bluuebunny/crossref_metadata_2025 huggingface dataset:
Modeling and Simulation of Ship-Borne Weapon Stabilization System Based On Double Position Loop Control
Authors: Dai Pu , Pan Jun , Cao Yan , Ma Qing , Du Manli
Published: November 2021

In order to solve the problem of increasing system cost and low control accuracy by adding gyroscope or inertial navigation system, a dual position loop control ship-borne stabilization system is proposed in the paper. The mathematical model is established to analyze the influence of ship rolling factors on the space pointing of single position loop weapon system. On this basis, the control strategy of double position loop is proposed, and the simulation structure and control method of inner position loop and double position loop are described respectively. The simulation results show that the method can isolate the factors of ship rolling, realize the closed-loop stabilization control of weapon system in geodetic coordinate without adding system hardware cost, and have good control effect as well.
Study on the Measurement Technology of Temperature Field at the Outlet of the Precooler in the Bleed air system of Civil Aircraft
Authors: Huang Xiaodan
Published: November 2021

In view of the phenomenon of uneven temperature field in the hot side outlet pipe of the precooler in the Bleed air system of civil aircraft, three thermocouple layout schemes were put forward. By comparing and analyzing the changes of the flow field in the pipe before and after the installation of the three schemes, the most effective scheme was determined. At the same time, the temperature uniformity before and after the mixing device at the outlet of the precooler is compared and analyzed, and the method of increasing the turbulence device to improve the accuracy of the average temperature measurement of the precooler is put forward.
Investigation of the optical properties of monocrystalline silicon with a deposited layer of spherical zinc sulfide nanoparticles
Authors: Egorova A V , Kochuev D A , Chernikov A S , Mikhalevsky V A , Kireev A V , Khorkov K.S.
Published: November 2021

In the present work zinc sulfide (ZnS) nanoparticles (NPs) were synthesized on silicon wafer by femtosecond pulsed ablation processing of ZnS bulk target using an electrostatic field in argon gas atmosphere. The morphology, size distribution and structural characterization of obtained ZnS NPs were investigated using scanning electron microscope (SEM), dynamic light scattering (DLS) technique and X-ray diffraction (XRD). Dominant size of obtained NPs lies in the range 10-20 nm, NPs are in spherical shape, particles of other shape and agglomerates of particles are absent. XRD investigation of synthesized NPs identified hexagonal wurtzite structure. There is a structural phase transition from the sphalerite ZnS bulk (target) structure to the structural phase of wurtzite (obtained NPs).The optical characterization of synthesized by laser ablation ZnS NPs was carried out using a photoluminescence (PL) measurement. ZnS NPs show a strong broad PL emission spectra covering the entire visible electromagnetic spectra region (range from 380 to 800 nm) centered at 513.7 nm.
Research of deformation compensation method in laser metal deposition process of 316L stainless steel product
Authors: Kovchik A , Babkin K , Vildanov A
Published: November 2021

It is exists the problem of big product manufacturing with minimal dimensions tolerances. To solve this problem it is necessary to compensate the deformations influence. In researching of method, it became clear that deformation degree has changed and depended on size and form of part. However, the amount of deformation degree to dimension of part is still independent of size. This fact has observed after production of axis-symmetrical parts. The simple axis-symmetrical part was built up. The dimensions of part was measured, and the compensation coefficient was calculated. The dimensions of part was scaled on this coefficient for compensation of shrinkage effect. After that the experiment was repeated.
Investigation of the surface overhanging elements of parts manufactured by the SLM
Authors: Oparin E S , Voznesenskaya A A , Kochuev D A , Raznoschikov A S
Published: November 2021

The work considers the task of modelling the mode of thermal action on the system of solid material and one aluminium granule. To improve the properties of synthesized parts and decrease porosity of the surface overhanging elements optimal processing parameters were calculated. The paper shows the result of modeling and predict surface roughness and porosity with four parameters pair of scanning speed and laser power.
Hybrid technology for manufacturing lightweight products with a cellular structure made of aluminum alloys
Authors: Roslova A A , Izotov V A
Published: November 2021

This article is devoted to one of the new directions of creating and obtaining porous materials. The cellular material part is a new element that provides high specific strength of the structure in combination with low density. An urgent task is to develop and obtain a cellular structure with optimal geometric parameters to ensure maximum manufacturability of the structure. The obtained data can be used in the development of technological processes of hybrid casting technologies, as well as for the manufacture of new lightweight parts with increased specific strength characteristics.
Solving flow shop scheduling problem based on improved non-dominated genetic algorithm
Authors: Wang Tiankui , Ji Chunlei , Hao Yuanfeng , He Jianli
Published: November 2021

Aiming at the multi-objective problem of flow workshop problem, a multi-objective optimization model was constructed and an improved non-dominated sorting genetic algorithm was proposed. Firstly, aiming at these problems, this paper proposes a two-stage chromosome coding method to adapt to the new production scenarios. Secondly, a new adaptive method is proposed to improve the convergence speed and the superiority of Pareto solution set. Finally, simulation results show that the optimality of the improved non-dominated sorting genetic algorithm is improved greatly.
User Deep Energy Efficiency Evaluation via Load Perception and Clustering
Authors: Zhai Shuran , Zhang Zhaojie
Published: November 2021

