bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enhanced light–matter interaction in two-dimensional transition metal dichalcogenides
Authors: Huang Lujun , Krasnok Alex , Alú Andrea , Yu Yiling , Neshev Dragomir , Miroshnichenko Andrey E 
Published: December 2021

Abstract
Two-dimensional (2D) transition metal dichalcogenide (TMDC) materials, such as MoS2, WS2, MoSe2, and WSe2, have received extensive attention in the past decade due to their extraordinary electronic, optical and thermal properties. They evolve from indirect bandgap semiconductors to direct bandgap semiconductors while their layer number is reduced from a few layers to a monolayer limit. Consequently, there is strong photoluminescence in a monolayer (1L) TMDC due to the large quantum yield. Moreover, such monolayer semiconductors have two other exciting properties: large binding energy of excitons and valley polarization. These properties make them become ideal materials for various electronic, photonic and optoelectronic devices. However, their performance is limited by the relatively weak light–matter interactions due to their atomically thin form factor. Resonant nanophotonic structures provide a viable way to address this issue and enhance light–matter interactions in 2D TMDCs. Here, we provide an overview of this research area, showcasing relevant applications, including exotic light emission, absorption and scattering features. We start by overviewing the concept of excitons in 1L-TMDC and the fundamental theory of cavity-enhanced emission, followed by a discussion on the recent progress of enhanced light emission, strong coupling and valleytronics. The atomically thin nature of 1L-TMDC enables a broad range of ways to tune its electric and optical properties. Thus, we continue by reviewing advances in TMDC-based tunable photonic devices. Next, we survey the recent progress in enhanced light absorption over narrow and broad bandwidths using 1L or few-layer TMDCs, and their applications for photovoltaics and photodetectors. We also review recent efforts of engineering light scattering, e.g., inducing Fano resonances, wavefront engineering in 1L or few-layer TMDCs by either integrating resonant structures, such as plasmonic/Mie resonant metasurfaces, or directly patterning monolayer/few layers TMDCs. We then overview the intriguing physical properties of different van der Waals heterostructures, and their applications in optoelectronic and photonic devices. Finally, we draw our opinion on potential opportunities and challenges in this rapidly developing field of research.
Improving the accuracy and robustness of RRAM-based in-memory computing against RRAM hardware noise and adversarial attacks
Authors: Kiran Cherupally Sai , Meng Jian , Siraj Rakin Adnan , Yin Shihui , Yeo Injune , Yu Shimeng , Fan Deliang , Seo Jae-Sun 
Published: December 2021

Abstract
We present a novel deep neural network (DNN) training scheme and resistive RAM (RRAM) in-memory computing (IMC) hardware evaluation towards achieving high accuracy against RRAM device/array variations and enhanced robustness against adversarial input attacks. We present improved IMC inference accuracy results evaluated on state-of-the-art DNNs including ResNet-18, AlexNet, and VGG with binary, 2-bit, and 4-bit activation/weight precision for the CIFAR-10 dataset. These DNNs are evaluated with measured noise data obtained from three different RRAM-based IMC prototype chips. Across these various DNNs and IMC chip measurements, we show that our proposed hardware noise-aware DNN training consistently improves DNN inference accuracy for actual IMC hardware, up to 8% accuracy improvement for the CIFAR-10 dataset. We also analyze the impact of our proposed noise injection scheme on the adversarial robustness of ResNet-18 DNNs with 1-bit, 2-bit, and 4-bit activation/weight precision. Our results show up to 6% improvement in the robustness to black-box adversarial input attacks.
Recovering the performance of irradiated high-temperature superconductors for use in fusion magnets
Authors: Unterrainer Raphael , Fischer David X , Lorenz Alena , Eisterer Michael 
Published: December 2021

Abstract
Magnets confining the plasma in future fusion devices will be exposed to a significant destructive flux of fast neutrons. In particular, in cost-efficient compact reactor designs, the degradation of the superconductor becomes an issue and directly impacts the commercial viability. We report on the influence of neutron radiation on the superconducting transition temperature,

