bluuebunny/crossref_metadata_2025 huggingface dataset:
Semi-supervised Deep Closest Point Method for Point Cloud Registration
Authors: Wu Yong , Shen Lingfeng , Li Ping
Published: February 2022

Point cloud registration is one of the key issues in fields that need 3D scenes with global vision, including 3D scene reconstruction in robot technology, high-accuracy 3D map reconstruction in automatic driving, 3D reconstruction of real-time monitoring underground tunnel. Recently, point cloud registration methods based on deep neural networks have made great progress by learning from a large amount of well-aligned point cloud pairs. However, these large-scale labelled samples are usually difficult to obtain in the real-world applications such as visual simultaneous location and mapping of mobile robots which obtains unlabelled scene images in real time. To solve this problem, we propose a deep point cloud registration method based on the semi-supervised learning. Specifically, for the problem of less supervised data, we use Iterative Closest Point (ICP) algorithm to generate pseudo labels for the point cloud samples with unknown rigid transformations, effectively increasing the number of supervised data; in order to amend the pseudo labels, we design an alternating optimization algorithm to jointly learn the pseudo labels and deep point cloud registration model. The deep point cloud registration model and ICP are used alternately to continuously improve the precision of pseudo labels and the performance of the deep model. Experimental results show that this method can still obtain a reliable point cloud registration model even when just observing a small amount of point cloud samples with ground truth labels.
Modeling and Simulation of Flexible Spacecrafts Based on Dual Quaternions
Authors: Zhang Wenzheng , Zhang Jiawei , Zhang Canheng , Chen Dianyin , He Yijie
Published: February 2022

This paper proposes a description of flexible spacecrafts’ motion and rotation uniformly with dual quaternions. The dynamic equations are derived by using Lagrange equations. Recursive Newton-Euler (RNE) method is introduced to calculate the nonlinear terms in the equations. The simulation results demonstrate the coupling effects during the actuating speed switch of the solar panel.
Research on A Low Computational Cost Vision-aided Inertial Navigation Method for Precision Landing on Asteroid
Authors: Lin Boyu , Cui Hutao
Published: February 2022

Asteroid exploration missions demand more autonomous navigation system that are precise and in real-time. Visual-aided inertial navigation systems combine the advantages of autonomy of inertial navigation systems and accuracy of visual navigation systems. However, both increase the computational burden for the information processing. With the goal of decreasing the computational load, this paper proposes a low cost vision-aided inertial navigation method which can calculate increment position, velocity and attitude directly in inertial frame by pre-integrating IMU measurements. For visual information, specific combinations of the features are selected according to information entropy theory to reduce computation. Simulation results show that the standard deviations of the spacecraft’s estimated position error is less than 1 m, the velocity less than 0.002 m/s and the attitude angle less than 0.005 deg, compatible with precision landing on asteroids.
Cycle-slip Detection Method Considering Clock Jump for GNSS Raw Observation of Smartphones
Authors: Liu Xing , Li Chunhua , Li Cailing , Gao Pan
Published: February 2022

Using low-cost sensors to achieve high-precision positioning has been a hot research topic in autonomous driving technology, and smartphones not only have the advantages of low cost and easy installation, but also can provide high-precision navigation and positioning services. In this paper, we focus on the cycle-slip detection method based on the original Global Navigation Satellite System (GNSS) observations of smartphones. In response to the problem that the phase value of smartphone GNSS has serious cycle slip and the existence of frequent clock jumps will affect the cycle slip detection, a cycle slip detection method that integrates clock jumps and single and dual frequency observations is proposed. The experiments show that the method can effectively detect the continuous and as small as 0.5 week of cycle slip for the phase observations with the sampling rate of 1 Hz. The root mean square error (RMSE) in the L1 and L5 phase residuals after the cycle slip repair is 7.0 mm and 8.3 mm, respectively, with an accuracy improvement of 18.16% and 9.62%, which indicates that this method is effective and feasible.
Improving the Transferability of Adversarial Examples by Using Generative Adversarial Networks and Data Enhancement
Authors: Xie Pengfei , Shi Shuhao , Xie Wanjing , Qin Ruoxi , Hai Jinjin , Wang Linyuan , Chen Jian , Hu Guoen , Yan Bin
Published: February 2022

Deep neural networks (DNNs) can be attacked by adversarial examples that are undetectable by humans. Generation-based approaches have recently gained popularity because they directly translate the input distribution to the distribution of adversarial instances, making them more effective and efficient. However, existing techniques are susceptible to overfitting on the substitute model, limiting the transferability of adversarial examples. In this paper, we introduce data augmentation into AdvGAN, called AdvGAN-DE, which can dramatically improve the transferability of adversarial examples. Experiments demonstrate that we enhance the success rate by 31.09% for defense models and 10.23% for typically trained models on average when compared to AdvGAN.
Research on Mosaic Method of UAV Low-altitude Remote Sensing Image based on SIFT and SURF
Authors: Zhu Hang , Jiang Yu , Zhang Cui , Liu Shu
Published: February 2022