With the constantly upgrading of the living level of urban residents, the types and quantities of domestic appliances continue to increase, and the electricity consumption behaviour is becoming more and more complex. Based on the fine-grained user energy consumption data obtained by load intelligent perception, this paper proposes a user energy efficiency evaluation method through fuzzy comprehension evaluation. First, the energy consumption characteristics at appliance level which can effectively represent energy consumption behaviour of users are designed and filtered, and then the energy efficiency evaluation strategy of appliances based on fuzzy comprehensive evaluation is established. The strategy adopts two-level fuzzy comprehensive evaluation. The first appliance-level fuzzy evaluation realizes the energy efficiency evaluation of the appliances. The second level is the comprehensive evaluation of user energy efficiency based on the appliance energy efficiency assessment, and the target users are divided into different energy efficiency levels. The measured data-based analysis results prove the effectiveness of the proposed method, and the results can guide users to optimize their electricity consumption behaviour scientifically and realize energy saving and carbon reduction.
Pedestrian detection algorithm based on improved muti-scale feature fusion
Authors: Liu Hui , Cheng Keyang
Published: November 2021

Aiming at the problem of false detection and missed detection of small targets and occluded targets in the process of pedestrian detection, a pedestrian detection algorithm based on improved multi-scale feature fusion is proposed. First, for the YOLOv4 multi-scale feature fusion module PANet, which does not consider the interaction relationship between scales, PANet is improved to reduce the semantic gap between scales, and the attention mechanism is introduced to learn the importance of different layers to strengthen feature fusion; then, dilated convolution is introduced. Dilated convolution reduces the problem of information loss during the downsampling process; finally, the K-means clustering algorithm is used to redesign the anchor box and modify the loss function to detect a single category. The experimental results show that the improved pedestrian detection algorithm in the INRIA and WiderPerson data sets under different congestion conditions, the AP reaches 96.83% and 59.67%, respectively. Compared with the pedestrian detection results of the YOLOv4 model, the algorithm improves by 2.41% and 1.03%, respectively. The problem of false detection and missed detection of small targets and occlusion has been significantly improved.
Research and Design of Adaptive Networking Method Based on LoRa
Authors: Zhang JianMin , Dai YaWen
Published: November 2021

An adaptive networking method based on LoRa(Long Range) technology is proposed. The data transmission of wireless sensor networks widely used in the Internet of things is studied. A star network composed of Lora wireless transmission technology is designed to build an adaptive data transmission network. The design process of network topology, hardware and adaptive networking method of adaptive networking are introduced. Aiming at solving the problems of limited node capacity, adjacent frequency interference, and unreasonable channel resource allocation in the LoRa network, an adaptive frequency hopping mechanism is used for networking. The system uses I. MX6ULL as the main control and integrates 8 LoRa modules at the same time, which greatly improves the node capacity of the gateway. The server can realize real-time viewing and monitoring of node equipment, and real-time evaluation of channel communication quality.
The Sparse Group Log Ridge for the Selection of Variable Groups
Authors: Yao Song , Cui Lipeng , Ma Sining
Published: November 2021

In recent years, the sparse model is a research hotspot in the field of artificial intelligence. Since the Lasso model ignores the group structure among variables, and can only achieve the selection of scattered variables. Besides, Group Lasso can only select groups of variables. To address this problem, the Sparse Group Log Ridge model is proposed, which can select both groups of variables and variables in one group. Then the MM algorithm combined with the block coordinate descent algorithm can be used for solving. Finally, the advantages of the model in terms of variables selection and prediction are shown through the experiment.
Optimization Method of Web Fuzzy Test Cases Based on Genetic Algorithm
Authors: Qu Sheng , Zhang Zheng , Ma Bolin , Shao Yuwen
Published: November 2021

In order to solve the problems of low code coverage, few vulnerabilities found, and poor fuzzing effect caused by the small number of test cases and single types in Web fuzzing, on the basis of studying the current Web fuzzing methods, the existing fuzzing Web applications are tested Program research. A genetic algorithm-based method for optimizing fuzzing test cases for Web applications is proposed. It analyzes and counts the traffic of public network website business with Web service attack characteristics, and uses genetic algorithms to generate a large number of test cases with various types to explore the Web service vulnerability that exists. Based on the creation of a Web attack signature database with weights, this method uses genetic algorithms to randomly pre-generate the test cases of the fuzzing test, and uses the response of the Web service to repeatedly iterate the weights of different attack signatures in the Web attack signature database. So as to generate the best test cases. Experimental analysis shows that this method effectively finds security vulnerabilities in Web applications.
Expedited Tensor Program Compilation Based on LightGBM
Authors: Liu Gonghan , Li Yue , Wang Xiaoling
Published: November 2021

