bluuebunny/crossref_metadata_2025 huggingface dataset:
A Factor Graph Approach to Simultaneous Robot-World and Hand-Eye Calibration Based on Lie Group
Authors: Lan Bin , Wang Xueqiang , Li Zhiheng
Published: February 2022

The existing robot-world and hand-eye calibration algorithms always treated the robot pose as a deterministic value. Its uncertainty is not taken into account, which affects the calibration accuracy when the robot’s position accuracy is low. In this paper, we proposed a factor graph approach to simultaneous robot-world and hand-eye calibration, which considers the measurement error of the robot joint encoder. And then, the nonlinear optimization of reprojection error minimization based on a product of exponentials (POE)-based model is implemented to solve the calibration problem based on lie groups. Through simulation and experiment, we validated that the proposed algorithm can get more accurate calibration results compared with the state algorithm.
Terrain Estimation with Least Squares and Virtual Model Control for Quadruped Robots
Authors: Qin Haipeng , Zhu Yaguang , Zhang Yaqiang , Wei Qibin
Published: February 2022

In this paper, a method to estimate the terrain based on footholds and least-squares is presented to make quadrupedal locomotion stable on the sloped terrains, with joint encoders, force sensors and inertial measurement unit (IMU) instead of visual sensors. This method which is tested successfully in simulation can predict two types of terrain, one for adjusting the robot pose and the other for adjusting the trajectory of swing phase. And to make full use of these new capabilities, general balance and locomotion controller is presented. The virtual model controller is embedded into architecture and it allows the robot to handle unexpected terrain. The quadrupedal locomotion in terrain with different inclination prove the robustness of this method.
Real-time Wide-depth-range Pose Estimation with Laser Scanning Point Cloud
Authors: Su Yu , Xu Tianlai , Zhang Zexu , Wang Yishi , Yuan Mengmeng
Published: February 2022

In this work, a real-time wide depth pose estimation method with laser scanning point cloud is proposed. Unlike other 6D pose estimation methods, the proposed method can maintain a robust and accurate tracking performance even when the target spacecraft has an obvious transformation in depth. To make full use of the known model, a pose estimation network based on CNNs is established to estimate the position. After the point cloud is translated according to the estimation result, the quaternion of the target is calculated independently. To evaluate the consequent of the network, we simulated the scanning point cloud with a wide depth range. The results show that the proposed method has strong robustness to the depth transformation and higher computational efficiency.
A Minimum Mutual Information Optimized SSD and Application In Bearing Fault Detection
Authors: Wang Zhenya , Liu Tao , Chu Wei
Published: February 2022

For purpose of efficiently extracting the fault detailed characteristics of rolling bearing vibration signals in the midst of heavy noise interference, the author combines the mutual information theory and proposes a singular spectrum decomposition effective component discrimination method based on the minimum mutual information criterion. First, the SSD algorithm is used to decompose the original vibration signal into a number of SSC; then the mutual information value between the original signal and each singular spectrum component is calculated separately, based on the nature of the bearing fault vibration signal and the mutual information theory for derivation and analysis, and the selection is determined the singular spectrum component with the smallest mutual information value among the original signal and each component is taken as the best component; finally, the best component selected based on the minimum mutual information value criterion is analysed by envelope spectrum to extract the fault characteristics of rolling bearing. Through the analysis of bearing fault simulation signal and the comparison with other indicators, it is shown that this set of experiments significantly extracts the characteristics of the vibration signals emitted by rolling bearings due to faults in noisy environments, thus revealing promising applications and development prospects.
Design and Implementation of Temperature Verification System Based on Robotic Arm
Authors: Qiu Shi , Yang Zongbo , Huang Lei , Zhang Xuehan
Published: February 2022

The key issue of meteorological temperature verification is how to operate itself without personnel deployment. This paper presents a meteorological temperature verification system based on robotic arm. Its goal is to realize the automatically operating by integrating robotic arm controller and verification software. The device level provides temperature verification instruments and the robotic arm. The communication function such as cyclic measurement of the resistance of temperature sensor, verification environmental data communication and temperature generator control can be realized by networking level. The interactive level ensures stable operation and fault location. Some key methods such as the system layout, the system workflow and the system integration method are also illustrated for system implementation.
A Configuration Design Method for Reconfigurable Robots Based on Terrain Understanding
Authors: Shang Hua , Xi Yurong , Xie Zhibing , Xiao Xueming , Yao Meibao
Published: February 2022

Reconfigurable robots have excellent reconfigurability and environmental adaptability, but the configuration design method to select appropriate configuration with different environments has not been fully studied. Most of the existing work is based on the known global environment information, or only focus on the structure design. How to effectively obtain rich terrain information for subsequent configuration design is the subject of this paper. In our work, a comprehensive method of obtaining robotic configuration is proposed which considering geometric information and semantic information of unstructured terrain. And it provides new possibilities for reconfigurable robots to perform autonomous tasks in complex scenarios. Results of simulation experiments prove the availability of the mentioned method.
Acceleration of Infrared Target Detection via Efficient Channel Pruning
Authors: Miao Zhuang , Zhang Yong , Li Weihua , Chen Ruimin
Published: February 2022