Unmanned aerial vehicle (UAV) low-altitude remote sensing image stitching is a new technology to promptly grasp the lodging situation of rice. The effect of image stitching depends on different application scenarios, so that it is necessary to explore low-altitude remote sensing image stitching algorithm suitable for rice lodging monitoring. The research adopts SIFT (Scale invariant feature transform) and SURF (Speeded up robust features) feature detection algorithms to conduct mosaic experiments based on drone images of a rice field in Dehui City, Jilin Province. The results demonstrate that the image stitching technology based on surf algorithm possesses better real-time performance, and the panorama obtained can well reflect the lodging condition of rice field. This research can provide technical reference for the actual lodging monitoring of rice field.
YOLOX-Mobile: A Target Detection Algorithm More Suitable for Mobile Devices
Authors: Ma Songzhe , Lu Huimin , Wang Yifan , Xue Han
Published: February 2022

With the continuous development of network, the increasing popularity of embedded technology, and the continuous improvement of mobile terminal computing ability, how to reduce the size of the model without affecting the accuracy as much as possible, and how to deploy the algorithm in embedded devices has become the current research hotspot. In this paper, we propose a new target detection algorithm called YOLOX-Mobile. Firstly, in order to further reduce the power consumption of embedded devices while maintaining the performance, we replaced the SiLU activation function in the original YOLOX with Mish activation function. Because SiLU activation functions occupy a certain amount of computing and storage resources, and the cost of calculating the Sigmoid type functions on mobile devices is much higher. Therefore, we use Mish functions that are smoother, non-monotonic, unbounded upper and lower bounds are used as activation functions to better meet the low power requirements of embedded devices. Secondly, we used Focal Loss as the Loss function of obj_output to achieve the balance of positive and negative samples, as well as hard-to-classify and easy-to-classify samples. Thirdly, we introduce the Involution operator and use it as the convolution kernel of 3×3. We validated the proposed algorithm on public dataset VOC2012 against the current mainstream YOLOX, YOLOX-M, and YOLOX-S algorithms. The mAP of our proposed algorithm is 78.22% and the detection speed is 55.26 FPS. Compared with the original YOlOX, YOLOX-M and YOLOX-S algorithms, the average FPS is improved by 1.99% and 4.13 FPS, and the average Params Size is reduced by 28.80%. Experimental results show that our proposed algorithm improves the accuracy and speed of detection on top of greatly reducing the network parameters and computation, making it a more suitable target detection algorithm for mobile devices.
Path Planning of Cleaning Robot for Cement Silo Inner Wall
Authors: Tian Jianying , Liu Shuchen , Sun Liwen , Jia Lianqin
Published: February 2022

This paper studies the design and control method of the cement silo wall cleaning robot system, analyzes the overall control requirements of the robot, and uses PLC as the control processor to design a cement silo wall cleaning robot control system, including action motor, pneumatic components and other components selection, which designs and completes working motors and sensors wiring schematic diagram. According to PLC input and output wiring diagram design, timing analysis and the human-computer interface of the upper computer, the design of the inner wall cleaning robot control system was completed, which can adapt to different circumstances, and has the characteristics of automatic cleaning, continuous operation and efficient completion. On this basis, aiming at the functional needs of the robot cleaning the inner wall of cement silo, cleaning path planning and control has been designed with better conditions.
Calibration of RGB-D Camera Using Depth Correction Model
Authors: Zhou Yang , Chen Danqing , Wu Jun , Huang Mingyi , Weng Yubin
Published: February 2022

This paper proposes a calibration method of RGB-D camera, especially its depth camera. First, use a checkerboard calibration board under auxiliary Infrared light source to collect calibration images. Then, the internal and external parameters of the depth camera are calculated by Zhang’s calibration method, which improves the accuracy of the internal parameter. Next, the depth correction model is proposed to directly calibrate the distortion of the depth image, which is more intuitive and faster than the disparity distortion correction model. This method is simple, high-precision, and suitable for most depth cameras.
Survey of Object Tracking Algorithm Based on Siamese Network
Authors: Zuo Mengle , Zhu Xuyang , Chen Yongchao , Yu Junyang
Published: February 2022

The network model using deep learning have been widely used in the sphereof visual object tracking for the past few years. The Siamese network can utilize the model based on deep learning to achieve a balance between the tracking accuracy and speed in the visual object tracking. This work mainly introduces the development process of the visual target tracking field and traditional target tracking algorithms. It focuses on the Siamese network structure and the improved the Siamese algorithm, and compares tracking results of related algorithms. Aiming at the deficiencies of existing Siamese object tracking algorithms, the future development trend is prospected.
A Review of Image Inpainting Automation Based on Deep Learning
Authors: Sun Qi , Zhai Rui , Zuo Fang , Zhong Yuhao , Zhang Yutao
Published: February 2022

The purpose of image inpainting is to automatically repair damaged areas using relevant information from preserved areas. Recent years, with the advancement of deep learning, significantly improved performance of image drawing has been achieved. In this paper, we are committed to reviewing the key techniques for automating image inpainting research. The article briefly describes conventional methods while focusing on deep learning-based inpainting methods, including model classification, strengths and weaknesses, range of usage, and performance comparison. Finally, the current issues and tendencies of image inpainting automation are discussed and predicted.
Adaptive Control Strategy with Threshold of Virtual Inertia and Virtual Damping for Virtual Synchronous Generator
Authors: Deng Wei , Zhong Jianwei , Huang Ming , Zhang Jixue , Zhang Zhekai
Published: February 2022