If the traditional deep learning framework needs to support a new operator, it usually needs to be highly optimized by experts or hardware vendors to be usable in practice, which is inefficient. The deep learning compiler has proved to be an effective solution to this problem, but it still suffers from unbearably long overall optimization time. In this paper, aiming at the XGBoost cost model in Ansor, we train a cost model based on LightGBM algorithm, which accelerates the optimization time without compromising the accuracy. Experimentation with real hardware shows that our algorithm provides 1.8× speed up in optimization over XGBoost, while also improving inference time of the deep networks by 6.1 %.
An Improved TextRank Multi-feature Fusion Algorithm For Keyword Extraction of Educational Resources
Authors: Zhao Hongyang , Xie Qiang
Published: November 2021

In view of the fact that the traditional graph model method which only considers statistical features or general semantic features when extracting keywords from existing massive educational resources, lacks the function of mining and utilizing multi-factor semantic features, this paper proposes an improved TextRank-based algorithm for keyword extraction of educational resources. According to the characteristics of Chinese text and the shortcomings of traditional TextRank algorithm, the improved algorithm featuring multi-feature fusion is developed using the importance of words in the corpus, the location information in the text and the attributes of words. Experimental results show that this method has higher accuracy, recall rate, and F-measure value than traditional algorithms in the process of keyword extraction of educational resources, which improves the quality of keyword extraction and is beneficial to better utilization and management of educational resources.
Implementation of Tactical Decision Aids Based on Event Knowledge Graph
Authors: Jia Zhen , Chu Yang , Liu Zhi
Published: November 2021

This paper proposes a new tactical decision aids method based on event knowledge graph (EventKG). In the warfare domain, EventKG can be constructed through event types design, event network construction and transition probability computation between events. Initially, four event classes are introduced in accordance with the OODA loop, and eighteen subclasses are further decomposed. With the aids of a common event template, all the events taking place in the battle field can be described. Event networks are built by adopting the hierarchical task network (HTN) and described through Bayesian network, to exhibit various relations between battle events. Transition probability, namely the occurrence probability of next possible event, is computed by using the prior probability and conditional probability of event occurring. On the basis of structured EventKG, entity knowledge graph (EKG) and entity relation knowledge graph (ERKG), tactical decision aid instructions can be generated by combining with the battlefield situation information.
Emotion recognition based on multi-channel EEG signals
Authors: Shi Huiping , Xie Hong , Wu Mengran
Published: November 2021

Emotion recognition is a key technology of human-computer emotional interaction, which plays an important role in various fields and has attracted the attention of many researchers. However, the issue of interactivity and correlation between multi-channel EEG signals has not attracted much attention. For this reason, an EEG signal emotion recognition method based on 2DCNN-BiGRU and attention mechanism is tentatively proposed. This method firstly forms a two-dimensional matrix according to the electrode position, and then takes the pre-processed two-dimensional feature matrix as input, in the two-dimensional convolutional neural network (2DCNN) and the bidirectional gated recurrent unit (BiGRU) with the attention mechanism layer Extract spatial features and time domain features in, and finally classify by softmax function. The experimental results show that the average classification accuracy of this model are 93.66% and 94.32% in the valence and arousal, respectively.
Lightweight target detection model for embedded platform
Authors: Li Yuhuan , Wang Jie , Shi Baodai
Published: November 2021

The detection speed of target detection algorithm depends on the performance of computer equipment. Aiming at the problems of slow detection speed and difficult trade-off between detection accuracy and detection speed when the target detection model is used in embedded devices, a lightweight target detection model based on the improved Tiny YOLO-V3 is proposed. Firstly, we analyze the time consumption of each layer structure in the convolutional neural network, and do a lot of experiments and tests. Then, we compress the time-consuming structure substantially. Secondly, we propose the segmentation and fusion module to improve the detection accuracy. Finally, we use the remote sensing data set of Wuhan University for experiments, and the experimental results show that compared with Tiny YOLO-V3, the detection speed is improved by 4 times, and the accuracy is improved by 2 percentage points.
Application of Convolutional Neural Network Method in Brain Computer Interface
Authors: Chen Lingzhi , Deng Wei , Ji Chunjin
Published: November 2021

Pattern Recognition is the most important part of the brain computer interface (BCI) system. More and more profound learning methods were applied in BCI to increase the overall quality of pattern recognition accuracy, especially in the BCI based on Electroencephalogram (EEG) signal. Convolutional Neural Networks (CNN) holds great promises, which has been extensively employed for feature classification in BCI. This paper will review the application of the CNN method in BCI based on various EEG signals.
Research on Face Recognition Algorithm Based on Multi-Class Support Vector Machine
Authors: Xue Liuxun , Li Hui , Wang Ping , Lin Zhiyang , Li Huanyu , Xu Jie , Shi Chongyue
Published: November 2021

