bluuebunny/crossref_metadata_2025 huggingface dataset:
Epitaxial registry and crystallinity of MoS2 via molecular beam and metalorganic vapor phase van der Waals epitaxy
Authors: Mortelmans Wouter , El Kazzi Salim , Groven Benjamin , Nalin Mehta Ankit , Balaji Yashwanth , De Gendt Stefan , Heyns Marc , Merckling Clement
Published: July 2020

Two-dimensional transition metal dichalcogenide (TMD) semiconductors have risen as an important material class for novel nanoelectronic applications. Molybdenum disulfide (MoS2) is the most representative TMD compound due to its superior stability and attractive properties for (opto-) electronic devices. However, the synthesis of single-crystalline and functional MoS2 across large-area substrates remains crucial for its successful integration in semiconductor industry platforms. Therefore, this work focuses on the study of MoS2 epitaxy via two well-established industry-compatible synthesis methods, promising for the large-area and single-crystalline integration of van der Waals (vdW) materials. These methods are molecular beam epitaxy (MBE) and metalorganic vapor phase epitaxy (MOVPE) and have studied MoS2 quasi-vdW heteroepitaxy on reconstructed sapphire substrates and MoS2 vdW homoepitaxy on exfoliated MoS2 flakes. By examining the MoS2 structural properties using diffraction and spectroscopy techniques, the epitaxial relation and crystal quality are assessed, which reveals insights into the prevalence of inter- and intragrain defects such as grain boundaries and sulfur vacancies. The MBE method yields superior epitaxial MoS2 registry on both sapphire and MoS2 surfaces as compared to MOVPE, although inferior defectivity arises from the typical lower MBE growth temperature and chalcogen partial pressure. Moreover, both synthesis methods generate high densities of twinned MoS2 grain boundaries, which hamper defect-free integration. As a result, this challenging integration might become an important bottleneck for industrial TMD-based applications with a low tolerance for material defects.
Tunability and switching of Fano and Lorentz resonances in PTX-symmetric electronic systems
Authors: Ye Zhilu , Farhat Mohamed , Chen Pai-Yen
Published: July 2020

Resonance is a ubiquitous phenomenon observed in a wide range of physical systems. Recently, with the Fano resonance exerting remarkable potential for optical, acoustic, atomic, and electronic applications, it is vital to control and even dynamically reconfigure the resonance line shape and bandwidth, in addition to its frequency. In this work, we introduce a parity-time-reciprocal scaling (PTX)-symmetric structure, which can offer a promising avenue for tailoring the resonance frequency and line shape of electronic circuits. We have theoretically studied the resonance behavior of such a PTX-symmetric electronic system, particularly for dependencies of resonant peaks and line-shapes on the non-Hermiticity, coupling coefficient, and the scaling coefficient introduced by the reciprocal scaling (X) transformation. Our results demonstrate that, at resonance frequencies, a transition between Fano and Lorentzian line-shapes is possible with a specific reciprocal scaling rule applied to lumped-element circuits.
Two-dimensional Raman spectroscopy of Lennard-Jones liquids via ring-polymer molecular dynamics
Authors: Tong Zhengqing , Videla Pablo E. , Jung Kenneth A. , Batista Victor S. , Sun Xiang
Published: July 2020

The simulation of multidimensional vibrational spectroscopy of condensed-phase systems including nuclear quantum effects is challenging since full quantum-mechanical calculations are still intractable for large systems comprising many degrees of freedom. Here, we apply the recently developed double Kubo transform (DKT) methodology in combination with ring-polymer molecular dynamics (RPMD) for evaluating multi-time correlation functions [K. A. Jung et al., J. Chem. Phys. 148, 244105 (2018)], providing a practical method for incorporating nuclear quantum effects in nonlinear spectroscopy of condensed-phase systems. We showcase the DKT approach in the simulation of the fifth-order two-dimensional (2D) Raman spectroscopy of Lennard-Jones liquids as a prototypical example, which involves nontrivial nonlinear spectroscopic observables of systems described by anharmonic potentials. Our results show that the DKT can faithfully reproduce the 2D Raman response of liquid xenon at high temperatures, where the system behaves classically. In contrast, liquid neon at low temperatures exhibits moderate but discernible nuclear quantum effects in the 2D Raman response compared to the responses obtained with classical molecular dynamics approaches. Thus, the DKT formalism in combination with RPMD simulations enables simulations of multidimensional optical spectroscopy of condensed-phase systems that partially account for nuclear quantum effects.
Comparison of umbrella sampling and steered molecular dynamics methods for computing free energy profiles of aromatic substrates through phospholipid bilayers
Authors: Noh Sang Young , Notman Rebecca
Published: July 2020