Virtual Synchronous Generator (VSG) control strategy can provide inertia and damping support for the power system by simulating the operating characteristics of synchronous generator, and improve the frequency response of the power system. First, analyse the influence of various parameters on the output characteristics of the system by establishing a mathematical model of VSG. Secondly, to fully utilize the flexible and controllable characteristics of VSG control strategy, the basic principle of VSG parameter adaptation is obtained through the analysis of the angular frequency oscillation characteristics of synchronous generators, and an adaptive control strategy of virtual inertia and virtual damping parameter is proposed, which can better track changes in frequency, and set the action threshold for adaptive control. Finally, compare the frequency and active power response between the traditional VSG control strategy and the adaptive one and verify the effectiveness of the proposed control strategy.
Application of Deep Learning in Wireless Networks for Channel Estimation: A Survey
Authors: Peng Guangyao , Zhu Zhengfa , Liang Yuanhai
Published: February 2022

The quality of channel estimation (CE) is critical to the performence of wireless communication systems. In addition to the conventional model based CE methods appeared in the past few decades, deep learning (DL) based CE methods, which is introduced to learn the statistical characteristics of wireless channels, has emerged as a promising methods in recently years. In this survey, different DL models applied to solve CE problems are discussed firstly, then DL-based CE problems in different wireless communication systems are investigated.
An Improved Algorithm for Target Detection in Low Light Conditions
Authors: Yin Dan , Tang Wengsheng , Chen Peng , Yang Bo
Published: February 2022

The accuracy of pig target detection is not ideal due to insufficient light in a real pig farm environment. An improved enhanced network helps to increase the accuracy for pig target detection. The ResNet-Attention-RetinexNet algorithm (RA-RetinexNet) is proposed to solve the problems that YOLO V4 has low accuracy in pig image detection under low light and Mosaic data enhancement method cannot improve the brightness of low light image. In this model, we build residual connection and add attention mechanism, decreasing data loss of RetinexNet and enhancing the brightness information of images. The experimental results show that the model achieves better performance for pig target detection under low light.
Wheel’s Slip Ratio and Sinkage Estimation for Planetary Rovers Moving on Deformable Terrain
Authors: Yang Huaiguang , Ding Liang , Gao Haibo , Deng Zongquan
Published: February 2022

Planetary rover plays a significant role in the lunar and Mars exploration missions and wheel’s move status such as sinkage and slip ratio are the critical Factor to estimate the moving ability of the Mars rover. A method is proposed to estimate wheel’s sinkage and skid angle based on terramechanic model and estimate slip ratio based on rover’s dynamic model using Extended Kalman Filter algorithm. The proposed method can avoid the errors caused by Visual Odometry and improves the estimation accuracy of rover state through the fusion with other sensors’ data. The effectiveness of the estimation method for planetary rovers is verified by comparing with the results obtained from dynamic simulation of Mars rovers.
Survey of Image Augmentation Based on Generative Adversarial Network
Authors: Yue Fei , Zhang Chao , Yuan MingYang , Xu Chen , Song YaLin
Published: February 2022

On the visual side of computer science, image data is very important in the training of neural network models. Sufficient training data can alleviate the over-fitting problem of the model during training and help the model obtain the optimal solution. However, in many computer vision assignments, it is not easy and costly to obtain sufficient training samples. Therefore, image augmentation has become a commonly used method to increase training samples. Generative Adversarial Network (GAN) is a generative method of machine learning that can generate realistic images and provide a new solution for image augmentation. This article first introduces image augmentation and its commonly used four types of methods. Secondly, the basic principles of GAN and its direct and integrated methods in image augmentation are introduced, and the typical methods used to calculate whether the images from the networks meets the requirements; then the research status of GAN in image augmentation is analyzed. Finally, the problems and development trends of GAN model in image augmentation are summarized and prospected.
Adaptive Particle Filter with Abnormal Detection for Wearable Indoor Pedestrian Navigation
Authors: Chen Ying , Chen Liang , Huang Lixiong , Liu Xiaoyan , Ma Zhen , Pan Yinghua
Published: February 2022

Currently, indoor location-based services (ILBSs) have increasing requirements in people’s daily life. In the meanwhile, the wearable devices are becoming more popular. In this paper, we studied a wearable system for indoor localization mainly based on INS/UWB. In order to achieve high-precision, stable, and continuous positioning, a sensor fusion method with anomaly detection is proposed. In the method, the sensor fusion method is derived from Bayesian estimation and a particle filter is developed to solve the nonlinearity problem and non-Gaussian errors for indoor positioning. In addition, the anomaly detection eliminates effects of NLoS and multipath effects significantly with the Mahalanobis distance. Two field experiments are conducted, and the results demonstrate that the 90% error of the proposed adaptive particle filter is 0.53 m, which is a 40% decrease compared with the PDR-only and UWB-only and classic PF, indicating better robustness and stability.
Application of Particle Swarm Optimization Algorithm in Photovoltaic Constant Pressure Water Supply Electrical Control System
Authors: Zhu Xianping , Li Shaowu , Cheng Hang , Fan Zhipeng
Published: February 2022