Facial recognition is one of the main research directions in the field of artificial intelligence and image processing. It has been widely used in identity authentication, video surveillance and biological detection. Because it is non-contact, natural, convenient and reliable, facial recognition has become a popular choice for biometric systems. The accuracy of facial recognition still needs to be improved, the main goal of this paper is to improve the accuracy of face recognition. Based on the support vector machine method, the focus is on the feature extraction and feature matching of face images. In view of the particularity of face images, the pre-processing of face images is studied. In this paper, grayscale normalization and geometric normalization pre-processing methods are used. In order to reduce the interference factors of the image as much as possible, the features are high-lighted, and the non-featured parts are weakened, this paper adopts the Histogram of Oriented Gradient feature extraction method. Then we proposed a new method based on SVM, which uses a one-to-many method to construct multiple SVM classifiers, selects the optimal parameters through repeated experiments, and selects ORL face database for testing. The recognition rate can reach about 98.5%.
Robustness Testing Framework For Neural Network Image Classifier
Authors: Li Duo , Dong Chaoqun , Liu Qianchao
Published: November 2021

Neural network has made remarkable achievements in the field of image classification, but they are threatened by adversarial examples in the process of application, making the robustness of neural network classifiers face danger. Programs or software based on neural network image classifiers need to undergo rigorous robustness testing before use and promotion, in order to effectively reduce losses and security risks. To comprehensively test the robustness of neural network image classifiers and standardize the test process, starting from the two aspects of generated content and interference intensity, a variety of robustness test sets are constructed, and a robustness testing framework suitable for neural network classifiers is proposed. And the feasibility and effectiveness of the test framework and method are verified by testing LENET-5 and the model reinforced by the adversavial training.
A L1 Regularized Logistic Regression Model for Highdimensional Questionnaire Data Analysis
Authors: Wang Jiasheng
Published: November 2021

The LI regularization method, or Lasso, is a technique for feature selection in high-dimensional statistical analysis. This method compresses the coefficients of the model by using the absolute value of the coefficient function as a penalty term. By adding L1 regularization to log-likelihood function of Logistic model, variable screening method based on the logistic regression model can be realized. The process of variable selection via Lasso is illustrated in Figure 1. The purpose of the experiment is to figure out the important factors that influence interviewees' subjective well-being using L1 regularized logistic regression. Experiments have been performed on CGSS 2017 data. Important features have been successfully selected by using the L1 regularization method.
Research on the Garbage Classification Problem Based on Convolutional Neural Network
Authors: Wu Shuang , Li Zeyu , Chen Xinqiong , Zhong Peiwen , Mei Liangcai , Cai Xing
Published: November 2021

In order to better promote garbage classification, machine learning models are used to discover and solve garbage classification problems. First, the factor analysis is used to conduct field investigation and data analysis on residents' perception of waste classification. Second, convolutional neural network (CNN) is used to classify and recognize garbage images, which is used to assist the judgment of garbage classification. We should put forward some reasonable classification suggestions to better promote the problem of garbage classification.
RESEARCH ON THE PREDICTION OF DEFORMATION PROPERTIES BASED ON NEURAL NETWORK
Authors: Jin Lu , Zhang Chunli , Meng Xiangyuan
Published: November 2021

The direct analysis method based on deformation properties needs to predict the lateral deformation of the steel frame-support structure in advance, but it takes time and labor to use both finite element numerical simulation and experimental research. In this paper, the BP neural network receives the parameters of the steel frame-support structure, such as the aspect ratio, the second-order effect parameters, the slenderness ratio of frame columns, the rigidity ratio of beam-column bus and the deformation limit, with the lateral displacement of the column as the output layer, the second-order lateral deformation properties of the steel frame-support structure can be quickly obtained. Comparing the prediction results of BP neural network with those of the existing fitting formulas, the absolute difference and the relative error ratio between them are very small, which shows that the accuracy of the second-order column roof migration using the BP neural network prediction is high. The BP neural network can be used to judge the applicability of the direct analysis method based on the deformation properties of the steel frame-support structure under various parameters.
IEPet: A Lightweight Multiscale Infrared Environmental Perception Network
Authors: Jiang Xinhao , Cai Wei , Yang Zhiyong , Xu Peiwei , Dong Qingjiang
Published: November 2021

In recent years, the development of unmanned driving technology requires continuous progress in environment perception technology. Aiming at the key research direction of infrared environment perception in unmanned driving technology, a lightweight real-time detection network model for infrared environment perception, IEPet, is proposed. The model backbone adds the BottleneckCSP module and the proposed DCAP attention module, which can significantly improve the detection ability and spatial position perception ability while maintaining light weight. At the same time, the model improves the detection accuracy by using a 3-scales detection head. Comparative experiments on the unmanned driving data set show that compared with the lightweight model YOLOv4-tiny, the model proposed in this paper has an increase in F1 Score by 1.48% and an average detection accuracy by 6.37% to reach 84.31%. And the model is lighter. It shows that the proposed IEPet model can better meet the excellent performance required for infrared environment perception.
A novel method for discriminating multi-sensor fusion based on coherent feature
Authors: Han Jun , Hu Yang , Zhou Ming , Tang Yuwen
Published: November 2021