Understanding the permeation of molecules through lipid membranes is fundamental for predicting the cellular uptake of solutes and drug delivery mechanisms. In molecular simulations, the usual approach is to compute the free energy (FE) profile of a molecule across a model lipid bilayer, which can then be used to estimate the permeability of the molecule. Umbrella Sampling (US), which involves carrying out a series of biased simulations along a defined reaction coordinate (usually the bilayer normal direction), is a popular method for the computation of such FE profiles. However, US can be challenging to implement because the results are dependent on the strength of the biasing potential and the spacing of windows along the reaction coordinate, which, in practice, are usually optimized by an inefficient trial and error approach. The Steered Molecular Dynamics implementation of the Jarzynski Equality (JE-SMD) has been identified as an alternative to equilibrium sampling methods for measuring the FE change across a reaction coordinate. In the JE-SMD approach, equilibrium FE values are evaluated from the average of rapid non-equilibrium trajectories, thus avoiding the practical issues that come with US. Here, we use three different corrections of the JE-SMD method to calculate the FE change for the translocation of two aromatic substrates, phenylalanine and toluene, across a lipid bilayer and compare the accuracy and computational efficiency of these approaches to the results obtained using US. We show evidence that when computing the free energy profile, the JE-SMD approach suffers from insufficient sampling convergence of the bilayer environment and is dependent on the characteristic of the aromatic substrate itself. We deduce that, despite its drawbacks, US remains the more viable approach of the two for computing the FE profile.
MicroRNA-141 participates in milk lipid metabolism by targeting SIRT1 in bovine mammary epithelial cells
Authors: Sun Yujia , Xia Hailei , Lu Xubin , Xu Chong , Li Mingxun , Chen Zhi , Yang Zhangping
Published: July 2020

Context The regulation of milk lipids is important for the evaluation of dairy cows’ performance. Lipids are produced and secreted by mammary gland under the regulation of steroid hormones, growth factors and microRNAs (miRNAs). MicroRNAs have been verified to be involved in numerous biological processes. Previous studies have shown that miR-141 is expressed at higher levels in dairy cows at peak lactation than in those at early lactation. However, the roles of miR-141 in bovine mammary epithelial cells (BMECs) and the mechanisms how it affects lipid metabolism are as yet unknown. Aims The aims of this study were to clarify (i) the molecular mechanisms of miR-141 in milk lipid metabolism, and (ii) how miR-141 affects milk lipid metabolism in BMECs. Methods Triglycerides were observed in BMECs using triglyceride analysis after overexpression or inhibition of miR-141; selected potential candidate genes that are targeted by miR-141 using TargetScan. The regulatory relationship among miR-141, SIRT1 gene and lipid metabolism-related genes (SREBF1, FASN and PPARγ) by using the dual luciferase assay, quantitative real-time PCR and western blotting. Key results Through overexpression or inhibition of miR-141 expression, we found that miR-141 promoted lipid metabolism in BMECs and an increase in triglycerides was observed in these cells. Further, miR-141 targets the 3′UTR of SIRT1 mRNA, and negative regulates the expression of SIRT1 gene in BMECs. Also, the expression levels of SREBF1, FASN and PPARγ, which are related to milk lipid metabolism, were also altered after overexpression miR-141. Conclusions Our results have revealed that miR-141 could promote milk lipid metabolism in BMECs by means of negative regulates SIRT1 gene and positive effects lipid metabolism-related genes (SREBF1, FASN and PPARγ) in BMECs. Implications Our research indicates that miR-141 could be considered a marker in cattle breeding to obtain high quality dairy products. It would be useful to study the function of miRNAs in milk lipid metabolism and synthesis. In the long term these findings might be helpful in developing practical means to improve the quality of ruminant milk.
Life on the farm: are zooplankton communities in natural ponds and constructed dams the same?
Authors: Le Quesne Kelly S. , Özkundakci Deniz , Duggan Ian C.
Published: July 2020

Constructed waters (e.g. dams and retired quarries) are commonly found to have a different zooplankton composition than are natural waters, and to be more readily invaded by non-indigenous species. Constructed ponds are common on farmland, but zooplankton research in these areas is scarce. Consequently, our aims were to (1) compare zooplankton communities between natural ponds and dams in rural environments and, (2) examine environmental determinants of zooplankton community composition among rural ponds. Thirty-eight ponds on farmland in New Zealand were sampled for zooplankton in winter–spring 2018 and summer 2019. All ponds were eutrophic, and zooplankton taxa typical of such conditions were common in both pond types (e.g. Brachionus, Keratella and Polyarthra species). Zooplankton community composition differed statistically between each type, although we deemed this difference to be ecologically insignificant (one-way ANOSIM, r=0.09, P=0.014). Prevalence of non-indigenous species was low, with 7% of farm dams and 2% of natural ponds being invaded, indicating that farm ponds are not acting as ‘stepping-stones’ for invaders across landscapes. Macrophyte abundance and concentration of humic substances were the dominant environmental variables measured determining zooplankton distribution among ponds.
Predicting the long-term stability of compact multiplanet systems
Authors: Tamayo Daniel , Cranmer Miles , Hadden Samuel , Rein Hanno , Battaglia Peter , Obertas Alysa , Armitage Philip J. , Ho Shirley , Spergel David N. , Gilbertson Christian , Hussain Naireen , Silburt Ari , Jontof-Hutter Daniel , Menou Kristen
Published: July 2020