The traditional constant water supply system has problems, low operation efficiency, and the strong nonlinear and large time delay of the control system. It did not meet the needs of water supply in remote and desert areas. To solve these problems, the PV constant voltage water supply system is designed to optimize the Proportion Integration Differentiation (PID) controller method based on the particle swarm optimization (PSO) algorithm. The programmable logic controller (PLC) and frequency converter are used as the core control mechanism of the system, the water pump motor as an actuator, the pressure sensor and the level sensor be used as a feedback mechanism, Photovoltaic power generation equipment is used in power supply agencies. In this paper, the objective utilizes the PSO algorithm to optimize the parameters of the PID controller, and the principle and control process of the PSO-PID algorithm are analyzed. Simulation results by MATLAB verify that the PSO-PID algorithm has better dynamic and steady-state performance than the traditional PID algorithm. Finally, the practicability and effectiveness of the water supply system are proved by the PLC of the OMRON-CP1H series and the touch screen of the WEINVIEW series.
Simulated Troubleshooting of Civil Aircraft Based on Operation and Maintenance Database
Authors: Lyu Huizi , Ma Zhenyuan
Published: February 2022

The characteristics of aircraft maintenance training are that the teaching hardware resources are few, the maintenance training items are many, and the students are difficult to master. To solve this problem, we developed a simulator for aircraft maintenance training by studying the key technologies of maintenance training devices (MTD). Based on the classic control theory to design the autopilot control law, using big data from aircraft operation and scheduled maintenance to achieve high simulation MTD, to solve the difference between traditional MTD teaching and actual aircraft maintenance makes CMTD more effective and vital.
Simulation of Robotic Arm Grasping Control Based on Proximal Policy Optimization Algorithm
Authors: Zhang Zhizhuo , Zheng Change
Published: February 2022

There are many kinds of inverse kinematics solutions for robots. Deep reinforcement learning can make the robot spend a short time to find the optimal inverse kinematics solution. Aiming at the problem of sparse rewards in the process of deep reinforcement learning, this paper proposes an improved PPO algorithm. Firstly, built a simulation environment for the operation of the robotic arm. Secondly, use a convolutional neural network to process the data read by the camera of the robotic arm, obtaining a network about Actor and Critic. Thirdly, based on the principle of inverse kinematics of the robotic arm and the reward mechanism in deep reinforcement learning, design a hierarchical reward function containing motion accuracy to promote the convergence of the PPO algorithm. Finally, compare the improved PPO algorithm with the traditional PPO algorithm. The results show that the improved PPO algorithm has improved both the convergence speed and the operating accuracy.
Research on Grasping Strategy Based on Residual Network
Authors: Li Shaobo , Bai Qiang , Yang Jing , Yu Liya
Published: February 2022

At present, the research on rectangular grasp strategy is generally based on object detection algorithm, which limits the improvement of model accuracy and generalization performance. This paper studies the semantic segmentation model based on residual network, and uses it to generate grasp strategies. The improved algorithm model not only achieves excellent rectangular grasping strategy prediction, but also has good generalization performance.
A Transformer Fault Diagnosis Based on Improved Squirrel Search Algorithm and Support Vector Machine
Authors: Wu Yuhan , Sun Xianbo , Zhang Yi , Zhong Xianjing , Cheng Lei
Published: February 2022

An improved Squirrel Search Algorithm (ISSA) is proposed to overcome the shortcoming of Support Vector Machine (SVM) parameters is difficult to select. Firstly, an adaptive probability formula is used to balance the exploration and foraging ability of SSA, so as to improve the optimization performance of SSA. In order to test the optimization performance of ISSA, two test functions are used. And results show that ISSA has better optimization performance than SSA. Finally, based on ISSA-SVM and SVM, and 3-fold cross-Validation is used, the fault diagnosis of DGA data is carried out. The diagnostic results show that ISSA-SVM has the highest diagnosis performance.
Health Condition Assessment of Braking System Based on Multi-agent Federated Learning
Authors: Gao Dianzhu , Peng Jun , Rong Jieqi , Yang Yingze , Zhang Xiaoyong , Zhao Qin , Liu Weirong , Huang Zhiwu
Published: February 2022

The brake system has the characteristics of multi-component, multi working conditions and complex degradation process, which brings great challenges to its health condition assessment. As it is difficult for a single brake agent to make a comprehensive and accurate health condition assessment, a health condition assessment model based on multi-agent federated learning is proposed in this paper. The different agents train their brake health data under different working conditions and states, which ensures the accuracy of health condition assessment and the safety of data of each agent. Aiming at the problem that it is difficult to determine the credibility of agent data in the process of federated learning, a credibility of agent data scheme based on evidence theory is proposed, which not only reduces the cost of calculation and communication, but also further improves the accuracy of health condition assessment. Simulation results show that the scheme not only has higher prediction accuracy, but also can ensure the security of agent data compared with the conventional centralized training scheme.
Intelligent Scheduling System for Production Line Automatic Matching Based on DSSM-XGBoost
Authors: Yang Shuaihu , Feng Min , Guan Danping
Published: February 2022