Deployment of modern detection models is difficult for infrared target detection used in robot vision systems due to their heavy computational burden. To alleviate this situation, a simple but efficient channel pruning method is proposed for model acceleration. Specifically, a soft-gated module combined with batch normalization (SGBN) is designed as a standalone layer to substitute the standard batch normalization (BN) layer during training. The conversion between SGBN and BN is easy, and the training overhead introduced is almost negligible after replacement. By controlling the sparsity of the scaling factor in SGBN, unimportant channels with small output are blocked automatically and globally, which is simultaneous with model training. Removing these redundant channels no longer requires fine-tuning, thus significantly speeding up the pruning process. Experiments of pruning different detection models on the infrared dataset show the effectiveness of our method. For example, the parameters and FLOPs of pruned CetnerNet are reduced by 72.70% and 40.20%, respectively, without accuracy loss. The inference speed on the CPU is 12.01ms faster. Extended studies on the classification task also demonstrate its great potential when transferring to other applications.
Positioning in Urban Scenarios Based on Smartphone Camera/IMU and Digital TV Signal
Authors: Pan Yinghua , Chen Liang , Jiao Zhenhang , Liu Xiaoyan , Chen Ying
Published: February 2022

The built-in sensors of smartphone, such as cameras, accelerometers and gyroscopes, provide abundant environmental information for positioning. VIO (visual-inertial odometry) can be derived from the embedded camera and inertial sensors to provide the estimation of a continuous and smooth trajectory of the pedestrian. However, the trajectory will drift due to the accumulation of system errors. Currently, DTV (digital TV) signal has been verified to be promising for precise ranging in urban scenarios. Therefore, this paper investigates a method of fusing the VIO with the ranging information from DTV signal to improve the positioning accuracy. An Extended Kalman filter is presented to estimate pedestrian mobile state. Field tests have been carried out and the fusion results can effectively reduce trajectory drift errors and improves positioning performance.
An SLAM Algorithm Based on Laser Radar and Vision Fusion with Loop Detection Optimization
Authors: Li Lina , Yuan Jing , Sun Hao , Song Yan
Published: February 2022

SLAM (Simultaneous Localization And Mapping, SLAM) is the key technology for mobile robots to obtain information about their position and surrounding environment, but both laser radar and vision sensors have limitations in solving SLAM problems, for example: a single laser sensor can’t do mapping effectively for hollowed-out obstacles, while a single vision sensor can’t work well in areas without texture. Besides, the existing loop detection technology still has some problems such as large calculation resources and errors. To solve the above problems, an improved SLAM algorithm laser and vision fusion was presented in this paper. In this algorithm, a Gaussian statistical linear interpolation algorithm was used to interpolate the laser point cloud, and then the interpolated laser raster map and visual raster map are fused by the Bayesian method to obtain a denser and more accurate map. Furthermore, the histogram was used to improve the loop detection part to further improve the positioning accuracy. The improved loop detection algorithm was simulated and verified by running the TUM data set. On this basis, a mobile robot experiment system was built to do mapping in the laboratory scene. The experimental results show that the algorithm proposed in this paper can map the hollowed-out area accurately and effectively, it plays a significant role in improving the drawing effect of the robot, and can provide more accurate location information, which has good practicability.
Design of an Improved Impedance Controller for Powered Transtibial Prosthesis
Authors: Xie Changle , Tu Mingyu , Liu Jingjing
Published: February 2022

With the rapid development of powered transtibial prostheses, the control of motorized actuators is a research hotspot. This study designs a double-loop impedance controller to improve the performance of the traditional controller. Including time-varying parameters, the designed controller is implemented on a novel elastic actuator. Under different loads of users’ weights, simulation tests are conducted in a working condition of walking, and the results show that the designed control system has outstanding performances in the control of an entire gait cycle. The error between output and reference of ankle joint position during walking is less than 0.013rad in each sample time. A position disturbance test demonstrates that the control also has a high tolerance of any position disturbance at the ankle joint. The double-loop impedance controller can replace traditional finite-state impedance control in the field of the powered transtibial prosthesis and has the potential to be applied in other relative fields.
The Execution Efficiency of the Response Time Analysis under Different Task Sequencing Strategies
Authors: Ma Li , Zhou Li , Zhou Qiupeng , Wang Qixin
Published: February 2022

Tasks in real-time systems have strict time requirements and need to be completed before their deadlines. Whenever the real-time task set changes, the real-time system reruns the schedulability analysis method to determine whether the tasks in the new task set can be completed before their deadlines. Since new tasks can not be executed until the execution of the schedulability analysis method is completed, the execution efficiency of the schedulability analysis method directly affects the actual performance of the real-time system. Response time analysis (RTA) is a typical method for schedulability analysis. It calculates the response times of tasks and tests the schedulability of tasks by comparing the calculation results and the relative deadlines of tasks. We found that the execution efficiency of existing RTAs can be improved by reasonably sorting the calculation order of the tasks. A task sequencing strategy is proposed to improve the execution efficiency of existing RTAs.
A Review of Hyperspectral Image Classification Based on Joint Spatial-spectral Features
Authors: Qu Shenming , Li Xiang , Gan Zhihua
Published: February 2022

