► A numerical model for photovoltaic–thermoelectric hybrid systems was developed. ► The performance of the GaAs–CoSb3 hybrid system is predicted by this model. ► Relations between critical parameters ...of the PV–TE hybrid system were studied. ► System optimization is carried out by analyzing the calculation results. ► Guidelines for PV–TE hybrid system design were provided.
This paper presents the numerical modeling and optimization of a spectrum splitting photovoltaic–thermoelectric (PV–TE) hybrid system. In this work, a simulation model is established in consideration of solar concentration levels and several heat dissipation rates. Exemplarily, the performance of a hybrid system composed of a GaAs solar cell and a skutterudites CoSb3 solar thermoelectric generator (TEG) is simulated. Analysis under different conditions has been carried out to evaluate the electrical and thermal performance of the hybrid system. Results show that the cutoff-wavelength of the GaAs–CoSb3 hybrid system is mainly determined by the band gap of solar cell, when the solar concentration ratio is ranged between 550 to 770 and heat transfer coefficient h=3000–4500W/m2K, the hybrid system has good electrical performance and low operating temperatures. Based on the analysis of the GaAs–CoSb3 hybrid system, guidelines for the PV–TE system design are proposed. It is also compared with a PV-only system working under the same cooling condition; results show that the PV–TE hybrid system is more suitable for working under high concentrations.
The productivity effect of infrastructure investment is controversial in the traditional literature using aggregate production function estimation due to reverse causality. This paper develops a new ...approach, using a structural model of firm-level production function, and matching Chinese firm-level production data with province-level infrastructure data. The estimated rates of return are about 6 percent averaged from 1999 to 2007. The returns triple if national-level spillover effects are taken into account. Controlling for the demand effect of public expenditure leads to smaller but still positive returns. The effect of infrastructure investment on firm-level productivity is heterogenous. With an increase in infrastructure investment, lower productivity firms are more likely to exit and higher productivity firms gain more market share.
•The productivity effect of infrastructure investment is controversial due to reverse causality.•This paper develops a structural model of firm-level production function matched Chinese province-level infrastructure data.•The estimated rates of return are about 6 percent averaged from 1999 to 2007.•The returns triple if national-level spillover effects are taken into account.•Controlling for the demand effect of public expenditure leads to smaller but still positive returns.
Plants develop tolerance to drought by activating genes with altered levels of epigenetic modifications. Specific transcription factors are involved in this activation, but the molecular connections ...within the regulatory system are unclear. Here, we analyzed genome-wide acetylated lysine residue 9 of histone H3 (H3K9ac) enrichment and examined its association with transcriptomes in
under drought stress. We revealed that abscisic acid-Responsive Element (ABRE) motifs in promoters of the drought-responsive genes
,
, and
are involved in H3K9ac enhancement and activation of these genes. Overexpressing these
genes in
resulted in strong drought-tolerance phenotypes. We showed that the ABRE binding protein PtrAREB1-2 binds to ABRE motifs associated with these
genes and recruits the histone acetyltransferase unit ADA2b-GCN5, forming AREB1-ADA2b-GCN5 ternary protein complexes. Moreover, this recruitment enables GCN5-mediated histone acetylation to enhance H3K9ac and enrich RNA polymerase II specifically at these
genes for the development of drought tolerance. CRISPR editing or RNA interference-mediated downregulation of any of the ternary members results in highly drought-sensitive
Thus, the combinatorial function of the ternary proteins establishes a coordinated histone acetylation and transcription factor-mediated gene activation for drought response and tolerance in
species.
ABSTRACTThe predictive analysis of solar flux distribution on the receiver surface is critical in optimizing the concentration processes of concentrating solar power (CSP) plants. Due to the ...difficulties of directly measuring the solar flux distribution of the heliostat field, tracking the Moon and measuring the lunar concentration ratio distribution become a promising option. However, many factors affect the flux distribution of a heliostat field. To obtain an accurate predictive model for the solar flux distribution, we propose a deep-learning method using conditional generative adversarial networks (cGAN) and lunar concentration images. The method can take account of tracking errors of individual heliostats, defects of reflecting surfaces, as well as atmospheric attenuation effects, and has the potential to give a reliable prediction of solar flux distribution. Mathematical relations between the solar flux distribution and the solar concentration ratio distribution are discussed in the paper. Experiments have been designed and carried out with an ordinary heliostat at the Beijing Badaling solar concentrating power station. Experimental results show that the AI-generated solar concentration ratio distributions are very close to the actual solar concentration ratio distributions, demonstrating the feasibility of AI models for the prediction of solar flux distribution.
With the continuous development of science and technology, intelligent surveillance technology using image processing and computer vision is also progressing. To improve the performance of target ...detection and tracking, an improved target tracking method is proposed, which uses a combination of the Canny operator and morphology for the detection part, and a Kalman filter extended Kernel Correlation Filter (KCF) tracking algorithm approach for the tracking part. First, a convolution kernel of
3
×
3
is improved to a convolution kernel of
2
×
2
in the traditional Canny algorithm, and the pixel gradient in the diagonal direction is increased. Secondly, a mathematical morphology theory of nonlinear filtering is applied to the Canny edge detection algorithm, and this method effectively improves the clarity of image edges. Finally, the extended kernel correlation filtering algorithm is applied to video surveillance and Online Object Tracking Benckmark2013 (OTB2013) datasets for testing. The experimental results show that the method proposed in this paper can accurately detect moving targets and the algorithm has good accuracy and success rate.
