Neural Aesthetic Image Reviewer

Recently, there is a rising interest in perceiving image aesthetics. The existing works deal with image aesthetics as a classification or regression problem. To extend the cognition from rating to reasoning, a deeper understanding of aesthetics should be based on revealing why a high- or low-aesthetic score should be assigned to an image. From such a point of view, we propose a model referred to as Neural Aesthetic Image Reviewer, which can not only give an aesthetic score for an image, but also generate a textual description explaining why the image leads to a plausible rating score. Specifically, we propose two multi-task architectures based on shared aesthetically semantic layers and task-specific embedding layers at a high level for performance improvement on different tasks. To facilitate researches on this problem, we collect the AVA-Reviews dataset, which contains 52,118 images and 312,708 comments in total. Through multi-task learning, the proposed models can rate aesthetic images as well as produce comments in an end-to-end manner. It is confirmed that the proposed models outperform the baselines according to the performance evaluation on the AVA-Reviews dataset. Moreover, we demonstrate experimentally that our model can generate textual reviews related to aesthetics, which are consistent with human perception.Comment: 8 pages, 13 figure

Intelligent modeling with physics-informed machine learning for petroleum engineering problems

The advancement in big data and artificial intelligence has enabled a novel exploration mode for the study of petroleum engineering. Unlike theory-based solution methods, the data-driven intelligent approaches demonstrate superior flexibility, computational efficiency and accuracy for dealing with complex multi-scale, and multi-physics problems. However, these intelligent models often disregard physical laws in pursuit of error minimization, which leads to certain uncertainties. Therefore, physics-informed machine learning approaches have been developed based on data, guided by physics, and supported by machine learning models. This study summarizes four embedding mechanisms for introducing physical information into machine learning models, including input databased embedding, model architecture-based embedding, loss function-based embedding, and model optimization-based embedding mechanism. These “data + physics” dualdriven intelligent models not only exhibit higher prediction accuracy while adhering to physic laws, but also accelerate the convergence to improve computational efficiency. This paradigm will facilitate the guide developments in solving petroleum engineering problems toward a more comprehensive and efficient direction.Cited as: Xie, C., Du, S., Wang, J., Lao, J., Song, H. Intelligent modeling with physics-informed machine learning for petroleum engineering problems. Advances in Geo-Energy Research, 2023, 8(2): 71-75. https://doi.org/10.46690/ager.2023.05.0

Ferroelectricity driven by magnetism in quasi-one-dimensional Ba9Fe3Se15

The spin-induced ferroelectricity in quasi-1D spin chain system is little known, which could be fundamentally different from those in three-dimensional (3D) system. Here, we report the ferroelectricity driven by a tilted screw spin order and its exotic dynamic in the spin-chain compound Ba9Fe3Se15. It is found that the spin-induced polarization has already occurred and exhibits magnetoelectric coupling behavior far above the long-range spin order (LRSO) at TN = 14 K. The polarized entities grow and their dynamic responses slow down gradually with decreasing temperature and permeate the whole lattice to form 3D ferroelectricity at TN. Our results reveal that the short-range spin orders (SRSOs) in the decoupled chains play a key role for the exotic dynamic in this dimension reduced system. Ba9Fe3Se15 is the only example so far which exhibits electric polarization above LRSO temperature because of the formation of SRSOs

Drip irrigation shapes the soil bacterial communities and enhances jujube yield by regulating the soil moisture content and nutrient levels

In the agricultural zones of the arid Xinjiang region of China, reducing irrigation is mandatory. However, irrigation affects the composition and diversity of the soil bacterial community which is vital to crop yield. To the best of our knowledge, very little research has been conducted on the relationships among the soil bacterial community, irrigation method, and yield as well as their underlying in jujube agroecosystems. Here, we investigated the soil physicochemistry and bacterial communities in jujube fields subjected to drip irrigation (DI) and traditional flood irrigation (FI), and their associations with yield at the flowering and fruit set (FFS) and end-of-growth (EG) stages. Under DI, the jujube yield was 8712.00 ± 24.54 kg/hm2, which was 7.64% higher than that obtained under FI (8094.33 ± 43.67 kg/hm2). DI increased the relative soil bacteria community diversity by decreasing the moisture content and increasing the nutrient levels in the soil. DI also transformed the soil bacterial community so that Bacteroidota predominated at the FFS stage and the probiotics Chloroflexi and Firmicutes predominated at the EG stage. A co-occurrence network analysis showed that DI created stable complex Soil bacteria communities in jujube fields, Though Dependentiae and Deferriberota had low relative abundance, they were nonetheless key nodes in the soil bacterial community network. A neutral community model (NCM) revealed that stochastic processes drove the soil bacterial community assembly whereas DI promoted deterministic processes by regulating the soil moisture content and nutrient levels. Partial least squares path modeling (PLS-PM) disclosed that DI affected the soil bacterial community structure by decreasing the moisture content (−0.342 **) and increasing the nutrient levels (0.557 **) in the soil. The PLS-PM also demonstrated that the observed change in the soil bacterial community structure was the main reason for the increase in jujube yield (1.098 **). The present work provides insights into the mechanisms underlying the correlations between the soil bacterial community and crop yield in response to changes in the irrigation method