In order to solve the problem of multi-sensor fusion discrimination of the same radar emitter, a multi-sensor fusion discrimination method based on coherent feature is proposed in this paper. First, the simultaneous interpreting of the pulse sequences received by different sensors is carried out. Then the peak value of the autocorrelation function between pulses is used to determine whether the same source is used. The simulation results show the effectiveness of the proposed method.
Research on Real-time Sensing Technology of Aerospace Interactive Behavior Based on Infrared Array Force Tactile
Authors: Ni Bo , Cao Jiaxing , Mao Zhiyong , Cheng Lei , Shen Huifeng , Chen Jigang
Published: November 2021

Aerospace robots can replace astronauts to complete dangerous work in the expanded space. How to efficiently and reliably interact with robots in complex and harsh space environment has become an urgent problem and technical challenge. Based on the infrared array force tactile technology, an infrared point force touch detection system is developed to realize the intention sensing of aerospace human-robot interaction. Based on multipoint positioning technology combined with image method and global combined optimal tracking method, the control software is designed, and the effective human-robot interaction in the process of cooperation is realized. Finally, the touch panel test effect of the system is given. This research can provide a theoretical basis for the practical application of aerospace human-robot interaction.
Linking and link complexity of geometrically constrained pairs of rings
Authors: Orlandini E , Tesi M C , Whittington S G
Published: November 2021

We investigate and compare the effects of two different constraints on the geometrical properties and linking of pairs of polygons on the simple cubic lattice, using Monte Carlo methods. One constraint is to insist that the centres of mass of the two polygons are less than distance d apart, and the other is to insist that the radius of gyration of the pair of polygons is less than R. The second constraint results in links that are quite spherically symmetric, especially at small values of R, while the first constraint gives much less spherically symmetric pairs, prolate at large d and becoming more oblate at smaller d. These effects have an influence on the observed values of the linking probability and link spectrum.
Evaluation of regional simulation of surface ozone over Southeast Asia using ground-based observation at two existing Global Atmospheric Watch stations
Authors: Permadi Didin Agustian , Oanh Nguyen Thi Kim
Published: November 2021

High level of ground level ozone concentrations was found in most of Southeast Asian (SEA) large cities and often exceeded the national ambient air quality standard. Ozone and PM10 are among of the critical air quality parameters that cause the unhealthy air quality index. Effort to mitigate ozone pollution is greatly complicated due to the photochemistry processes therefore photochemical smog modelling has been widely used. Surface ozone simulation in SEA was done using CHIMERE and weather research forecast (WRF) model. Emission inventory of ozone precursors was done for three countries in the domain, i.e. Indonesia, Thailand and Cambodia. Modelling performance evaluation for meteorological parameters and ozone at the SEA big cities was done in another study. This paper focused on the model evaluation conducted at the two remote sites represented by 2 (two) global atmospheric watch (GAW) remote stations of Bukit Kototabang (BKT) and Danum Valley (DNV). Evaluation result showed an overestimation of observed ozone in BKT while a contradictive result was seen in DNV station which was due to the ozone chemistry and inaccurate estimation of emissions (both anthropogenic and biogenic emission). The evaluation conducted at the remote sites was not even better than that conducted previously at the urban areas. Statistically, only mean normalized gross error and unpaired peak accuracy values that satisfy the criteria for surface ozone modelling suggesting major improvement required for ozone precursors emission inventory data.
Numerical simulation of hydraulic performance with free overfall flow
Authors: Yao Li , Ye Xiao-hui , Huang Xiao-chong , Zheng Ke-hong , Fitri Arniza , Lestari Fera
Published: November 2021

Recently, free overfall flows have attracted attention of many researchers in the world. It is because the free overfall can be used as a tool in measuring the discharges and flows in any open channel systems or water collection systems for various flow regime at different shape of channels. In this study, the hydraulic performances of free overfall flow were explored using numerical simulation for three shapes of open channels including uniform rectangular, rectangular circumscribed, and rectangular inscribed steps. The hydraulic performances discussed in this study are including the flow regime, the maximum pressure, drop length and the energy dissipation rate. Numerical simulations were performed under different discharges Q (between 0.0005 m3/s and 0.0015 m3/s) and excise repair densities (y/H) for 1:2, 1:3 and 1:4. Visual observations identified various flow regimes with largely identical flow patterns in different discharges and configurations. Comparisons of properties indicated that there is a better depressurization and energy dissipation performances at the open channel with the shape of rectangular inscribed steps compare to the uniform rectangular in the small charge. Comparative study also revealed that the large excise repair densities of rectangular circumscribed steps might be optimal in terms of energy dissipation performances.
A study on carbon cap and trade effect to cost of electricity in accordance with the Merit Order of 300-400 MW coal power plants
Authors: Putra A S , Garniwa I , Arilanggaaji E S , Pradana S S
Published: November 2021