Hyperspectral image classification technology is a basic work in the application of hyperspectral images. In recent years, with the innovation and development of hyperspectral image classification technology, the method and performance of hyperspectral image classification based on joint spatial-spectral features have made breakthroughs, and have gradually become the focus of researchers. In order to further promote the development of the spatial-spectral feature union class method and improve the classification accuracy of hyperspectral images, this paper summarizes the commonly used spatial-spectral union algorithms. Firstly, the introduction briefly outlines the background and research status of this field. Some common problems in the process of hyperspectral image classification are listed. Finally, some current hyperspectral image classification methods based on joint spatial-spectral features are introduced. The main roles and existing problems of spatial-spectral joint features in the field of hyperspectral image classification are summarized in detail, and the future research directions are prospected.
Design of Diesel Oxidation Catalyst Temperature Control System Based on Fuzzy Adaptive PID
Authors: Wang Ning , Sun Yao , Hu Yunfeng , Zhao Jinghua , Gong Xun
Published: February 2022

This paper presents a temperature control framework of Diesel Oxidation Catalyst (DOC) of after-treatment system (ATS) which aims to increase the control precise of DOC temperature under different exhaust flow rates. To start with, a control-oriented DOC temperature model is established. Considering the sensitivity of DOC to different exhaust flow rate, a fuzzy adaptive PID temperature control system framework under the Smith predictor is designed. Finally, the controller is verified in the Matlab/Simulink. The results show that when tracking the desired temperature, the overshoot is reduced and the response speed is faster at different exhaust flow rates compared with the traditional PID control system.
A Cloud Computing Platform Technology Based on Space-based Intelligent Information Network
Authors: Ding Yueli , Shao Yingzhao , Zhang Jianhua , Nie Xiaokai , Yang Pengfei , Lv Wenkai
Published: February 2022

Based on large-scale storage and networked computing resources, cloud computing technology provides a strong computing platform which could support for massive data storage and big data intelligent application in the different field. Space-based information infrastructure has matured over the years, but most of the satellite systems are independent, which cause the resources of the satellite computing platform are isolated from each other. In this case, storage and computing resources cannot be utilized fully, which also cannot effectively support space-based intelligent application platform[1]. In this paper, a space-based embedded cloud computing platform is designed by combining the application requirements of space-based intelligent computing and ground-based cloud computing system, which includes sharing and using large-scale computing resources, concurrent monitoring and management of multi-tasks, fault-tolerant migration of tasks, and distributed storage retrieval. The results of this paper are committed to building an intelligent information network computing and storage platform, which could support for space-based intelligent infrastructure application.
Design of Quadrotor System for Transmission Line Inspection
Authors: Lin Xumei , Miao Fangrong , Chen Yige , Zai Wenqiang , Ding Yan , Pan Jiaxin
Published: February 2022

A novel robust adaptive control is proposed for target detection and recognition in transmission line inspection, this paper proposes a quadrotor inspection system based on robust adaptive control of multi-sensor fusion. Robust adaptive control ensures the stability and anti-disturbance ability of the quadrotor during the flight. It is measurable for the distance between the quadrotor and cables by laser sensor and ultrasonic sensor. It can be used to detect and identify the target objects on cable lines by OpenMV. According to the proposed Lyapunov function, the asymptotically stable conditions is obtained, in which all trajectories converge in finite time to the desired location. Finally, the effectiveness of inspections is validated experimentally, and the system has strong anti-interference ability.
A Simple Data Privacy and Security Protection Algorithm Based on Federated Learning
Authors: Wang Jingtian , Chen Shenbo , Li Boyuan , Wang Guanghui
Published: February 2022

Although federated learning can break the data island and enhance privacy, it is not absolutely secure. External eavesdroppers can intercept the model when clients upload their models to the server, which might be used to infer the data information. A natural solution is to add noise to the uploaded model. However, this solution has its drawback, because the added noise cannot be eliminated when the models are aggregated at the server, which leads to poor performance. In order to tackle this problem, we propose a simple but effective algorithm, FedNoise. In the vanilla federated learning algorithm, a gradually decreasing learning rate is applied at the client side. While in our algorithm, we divide the parameter update into client-side and server-side. The stochastic gradient descent update with constant learning rate is performed at the client side and a gradually decreasing learning rate is performed at the server. Our algorithm can not only ensure the data security, but also maintain comparable system performance. Simulation results validate our conclusion.
Augmented Value Iteration Networks with Artificial Potential Fields for Path Planning
Authors: Jin Xiang , Lan Wei , Yu Pengyao , Wang Tianlin
Published: February 2022

