedding schemes. One is the real fermion embedding, and the other is the dual fermion embedding. Our numerical tests show that the real fermion and dual fermion embedding approaches converge to essentially the same result. The application on the Anderson disorder and Hubbard models shows that these embedding algorithms converge more quickly with system size as compared to the conventional dual fermion method, for the calculation of both single-particle and two-particle quantities.Comment: 10 pages, 10 figure

Dual Fermion Method for Disordered Electronic Systems

While the coherent potential approximation (CPA) is the prevalent method for the study of disordered electronic systems, it fails to capture non-local correlations and Anderson localization. To incorporate such effects, we extend the dual fermion approach to disordered non-interacting systems using the replica method. Results for single- and two- particle quantities show good agreement with cluster extensions of the CPA; moreover, weak localization is captured. As a natural extension of the CPA, our method presents an alternative to the existing cluster theories. It can be used in various applications, including the study of disordered interacting systems, or for the description of non-local effects in electronic structure calculations.Comment: 5 pages, 4 figure

A data driven deep neural network model for predicting boiling heat transfer in helical coils under high gravity

In this article, a deep artificial neural network (ANN) model has been proposed to predict the boiling heat transfer in helical coils under high gravity conditions, which is compared with experimental data. A test rig is set up to provide high gravity up to 11 g with a heat flux up to 15100 W/m 2 and the mass velocity range from 40 to 2000 kg m −2 s −1. In the current work, a total 531 data samples have been used in the ANN model. The proposed model was developed in a Python Keras environment with Feed-forward Back-propagation (FFBP) Multi-layer Perceptron (MLP) using eight features (mass flow rate, thermal power, inlet temperature, inlet pressure, direction, acceleration, tube inner surface area, helical coil diameter) as the inputs and two features (wall temperature, heat transfer coefficient) as the outputs. The deep ANN model composed of three hidden layers with a total number of 1098 neurons and 300,266 trainable parameters has been found as optimal according to statistical error analysis. Performance evaluation is conducted based on six verification statistic metrics (R 2, MSE, MAE, MAPE, RMSE and cosine proximity) between the experimental data and predicted values. The results demonstrate that a 8-512-512-64-2 neural network has the best performance in predicting the helical coil characteristics with (R 2=0.853, MSE=0.018, MAE=0.074, MAPE=1.110, RMSE=0.136, cosine proximity=1.000) in the testing stage. It is indicated that with the utilisation of deep learning, the proposed model is able to successfully predict the heat transfer performance in helical coils, and especially achieved excellent performance in predicting outputs that have a very large range of value differences

Global supply chain of biomass use and the shift of environmental welfare from primary exploiters to final consumers

With the increasingly frequent interregional trade that leads to the geographical separation of production and consumption, the invisible shift of the environmental welfare bestowed by biomass use is brought to attention. Using a systems embodiment accounting model, this study tracked the dynamic process of interregional transfer of biomass use from primary supply to final consumption via the global supply chain. The results reveal that biomass use embodied in global trade is 87% of total global biomass exploitation. Moreover, the intermediate trade volume is 1.7 times higher than the final trade volume. In terms of biomass use, the United States, South Korea, mainland China, Japan, and the United Kingdom are revealed as the five leading net importers and also the main final consumers. Brazil, India, Cyprus, Indonesia, and Latvia are demonstrated to be the top five net exporters and also the main exploiters of biomass resources. The biomass self-sufficiency rate by source and that by sink for each country are then discussed. The outcome shows that through the channels of global supply chain, the shift of environmental welfare from biomass-exporting nations to biomass-importing nations occurs along with interregional trade. For Brazil and India, we suggest that they should strike a balance between economic revenues and long-term sustainability. Regarding the consumption-oriented nations such as the United States, an increase in the energy efficiency of high value-added industries is recommended