Significance

Observations of planets beyond our solar system (exoplanets) yield uncertain orbital parameters. Particularly in compact multiplanet systems, a significant fraction of observationally inferred orbital configurations can lead to planetary collisions on timescales that are short compared with the age of the system. Rejection of these unphysical solutions can thus sharpen our view of exoplanetary orbital architectures. Long-term stability determination is currently performed through direct orbital integrations. However, this approach is computationally prohibitive for application to the full exoplanet sample. By speeding up this process by up to five orders of magnitude, we enable precise exoplanet characterization of compact multiplanet systems and our ability to examine the stability properties of the multiplanet exoplanet sample as a whole.
Site-specific ubiquitination of pathogenic huntingtin attenuates its deleterious effects
Authors: Hakim-Eshed Vicky , Boulos Ayub , Cohen-Rosenzweig Chen , Yu-Taeger Libo , Ziv Tamar , Kwon Yong Tae , Riess Olaf , Phuc Nguyen Hoa Huu , Ziv Noam E. , Ciechanover Aaron
Published: July 2020

Significance

The hallmark of neurodegenerative disorders (e.g., Alzheimer’s, Parkinson’s, and Huntington’s disease) is the generation of aggregated proteins in different brain regions. These large aggregates originate from smaller, invisible, and soluble oligomers/aggregates and are believed to be sequestered from essential cellular machineries and, therefore, to attenuate/postpone the pathologic process. That, in contrast to the smaller ones that are thought to inhibit basic cellular pathways by trapping their components. The role played in this process by ubiquitin—which normally targets proteins for degradation—is unknown. Here, we show that ubiquitination of pathogenic Htt is important for its efficient aggregation and probably sequestration. Abolishing its ubiquitination results in generation of numerous smaller and less visible aggregates that are particularly harmful to cells.
EGF receptor–mediated FUS phosphorylation promotes its nuclear translocation and fibrotic signaling
Authors: Chiusa Manuel , Hu Wen , Zienkiewicz Jozef , Chen Xiwu , Zhang Ming-Zhi , Harris Raymond C. , Vanacore Roberto M. , Bentz Jennifer A. , Remuzzi Giuseppe , Benigni Ariela , Fogo Agnes B. , Luo Wentian , Mili Stavroula , Wilson Matthew H. , Zent Roy , Hawiger Jacek , Pozzi Ambra
Published: July 2020

Excessive accumulation of collagen leads to fibrosis. Integrin α1β1 (Itgα1β1) prevents kidney fibrosis by reducing collagen production through inhibition of the EGF receptor (EGFR) that phosphorylates cytoplasmic and nuclear proteins. To elucidate how the Itgα1β1/EGFR axis controls collagen synthesis, we analyzed the levels of nuclear tyrosine phosphorylated proteins in WT and Itgα1-null kidney cells. We show that the phosphorylation of the RNA-DNA binding protein fused in sarcoma (FUS) is higher in Itgα1-null cells. FUS contains EGFR-targeted phosphorylation sites and, in Itgα1-null cells, activated EGFR promotes FUS phosphorylation and nuclear translocation. Nuclear FUS binds to the collagen IV promoter, commencing gene transcription that is reduced by inhibiting EGFR, down-regulating FUS, or expressing FUS mutated in the EGFR-targeted phosphorylation sites. Finally, a cell-penetrating peptide that inhibits FUS nuclear translocation reduces FUS nuclear content and collagen IV transcription. Thus, EGFR-mediated FUS phosphorylation regulates FUS nuclear translocation and transcription of a major profibrotic collagen gene. Targeting FUS nuclear translocation offers a new antifibrotic therapy.
A numerical damped oscillator approach to constrained Schrödinger equations
Authors: Ögren M , Gulliksson M
Published: July 2020

This article explains and illustrates the use of a set of coupled dynamical equations, second order in a fictitious time, which converges to solutions of stationary Schrödinger equations with additional constraints. In fact, the method is general and can solve constrained minimization problems in many fields. We present the method for introductory applications in quantum mechanics including three qualitative different numerical examples: the radial Schrödinger equation for the hydrogen atom; the 2D harmonic oscillator with degenerate excited states; and a nonlinear Schrödinger equation for rotating states. The presented method is intuitive, with analogies in classical mechanics for damped oscillators, and easy to implement, either with coding or with software for dynamical systems. Hence, we find it suitable to introduce it in a continuation course in quantum mechanics or generally in applied mathematics courses which contain computational parts. The undergraduate student can, for example, use our derived results and the code (supplemental material (https://stacks.iop.org/EJP/41/065406/mmedia)) to study the Schrödinger equation in 1D for any potential. The graduate student and the general physicist can work from our three examples to derive their own results for other models including other global constraints.
An improved formula for the deflection of light by a massive object
Authors: Hurkx Fred
Published: July 2020