Value iterative networks (VINs), as a differentiable path planning method, taking environmental images as input, can solve path planning problems in new and unseen environments with powerful generalization capabilities. But the planning success rate decreases rapidly as the planning space scales. We propose the potential field augmented VIN by replacing the environmental map with the potential map as input and implementing this process in the form of dilated convolutions, giving more a priori information to the subsequent planning computation while hardly increasing the computational cost. Experiments on 2D grid-world show that the improved method has higher path planning success rates and smaller difference of predicted paths from shortest paths, as well as smaller performance degradation with increasing map size.
Dynamical Modelling and Vibration Analysis of the Non-coplanarity of the Orthogonal Support of Gyrowheel
Authors: Liu Xiaokun , Tan Minglang , Yang Guanglie , Mo Chaoming , Gu Dongqing , Song Bin , Wu Shufan
Published: February 2022

In the engineering implementation of gyrowheel, the influence of orthogonal support non-coplanar is inevitable. However, when the gyrowheel is used for satellite inertial angular velocity measurement, this non-ideal factor will inevitably have an adverse impact on the accuracy of angular velocity measurement. Therefore, the dynamic model of gyrowheel with non-coplanar orthogonal support is established, and the influence of the non-coplanarity support on the vibration frequency is analysed. Finally, the correctness of this research is verified by simulation.
An Optimal Evader-Pursuer Strategy based on Machine Learning and Differential Game
Authors: Wang Jingjie , Chen Xiangwei , Yu Yongjia , Yuan Bingchang , Jin Chufeng , Zhu Ren , Li Heng
Published: February 2022

Based on the special differential game model of evader and pursuers, in this paper, we propose an optimal escape and containment strategies of evader and pursuers, and gives the escape boundary conditions of evader. Through the training of DDPG and A3C algorithms, the evader have the ability to escape autonomously, and the evaluation system is established with the reward value, the number of rounds, the success rate of escape and the ratio of escape speed as the main indicators. Taking different pursuer-evader speed ratios under DDPG and A3C as key indicators, a quantitative evaluation system was established, and the evaluation results verify the effectiveness of the proposed approach.
Knowledge Base Question Answering Based on Multi-head Attention Mechanism and Relative Position Coding
Authors: Gan Ling , Xiao Yang
Published: February 2022

Most current knowledge base question answering models mainly use RNN and its various derivative versions such as BiLSTM to model the problem, which limits Parallel computing capabilities of the model. In response to this problem, we try to use TransformerEncoder instead of BiLSTM to model and encode the problem, and hope to improve the parallel computing efficiency of the model. At the same time, to solve the problem of insufficient relative position information obtained by using absolute position coding in TransformerEncoder, it is proposed to use relative position coding instead of absolute position coding. According to the experimental results, our model effectively reduces a certain amount of training time and has achieved certain results on the WebQuestions benchmark data set.
A Novel Brain Tumor Segmentation Method Based on Im-proved Spatial Attention Mechanism and Multi-path Deep Neural Network
Authors: Wang Guizeng , Lu Huimin , Wang Yifan , Zhao Chengcheng
Published: February 2022

Preoperative Magnetic Resonance Image (MRI) brain tumor diagnosis is an effective technical approach. To accurately segment tumor regions, we propose a novel brain tumor segmentation method based on improved Spatial Attention mechanism and Multi-path neural network (SAMPU-Net). Firstly, we propose a multipath input method to extract feature information of different scales by using convolution kernels of different sizes, so as to fully extract MRI feature information. Secondly, we improve the spatial attention mechanism by adding convolution layer of pyramid structure to it to obtain the features of different receptive fields. In the convolution layer of this pyramid structure, the larger the convolution kernel is, the more global features will be extracted; conversely, the smaller the convolution kernel is, the more local features will be extracted. Thirdly, we use more multi-mode MRI information to segment the brain tumor images. In practical application, due to the fuzzy tumor regions in some MRIs, we use the method of restricted contrast adaptive histogram equalization to perform local enhancement of images. The proposed model and several other mainstream segmentation methods are trained and tested on the BraTS2019 public dataset. Experimental results indicate that using our method, the Dice coefficient of tumor core and tumor enhancement region is increased by 2.4% and 1.3% respectively, and our proposed method has better segmentation effect than other methods.
The Research of RoboCup2D Player Shooting Technique Based on Fuzzy Control
Authors: Yu Junfeng , Bahitbek Ardah , Yang Yingze
Published: February 2022

Multi-agent collaboration is the core task of multi-agent system (MAS) research. In order to explore a kind of multi-agent collaboration way with dynamic environment, this paper proposes a method based on fuzzy learning and applies it to RoboCup2D. In the RoboCup2D simulation competition, eleven players need to cooperate to win, so score becomes extremely critical. Therefore, this paper focus on shooting technique based on fuzzy control. In order to increase the scoring rate, We speculate through observation and testing that finding the correct shooting position and angle is the key. For solving the problem, this paper firstly design fuzzy controller for selecting the shooting path; and then, this paper purpose fuzzy neural network for optimizing shooting time; At last, this paper do lots of experiments to verify its effectiveness and find it does work on increasing the scoring rate. After optimization on the fuzzy control, we are fortunate to have won the national second prize in 2021 RoboCup China Open, and the goal accuracy has increased by 21.36%.
A Closed-Loop Assembly Network Optimization Based on Chance Constraint with Robust Approximation
Authors: Wei Dongyuan , Wang Yue , Li Xinchao , Lu Shan
Published: February 2022