Electricity is the basis of national development in a country. Power plants in Indonesia produces up to 283.8 TWh and are dominated by coal power plants which increase the amount of the greenhouse gases (GHG). In order to prevent more environmental problems, Indonesia ratified Paris Agreement by publishing the roadmap of Nationally Determined Contribution (NDC) that committed in reducing 29% of GHG emissions in 2030, which 11% of them are from the energy sector contributions. This research focuses on the implementation of the carbon cap and trade (CAT) between coal power plants having 300-400 MW capacity, which can affect their cost of electricity (Rp/kWh). It is well known that cap and trade (CAT) is a method used for reducing the mitigation cost of emission reduction in an effective way. From this research, it is found that the highest rise of incremental cost belongs to the 300 MW power plant in scenario 9 and the increase is from Rp.431.00/kWh to Rp.462.77/kWh, or approximately 7.37%. This research also shows that the most optimal carbon price is in the range of Rp. 130,165 to Rp.130,183 because the rank of the 330 MW and 400 MW power plant in merit order changes over in this condition. In the future, this research can be used as a comparison with the higher coal power plant capacity, so that an alternative way is obtained to determine the more optimal merit order.
Eight-element liquid crystal based 32 GHz phased array antenna with improved time response
Authors: Nobles Jason E. , Smiley K. , Bueno Baques D. , Economou E. , Herman Jakub , Harward Ian , Glushchenko Anatoliy (Tolya) , Camley Robert E , Celinski Z
Published: November 2021

Phased array radar systems are used for a wide variety of applications including the precise tracking of airborne craft for air traffic control and providing accurate atmospheric condition information important in weather forecasting. Reducing the cost and size of these radar systems will open new fields to the use of this technology. Using phase control implemented through liquid crystal materials we have created a compact, phased array radar system operating in the microwave range. We report on the construction and testing of a linear, eight element phased array antenna system operating at 32 GHz with element phase controlled by a dual frequency nematic liquid crystal media used as a tunable dielectric. The system was designed using CST Design Studios and Ansys HFSS software. Dual frequency liquid crystals are used to improve beam steering response times. We demonstrate 42 millisecond beam switching times, defined as the time to change the beam focus from one point to another point, controllable beam formation, and beam steering profiles consistent with analytical results and simulation models. The device footprint is a square with sides 9.5 cm long and a thickness less than 2.5 mm. Such a module is easily stackable to create an 8 × 8 phased array system. Our design incorporates a modular construction using PCB for the antennas and input circuitry and a liquid crystal phase control cell with microwave glass substrates. This design simplifies design, construction, and testing as compared to on-glass designs. The device shows an improvement in point-to-point scanning speeds by a factor of 3 as compared to similar liquid crystal based devices and provides continuously variable tuning. Such a device can be used in a system for reduced visibility, directional range finding suitable for automobile collision avoidance systems and rotary wing aircraft landing aids.
316L WAAM and pressure machining influence
Authors: Kan Xinfeng , Yang Dengcui , Zhao Zhengzhi , Sun Jiquan
Published: November 2021

Wire arc additive manufacture (WAAM) technology has a good development prospect, and can be used to manufacture large metal components with complex shapes in combination with traditional machining equipment. This paper adjusts the parameters from the perspective of heat input and arc control. It is found that the stacking quality of 316L stainless steel is the best when the arc voltage is 40V and the arc current is 360A. It is proposed to obtain the flat layers by pressure machining after every layer is stacked, which can create favorable conditions for manufacturing large-size components. And through the hot rolling experiment, it is proved that pressure machining can improve the density and uniformity of the microstructure, and thus enhance the comprehensive mechanical properties of components built by WAAM.
Statistically-informed deep learning for gravitational wave parameter estimation
Authors: Shen Hongyu , Huerta E A , O’Shea Eamonn , Kumar Prayush , Zhao Zhizhen
Published: November 2021

We introduce deep learning models to estimate the masses of the binary components of black hole mergers,(m1,m2), and three astrophysical properties of the post-merger compact remnant, namely, the final spin,af, and the frequency and damping time of the ringdown oscillations of the fundamentalℓ=m=2bar mode,(ωR,ωI). Our neural networks combine a modifiedWaveNetarchitecture with contrastive learning and normalizing flow. We validate these models against a Gaussian conjugate prior family whose posterior distribution is described by a closed analytical expression. Upon confirming that our models produce statistically consistent results, we used them to estimate the astrophysical parameters(m1,m2,af,ωR,ωI)of five binary black holes:GW150914,GW170104,GW170814,GW190521andGW190630. We usePyCBC Inferenceto directly compare traditional Bayesian methodologies for parameter estimation with our deep learning based posterior distributions. Our results show that our neural network models predict posterior distributions that encode physical correlations, and that our data-driven median results and 90% confidence intervals are similar to those produced with gravitational wave Bayesian analyses. This methodology requires a single V100NVIDIAGPU to produce median values and posterior distributions within two milliseconds for each event. This neural network, and a tutorial for its use, are available at theData and Learning Hub for Science.
An extended Galerkin analysis in finite element exterior calculus
Authors: Hong Qingguo , Li Yuwen , Xu Jinchao
Published: November 2021