A straightforward derivation of the integral governing the trajectory of light around a massive object is presented. By a proper choice of coordinate transformation the singularity in the integrand of this equation is removed, which makes it more suitable for numerical calculations of trajectories. An improved approximate formula for the deflection angle is presented.
Evaluation of laser diffraction-based particle size measurements using digital inline holography
Authors: Kumar S Santosh , He Zilong , Hogan Christopher J , Fredericks Steven A , Hong Jiarong
Published: July 2020

The measurements of size distribution of small particles (e.g. dusts, droplets, bubbles, etc) are critical for a broad range of applications in environmental science, public health, industrial manufacturing, etc. Laser diffraction (LD), a widely used method for such applications, depends on model-based inversion with underlying assumptions on particle properties. Furthermore, the presence of sampling biases such as velocity differentials are often overlooked in simple ex-situ calibrations, which introduces as an additional source of error. In contrast, digital inline holography (DIH), a single camera coherent imaging technique, can both measure particle size distributions without the need for a model-based inversion and can directly provide information on the shape characteristics of the particles. In this study, we evaluate the performance of an LD system in characterizing polydisperse droplets produced in a flat fan spray using in-situ DIH based imaging as a reference. The systematic differences in the two techniques are examined. A droplet-trajectory-based correction for the LD-inferred size distributions is proposed to compensate for the observed differences. We validate the correction using NIST standard polydisperse particles undergoing differential settling, and then apply the correction to polydisperse spray droplet measurements. The correction improves agreement between LD and DIH size distributions for droplets over two orders of magnitude, but with LD still underestimating the fraction of droplets at sizes above ∼1 mm. This underestimation is possibly linked to the complex oscillatory and rotational motion of droplets which cannot be faithfully captured by measurement or modelled by the correction algorithm without additional information.
Anisotropic failure behavior of ordered intermetallic TiAl alloys under pure mode-I loading
Authors: Neogi Anupam , Alam Masud , Hartmaier Alexander , Janisch Rebecca
Published: July 2020

Whether a metallic material fractures by brittle cleavage or by ductile rupture is primarily governed by the competition between cleavage and dislocation emission at the crack tip. The linear elastic fracture mechanics (LEFM) based criterion of Griffith, respectively the one for dislocation emission of Rice, are sufficiently reliable for determining the possible crack tip propagation mechanisms in isotropic crystalline metals. However, the applicability of these criteria is questionable when non-cubic, anisotropic solids are considered, as e.g. ordered intermetallic TiAl phases, where slip systems are limited and elastic anisotropy is pronounced. We study brittle versus ductile failure mechanisms in face-centered tetragonal TiAl and hexagonal Ti3Al using large-scale atomistic simulations and compare our findings to the predictions of LEFM-based criteria augmented by elastic anisotropy. We observe that the augmented Griffith and Rice criteria are reliable for determining the direction dependent crack tip mechanisms, if all the available dislocation slip systems are taken into account. Yet, atomistic simulations are necessary to understand crack blunting due to mixed mechanisms, or shear instabilities other than dislocation emission. The results of our systematic study can be used as basis for modifications of the Griffith/Rice criteria in order to incorporate such effects.
Simulation of attosecond transient soft x-ray absorption in solids using generalized Kohn–Sham real-time time-dependent density functional theory
Authors: Pemmaraju C D
Published: July 2020

Time-dependent density functional theory (TDDFT) simulations of transient core-level spectroscopies require a balanced treatment of both valence- and core-electron excitations. To this end, tuned range-separated hybrid exchange–correlation functionals within the generalized Kohn–Sham scheme offer a computationally efficient means of simultaneously improving the accuracy of valence and core excitation energies in TDDFT by mitigating delocalization errors across multiple length-scales. In this work range-separated hybrid functionals are employed in conjunction with the velocity-gauge formulation of real-time TDDFT to simulate static as well as transient soft x-ray near-edge absorption spectra in a prototypical solid-state system, monolayer hexagonal boron nitride, where excitonic effects are important. In the static case, computed soft x-ray absorption edge energies and line shapes are seen to be in good agreement with experiment. Following laser excitation by a pump pulse, soft x-ray probe spectra are shown to exhibit characteristic features of population induced bleaching and transient energy shifts of exciton peaks. The methods outlined in this work therefore illustrate a practical means for simulating attosecond time-resolved core-level spectra in solids within a TDDFT framework.
A Hybrid Machine Learning Model with Cost-function Based Outlier Removal and Its Application on Credit Rating
Authors: Mu Aurora Y.
Published: July 2020

This paper establishes a methodology to build hybrid machine learning models, aiming to combine the power of different machine learning algorithms on different types of features and hypothesis. A generic cost-based outlier removal algorithm is introduced as a step of pre-process of training data. Using this methodology, an experimental hybrid machine learning model is implemented for a crediting problem, which combines three types of machine learning algorithms SVM, DT and LR. Different metrics of training time, recall, precision, F1 score, and AUC are evaluated and compared with the results from single model of single SVM, DT, and LR. The new hybrid models in general shows improvement in performance on multiple metrics. this study further demonstrated that one learning model may work well on certain type of variables but not as effectively on other variables. Well defined procedures can be followed to create hybrid models to combine the power of different learning models, which are superior in dealing with dataset with complicate feature selection. This methodology can be explored with other algorithms and applications.
The Shortest Path Planning Based on Reinforcement Learning
Authors: Wang Xiaoqi , Jin Lina , Wei Haiping
Published: July 2020