In this paper, an optimization algorithm of forward-reverse cyclic assembly network under uncertainty is studied. A chance constrained algorithm based on robust approximation is adopted, in which the size of uncertain set is used to describe the violation probability of constraint. Further, some parameters are set to describe the confidence probability of the model. By implementing the proposed algorithm, we can reduce the manufacturing cost of the final forward-reverse cycle assembly network by 3.17% while ensuring a certain confidence probability, and relax the lower bound of the probability of the model, which makes the model more adaptable. By calculating the scheduling data and comparing with flexible robust optimization, the effectiveness of the algorithm is proved.
Distributed Cruise Control and Comfort Evaluation for High-speed Trains Under Moving Block Mode
Authors: Zhou Feng , Tao Kewu , Zheng Ge
Published: February 2022

This paper investigates the problems of distributed cooperative cruise control and train running comfort for high-speed trains with input constraints under moving block mode. Firstly, inspired by the mode of hitting a hard wall for moving block, a distributed cooperative cruise control algorithm based on the position and speed state information of adjacent trains is proposed. Under this control algorithm, all trains can track the given speed and realize speed cruise. In this algorithm, the relationship between speed and displacement is used to improve the position term, so as to achieve the minimum safe distance between trains by adjusting the speed. Secondly, aiming at the problem of the excessive acceleration of each train in the moving block mode, the hyperbolic tangent function is introduced to limit the acceleration of each train in an appropriate range by limiting the control input. Finally, the stability of the closed-loop system in error state is proved by using Lyapunov stability theorem, and the feasibility of the algorithm is verified by simulation.
A Robot Pick and Place Skill Learning Method Based on Maximum Entropy and DDQN Algorithm
Authors: Wu Peiliang , Zhang Yan , Li Yao , Mao Bingyi , Chen Wenbai , Gao Guowei
Published: February 2022

Pick and place (PAP) skill learning is a fundamental ability of intelligent robots, such as home service robot. Due to the NP-hard nature of the PAP problem, it takes a long time for an intelligent robot to learn the PAP skill based on current methods. In order to improve the learning efficiency of robot PAP skills, this paper proposes a Soft-DDQN-based PAP skill learning method. Firstly, the Soft-DDQN is proposed by introducing maximum entropy into robot DDQN framework, and the learning goal of Soft-DDQN is to maximize reward and information entropy. Secondly, PAP problem is modelled as a discrete form and Soft-DDQN is applied to solve the PAP problem. Finally, in order to verify the efficiency of Soft-DDQN-based PAP skill learning, comparisons have been given from two standard perspectives and shown that Soft-DDQN improves efficiency of PAP skill learning evidently.
Research on Control Strategy of Grid-connected Inverter with Active Filter Function
Authors: Jia Die , Zhong Jianwei , Yang Yongchao , Tian Shidong , Zheng Wenli
Published: February 2022

Grid-connected inverters incorporate distributed energy sources such as photovoltaics and wind energy into the micro-grid. Due to the volatility of distributed energy generation and the presence of a large number of non-linear loads in the micro-grid, harmonic wave seriously affect power quality. The grid-connected inverter with active filtering function is formed by adding the harmonic wave suppression function to the grid-tie inverter, which not only realizes the inverter grid connection but also achieves the purpose of harmonic wave compensation. The traditional model predictive control (MPC) has the characteristics of strong robustness and fast dynamic response, but its high switching frequency causes serious heating of the inverter. In response to the above problems, combined with hysteresis control theory, a model control strategy based on hysteresis is proposed in this paper, which improves the accuracy of grid-connected current of the grid-connected inverter with active filter function and inhibits the harmonic component of the system, verifying the effectiveness of the strategy.
Study on time measurement for CSA-based readout electronics in STCF ECAL
Authors: Luo L. , Jia Z. , Shen Z. , Zhang Y. , Liu S.
Published: February 2022