Intelligent production scheduling is an important part of intelligent production, and its production time and intensity are reasonably arranged, which can fully improve production efficiency. This article provides an intelligent model for engine manufacturing and scheduling. After the model decomposes the order into processes, the DSSM algorithm solves the semantic similarity of the text in the production content of the process, and generates a semantic vector to convert unstructured data into structured data. Based on XGBoost algorithm, the required production line is marked to establish the association between the production content and the production line, and realize automatic matching. After classification, the average working hours and other information are estimated according to the established logic. Finally, schedule production based on the principle of maximum utilization of the production line. Through testing and evaluation, the scheduling results of this smart scheduling model can be completed in just a few seconds, which saves time and costs in the scheduling process.
Short-term Impact Load Forecasting Model Based on Multi-objective Antlion Optimization Algorithm
Authors: Chen Jing , Tan Aiguo , Zhong Jianwei , Han Mingchong
Published: February 2022

In this study, a novel multi-objective antlion optimization (MOALO) algorithm-based impact load prediction model is proposed to address forecasting problem in the area with a lot of impact load. To reduce the impact of glitch in impact load data, ensemble empirical mode decomposition(EEMD) is applied to decompose the primitive load data into a set of sub-layers. Then, a novel MOALO-based extreme learning machine (ELM) forecasting model is put forward to make short-term prediction by using the decomposed sub-series. Finally, superimpose the prediction results. According to case study, the proposed EEMD-MOALO-ELM model has the best prediction accuracy and stability.
The TaintDroid Based Honeypot Monitoring System for Embedded Device
Authors: Cheng Kai , Wu Zhan , Li Dongkun , Li Xin , Ren Mu
Published: February 2022

Honeypot is a proactive defense technology introduced by defenders. Through the honeypots, defenders can deceive attackers to illegally take advantage of the honeypots and capture and analyze the attack behaviors to understand the attack tools and methods. To build honeypots, defenders first imitate vulnerable systems to entice the attacker to attack, then deploy monitoring systems that is responsible for monitoring and recording the attacker’s behavior. It is of concern that monitoring system is the key to determine the performance of honeypots, because obtaining attackers’ behavior is the main purpose of deploying honeypots, and monitoring system’s performance determines whether attackers’ behavior can be accurately and comprehensively recorded. In this paper, we introduce a novel TaintDroid based honeypot monitoring system for embedded device. This system uses TaintDroid to mark the attackers who hack into the honeypot, monitors the behavior of the marked attackers and then records. Moreover, we tested the feasibility of this system by building a monitoring system based on TaintDroid.
Accelerator and detector control for the EIC with machine learning
Authors: Britton T. , Nachman B.
Published: February 2022

This document provides a brief overview of the Accelerator and Detector Control session at the AI4EIC workshop in September 2021.
Detection of runaway electrons at the COMPASS tokamak using a Timepix3-based semiconductor detector
Authors: Kulkov S. , Marcisovsky M. , Svihra P. , Tunkl M. , van Beuzekom M. , Caloud J. , Cerovsky J. , Ficker O. , Macusova E. , Mlynar J. , Weinzettl V. , Svoboda V.
Published: February 2022

Runaway electrons are considered dangerous for the integrity
of tokamak vacuum vessels. To secure the success of the future
tokamak-based machines, reliable diagnostics and mitigation
strategies are necessary. The COMPASS tokamak supported the research
of runaway electron physics via regular experimental
campaigns. During the last two experimental campaigns dedicated to
runaway electrons, a semiconductor detector with a Timepix3 readout
chip, Si sensor, and the SPIDR readout system was tested. Time
evolution signals, energy measurements, and sensor snapshots
collected with the Timepix3-based detector are presented.
Determination of ionizing threshold of trimethylamine ions with a high resolution RGA mass spectrometer
Authors: Trindade A.M.F. , Escada J. , Rajado Silva M. , Borges F.I.G.M. , Santos F.P.
Published: February 2022

In this work, a mass spectrometer residual gas analyzer
(RGA) was used to determine the ionization thresholds (ionization
energy and appearance energy) in trimethylamine (TMA,
(CH3)3N), as well as the relative abundance of the ions
produced by electron impact in the 7.5–70 eV electron energy
range. The ten most intense peaks in the RGA mass spectra,
corresponding to the most abundant ions formed by electron impact at
70 eV in TMA, were analyzed. The ionization energy for TMA, as
assessed by this experiment, is 7.9 ± 0.2 eV and it is the
threshold of formation of the C3H9N+ ion. This ion
species is the most abundant one below 18 eV electron impact
energy, being surpassed by the C3H8N+ ion above this
energy.