T

c

, and the critical current density,

j

c

, and discuss possibilities to counteract the degradation using thermal treatments. We found that the degradation in

T

c

and

j

c

are closely related to each other, likely due to the expected loss of superfluid density; thus,

T

c

is a very useful indicator for the magnets’ degradation. It increases linearly with the annealing temperature, and around 25% of the decrease can be recovered by annealing at 150 ∘C and about 60% at 400 ∘C, which would more than double the magnet’s lifetime. However, a loss of oxygen has to be impeded in the latter case.
Protecting the quantum state from a finite temperature decoherence source by parity-time symmetric operation
Authors: Leng Yang , Yang Li 
Published: December 2021

Abstract
We examine the validity of the parity-time (

P
T

)-symmetric operation in protecting quantum state and entanglement in the non-zero temperature environment. Special attention is paid to the dependence of quantum fidelity and entanglement on the temperature. In particular, by solving the master equation, we get the exact analytical or numerical simulation expressions of the explicit formulas of protection, showing explicitly that

P
T

-symmetric operation does indeed help in protecting quantum state from finite temperature decoherence.
Two-proton radioactivity within Coulomb and proximity potential model *
Authors: Zhu De-Xing , Liu Hong-Ming , Xu Yang-Yang , Zou You-Tian , Wu Xi-Jun , Chu Peng-Cheng , Li Xiao-Hua 
Published: December 2021

Abstract
Considering the preformation probability of the two emitted protons in the parent nucleus, we extend the Coulomb and proximity potential model (CPPM) to systematically study two-proton (2p) radioactivity half-lives of the nuclei close to proton drip line. The proximity potential chosen is Prox. 81 proposed by Blocki et al. in 1981. Furthermore, we apply this model to predict the half-lives of possible 2p radioactive candidates whose 2p radioactivity is energetically allowed or observed but not yet quantified in the evaluated nuclear properties table NUBASE2016. The predicted results are in good agreement with those from other theoretical models and empirical formulas, namely the effective liquid drop model (ELDM), generalized liquid drop model (GLDM), Gamow-like model, Sreeja formula and Liu formula.
Knowledge-driven feature component interpretable network for motor imagery classification
Authors: Niu Xu , Lu Na , Kang Jianghong , Cui Zhiyan 
Published: December 2021

Abstract

Objective. The end-to-end convolutional neural network (CNN) has achieved great success in motor imagery (MI) classification without a manual feature design. However, all the existing deep network solutions are purely datadriven and lack interpretability, which makes it impossible to discover insightful knowledge from the learned features, not to mention to design specific network structures. The heavy computational cost of CNN also makes it challenging for real-time application along with high classification performance. Approach. To address these problems, a novel knowledge-driven feature component interpretable network (KFCNet) is proposed, which combines spatial and temporal convolution in analogy to independent component analysis and a power spectrum pipeline. Prior frequency band knowledge of sensory-motor rhythms has been formulated as band-pass linear-phase digital finite impulse response filters to initialize the temporal convolution kernels to enable the knowledge-driven mechanism. To avoid signal distortion and achieve a linear phase and unimodality of filters, a symmetry loss is proposed, which is used in combination with the cross-entropy classification loss for training. Besides the general prior knowledge, subject-specific time-frequency property of event-related desynchronization and synchronization has been employed to construct and initialize the network with significantly fewer parameters. Main results. Comparison of experiments on two public datasets has been performed. Interpretable feature components could be observed in the trained model. The physically meaningful observation could efficiently assist the design of the network structure. Excellent classification performance on MI has been obtained. Significance. The performance of KFCNet is comparable to the state-of-the-art methods but with much fewer parameters and makes real-time applications possible.
Collective behavior in the North Rhine-Westphalia motorway network
Authors: Wang Shanshan , Gartzke Sebastian , Schreckenberg Michael , Guhr Thomas 
Published: December 2021