Super Tau-Charm Facility (STCF) will provide unique support
for particle physics research in China owing to its high luminosity
and large dynamic range. Its electromagnetic calorimeter (ECAL) is
designed as a new type of calorimeter which can perform
high-precision energy and time measurement at the same
time. Considering the high event rate and pile up induced by high
luminosity, and the large channel number scale of ECAL, the readout
electronics needs to adopt a high-precision, unconventional, and
real-time processing method to reduce the data amount in
transmission. Based on the charge-sensitive front-end electronics in
energy measurement, an online time measurement method based on
waveform fitting algorithm has been studied and implemented in this
work. The parameters which would affect the time resolution are
analyzed according to simulation and experimental test. After the
optimization of electronics, a time resolution of 165 ps at the
equivalent deposition energy of 1 GeV is achieved, which indicates
that the waveform fitting method based on charge-sensitive readout
electronics can meet the future requirements.
The straw tracking detector for the Fermilab Muon g-2 Experiment
Authors: King B.T. , Albahri T. , Al-Kilani S. , Allspach D. , Beckner D. , Behnke A. , Bowcock T.J.V. , Boyden D. , Carey R.M. , Carroll J. , Casey B.C.K. , Charity S. , Chislett R. , Eads M. , Epps A. , Foster S.B. , Gastler D. , Grant S. , Halewood-Leagas T. , Hardin K. , Hazen E. , Hesketh G. , Hollywood D.J. , Jones T. , Kenziora C. , Keshavarzi A. , Kiburg M. , Kinnaird N. , Kintner J. , Lancaster M. , Lucà A. , Lukicov G. , Luo G. , Mapar L. , Maxfield S.J. , Mott J. , Motuk E. , Mourato H. , Pohlman N. , Price J. , Roberts B.L. , Sathyan D. , Shenk M. , Sim D. , Stuttard T. , Sweetmore G. , Thayer G. , Thomson K. , Turner W. , Vasilkova D. , Velho J. , Voirin E. , Walton T. , Warren M. , Welty-Rieger L. , Whitley M. , Wormald M.
Published: February 2022

The Muon g-2 Experiment at Fermilab uses a gaseous straw
tracking detector to make detailed measurements of the stored muon
beam profile, which are essential for the experiment to achieve its
uncertainty goals. Positrons from muon decays spiral inward and pass
through the tracking detector before striking an electromagnetic
calorimeter. The tracking detector is therefore located inside the
vacuum chamber in a region where the magnetic field is large and
non-uniform. As such, the tracking detector must have a low leak
rate to maintain a high-quality vacuum, must be non-magnetic so as
not to perturb the magnetic field and, to minimize energy loss, must
have a low radiation length. The performance of the tracking
detector has met or surpassed the design requirements, with adequate
electronic noise levels, an average straw hit resolution of
(110 ± 20) μm, a detection efficiency of 97% or higher,
and no performance degradation or signs of aging. The tracking
detector's measurements result in an otherwise unachievable
understanding of the muon's beam motion, particularly at early times
in the experiment's measurement period when there are a
significantly greater number of muons decaying. This is vital to the
statistical power of the experiment, as well as facilitating the
precise extraction of several systematic corrections and
uncertainties. This paper describes the design, construction,
testing, commissioning, and performance of the tracking detector.
A new technique to enhance the position resolution of large area plastic scinitillators to reconstruct the cosmic muon tracks
Authors: Sehgal R. , Dey R. , Behera S.P. , Netrakanti P.K. , Mishra D.K. , Mulmule D. , Jha V. , Pant L.M.
Published: February 2022

In this paper, we present a study to use thick plastic
scintillators to reconstruct the cosmic muon tracks, that can be
used for the applications like Muon Tomography. At Bhabha Atomic
Research Centre (BARC), India, a plastic scintillator array —
`ISMRAN (Indian Scintillator Matrix for Reactor Anti-Neutrinos),'
with a total weight of 1.0 ton has been configured for neutrino
physics study. Using the ISMRAN scintillators matrix, we present a
technique of the position calibration of thick plastic scintillators
using cosmic muons as a probe. The position resolution obtained from
the cosmic muons based calibration method is compared with the one
obtained from the traditional calibration method using the
radioactive source. Finally, the accuracy of reconstructed cosmic
muon tracks from the two position calibration techniques is compared
using the χ2/ndf distribution of the fitted cosmic muon
tracks.
Fabrication and Characterization of the self support enriched 107, 109Ag thin targets for heavy ion induced transfer reaction
Authors: Vinayak A. , Abhilash S.R. , Hosamani M.M. , Hiremath G.B. , Umapathy G.R. , Khandelwal D. , Madhavan N. , Kabiraj D. , Badiger N.M.
Published: February 2022

The enriched, self-supported, and thin isotopic targets of
107Ag and 109Ag were fabricated at the target development
laboratory, IUAC New-Delhi, using a vacuum deposition facility. The
targets were fabricated by the thermal evaporation technique with
minimum material consumption. The simple and efficient method for
the fabrication of thin targets with high quality without major
impurities was discussed in detail. The prepared targets were
characterised by using Rutherford Backscattering Spectrometry, X-Ray
Diffraction, Total-Reflection X-Ray-Fluorescence and
Scanning-Electron-Microscope techniques. From the characterizations,
it was found that the fabricated targets were quite stable, had the
same crystalline nature, the absence of any other elements, and were
suitable for nuclear reaction experiments.
Multi-year variation of near-surface ozone at Zhongshan Station, Antarctica
Authors: Tian Biao , Ding Minghu , Putero Davide , Li Chuanjin , Zhang Dongqi , Tang Jie , Zheng Xiangdong , Bian Lingen , Xiao Cunde
Published: February 2022