In order to assess the method used, methane (CH4), whose
ionization properties are well established, was previously
studied. The agreement between the results obtained for CH4 and
those found in the literature provided the validation of the
experimental method used in this work.
Research on proton beam spot imaging based on pixelated gamma detector
Authors: Sun Y.K. , Jing H.T. , Tian B.B. , Gao X.L. , Yang X.Y.
Published: February 2022

The secondary particles from the spallation target of the
China Spallation Neutron Source are mainly gammas and neutrons,
which are related to the incident proton. The reconstruction of the
proton beam spot could be implemented based on the distribution of
the positions of secondary gammas or neutrons. The methods of
pinhole imaging and Compton imaging are developed by measuring the
position distribution of gammas based on the pixelated detector. The
secondary gammas could be detected by the pixelated gamma detector
directly. The neutron can be identified by detecting the
characteristic (478 keV) γ-rays from the
10B(n, α) reactions. In order to detect secondary
neutrons, a layer of 10B converter is added before the
pixelated gamma detector. The pixelated gamma detector is sensitive
to the characteristic (478 keV) γ-rays and then the neutron
imaging could be achieved based on measuring the position
distribution of the characteristic (478 keV) γ-rays.
A detector-emulation method for realistic readout-electronics tests. A case study of VMM3a ASIC for sTGC detector.
Authors: Moleri L. , Lupu N. , Vdovin A. , Kajomovitz E.
Published: February 2022

We present a detector-emulation method for developing,
optimizing, and testing front-end electronics for particle detectors
under realistic experimental conditions. The method is capable of
reproducing bipolar signals shape and amplitude, rates, pileup, and
radiation backgrounds. By controlling the different components of
the experimental environment, the method allows for the assessment
of their individual and combined effects, which is difficult to
achieve in test beam or experiment environment.

The technique emulates the output current of AC coupled detector
terminals for an experimental environment of interest, by combining
a sequence of recorded or simulated signal waveforms. Voltage
waveforms produced with off the shelf arbitrary waveform generators
are converted to current by a simple circuit, which also accounts
for the characteristic capacitance of the detector, and injected
into the front-end electronics. We demonstrate the technique by
applying it in the optimization and characterization of the
front-end electronics of the sTGC detector for the
ATLAS experiment.
Solar park management and design to boost bumble bee populations
Authors: Blaydes H , Gardner E , Whyatt J D , Potts S G , Armstrong A
Published: February 2022

Solar photovoltaics is projected to become the dominant renewable, with much capacity being installed as ground-mounted solar parks. Land use change for solar can affect ecosystems across spatial scales and solar parks offer a unique opportunity for ecological enhancement. One compelling potential benefit beginning to be deployed by the solar industry is management for insect pollinators. Specifically, solar parks can provide refuge for pollinators through the provision of suitable habitat, potentially contributing to halting and reversing widespread declines recorded in some pollinator groups. There is scope to both manage and design solar parks for pollinators, but understanding is limited. Using a geographic information system and a process-based pollinator model, we explore how solar park management, size, shape and landscape context might impact ground-nesting bumble bee density, nest density and nest productivity inside existing solar parks and surrounding landscapes in the UK. We show that bumble bee density and nest density is driven by solar park management, with twice as many bumble bees foraging and nesting inside solar parks managed as wildflower meadows, compared to those with only wildflower margins. In comparison, solar park size, shape and landscape context have a smaller impact on bumble bee response inside solar parks. However, large, elongated resource-rich solar parks were most effective at increasing bumble bee density in surrounding landscapes, with implications for local crop pollination. Specifically, there were double the number of foraging bumble bees surrounding large solar parks managed as meadows compared to smaller parks managed as turf grass. If designed and managed optimally, solar parks therefore have the potential to boost local bumble bee density and potentially pollination services to adjacent crops. Our results demonstrate how incorporating biodiversity into solar park management and design decisions could benefit groups such as pollinators and contribute to the wider environmental sustainability of solar parks.
In vitro and in vivo characterization of a novel tricalcium silicate-based ink for bone regeneration using laser-assisted bioprinting
Authors: Touya Nicolas , Devun Mathilde , Handschin Charles , Casenave Sophia , Ahmed Omar Naïma , Gaubert Alexandra , Dusserre Nathalie , De Oliveira Hugo , Kérourédan Olivia , Devillard Raphaël
Published: February 2022