This paper proposes a shortest path planning of agent in an environment based on reinforcement learning. This method adopts the Q-learning algorithm, which has gained increasingly using in agent path planning recently. This algorithm can be fully designed to a reasonable environment model and applied to other professional fields. Yet, reinforcement learning in path planning still needs to improve the rate of convergence. The learning process takes several iterations and spends a long time to find the final path. A local optimal problem in search process deals with other explorations that provided shortest path for agent to take optimal actions. For the agent, the reward value of inclined movement is introduced to find the shortest path. The autonomous obstacle avoidance is utilized to obtain the optimal path in the process. Finally, the model of intelligent agent movement environment has established in matlab. The effectiveness of the algorithm has proved by simulation results.
Research on Application of Improved Random Forest in Medical Ultrasound Image Classification
Authors: Min Cui , Haijiang Zhu
Published: July 2020

Gray-scale ultrasound imaging methods are commonly used to assess synovitis in Rheumatoid Arthritis (RA) in clinical practice. This paper proposed an improved classification algorithm for ultrasound image of metacarpophalangeal joint in Rheumatoid Arthritis(RA). Using several features of gray scale co-occurrence matrix and improved Random Forest method to achieve automatic classification of Rheumatoid Arthritis(RA). Three grading experiments were carried out in the experiment: the first one is for the binary classification of grade 0 (normal) and grade 3 (lesion) ultrasound images of 80 × 50 pixels, 130 × 70 pixels, and 185 × 90 pixels. The second is for the binary classification of grade 0 (normal) and grade 1/2/3 (lesion) ultrasound images of 80 × 50 pixels, 130 × 70 pixels, and 185 × 90 pixels. The third is for four-grade classification of ultrasound images at grade 0(normal), grade 1(mild), grade 2(moderate), and grade 3(severe). Conclusion shows that use the method of combing gray scale co-occurrence matrix and improved Random Forest algorithm can achieve automatic classification of Rheumatoid Arthritis(RA), which has a high Classification Accuracy.
Tobacco-disease Image Recognition via Multiple-Attention Classification Network
Authors: Sun Y , Wang H Q , Xia Z Y , Ma J H , Lv M Z
Published: July 2020

The recognition of disease images is of great importance in modern tobacco agriculture. Compared with other plant diseases, the early disease spots of tobacco are highly similar, and the quantity of tobacco disease images is scarce due to the difficulty of data collection. As such, the image recognition of early tobacco diseases is challenging. In response to these problems, this study proposes a novel image-classification method, namely, Multiple-Attention Classification Network (MACN). It improves the existing image classification network from two aspects: 1) the acquisition of low-level features based on transfer-learning and 2) the extraction of high-level features by modelling the multiple dependencies in low-level features. The pretrained model is used to obtain the initial feature map, thereby breaking the limitation of small-scale dataset. A novel multiple-attention module (MAM) is designed to learn fine-grained differences between classes. To evaluate the proposed method, this paper conducts experiments on field-collected images. Results show that this method is superior in identifying early tobacco diseases and has demonstrated better robustness when dealing with the actual field images with complex factors.
Application of Neural Network Based Knowledge Graph in Vertical Industry
Authors: Zhao Xiaohong , Zhao Yang , Wang Wenting , Ren Tiancheng , Zhang Hao
Published: July 2020

Knowledge graph was mentioned first by Google, now is used to refer to a wide variety of large-scale knowledge bases, in both general areas and vertical industry areas. Techniques such as knowledge fusion, knowledge extraction, knowledge reasoning and knowledge expression are key techniques in knowledge graph which need to be researched urgently. Neural network technologies have achieved hugely these years, the technique has been readily applied to a variety of practical engineering problems such as pattern recognition, signal processing and control systems. In this work, we represent how to implement the neural network technique to knowledge reasoning technology, to achieve the completion of knowledge base, to predict hidden relationships among entities through reasoning inside a specific knowledge base. We also show the possibility of knowledge graph applied to one vertical industry, namely the electrified power grid industry. This paper shows that knowledge graph can apply to the whole power processing procedures, such as electric power producing, operating and marketing procession, and electric power equipment operation and maintenance, as well as customer service.
Multi-Sensor Data Fusion Algorithm Based on BP Neural Network
Authors: Shuai Liu
Published: July 2020