The Control Algorithm and Experimentation of Coaxial Rotor Aircraft Trajectory Tracking Based on Backstepping Sliding Mode

In view of the uncertainty of model parameters, the influence of external disturbances and sensor noise on the flight of coaxial rotor aircraft during autonomous flight, a robust backstepping sliding mode control algorithm for the position and attitude feedback control system is studied to solve the trajectory tracking problem of an aircraft in the case of unknown external interference. In this study, a non-linear dynamic model based on a disturbed coaxial rotor aircraft was established for an unknown flight. Then, a non-linear robust backstepping sliding mode controller was designed, which was divided into two sub-controllers: the attitude controller and the position controller of the coaxial rotor aircraft. In the controller, virtual control was introduced to construct the Lyapunov function to ensure the stability of each subsystem. The effectiveness of the proposed controller was verified through numerical simulation. Finally, the effectiveness of the backstepping sliding mode control algorithm was verified by flight experiments

Biomedical Applications of Electrets: Recent Advance and Future Perspectives

Recently, electrical stimulation, as a non-pharmacological physical stimulus, has been widely exploited in biomedical and clinical applications due to its ability to significantly enhance cell proliferation and differentiation. As a kind of dielectric material with permanent polarization characteristics, electrets have demonstrated tremendous potential in this field owing to their merits of low cost, stable performance, and excellent biocompatibility. This review provides a comprehensive summary of the recent advances in electrets and their biomedical applications. We first provide a brief introduction to the development of electrets, as well as typical materials and fabrication methods. Subsequently, we systematically describe the recent advances of electrets in biomedical applications, including bone regeneration, wound healing, nerve regeneration, drug delivery, and wearable electronics. Finally, the present challenges and opportunities have also been discussed in this emerging field. This review is anticipated to provide state-of-the-art insights on the electrical stimulation-related applications of electrets

Simulating gas-water relative permeabilities for nanoscale porous media with interfacial effects

This paper presents a theoretical method to simulate gas-water relative permeability for nanoscale porous media utilizing fractal theory. The comparison between the calculation results and experimental data was performed to validate the present model. The result shows that the gas-water relative permeability would be underestimated significantly without interfacial effects. The thinner the liquid film thickness, the greater the liquid-phase relative permeability. In addition, both liquid surface diffusion and gas diffusion coefficient can promote gas-liquid two-phase flow. Increase of liquid surface diffusion prefer to increase liquid-phase permeability obviously as similar as increase of gas diffusion coefficient to increase gas-phase permeability. Moreover, the pore structure will become complicated with the increase of fractal dimension, which would reduce the gas-water relative permeability. This study has provided new insights for development of gas reservoirs with nanoscale pores such as shale

The Effect of Consistency and Freeness on the Yield Stress of Chemical Pulp Fibre Suspensions

To study the influence of mechanical treatments on the yield stress of chemical pulp suspensions, a traditional rheometer, coupled with local velocity measurements (ultrasonic Doppler velocimetry), was used to measure the yield stress of two types of commercial chemical pulp suspensions with different freeness values at mass concentrations (consistencies) ranging from 0.5 to 1.5%. Over the range of consistencies tested, the yield stress was found to depend on the consistency through a power law relationship for all tested samples. Moreover, the results showed that as the freeness decreased, the yield stress of hardwood suspensions increased to a maximum value then decreased. This variation in yield stress was also observed in softwood suspensions with mass concentrations above 1%. However, when the consistency was lower than 0.75%, the yield stress of softwood suspensions increased with decreasing freeness.This behaviour can be understood based on the underlying fibre properties of fibrillation, curl, and stiffness, suggesting that fibre morphology plays a significant role on the yield stress of pulp suspensions over the concentration range studied

Potential for Vertical Heterogeneity Prediction in Reservoir Basing on Machine Learning Methods

With the rapid development of computer technology, some machine learning methods have begun to gradually integrate into the petroleum industry and have achieved some achievements, whether in conventional or unconventional reservoirs. This paper presents an alternative method to predict vertical heterogeneity of the reservoir utilizing various deep neural networks basing on dynamic production data. A numerical simulation technique was adopted to obtain the required dataset, which contains dynamic production data calculated under different heterogeneous reservoir conditions. Machine learning models were established through deep neural networks, which learn and capture the characteristics better between dynamic production data and reservoir heterogeneity, so as to invert the vertical permeability. On the basis of model validation, the results show that machine learning methods have excellent performance in predicting heterogeneity with the RMSE of 12.71 mD, which effectively estimated the permeability of the entire reservoir. Moreover, the overall AARD of the predictive result obtained by the CNN method was controlled at 11.51%, revealing the highest accuracy compared with BP and LSTM neural networks. And the permeability contrast, an important parameter to characterize heterogeneity, can be predicted precisely as well, with a derivation of below 10%. This study proposed a potential for vertical heterogeneity prediction in reservoir basing on machine learning methods