Grafts aside, current strategies employed to overcome bone loss still fail to reproduce native tissue physiology. Among the emerging bioprinting strategies, laser-assisted bioprinting (LAB) offers very high resolution, allowing designing micrometric patterns in a contactless manner, providing a reproducible tool to test ink formulation. To this date, no LAB associated ink succeeded to provide a reproducible ad integrum bone regeneration on a murine calvaria critical size defect model. Using the Conformité Européenne (CE) approved BioRoot RCS® as a mineral addition to a collagen-enriched ink compatible with LAB, the present study describes the process of the development of a solidifying tricalcium silicate-based ink as a new bone repair promoting substrates in a LAB model. This ink formulation was mechanically characterized by rheology to adjust it for LAB. Printed aside stromal cells from apical papilla (SCAPs), this ink demonstrated a great cytocompatibility, with significant in vitro positive impact upon cell motility, and an early osteogenic differentiation response in the absence of another stimulus. Results indicated that the in vivo application of this new ink formulation to regenerate critical size bone defect tends to promote the formation of bone volume fraction without affecting the vascularization of the neo-formed tissue. The use of LAB techniques with this ink failed to demonstrate a complete bone repair, whether SCAPs were printed or not of at its direct proximity. The relevance of the properties of this specific ink formulation would therefore rely on the quantity applied in situ as a defect filler rather than its cell modulation properties observed in vitro. For the first time, a tricalcium silicate-based printed ink, based on rheological analysis, was characterized in vitro and in vivo, giving valuable information to reach complete bone regeneration through formulation updates. This LAB-based process could be generalized to normalize the characterization of candidate ink for bone regeneration.
All-in-one biofabrication and loading of recombinant vaults in human cells
Authors: Martín Fernando , Carreño Aida , Mendoza Rosa , Caruana Pablo , Rodriguez Francisco , Bravo Marlon , Benito Antoni , Ferrer-Miralles Neus , Céspedes M Virtudes , Corchero José Luis
Published: February 2022

One of the most promising approaches in the drug delivery field is the use of naturally occurring self-assembling protein nanoparticles, such as virus-like particles, bacterial microcompartments or vault ribonucleoprotein particles as drug delivery systems (DDSs). Among them, eukaryotic vaults show a promising future due to their structural features, in vitro stability and non-immunogenicity. Recombinant vaults are routinely produced in insect cells and purified through several ultracentrifugations, both tedious and time-consuming processes. As an alternative, this work proposes a new approach and protocols for the production of recombinant vaults in human cells by transient gene expression of a His-tagged version of the major vault protein (MVP-H6), the development of new affinity-based purification processes for such recombinant vaults, and the all-in-one biofabrication and encapsulation of a cargo recombinant protein within such vaults by their co-expression in human cells. Protocols proposed here allow the easy and straightforward biofabrication and purification of engineered vaults loaded with virtually any INT-tagged cargo protein, in very short times, paving the way to faster and easier engineering and production of better and more efficient DDS.
Silver nanoparticles synthesized via green chemistry with the aid of Delphinium denudatum Wall. root extract modulated gentamicin nephrotoxicity activity with respect to oxidative potential
Authors: Siddique N A , Al-Samman A M M A
Published: February 2022

Inpresent study, we explored the nephroprotective potential of Delphinium denudatum derived silver nanoparticles (DdrAgNPs). Characterisation of DdrAgNPs were carried out using UV–vis spectroscopy, X-ray diffraction (XRD), scanning electron microscope analysis (SEM), high resolution transmission electron microscopy (HR-TEM), energy dispersive x-rays (EDX) and Fourier transform infrared spectroscopy (FTIR). The biosynthesised DdrAgNPs displayed maximum absorbance at 415 nm, were polyspread in nature, sphere-shaped, and had a particle size ranging from 10 to 63 nm and the average diameter was ∼28.2 nm. Because nephroprotection could not be obtained solely through one mechanism, a considerable relationship of DdrAgNPs has been established between antioxidant potential and polyhydroxy herbal contents, suggesting that these blends may be complementary drug candidates to patients on gentamicin (GM) therapy. Biological performance of DdrAgNPs was assessed by estimating the level of renal biomarkers (blood urea, uric acid, creatinine) via antioxidants potential (GSH, CATALASE, SOD and TBARS) in experimental rats against gentamicin and corroborated with histopathological examination of renal tissue. In all, the proposed results indicate that DdrAgNPs are a potent nephroprotective agent, which can be utilised as a therapeutic adjunct to prevent gentamicin-nephrotoxicity. It is first time nephroprotection of silver nanoparticles has been reported using Delphinium denudatum as a plant extract.
Large deflection deformation detection method of steel strip based on ultra-weak fiber bragg grating
Authors: Luo Zhihui , Wang Yemeng , Cheng Wensheng , He Huiling
Published: February 2022

Large deflection deformation of a steel strip is an important part of structural deformation detection. Deformation detection has been achieved using ultra-weak fiber Bragg gratings (FBGs), and the deformation reconstruction inversion algorithm of the tangent recursive algorithm has been analyzed theoretically. The strain distribution of the steel strip (2.5 m × 0.050 m × 0.002 m) with large deflection under different loads was obtained by pasting eight ultra-weak FBG sensors with spacing of 0.3 m on the surface of the strip. The steel strip deformation was inversed by tangent recursive algorithm, and deformation error under loading condition was analyzed by finite element simulation. The inversion value is compared with the theoretical value and the measured value, which proves the effectiveness and accuracy of the ultra weak FBG method proposed in this paper. Results show that the large deflection deformation of a steel strip under different loads can be reconstructed using the tangent recursive algorithm, and the relative errors of the coordinates of the sampling points are less than 6.21%. This paper provides a reference for the detection of structural deformation.
Stepping up to the thermogradient plate: a data framework for predicting seed germination under climate change
Authors: Collette Justin C , Sommerville Karen D , Lyons Mitchell B , Offord Catherine A , Errington Graeme , Newby Zoe-Joy , von Richter Lotte , Emery Nathan J
Published: February 2022

Background and Aims

Seed germination is strongly influenced by environmental temperatures. With global temperatures predicted to rise, the timing of germination for thousands of plant species could change, leading to potential decreases in fitness and ecosystem-wide impacts. The thermogradient plate (TGP) is a powerful but underutilized research tool that tests germination under a broad range of constant and alternating temperatures, giving researchers the ability to predict germination characteristics using current and future climates. Previously, limitations surrounding experimental design and data analysis methods have discouraged its use in seed biology research.