The concentrated solar power (CSP) system integrated with supercritical carbon dioxide (sCO 2) Brayton cycle is considered as the major development trend of clean energy technology in mitigating ...climate change and promoting sustainable energy owing to its more compact structure, higher efficiency and lower cost. While the system integration and control are more complex because the thermal energy storage and heat exchange units are obliged to be provided with capacity and property matching with sCO 2 compression and expansion units. Therefore, there is an urgent need for the whole system modeling and dynamic simulation of sCO 2-CSP plants. In the paper, running logic of the integrated system is determined and novel system dynamic simulation platform is built. Moreover, for the sake of optimizing the system design, different control strategies are further studied. When the extremum-seeking control strategy is used, the average daily generating capacity of the system is 2.06% higher than that of the traditional fixed inventory control strategy; and when iterative learning control strategy is used, the average daily generating capacity of the system is 2.15% higher than that of the traditional fixed inventory control strategy. The progress of the technology will promote the development of new power systems and early realization of carbon neutrality.
Nickelous oxide (NiO) is a promising anode for Lithium ion (Li-ion) batteries. However it suffers from rapid degradation due to large volume change upon cycling. In this work, a novel strategy to ...accommodate the volume change of NiO-based anodes during charge/discharge cycling through employment of the advantages of bimodal porous Nickel–Silicon (Ni–Si) network and Nickelous oxide@Nickel (NiO@Ni) shell@core structure is proposed. The designed bimodal nanoporous NiO@Ni–Si network exhibits a stable Li-ion storage property with an extremely high reversible capacity of 1656.9 mAh g−1 at 200 mA g−1 after 300 repeated cycles and 1387.1 mAh g−1 at 500 mA g−1 after 1000 cycles. It also shows a good rate performance, delivering about 400 mAh g−1 even at a current density of 2000 mA g−1. Post-cycling microscopy and impedance studies reveals the minor changes in the electrode structure that, in turn, results in an extremely low capacity degradation rate of 0.03%/cycle. The employed strategy enriches the structural design idea of dealloying products, which may further promote the development of the dealloying field and can be applied in future to prepare various types of porous shell@core anodes for Li-ion battery applications.
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•Bimodal nanoporous NiO@Ni–Si network is prepared by dealloying method.•The material contains bimodal porous Ni–Si network and NiO@Ni shell@core structure.•Huge volume change is accommodated by bimodal porous shell@core network.•The composite shows stable storage performance as an anode for Li-ion battery.
► We present a transient 2-D two-phase model for the packed-bed thermocline system. ► We investigate the general thermal behavior of a discharging process. ► We evaluate several heat transfer ...correlations and parameters. ► Solid conductivity and interstitial heat transfer influence the thermocline thickness.
In this paper, a comprehensive transient, two-dimensional, two-phase model for heat transfer and fluid dynamics within the packed-bed molten salt thermocline thermal storage system is presented. After model validation, the developed model is used to investigate the general thermal behavior of a discharging process of the pack-bed thermocline system and evaluate the interstitial heat transfer coefficient, the effective thermal conductivity and effect of the thermal conductivity of solid fillers. The results show that the thermocline region is moving upward with slight expansion during the discharging process. With the use of two insulation layers, a uniform cross-sectional temperature distribution is well achieved. The use of different correlations for the interstitial heat transfer coefficient or the effective thermal conductivity from the literature leads to negligible difference in the predicted thermal performance. It is also found that decreasing the heat transfer rate between fluid and solid fillers, or increasing the thermal conductivity of solid fillers, results in an increase in the thermocline thickness which finally decreases the effective discharging time and the effective discharging efficiency.
With the development of online educational platforms, numerous research works have focused on the knowledge tracing task, which relates to the problem of diagnosing the changing knowledge proficiency ...of learners. Deep-neural-network-based models are used to explore the interaction information between students and their answer logs in the current field of knowledge tracing studies. However, those models ignore the impact of previous interactions, including the exercise relation, forget factor, and student behaviors (the slipping factor and the guessing factor). Those models also do not consider the importance of the Q-matrix, which relates exercises to knowledge points. In this paper, we propose a novel relational attention knowledge tracing (RAKT) to track the students’ knowledge proficiency in exercises. Specifically, the RAKT model incorporates the students’ performance data with corresponding interaction information, such as the context of exercises and the different time intervals between exercises. The RAKT model also takes into account the students’ interaction behaviors, including the slipping factor and the guessing factor. Moreover, consider the relationship between exercise sets and knowledge sets and the relationship between different knowledge points in the same exercise. An extension model of RAKT is called the Calibrated Q-matrix relational attention knowledge tracing model (QRAKT), which was developed using a Q-matrix calibration method based on the hierarchical knowledge levels. Experiments were conducted on two public educational datasets, ASSISTment2012 and Eedi. The results of the experiments indicated that the RAKT model and the QRAKT model outperformed the four baseline models.
The detection of defects on irregular surfaces with specular reflection characteristics is an important part of the production process of sanitary equipment. Currently, defect detection algorithms ...for most irregular surfaces rely on the handcrafted extraction of shallow features, and the ability to recognize these defects is limited. To improve the detection accuracy of micro-defects on irregular surfaces in an industrial environment, we propose an improved Faster R-CNN model. Considering the variety of defect shapes and sizes, we selected the K-Means algorithm to generate the aspect ratio of the anchor box according to the size of the ground truth, and the feature matrices are fused with different receptive fields to improve the detection performance of the model. The experimental results show that the recognition accuracy of the improved model is 94.6% on a collected ceramic dataset. Compared with SVM (Support Vector Machine) and other deep learning-based models, the proposed model has better detection performance and robustness to illumination, which proves the practicability and effectiveness of the proposed method.
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