Abstract
To understand the dynamics on complex networks, measurement of correlations is indispensable. In a motorway network, it is not sufficient to collect information on fluxes and velocities on all individual links, i.e. parts of the freeways between ramps and highway crosses. The interdependencies and mutual connections are also of considerable interest. We analyze correlations in the complete motorway network in North Rhine-Westphalia, the most populous state in Germany. We view the motorway network as a complex system consisting of road sections which interact via the motion of vehicles, implying structures in the corresponding correlation matrices. In particular, we focus on collective behavior, i.e. coherent motion in the whole network or in large parts of it. To this end, we study the eigenvalue and eigenvector statistics and identify significant sections in the motorway network. We find collective behavior in these significant sections and further explore its causes. We show that collectivity throughout the network cannot directly be related to the traffic states (free, synchronous and congested) in Kerner’s three-phase theory. Hence, the degree of collectivity provides a new, complementary observable to characterize the motorway network.
Stochastic rumors on random trees
Authors: Junior Valdivino V , Rodriguez Pablo M , Speroto Adalto 
Published: December 2021

Abstract
The Maki–Thompson rumor model is defined by assuming that a population represented by a graph is subdivided into three classes of individuals; namely, ignorants, spreaders and stiflers. A spreader tells the rumor to any of its nearest ignorant neighbors at rate one. At the same rate, a spreader becomes a stifler after a contact with other nearest neighbor spreaders, or stiflers. In this work we study the model on random trees. As usual we define a critical parameter of the model as the critical value around which the rumor either becomes extinct almost-surely or survives with positive probability. We analyze the existence of phase-transition regarding the survival of the rumor, and we obtain estimates for the mean range of the rumor. The applicability of our results is illustrated with examples on random trees generated from some well-known discrete distributions.
Hydroelastic analysis of floating structures under wave loads
Authors: Deng Xuhui , Ding Liang , Meng Liuyang 
Published: December 2021

Abstract
Accurate prediction of hydroelastic response in ocean waves is of great significance to the structural design and reliability design of floating structures. In this paper, based on the potential flow theory, a large floating structure is simplified as a thin-plate material, and the hydrodynamic characteristics of the structure are calculated by using the modal expansion method and the boundary element method. The correctness of the theory and calculation is verified by comparing the experimental and numerical results. Further, the wave properties and structural materials characterization were changed, this paper calculates the stress and deflection of the structure under wave action, and analyzes the effects of hydroelastic response on the safety of the structure.
Development of auto loading device in bearing positioning lip stamping
Authors: Qi Pengyuan , Wang Jingbo , Liu Zhe , Ren Xu , Wang Lei 
Published: December 2021

Abstract
With the continuous development of our economy, the types and quantity of engine bearing are increasing. In order to prevent the bearing moving in the bearing pedestal, one or both ends of the bearing need to make positioning lip for axial positioning. In order to meet the needs of mass bearing production, an auto loading device for positioning lip of bearing is proposed in this paper. The horizontal conveyor belt and loading guide are used to make the blank turn to the required angle, and then the double cylinder valve and push rod motor are used to realize the time-sharing conveying and continuous loading of the blank. During the loading process, there are inclined slideway and blocking plate to avoid the collision between blanks and ensure the reliable loading bearings. Through the actual production verification, the developed device has the advantages of simple structure, low cost, fine working stability, and made great improving in the efficiency and safety for production of bearing.
Development of Dry Drilling Diamond Bit and Drilling System
Authors: Xu Liang , Liu Yibo , Sun Yanlong 
Published: December 2021