Methods

Here, we have developed a freely available R script that uses TGP data to analyse seed germination responses to temperature. We illustrate this analysis framework using three example species: Wollemia nobilis, Callitris baileyi and Alectryon subdentatus. The script generates >40 germination indices including germination rates and final germination across each cell of the TGP. These indices are then used to populate generalized additive models and predict germination under current and future monthly maximum and minimum temperatures anywhere on the globe.

Key Results

In our study species, modelled data were highly correlated with observed data, allowing confident predictions of monthly germination patterns for current and future climates. Wollemia nobilis germinated across a broad range of temperatures and was relatively unaffected by predicted future temperatures. In contrast, C. baileyi and A. subdentatus showed strong seasonal temperature responses, and the timing for peak germination was predicted to shift seasonally under future temperatures.

Conclusions

Our experimental workflow is a leap forward in the analysis of TGP experiments, increasing its many potential benefits, thereby improving research predictions and providing substantial information to inform management and conservation of plant species globally.
The Microbiota–Gut–Brain Axis in Alzheimer’s Disease: A Review of Taxonomic Alterations and Potential Avenues for Interventions
Authors: Murray Emily R , Kemp Mylon , Nguyen Tanya T
Published: February 2022

Objective

The gut microbiome is a complex community of microorganisms that inhabit the gastrointestinal tract. The microbiota–gut–brain axis encompasses a bidirectional communication system that allows the gut to influence the brain via neural, endocrine, immune, and metabolic signaling. Differences in the gut microbiome have been associated with psychiatric and neurological disorders, including Alzheimer’s Disease (ad). Understanding these ad-associated alterations may offer novel insight into the pathology and treatment of ad.

Method

We conducted a narrative review of clinical studies investigating the gut microbiome in ad, organizing the results by phyla to understand the biological contributions of the gut microbial community to ad pathology and clinical features. We also reviewed randomized clinical trials of interventions targeting the microbiome to ameliorate ad symptoms and biomarkers.

Results

Alpha diversity is reduced in patients with ad. Within Firmicutes, taxa that produce beneficial metabolites are reduced in ad, including Clostridiaceae, Lachnospiraceae, Ruminococcus, and Eubacterium. Within Bacteroidetes, findings were mixed, with studies showing either reduced or increased abundance of Bacteroides in mild cognitive impairment or ad patients. Proteobacteria that produce toxins tend to be increased in ad patients, including Escherichia/Shigella. A Mediterranean-ketogenic dietary intervention significantly increased beneficial short-chain fatty acids and taxa that were inversely correlated with changes in ad pathological markers. Probiotic supplementation with Lactobacillus spp. and Bifidobacterium spp. improved cognitive function and reduced inflammatory and metabolic markers in patients with ad.

Conclusions

The gut microbiome may provide insight into ad pathology and be a novel target for intervention. Potential therapeutics include probiotics and dietary intervention.
AFSE: towards improving model generalization of deep graph learning of ligand bioactivities targeting GPCR proteins
Authors: Yin Yueming , Hu Haifeng , Yang Zhen , Jiang Feihu , Huang Yihe , Wu Jiansheng
Published: February 2022

Ligand molecules naturally constitute a graph structure. Recently, many excellent deep graph learning (DGL) methods have been proposed and used to model ligand bioactivities, which is critical for the virtual screening of drug hits from compound databases in interest. However, pharmacists can find that these well-trained DGL models usually are hard to achieve satisfying performance in real scenarios for virtual screening of drug candidates. The main challenges involve that the datasets for training models were small-sized and biased, and the inner active cliff cases would worsen model performance. These challenges would cause predictors to overfit the training data and have poor generalization in real virtual screening scenarios. Thus, we proposed a novel algorithm named adversarial feature subspace enhancement (AFSE). AFSE dynamically generates abundant representations in new feature subspace via bi-directional adversarial learning, and then minimizes the maximum loss of molecular divergence and bioactivity to ensure local smoothness of model outputs and significantly enhance the generalization of DGL models in predicting ligand bioactivities. Benchmark tests were implemented on seven state-of-the-art open-source DGL models with the potential of modeling ligand bioactivities, and precisely evaluated by multiple criteria. The results indicate that, on almost all 33 GPCRs datasets and seven DGL models, AFSE greatly improved their enhancement factor (top-10%, 20% and 30%), which is the most important evaluation in virtual screening of hits from compound databases, while ensuring the superior performance on RMSE and $r^2$. The web server of AFSE is freely available at http://noveldelta.com/AFSE for academic purposes.