In multi-sensor detection system, the application of multi-sensor accurate detection system parameters is limited due to the existence of measurement noise. Using multi-source data fusion technology can be more accurate, timely detection and data processing system. Data fusion is a basic function in humans and other biological systems. In this paper, in order to make the system adaptive multi-source data fusion, using the BP neural network algorithm is a good way to deal with incomplete test data and test the noise problem. In this paper, the characteristics of three levels of data fusion and the derivation process of BP neural network algorithm are introduced in detail. In order to verify the role of BP neural network algorithm in the process of detection system filtering, a MATLAB simulation experiment is carried out. The experimental results show that the BP neural network algorithm can effectively reduce the measurement error of multi-sensor detection system and improve the detection accuracy.
Anti-reconnaissance Model of Host Fingerprint Based on Virtual Node
Authors: Zhang Tao , Lu Bin , Li Ding , He Kang , Zhu Yuefei
Published: July 2020

Aiming at the problem of insufficient defense ability of fingerprint detection, the anti-reconnaissance model of host fingerprint based on virtual node is proposed. The model constructs periodically reconfigurable virtual nodes, dynamically camouflages the fingerprint information of the host to deceive the detector, and redirect attack traffic targeting virtual nodes to honeypots that can capture and analyze attack behavior. Honeypot, as an active defense technology, can effectively improve the model’s defense capabilities. This paper introduces probabilistic models for the defense model to provide a deeper understanding of the theoretical effect their parameters have for cybersecurity, which quantifies the impact of different parameters on the probability of attack success, such as the number of probes, number of honeypot mapping rules, the virtual node deception rate, the honeypot detection rate and allowable losses. Furthermore, our prototype system using Software Defined Network (SDN) and Data Plane Development Kit (DPDK) verifies the effectiveness of the model against reconnaissance.
Application of Directional Transmission Data Technology for Transmission Lines
Authors: Bai Demeng , Lv Xuebin , Wang Jian , Zheng Wenjie
Published: July 2020

The transmission line is realized by boosting the electric energy from the generator with a transformer and then connecting it to the transmission line through the circuit breaker and other control equipment. Structure, transmission lines are divided into overhead transmission lines and cable lines. At present, the real-time state acquisition of overhead transmission line mainly depends on the online monitoring device installed by itself. After the equipment obtains the monitoring information, it will send the relevant information to the remote monitoring system through the network formed by the network. However, the length of the working time of the online monitoring device is limited by the battery, so improving the power utilization rate of the transmission line monitoring device can significantly extend the working time of the device. This paper proposes adirectional transmission data technology which can solve the problems of high energy consumption and poor signal transmission quality of monitoring devices, and effectively extend the network lifetime.
Research on Channel Estimation Technology in Power Wireless Communication Based on MIMO Systems
Authors: Zhao Yang , Zhao Xiaohong , Ren Tiancheng , Liu Xin , Liu Donglan
Published: July 2020

Power wireless communication system has received more and more attention in recent years. At present, power wireless communication system has not been applied on a large scale in Shandong Province. With the proposal of ubiquitous power Internet of things (UPIOT), the State Grid Corporation of China (SGCC) is paying more and more attention to the construction of wireless networks. Shandong Province is gradually carrying out the construction of wireless communication networks, with corresponding research undertaken accordingly. This paper mainly studies the channel estimation technology of MIMO based power wireless communication system. According to the movement patterns of the user terminal, we manage to model the downlink channel, as well as give out the channel estimation and the corresponding training sequence design method. The relationship of channel capacity and training sequence length is verified by simulation, and the optimal value of training sequence length under given parameters is obtained. At the same time, the channel training optimization scheme is given by the use of channel correlation, in which the specific parameters are determined by the specific system.
Understandable Capacity Level Assessment Method for Networking Information-Centric System-of-Systems Interoperability
Authors: Wang Shuang-Ling
Published: July 2020

According to the characteristics of the networking information-centric system-of-systems, starting from the understanding ability of the mutual understanding subjects and the information sharing degree of the mutual understanding objects, this paper analyzes the influencing factors of mutual understanding of networking information-centric system-of-systems, extracts four evaluation attributes of education, situation, decision-making and control, describes the selection process of four evaluation attributes, analyzes the factors that affect the mutual understanding level of the networking information-centric system-of-systems in each attribute field, constructs the mutual understanding maturity model of the networking information-centric system-of-systems, analyzes the basic characteristics of each grade, constructs the mutual understanding grade model of the networking information-centric system-of-systems, analyzes the symbolic characteristics of each attribute corresponding to each grade, and puts forward the mutual understanding evaluation method of the networking information-centric system-of-systems, which provides guidance for measuring the ability of the networking information-centric system-of-systems to achieve information advantages and decision-making advantages.
Situation Plotting Algorithm and System Implementation Based on Web
Authors: Chen N Y , Chen L , Xiong W
Published: July 2020

Based on the integration of existing programming languages and framework technologies, this paper designs and implements a Web-based situational plotting system. This system combines Web-oriented and map services, it uses Leaflet framework and SVG (Scalable Vector Graphics) technology design and implementation, classify the symbols and generate corresponding situational symbol frameworks, which can be extended to different areas such as emergency rescue, logistics coordination, communication planning, and realize the situational symbol mapping and editing, map browsing, and plotting Sample set saving and uploading functions have good real-time, cross-platform and interactive features. At the same time, aiming at the problem of generating linear plotting symbols, a plotting algorithm based on Centripetal Catmull–Rom curve is proposed, which avoids sharp angle and self-intersections in the process of plotting linear symbols. The experimental results prove that compared with the traditional curve plotting algorithm, the proposed algorithm has the characteristics of fewer control points and good fitting effects, which improves the plotting efficiency.
Openflow Table Decision Method under Mimic Defense
Authors: Lei Rui , Li Chuanhuang , Tang Zhongyun
Published: July 2020