Abstract
This paper summarizes the current situation, market demand and existing problems of dry drilling for stone and building materials construction at home and abroad. The use and characteristics of dry drilling, the structural design of dry drilling diamond bit and the selection of matrix formula are introduced. Finally, through the test of drilling reinforced concrete with long dry drilling bits, the related equipment of the complete dry drilling system is introduced in detail.
Research and Application of Remote Control Test Method of Amplifier Indicators
Authors: Liaoyuemin  , Liaohaiqian  
Published: December 2021

Abstract
As we all know, there are many test indicators for amplifiers, including saturated output power, 1dB compressed output power (P-1), harmonic suppression, etc. The saturated output power and 1dB compressed output power are tested step by step, and the harmonic suppression needs to traverse every frequency point to calculate the results, which require testers to pay more time and effort. When there are batches of power amplifiers to be tested, manual testing methods are unrealistic. In order to simplify the test steps, reduce the input of testers, and speed up the test progress, this article introduces a test method about instrument remote control, based on GPIB and C# after the study and research for the GPIB bus of the GPIB instrument. This method calls the VISA library to drive the GPIB card to communicate with the test instrument by SCPI programmable instrument standard commands and the respective remote control commands of each instrument to perform mixed programming with C#. Finally, we can complete the measurement, display and get the data by controlling the GPIB instruments on the PC side.
Automatic Measurement System of High Resistance up to100TΩ
Authors: Sun Wen , Peng Jiajun , Zhang Huan , Wang Ying , Chen Jiaxu 
Published: December 2021

Abstract
To solve the problems exist in the measurements high resistances in range of 1 MΩ~100 TΩ, such as low precision, poor repeatability, long time consuming and easy to be affected by the external environment, an adapted Wheatstone bridge for high resistances is described. The system uses two DC voltage sources to build a standard proportion, a pico-ammeter as the Null-indicator. The automatic balancing of the bridge is realized through the balance algorithm. The measurement software is compiled to realize the automatic measurement of high resistance. The shielding system is studied, the methods to improve the accuracy are summarized. The experimental results show that the standard uncertainty at 100GΩ reaches 3.2×10−5. The system can meet the calibration requirements of high value resistance, and has been applied in practical work.
Performance of different Formation control methods between Multiple Spacecraft with local part communications
Authors: Zhang Bokun 
Published: December 2021

Abstract
With the development of network and distributed computing, many applications of multi vehicle system have become possible, and motion transformation can be realized. In multi vehicle coordination, the interaction between information exchange topology and control plays an important role. Different formation control methods have different performance. Thus, in order to figure out which method is suitable for multiple spacecraft, the author simulated the signal unstable environment in space to test the robustness and consensus speed of the following three methods. The author use Bearing-only Formation Control, A leader–follower Formation Control and Affine Formation Control to test their performance with local part communications. The author use matlab and simulink to analog communication process of multiple spacecraft. Finally, the author find Affine Formation Control’s performance is the best. It has the best robustness and the fast consensus speed but more energy and signal paths to communicate.
An Attention-Based Photovoltaic Forecasting Scheme Combined with LSTM Model
Authors: Wei H , Chen X , Mi W 
Published: December 2021

Abstract
With the development of new energy around the world, the proportion of photovoltaic energy used as a clean energy in the distribution network is gradually rising. The forecast of photovoltaic power plants is vital to many energy providers for their marketing analysis. Thus, photovoltaic forecasting has become an important research direction at present. However, owing to the high volatility and intermittent characteristics of photovoltaic power generation, it is still a challenge to predict photovoltaic power accurately. As far as traditional photovoltaic forecasting methods are concerned, SVM and ARIMA, as machine learning methods can solve the timing prediction problem of certain scenarios, but they are often not appropriate for some time series closely related to features. To address this problem, this paper proposes a short-term photovoltaic load forecasting model based on the Attention mechanism and LSTM model. Firstly, the correlation coefficient and LASSO regression are used for feature selection to filter out redundant features. Secondly, a long short-term memory network (LSTM) is used to make predictions to solve the problem of gradient disappearance during model training. Finally, the Attention mechanism is added to better capture feature weights and further improve the prediction accuracy of the training model. The proposed method can predict the change trend well. Comparative results confirm that the proposed method with feature selection can has better effect than ARIMA, SVM, and ELM.
Research status and development trend of ceramifiable silicone rubber composites: a brief review
Authors: Li Jianhua 
Published: December 2021

