Vector semi-rational rogon-solitons and asymptotic analysis for any multi-component Hirota equations with mixed backgrounds

The Hirota equation can be used to describe the wave propagation of an ultrashort optical field. In this paper, the multi-component Hirota (alias n-Hirota, i.e. n-component third-order nonlinear Schrödinger) equations with mixed non-zero and zero boundary conditions are explored. We employ the multiple roots of the characteristic polynomial related to the Lax pair and modified Darboux transform to find vector semi-rational rogon-soliton solutions (i.e. nonlinear combinations of rogon and soliton solutions). The semi-rational rogon-soliton features can be modulated by the polynomial degree. For the larger solution parameters, the first m (m < n) components with non-zero backgrounds can be decomposed into rational rogons and grey-like solitons, and the last n − m components with zero backgrounds can approach bright-like solitons. Moreover, we analyze the accelerations and curvatures of the quasi-characteristic curves, as well as the variations of accelerations with the distances to judge the interaction intensities between rogons and grey-like solitons. We also find the semi-rational rogon-soliton solutions with ultra-high amplitudes. In particular, we can also deduce vector semi-rational solitons of the n-component complex mKdV equation. These results will be useful to further study the related nonlinear wave phenomena of multi-component physical models with mixed background, and even design the related physical experiments.     

Realizing number recognition with simulated quantum semi-restricted Boltzmann machine

Quantum machine learning based on quantum algorithms may achieve an exponential speedup over classical algorithms in dealing with some problems such as clustering. In this paper, we use the method of training the lower bound of the average log likelihood function on the quantum Boltzmann machine (QBM) to recognize the handwritten number datasets and compare the training results with classical models. We find that, when the QBM is semi-restricted, the training results get better with fewer computing resources. This shows that it is necessary to design a targeted algorithm to speed up computation and save resources.     

When null energy condition meets ADM mass

We give a conjecture on the lower bound of the ADM mass M by using the null energy condition. The conjecture includes a Penrose-like inequality $3M\geqslant \kappa { \mathcal A }/(4\pi )+\sqrt{{ \mathcal A }/4\pi }$ and the Penrose inequality $2M\geqslant \sqrt{{ \mathcal A }/4\pi }$ with ${ \mathcal A }$ the event horizon area and κ the surface gravity. Both the conjecture in the static spherically symmetric case and the Penrose inequality for a dynamical spacetime with spherical symmetry are proved by imposing the null energy condition. We then generalize the conjecture to a general dynamical spacetime. Our results raise a new challenge for the famous unsettled question in general relativity: in what general case can the null energy condition replace other energy conditions to ensure the Penrose inequality?     

Meaningful Secret Image Sharing with Saliency Detection

Secret image sharing (SIS), as one of the applications of information theory in information security protection, has been widely used in many areas, such as blockchain, identity authentication and distributed cloud storage. In traditional secret image sharing schemes, noise-like shadows introduce difficulties into shadow management and increase the risk of attacks. Meaningful secret image sharing is thus proposed to solve these problems. Previous meaningful SIS schemes have employed steganography to hide shares into cover images, and their covers are always binary images. These schemes usually include pixel expansion and low visual quality shadows. To improve the shadow quality, we design a meaningful secret image sharing scheme with saliency detection. Saliency detection is used to determine the salient regions of cover images. In our proposed scheme, we improve the quality of salient regions that are sensitive to the human vision system. In this way, we obtain meaningful shadows with better visual quality. Experiment results and comparisons demonstrate the effectiveness of our proposed scheme.     

Impact of Extracellular Polymeric Substance in the Inactivation of Harmful Algae by Ag2O/g-C3N4 under Visible Light

Photocatalysis has attracted much attention as an emerging algae removal technology, but the inactivation performance is inevitably affected by the extracellular polymeric substance (EPS) produced by algae. In this study, a photocatalyst (Ag₂O/g-C₃N₄) with efficient algae inactivation is adopted to investigate the interactions with EPS, and the impact of EPS on photocatalytic algae removal is studied. The results show that EPS can adhere to the surface of Ag₂O/g-C₃N₄ by electrostatic force. The interaction with EPS decreases the surface zeta potential of the Ag₂O/g-C₃N₄ from 7.71 to −22.3 mV with the increase in EPS concentration, and the maximum ratio of particle size increases from 825 to 1281 nm. In addition, the interaction with EPS inhibits the release of Ag⁺ in Ag₂O/g-C₃N₄ by half, thus, the toxicity of metal ions will be alleviated. Meanwhile, EPS can also be degraded by Ag₂O/g-C₃N₄, indicating that EPS can work as a radical scavenger to protect the algae cells. Without the protection of EPS, 97.8% of algae cells are inactivated after 5 h photocatalysis. Therefore, more attention should be given to the interaction between EPS and photocatalyst to promote the design and application of the photocatalytic.     

Amorphous High-Entropy Hydroxides of Tunable Wide Solar Absorption for Solar Water Evaporation

The high-entropy materials have raised much attention in recent years due to their extraordinary performances in mechanical, catalysis, energy storage fields. Herein, a new type of high-entropy hydroxides (e.g., NiFeCoMnAl(OH)_x) that are amorphous and capable of broad solar absorption is reported. A facile one-pot co-precipitation method is employed to synthesize these amorphous high-entropy hydroxides (a-HEHOs) under ambient conditions. The a-HEHOs thus obtained display widely tunable bandgap (e.g., from 2.6 to 1.1 eV) due to their high-entropy and amorphous characteristics, enabling efficient light absorbance and photothermal conversion in the solar regime. Further solar water evaporation measurements show that the a-HEHOs delivered a considerable energy conversion efficiency of 55%, comparable to black titanium oxides that are synthesized using more complex and expensive methods.     