SDN is an implementation of network virtualization. It implements flexible control of network traffic by separating control and forwarding of network equipment. However, there are security issues in SDN networks. If the controller is hijacked, an attacker can manipulate the entire network and tamper with and intercept network traffic at will, causing serious problems such as network paralysis. Based on the overall architecture of the mimic controller, this paper proposes a controller flow table decision method for the key decision module. Finally, the decision algorithm is developed and implemented, and the attack defense verification is performed. The flow table decision method in this paper greatly improves the decision efficiency, and successfully implements the random active defense of the controller’s flow table.
Research on Quality Evaluation of Customized Plank Furniture Based on the Entropy Weight-Analytical Hierarchy Process
Authors: Li Ying , Xia Jianbing , Xu Yingcheng , Huang Dongmei
Published: July 2020

Custom-made furniture is a product of the marketplace. At present, the concept of whole-house custom-made furniture has not been clearly defined. In the market, custom-made furniture for the whole house is mainly designated panel furniture. As the custom furniture market continues to expand, consumer complaints about custom furniture are increasing. However, there is no effective supervision model for the new decoration business of whole-house custom-made furniture. The market for customized furniture is constantly expanding. Under the current circumstances, the regulatory approach is difficult to implement effectively for a time. Faced with this transitional stage, this article proposes to evaluate the quality risk of custom-made furniture by means of evaluation. Combined with industry experts’ discussion on consumer complaints and concerns, this article builds a customized furniture product quality evaluation system. According to the constructed quality evaluation system of customized furniture products, with the help of the basic theory of entropy weight method and analytic hierarchy process, determine the weight of each index. Finally, through empirical research, it is proved that this method has a strong operability for the quality evaluation of customized furniture.
A Design of Simulation Environment for Small Fixed-wing Aircraft
Authors: Wang Zi-Quan , Yang Cheng-Wei , Hu Xiao-Lin , Xiong Jing , Li Juan , Liu Chang
Published: July 2020

Multi-Agent Particle Environment (MPE) [1] proposed by OpenAI is applied to the study of multi-agent reinforcement learning strategies. However, the motion rules of the agent are excessively simplified. In order to make the environment more suitable to small fixed-wing aircraft, we have made following improvements: 1. The dynamic model of the agent in the MPE does not conform to the characteristics of the fixed-wing aircraft. In order to simulate the dynamic characteristics of the fixed-wing aircraft, a speed-related damping mechanism is introduced into the two-dimensional motion environment. 2. Since the MPE lacks the control module for single agent, the MPE cannot meet the challenges raised by single agent control. A two-layer controller is proposed which includes the outer layer (Total Energy Control System and L_1) and the inner layer (PID). 3. The MPE does not contain any decision module. In order to comprehensively study the collaborative decision-making behavior of aircrafts in target allocation, a swarm decision module is added to the environment. In addition, the concept of control period is introduced to reduce the gap between simulation and the actual situation. Finally, several simulations were carried out to test the improved Multi-Agent Aircraft Environment (MAE). The test cases include the outer layer with L1 and Total Energy Control System (TECS) algorithm in two dimensions, the PID inner layer control algorithm and the designed auction algorithm. The tests complete the process of single aircraft flight, Multiple aircrafts scan-search flight and Multiple aircrafts dynamical-waypoint flight, which verifies the effectiveness of MAE.
An Improved Electromagnetic Flowmeter
Authors: Li Xuejing , Yao Xinhong , Wang Chongyuan , Zhu Liang
Published: July 2020

Electromagnetic flowmeters are widely used in industrial production or control. However, the structure of the traditional electromagnetic flowmeter makes it restricted in some applications. With the development of medicine, aviation and other fields, people’s demand for multiphase flow measurement is increasing. However, the theoretical study of the multi-electrode electromagnetic flowmeter has always lacked systemicity, especially in the relationship between the tube wall, the electrical characteristics of the fluid and the output signal. Based on the existing research, this paper studies several key technologies of multi-electrode electromagnetic flowmeter. The structure of the existing electromagnetic flowmeter is improved, and a multi electrode non insulated pipe wall electromagnetic flow sensor is proposed. According to the equivalent circuit, the influence of the sensor structure on the measurement is analyzed, and the disturbance of the electrical characteristics of the tube wall to the output signal is obtained by combining with the weight function analysis. According to the results of theoretical calculation and analysis, a negative feedback circuit is designed. According to the experimental results, the accuracy and reliability of the novel electromagnetic flowmeter are verified.
Urban Hotspots Mining Based on Improved FDBSCAN Algorithm
Authors: Chen Jinhua , Zhao Can , Zhang Kai , Li Zhiheng
Published: July 2020