Abstract
Ceramifiable silicone rubber composites (CSR) are one of the most important industrially produced elastic technical materials. At present, there is a strong demand for CSR in the high-tech fields of high-speed rail, nuclear power, rocket and aerospace, which are still to be met. Many scholars have committed to the research work of improving the thermal stability, flame retardant, mechanical properties by changing the silicone rubber (SR) matrixes and filler, tremendous advances in CSR have been made for over the last decades where CSR intended for the high technology fields has evolved through different generations. In summary, the first-generation research is based on matrix reaction, the second-generation research is on reaction products, and the third-generation research is on fillers effects. In this review, the evolution of CSR and the synthesis routes, reaction mechanism, and degradation mechanism are introduced and analyzed; fillers, various types of CSR based composites as well as the flame retardancy and mechanical properties are reviewed. Finally, the problems of developing high-performance CSRs are proposed and discussed. This review provides a theoretical basis and supporting data for the application of high-performance CSR, as well as provides details on the fire prevention mechanism of CSR.
Study of the temperature dependence of a carbidized layer formation on the tungsten surface under plasma irradiation
Authors: Baklanov Victor , Zhanbolatova Gainiya , Skakov Mazhyn , Miniyazov Arman , Sokolov Igor , Tulenbergenov Timur , Kozhakhmetov Yernat , Bukina Olga , Orazgaliev Nurkhat 
Published: December 2021

Abstract
The paper considers a method of tungsten surface carbidization using a beam-plasma discharge (BPD), which was implemented in a plasma-beam installation (PBI). The advantage of this method is to create conditions for chemical reactions and physical processes as close as possible to those possible in thermonuclear installations. The BPD makes it possible to generate plasma using different working gases. Methane was used as a plasma-forming gas. The working gas pressure in a chamber was (1,3–1,4)·10–1 Pa. The temperature dependence of the carbidized layer formation on the tungsten surface under plasma irradiation was determined in the temperature range of 700 °C–1700 °C. The formation of tungsten carbides in surface layers was confirmed by SEM and x-ray diffraction analysis. It was found that interaction between tungsten and methane in a wide temperature range can proceed with simultaneous or sequential formation of the carbide phases W2C and WC.
Effects of particle grading composition of SiC on properties of silicon-bonded SiC porous ceramics
Authors: Zhao Changzhi , Hu Huajian , Zhuo Meizhen , Shen Chunying 
Published: December 2021

Abstract
Silicon-bonded silicon carbide (SBSC) porous ceramics had been prepared by mixing two different particle size of SiC powder (coarse and fine) as aggregates for silicon carbide porous ceramics, adding metallic Si as the binder phase and firing at 1450 °C under argon atmosphere. Various combinations of SiC mixtures consisting of two different particle size and packing density were prepared, and the samples were investigated to understand apparent porosity, bending strength, pore size distribution, and microstructure. The result showed that mixing an appropriate proportion of SiC coarse and fine powders could not only improve the pore size distribution of SBSC porous ceramics but also significantly increase the bending strength compared with the single-particle size sample. The system had the highest free packing density when the ratio of coarse to fine SiC size was >2 and the coarse powder content was 60–70 wt%. The optimal bending strength, and apparent porosity were 37.53 MPa and 37.11% respectively when mixing 70 wt% of coarse powder (50.8 μm) and 30 wt% of fine powder (9.5 μm) and sintered at 1450 °C in an argon atmosphere. The material created had 100.3% increased bending strength, and 0.99% decreased porosity compared with the single-particle size sample (50.8 μm).