For the Hodge–Laplace equation in finite element exterior calculus, we introduce several families of discontinuous Galerkin methods in the extended Galerkin framework. For contractible domains, this framework utilizes seven fields and provides a unifying inf-sup analysis with respect to all discretization and penalty parameters. It is shown that the proposed methods can be hybridized as a reduced two-field formulation.
LR-GNN: a graph neural network based on link representation for predicting molecular associations
Authors: Kang Chuanze , Zhang Han , Liu Zhuo , Huang Shenwei , Yin Yanbin
Published: November 2021

In biomedical networks, molecular associations are important to understand biological processes and functions. Many computational methods, such as link prediction methods based on graph neural networks (GNNs), have been successfully applied in discovering molecular relationships with biological significance. However, it remains a challenge to explore a method that relies on representation learning of links for accurately predicting molecular associations. In this paper, we present a novel GNN based on link representation (LR-GNN) to identify potential molecular associations. LR-GNN applies a graph convolutional network (GCN)-encoder to obtain node embedding. To represent associations between molecules, we design a propagation rule that captures the node embedding of each GCN-encoder layer to construct the LR. Furthermore, the LRs of all layers are fused in output by a designed layer-wise fusing rule, which enables LR-GNN to output more accurate results. Experiments on four biomedical network data, including lncRNA-disease association, miRNA-disease association, protein–protein interaction and drug–drug interaction, show that LR-GNN outperforms state-of-the-art methods and achieves robust performance. Case studies are also presented on two datasets to verify the ability to predict unknown associations. Finally, we validate the effectiveness of the LR by visualization.
Efficient Parameter Server Placement for Distributed Deep Learning in Edge Computing
Authors: Wu Yalan , Yan Jiaquan , Chen Long , Wu Jigang , Li Yidong
Published: November 2021

Parameter servers (PSs) placement is one of the most important factors for global model training on distributed deep learning. This paper formulates a novel problem for placement strategy of PSs in the dynamic available storage capacity, with the objective of minimizing the training time of the distributed deep learning under the constraints of storage capacity and the number of local PSs. Then, we provide the proof for the NP-hardness of the proposed problem. The whole training epochs are divided into two parts, i.e. the first epoch and the other epochs. For the first epoch, an approximation algorithm and a rounding algorithm are proposed in this paper, to solve the proposed problem. For the other epochs, an adjustment algorithm is proposed, by continuously adjusting the decisions for placement strategy of PSs to decrease the training time of the global model. Simulation results show that the proposed approximation algorithm and rounding algorithm perform better than existing works for all cases, in terms of the training time of global model. Meanwhile, the training time of global model for the proposed approximation algorithm is very close to that for optimal solution generated by the brute-force approach for all cases. Besides, the integrated algorithm outperforms the existing works when the available storage capacity varies during the training.
Attaining the Exponent 5/4 for the Sum-Product Problem in Finite Fields
Authors: Mohammadi Ali , Stevens Sophie
Published: November 2021

We improve the exponent in the finite field sum-product problem from $11/9$ to $5/4$, improving the results of Rudnev et al. [16]. That is, we show that if $A\subset \mathbb {F}_p$ has cardinality $|A|\ll p^{1/2}$, then $ \max \{|A\pm A|,|AA|\} \gtrsim |A|^\frac 54 $ and $ \max \{|A\pm A|,|A/A|\}\gtrsim |A|^\frac 54 $.
BSAC Vanguard Series: Are we finally finding our voice on antimicrobial resistance?
Authors: Davies Dame Sally
Published: November 2021

Modern medicine is a miracle, as recently demonstrated by the successful delivery of COVID-19 vaccines. However, the sad reality is that antimicrobial resistance (AMR) is simultaneously undermining our progress. We need to take urgent action to prevent devastating effects on lives, poverty and global development. Late last year, the heads of the World Health Organization, Organisation for Animal Health and the Food and Agriculture Organization came together to launch a new group of global leaders and experts from right across the world. I’m honoured to be a member of their Global Leaders Group. Many feel powerless in the face of AMR, but the truth is there are many things we can do. I outline some here, from simple but very effective steps such as proper handwashing, right through to putting consumer pressure on food suppliers to reduce reliance on antibiotics in the food supply chain. Change won’t happen overnight, but we can and must make a difference.
Analysis of Genetic Diversity in the American Standardbred Horse Utilizing Short Tandem Repeats and Single Nucleotide Polymorphisms
Authors: Esdaile Elizabeth , Avila Felipe , Bellone Rebecca R
Published: November 2021