With the support of the Chinese National Antarctic Research Expedition, near-surface ozone (O3) was continuously monitored at Zhongshan Station (ZOS) (69°22′12″ S, 76°21′49″ E, 18.5 m above sea level) in East Antarctica from 2008 to 2020. The seasonal and diurnal variability of near-surface O3 at ZOS were investigated. O3 enhancement events (OEEs) were frequently observed in the warm season (OEEs in January accounted for 23.0% of all OEEs). The OEEs at ZOS were related to the photochemical reaction processes under the influences of O3 and solar radiation in the stratosphere and synoptic-scale air mass transport from coastal areas (Princess Elizabeth Land, Wilkes Land, and Queen Mary Land), as evidenced by the recorded wind speed, solar shortwave irradiance, and total column ozone data and the computed potential source contribution function and concentration-weighted trajectory models. The results computed by the tool Stratosphere-to-Troposphere Exchange Flux indicated that stratosphere-to-troposphere transport had no direct impact on OEEs at ZOS. Therefore, synoptic-scale air mass transport is the main cause of OEEs in Antarctica, which is consistent with previous studies. Unlike OEEs at inland Antarctic stations, which are mainly affected by air mass transport from inland plateaus, OEEs at ZOS, a coastal station, are mainly affected by air mass transport from coastal land in East Antarctica.
Mapping public support for climate solutions in France
Authors: Schmid Nicolas , Guinaudeau Benjamin
Published: February 2022

Although successful sustainability transitions depend on public support, we still know little about citizens’ opinions on climate solutions. Existing research often focuses on the problem perception of climate change rather than analyzing attitudes toward specific climate solutions. Studies also largely use closed questions to assess public opinion, posing a problem of ecological validity. Here, we address these gaps by leveraging data from a large-scale public consultation process, the “Grand Débat National”, launched by the French government in response to the Yellow Vest movement in 2019. Combining structural topic modelling, dictionary-based text analysis and qualitative coding, we map the salience and directionality of public opinion on climate solutions. We find that consultation participants perceive climate change as the most salient environmental problem. Transforming the transport and energy sectors is the most supported solution for addressing climate change. For these two sectors, substitution-based climate solutions - as opposed to sufficiency- or efficiency-based measures - are most salient. For instance, participants stress the need to expand public transport infrastructure and switch to renewable energy technologies for power generation. Our findings demonstrate a strong public consensus on most substitution-based climate solutions, except for the role of cars and nuclear energy. While most participants do not link climate solutions to specific policy instruments, we find preferences for authority-based instruments in the context of phasing out polluting technologies, and treasury-based instruments for supporting innovation and phasing in low carbon technologies.
The use of GEDI canopy structure for explaining variation in tree species richness in natural forests
Authors: Marselis Suzanne M , Keil Petr , Chase Jonathan M , Dubayah Ralph
Published: February 2022

Variables describing the abiotic environment (e.g. climate, topography or biogeographic history) have a long tradition of use as predictors of tree species richness patterns. However, these variables may capture variations in richness related to climate, but not those that are related to soil type or forest disturbance. Canopy structure has previously been shown to provide information on the variation of tree species richness, with richness generally increasing with larger canopy heights and denser foliage. The use of canopy structure is increasingly relevant with the availability of such data from the Global Ecosystem Dynamics Investigation (GEDI), a lidar mission onboard the International Space Station. In this analysis we show that GEDI canopy structure explains up to 66% of the variation in tree species richness in natural forests without a history of recent disturbance across the globe. However, this portion overlaps with the variation (up to 80%) explained by environmental and biogeographical variables. Our results show that relationships between tree species richness on one side and climate and canopy structure on the other side are not as straightforward as we initially expected, and should be further investigated across both natural and disturbed forests.
Thermally actuated micro-/nanoscale deformations for optical reconfigurations
Authors: Zhao Yinghao , Ji Chang-Yin , Yang Hengzhang , Wang Yang , Xie Huikai , Li Jiafang
Published: February 2022

The unique three-dimensional (3D) deformations caused by nano-kirigami have enabled a new degree of freedom for reconfigurable optics. Here, we demonstrate a facile nano-kirigami method that can create 3D deformed structures, which can flexibly manipulate optical properties using thermally actuated micro-/nanoscale deformations. By connecting four pairs of thermal actuators to the four sides of a gradient metasurface, large-angle beam steering (∼90°) can be achieved by adjusting the temperature of the actuators. The amplitude of circular dichroism can be adjusted by thermally actuating micro-/nanoscale deformations. The 2D-to-3D transformation of the curved arm structure on metallic substrate results in enhanced structural absorption, inducing an almost perfect absorption at specific wavelengths. Curved asymmetric structures can also be created by thermally actuated micro-/nanoscale deformations, which provides a novel method for cross-polarized light conversion. The proposed design with thermally actuated micro-/nanoscale deformations provides a new methodology to explore versatile reconfigurable functionalities.
Multivariate normal distribution for integral points on varieties
Authors: El-Baz Daniel , Loughran Daniel , Sofos Efthymios
Published: February 2022

Given a variety with coefficients in

Z

\mathbb {Z}

, we study the distribution of the number of primes dividing the coordinates as we vary an integral point. Under suitable assumptions, we show that this has a multivariate normal distribution. We generalise this to more general Weil divisors, where we obtain a geometric interpretation of the covariance matrix. For our results we develop a version of the Erdős–Kac theorem that applies to fairly general integer sequences and does not require a positive exponent of level of distribution.
ABO Blood Groups Are Not Associated With COVID-19 Disease Incidence and Severity When Correcting for Ethnicity Differences in Blood Type
Authors: Pasko Bryce E , Abbott Diana , Bocsi Gregary T , Draper Nicole L
Published: February 2022

Objectives

To determine if blood type is a risk factor for coronavirus disease 2019 (COVID-19) disease incidence and severity after correcting for ethnicity differences between novel infections and known ABO blood type frequency differences.

