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.     

BESIII 实验对奇特态 X(3872) 的研究

量子色动力学允许超出夸克模型中重子和介子的奇特态的存在。对奇特态的研究是当前高能物理实验的热门话题之一。X(3872)是第一个被实验上观测到的奇特态候选者。实验和理论上对X(3872)的研究从未间断,这使得X(3872)成为了解最好的奇特态。运行在北京正负电子对撞机(BEPCII)上的北京谱仪(BESIII)实验积累了大量的质心能量从3.87 到 4.70 GeV的正负电子对撞数据样本。利用这些数据样本,得到了很多关于X(3872)的实验结果。本文将回顾BESIII上关于X(3872)所取得一系列成果及最新实验进展。对X(3872)等奇特态的研究,将有利于我们对自然界的基本相互作用的理解,也将促进标准模型理论的完善。     

大学物理教学改革实践浅析——“问题导向”式学习的意义及应用

针对当前我国高校大学物理教学中存在诸多问题的现状,提出"问题导向"式教学的解决途径."问题导向"学习中"教"与"学"的方式有较大跨度的转变,它以学生为中心,以问题为导向,其中问题的设置环节对师生良好互动的实现至关重要.文章就振动内容问题的设计和提出进行简单应用尝试,以期为后续大学物理教学改革的持续进行提供帮助.     

Intelligent Fault Diagnosis of Rolling-Element Bearings Using a Self-Adaptive Hierarchical Multiscale Fuzzy Entropy

The fuzzy-entropy-based complexity metric approach has achieved fruitful results in bearing fault diagnosis. However, traditional hierarchical fuzzy entropy (HFE) and multiscale fuzzy entropy (MFE) only excavate bearing fault information on different levels or scales, but do not consider bearing fault information on both multiple layers and multiple scales at the same time, thus easily resulting in incomplete fault information extraction and low-rise identification accuracy. Besides, the key parameters of most existing entropy-based complexity metric methods are selected based on specialist experience, which indicates that they lack self-adaptation. To address these problems, this paper proposes a new intelligent bearing fault diagnosis method based on self-adaptive hierarchical multiscale fuzzy entropy. On the one hand, by integrating the merits of HFE and MFE, a novel complexity metric method, named hierarchical multiscale fuzzy entropy (HMFE), is presented to extract a multidimensional feature matrix of the original bearing vibration signal, where the important parameters of HMFE are automatically determined by using the bird swarm algorithm (BSA). On the other hand, a nonlinear feature matrix classifier with strong robustness, known as support matrix machine (SMM), is introduced for learning the discriminant fault information directly from the extracted multidimensional feature matrix and automatically identifying different bearing health conditions. Two experimental results on bearing fault diagnosis show that the proposed method can obtain average identification accuracies of 99.92% and 99.83%, respectively, which are higher those of several representative entropies reported by this paper. Moreover, in the two experiments, the standard deviations of identification accuracy of the proposed method were, respectively, 0.1687 and 0.2705, which are also greater than those of the comparison methods mentioned in this paper. The effectiveness and superiority of the proposed method are verified by the experimental results.     

An Adaptive Deblurring Vehicle Detection Method for High-Speed Moving Drones: Resistance to Shake

Vehicle detection is an essential part of an intelligent traffic system, which is an important research field in drone application. Because unmanned aerial vehicles (UAVs) are rarely configured with stable camera platforms, aerial images are easily blurred. There is a challenge for detectors to accurately locate vehicles in blurred images in the target detection process. To improve the detection performance of blurred images, an end-to-end adaptive vehicle detection algorithm (DCNet) for drones is proposed in this article. First, the clarity evaluation module is used to determine adaptively whether the input image is a blurred image using improved information entropy. An improved GAN called Drone-GAN is proposed to enhance the vehicle features of blurred images. Extensive experiments were performed, the results of which show that the proposed method can detect both blurred and clear images well in poor environments (complex illumination and occlusion). The detector proposed achieves larger gains compared with SOTA detectors. The proposed method can enhance the vehicle feature details in blurred images effectively and improve the detection accuracy of blurred aerial images, which shows good performance with regard to resistance to shake.     

Passivation of aluminium–n+ silicon contacts for solar cells by ultrathin Al2O3 and SiO2 dielectric layers

Ultra‐thin thermally grown SiO₂ and atomic‐layer‐deposited (ALD) Al₂O₃ films are trialled as passivating dielectrics for metal–insulator–semiconductor (MIS) type contacts on top of phosphorus diffused regions applicable to high efficiency silicon solar cells. An investigation of the optimum insulator thickness in terms of contact recombination factor J_{0_cont} and contact resistivity ρ_c is undertaken on 85 Ω/□ and 103 Ω/□ diffusions. An optimum ALD Al₂O₃ thickness of ～22 Å produces a J_{0_cont} of ～300 fAcm^–2 whilst maintaining a ρ_c lower than 1 mΩ cm² for the 103 Ω/□ diffusion. This has the potential to improve the open‐circuit voltage by a maximum 15 mV. The thermally grown SiO₂ fails to achieve equivalently low J_{0_cont} values but exhibits greater thermal stability, resulting in slight improvements in ρ_c when annealed for 10 minutes at 300 °C without significant changes in J_{0_cont}. The after‐anneal J_{0_cont} reaches ～600 fAcm^–2 with a ρ_c of ～2.5 mΩ cm² for the 85 Ω/□ diffusion amounting to a maximum gain in open‐circuit voltage of 6 mV. (© 2013 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)