Enormous human activities happen in modern cities nowadays. These activities generate trajectory information, which can play a big role in urban planning, transportation improvement, behavior analysis, and many location-based services. Currently, GPS devices are installed in many cities’ taxies. These positioning devices are the ideal source of trajectory data. Urban hotspots can be extracted by applying spatial clustering algorithm to the taxi trajectory data. Since the classical DBSCAN clustering algorithm is short of execution efficiency and its variant FDBSCAN clustering algorithm fails to make fine-grained clustering on the taxi trajectory data, this paper proposes an improved FDBSCAN clustering algorithm, O-FDBSCAN clustering algorithm. The core of the O-FDBSCAN clustering algorithm is, it adds some coordinate offsets to the original trajectory data point based on some background geographic region knowledge. The background knowledge contains the collected functional domain information in the nearby area. By adding this kind of weight to the trajectory data point, the clustering result will be closer to the real target place and therefore give a more fine-grained clustering result. In this paper, we extract the urban hotspots and their spatiotemporal pattern by using the O-FDBSCAN clustering algorithm. The comparison result shows that, the proposed algorithm has better fine-grained clustering results than FDBSCAN. be taken in constructing both.
Multi-Target Adaptive Radar Waveform Design Based on SINR
Authors: Li Shumin , Wang Bin , Liu Wei , Xin Fengming
Published: July 2020

Aiming at the radar waveform design problem of detecting multiple targets in the presence of clutter, a novel algorithm based on linear probability-weighted summation (PWS) of multi-target impulse response of signal to interference plus noise ratio (SINR) is proposed. In view of the traditional water-filling algorithm, the multi-target detection algorithm is further studied in a new way to improve the overall performance of the system. This method uses Jensen’s inequality to make a lot of derivations to determine the target function of the algorithm and the constraints. Simulation results show that the proposed algorithm has better detection performance and more accurate target information.
An Accurate Efficiency Calculation of Self-excited Induction Generator Including Effects of All Machine Parameters
Authors: Nasir Bilal Abdullah
Published: July 2020

In this paper an accurate method for efficiency calculation of self-excited induction generator (SEIG) at different operating conditions is proposed, considering the effect of stator and rotor iron core losses, stator and rotor circuits stray load losses, stator and rotor winding (copper) losses, mechanical losses, skin-effect losses, and skew-effect losses. The proposed procedure of efficiency calculation is based on the conventional machine tests such as no-load test, load test at variable supply voltage and temperature test without additional measurements. From these conventional tests, all the SEIG losses can be calculated accurately by their related formulas derived based on the SEIG equivalent circuit. The reduction of these losses causes an increase in the SEIG percentage efficiency, and this is a very important feature in electric energy saving by minimization of SEIG equivalent circuit components.
Specification for water balance test of cooling towers
Authors: Song Baohong , Song Yu
Published: July 2020

This test procedure describes the evaluation method used to determine the water consumption performance of cooling towers and evaporative cooling equipment, which provides a unified test instrument, test procedure, parameter measurement, test data processing, and test results. This test procedure provides the manufacturer and the owner an objective and fair evaluation, outlines practical methods for monitoring the water consumption performance of cooling towers. The loss of water in cooling tower is measured by U-type liquidometer, and the law of collecting basin liquid level. When the liquid level of the collecting basin decreased by 1 mm, the water quantity loss of one cooling tower diameter D=42 m was 1.38 m3. And cooling tower water loss and specification for water balance test is established. The water quantity loss of cooling tower can be obtained conveniently, accurately and quickly. This provides conditions for further accurate analysis of water quantity loss of cooling tower, etc. It is widely used and has great significance in the hydraulic design of cooling tower, the analysis of air status in the tower, the water balance test to determine the water used in cooling tower, the reduction of water loss and the drift recovery. And provides explicit test procedures that yield results with the highest level of accuracy and consistent with the best current engineering practices and knowledge in this field.
Adaptive time resolution rolling dispatch with high renewable penetration
Authors: Zhao Hongyang , Wang Xiuli , Li Nailiang , Kuang Yi , Sun Qiang
Published: July 2020

To cope with the energy crisis and the environment issues caused by the fossil fuels, the renewable energy generation developed rapidly. However, the inherent characteristics of uncertainty bring about difficulty to the operation of power system. Rolling dispatch can use the updated forecast data to adjust unit planning and it is a good method to cope with the volatility of renewable energy. But with the dispatch time resolution be finer, the computation burden of dispatch agent increases significantly and the allowable time limit of each round rolling dispatch shrunk. This paper proposed a time sections clustering method, which could adaptively reduce the number of time sections that should be considered in the optimization model. In order to avoiding the interval of two successive dispatch horizon, this paper use an extended horizon to cover a first few hours of next day. To adopt the variable time resolution, a unified unit commitment model is proposed. The numerical experiment on a real provincial system in northwest China shows the effectiveness of the proposed model.