Methods

We performed a retrospective analysis on all severe acute respiratory system coronavirus 2 (SARS-CoV-2) infections and disease severity across two major testing sites in Colorado. We evaluated all individuals with a SARS-CoV-2 nucleic acid test (NAT) and a known blood type between March 1, 2020, and June 1, 2020. We then created a prediction algorithm based on the corrected blood types by ethnicity using data from the Colorado Department of Health and established blood types by ethnicity. We applied this prediction algorithm to all patients in our sample.

Results

Of 8,676 patients, 485 (5.6%) had a positive SARS-CoV-2 NAT test and 8,191 (94.4%) had a negative test. All patients had ABO blood types that mirrored the expected blood type distribution within the state of Colorado (P = .15, χ 2 statistic = 5.31). No differences in expected blood groups were present between ethnicity-adjusted SARS-CoV-2–negative and SARS-CoV-2–positive patients (χ 2 = 3.416313, P = .332).

Conclusions

Blood type is not associated with COVID-19 disease incidence or severity after correcting for ethnicity differences in expected blood type frequencies.
cSurvival: a web resource for biomarker interactions in cancer outcomes and in cell lines
Authors: Cheng Xuanjin , Liu Yongxing , Wang Jiahe , Chen Yujie , Robertson Andrew Gordon , Zhang Xuekui , Jones Steven J M , Taubert Stefan
Published: February 2022

Survival analysis is a technique for identifying prognostic biomarkers and genetic vulnerabilities in cancer studies. Large-scale consortium-based projects have profiled >11 000 adult and >4000 pediatric tumor cases with clinical outcomes and multiomics approaches. This provides a resource for investigating molecular-level cancer etiologies using clinical correlations. Although cancers often arise from multiple genetic vulnerabilities and have deregulated gene sets (GSs), existing survival analysis protocols can report only on individual genes. Additionally, there is no systematic method to connect clinical outcomes with experimental (cell line) data. To address these gaps, we developed cSurvival (https://tau.cmmt.ubc.ca/cSurvival). cSurvival provides a user-adjustable analytical pipeline with a curated, integrated database and offers three main advances: (i) joint analysis with two genomic predictors to identify interacting biomarkers, including new algorithms to identify optimal cutoffs for two continuous predictors; (ii) survival analysis not only at the gene, but also the GS level; and (iii) integration of clinical and experimental cell line studies to generate synergistic biological insights. To demonstrate these advances, we report three case studies. We confirmed findings of autophagy-dependent survival in colorectal cancers and of synergistic negative effects between high expression of SLC7A11 and SLC2A1 on outcomes in several cancers. We further used cSurvival to identify high expression of the Nrf2-antioxidant response element pathway as a main indicator for lung cancer prognosis and for cellular resistance to oxidative stress-inducing drugs. Altogether, these analyses demonstrate cSurvival’s ability to support biomarker prognosis and interaction analysis via gene- and GS-level approaches and to integrate clinical and experimental biomedical studies.
Do deep learning models make a difference in the identification of antimicrobial peptides?
Authors: García-Jacas César R , Pinacho-Castellanos Sergio A , García-González Luis A , Brizuela Carlos A
Published: February 2022

In the last few decades, antimicrobial peptides (AMPs) have been explored as an alternative to classical antibiotics, which in turn motivated the development of machine learning models to predict antimicrobial activities in peptides. The first generation of these predictors was filled with what is now known as shallow learning-based models. These models require the computation and selection of molecular descriptors to characterize each peptide sequence and train the models. The second generation, known as deep learning-based models, which no longer requires the explicit computation and selection of those descriptors, started to be used in the prediction task of AMPs just four years ago. The superior performance claimed by deep models regarding shallow models has created a prevalent inertia to using deep learning to identify AMPs. However, methodological flaws and/or modeling biases in the building of deep models do not support such superiority. Here, we analyze the main pitfalls that led to establish biased conclusions on the leading performance of deep models. Also, we analyze whether deep models truly contribute to achieve better predictions than shallow models by performing fair studies on different state-of-the-art benchmarking datasets. The experiments reveal that deep models do not outperform shallow models in the classification of AMPs, and that both types of models codify similar chemical information since their predictions are highly similar. Thus, according to the currently available datasets, we conclude that the use of deep learning could not be the most suitable approach to develop models to identify AMPs, mainly because shallow models achieve comparable-to-superior performances and are simpler (Ockham’s razor principle). Even so, we suggest the use of deep learning only when its capabilities lead to obtaining significantly better performance gains worth the additional computational cost.