American Standardbreds were developed as a harness racing horse breed. The United States Trotting Association closed the studbook in 1973 and implemented a book size cap in 2009. This study aimed to investigate genetic diversity in the American Standardbred after the studbook cap was introduced using short tandem repeats (STRs) and single-nucleotide polymorphisms (SNPs). Sixteen STRs from horses foaled from 2010 to 2015 and their sires and dams (n = 50 621) were utilized to examine allelic richness (Ar), expected heterozygosity (HE), observed heterozygosity (HO), unbiased heterozygosity (HU), inbreeding coefficient (FIS), and fixation index (FST). These analyses found that trotting and pacing sires were less genetically diverse than dams (HEPBonferroni = 0.029 and 6.3 × 10−5, respectively) and their offspring (ArPBonferroni = 0.034 and 6.9 × 10-6, respectively), and pacing offspring were significantly less diverse than their dams (HEPBonferroni = 2 × 10-3). Inbreeding coefficients for trotters (FIS = −0.014) and pacers (FIS = −0.012) suggest that breeding practices have maintained diversity. Moderate levels of genetic differentiation (0.066 < FST < 0.11) were found between pacing and trotting groups. Additionally, 10 of the most prolific trotting sires and their male offspring (n = 84) were genotyped on the 670K Axiom Equine HD Array. HO values higher than HE (P < 0.001), low inbreeding coefficients (mean F = −0.064), and mean FROH = 21% indicate relatively high levels of diversity in this cohort, further supporting the STR data. However, in contrast, HO values were higher for trotting sires (0.41) than their offspring (0.36). This observation warrants further monitoring of diversity over time. These data provide an updated foundation of diversity indices for further, long-term analysis in the breed.
Electoral Sentencing Cycles
Authors: Abrams David , Galbiati Roberto , Henry Emeric , Philippe Arnaud
Published: November 2021

We add to our understanding of the optimal method of judicial selection by exploiting an unusual feature in North Carolina: judges rotate location every 6 months. This allows us to identify the existence and source of sentencing variation over the electoral cycle. We show that when elections approach, felony sentences rise. This increase is found exclusively when judges are sentencing in their district of election, and only when elections are contested. When judges hear cases outside their home district, sentences do not significantly vary over the electoral cycle. Our results show that electoral sentencing cycles can be explained by strategic sentencing by judges in an attempt to please voters. The unique setting allows us to reject alternative behavioral or contextual explanations for the rise in sentences as elections approach. (JEL K42).
Flow, form, and force: methods and frameworks for field studies of macroalgal biomechanics
Authors: Burnett Nicholas P , Gaylord Brian
Published: November 2021

Macroalgae are ecologically important organisms that often inhabit locations with physically challenging water motion. The biomechanical traits that permit their survival in these conditions have been of interest to biologists and engineers alike, but logistical and technical challenges of conducting investigations in macroalgal habitats have often prevented optimal study of these traits. Here, we review field methods for quantifying three major components of macroalgal biomechanics in moving water: fluid flow, macroalgal form, and hydrodynamic force. The implementation of some methodologies is limited due to the current state and accessibility of technology, but many of these limitations can be remedied by custom-built devices, borrowing techniques from other systems, or shifting lab-based approaches to the field. We also describe several frameworks for integrating flow, form, and force data that can facilitate comparisons of macroalgal biomechanics in field settings with predictions from theory and lab-based experiments, or comparisons between flow conditions, habitats, and species. These methods and frameworks, when used on scales that are relevant to the examined processes, can reveal mechanistic information about the functional traits that permit macroalgae to withstand physically challenging water motion in their habitats, using the actual fluid flows, macroalgal forms, and physical forces that occur in nature.
Transcriptional Analysis in the Arabidopsis Roots Reveals New Regulators that Linkrac-GR24 Treatment with Changes in Flavonol Accumulation, Root Hair Elongation and Lateral Root Density
Authors: Struk Sylwia , Braem Lukas , Matthys Cedrick , Walton Alan , Vangheluwe Nick , Van Praet Stan , Jiang Lingxiang , Baster Pawel , De Cuyper Carolien , Boyer François-Didier , Stes Elisabeth , Beeckman Tom , Friml Jiří , Gevaert Kris , Goormachtig Sofie
Published: November 2021

The synthetic strigolactone (SL) analog, rac-GR24, has been instrumental in studying the role of SLs as well as karrikins because it activates the receptors DWARF14 (D14) and KARRIKIN INSENSITIVE 2 (KAI2) of their signaling pathways, respectively. Treatment with rac-GR24 modifies the root architecture at different levels, such as decreasing the lateral root density (LRD), while promoting root hair elongation or flavonol accumulation. Previously, we have shown that the flavonol biosynthesis is transcriptionally activated in the root by rac-GR24 treatment, but, thus far, the molecular players involved in that response have remained unknown. To get an in-depth insight into the changes that occur after the compound is perceived by the roots, we compared the root transcriptomes of the wild type and the more axillary growth2 (max2) mutant, affected in both SL and karrikin signaling pathways, with and without rac-GR24 treatment. Quantitative reverse transcription (qRT)-PCR, reporter line analysis and mutant phenotyping indicated that the flavonol response and the root hair elongation are controlled by the ELONGATED HYPOCOTYL 5 (HY5) and MYB12 transcription factors, but HY5, in contrast to MYB12, affects the LRD as well. Furthermore, we identified the transcription factors TARGET OF MONOPTEROS 5 (TMO5) and TMO5 LIKE1 as negative and the Mediator complex as positive regulators of the rac-GR24 effect on LRD. Altogether, hereby, we get closer toward understanding the molecular mechanisms that underlay the rac-GR24 responses in the root.