Cyclodextrin Functionalized Carbon Dots Using IFE and FRET Dual Mechanism for Sequential Detection of Morin and Al3+

Carbon dots (CDs) are emerging photoluminescent materials with excellent optical properties. However, the lack of active sites in primitive CDs has limited their development applications. Herein, functionalized carbon dots (Z-CDs) are successfully prepared by surface modification of CDs with mono (6-amino-6-deoxy) cyclodextrin (β-CD). The introduction of β-CD increases the spatial potential resistance between CDs, which effectively reduces the self-quenching effect. Moreover, the conjugated domains of Z-CDs are expanded, which improves the optical properties with a quantum yield of 48.74%. Z-CDs are able to be used in the sequential detection of morin and Al³⁺, and the fluorescence mechanisms are confirmed to be internal filtration effect and fluorescence resonance energy transfer, respectively. The limits of detection are 0.817 and 0.231 × 10⁻⁶ m . This study not only provides an idea to solve the problem of self-quenching of CDs but also enriches the detection means of flavonoids and ions, which is expected to be applied to biosensing and environmental monitoring.     

Porcelain Insulator Crack Location and Surface States Pattern Recognition Based on Hyperspectral Technology

A porcelain insulator is an important part to ensure that the insulation requirements of power equipment can be met. Under the influence of their structure, porcelain insulators are prone to mechanical damage and cracks, which will reduce their insulation performance. After a long-term operation, crack expansion will eventually lead to breakdown and safety hazards. Therefore, it is of great significance to detect insulator cracks to ensure the safe and reliable operation of a power grid. However, most traditional methods of insulator crack detection involve offline detection or contact measurement, which is not conducive to the online monitoring of equipment. Hyperspectral imaging technology is a noncontact detection technology containing three-dimensional (3D) spatial spectral information, whereby the data provide more information and the measuring method has a higher safety than electric detection methods. Therefore, a model of positioning and state classification of porcelain insulators based on hyperspectral technology is proposed. In this model, image data were used to extract edges to locate cracks, and spectral information was used to classify the surface states of porcelain insulators with EfficientNet. Lastly, crack extraction was realized, and the recognition accuracy of cracks and normal states was 96.9%. Through an analysis of the results, it is proven that the crack detection method of a porcelain insulator based on hyperspectral technology is an effective non-contact online monitoring approach, which has broad application prospects in the era of the Internet of Things with the rapid development of electric power.     

GIS Partial Discharge Pattern Recognition Based on a Novel Convolutional Neural Networks and Long Short-Term Memory

Distinguishing the types of partial discharge (PD) caused by different insulation defects in gas-insulated switchgear (GIS) is a great challenge in the power industry, and improving the recognition accuracy of the relevant models is one of the key problems. In this paper, a convolutional neural network and long short-term memory (CNN-LSTM) model is proposed, which can effectively extract and utilize the spatiotemporal characteristics of PD input signals. First, the spatial characteristics of higher-level PD signals can be obtained through the CNN network, but because CNN is a deep feedforward neural network, it does not have the ability to process time-series data. The PD voltage signal is related to the time dimension, so LSTM saves and analyzes the previous voltage signal information, realizes the modeling of the time dependence of the data, and improves the accuracy of the PD signal pattern recognition. Finally, the pattern recognition results based on CNN-LSTM are given and compared with those based on other traditional analysis methods. The results show that the pattern recognition rate of this method is the highest, with an average of 97.9%, and its overall accuracy is better than that of other traditional analysis methods. The CNN-LSTM model provides a reliable reference for GIS PD diagnosis.     

Assessment and Prediction of Water Resources Vulnerability Based on a NRS-RF Model: A Case Study of the Song-Liao River Basin,China

The vulnerability of water resources is an important criterion for evaluating the carrying capacity of water resources systems under the influence of climate change and human activities. Moreover, assessment and prediction of river basins’ water resources vulnerability are important means to assess the water resources security state of river basins and identify possible problems in future water resources systems. Based on the constructed indicator system of water resources vulnerability assessment in Song-Liao River Basin, this paper uses the neighborhood rough set (abbreviated as NRS) method to reduce the dimensionality of the original indicator system to remove redundant attributes. Then, assessment indicators’ standard values after dimensionality reduction are taken as the evaluation sample, and the random forest regression (abbreviated as RF) model is used to assess the water resources vulnerability of the river basin. Finally, based on data under three different future climate and socio-economic scenarios, scenario predictions are made on the vulnerability of future water resources. The results show that the overall water resources vulnerability of the Song-Liao River Basin has not improved significantly in the past 18 years, and the overall vulnerability of the Song-Liao River Basin is in the level V of moderate to high vulnerability. In the future scenario 1, the overall water resources vulnerability of the river basin will improve, and it is expected to achieve an improvement to the level III of moderate to low vulnerability. At the same time, the natural vulnerability and vulnerability of carrying capacity will increase significantly in the future, and the man-made vulnerability will increase slowly, which will deteriorate to the level V of moderate to high vulnerability under Scenario 3. Therefore, taking active measures can significantly reduce the vulnerability of nature and carrying capacity, but man-made vulnerability will become a bottleneck restricting the fragility of the overall water resources of the river basin in the future.