bluuebunny/crossref_metadata_2025 huggingface dataset:
Method of Differential-Based Remote Upgrade for ECUs on Vehicles*
Authors: Wu Xingchen , Gao Tianyu , Lee Guodong
Published: April 2022

In view of the problem that the current update flashing of the traditional automotive electronic control unit (ECU) software highly relies on professional maintenance personnel and external equipment, a method for ECU remote data flashing is proposed to achieve remote and efficient upgrade of ECU software. Furthermore, through the introduction of backup partitions and differential upgrade methods, the problems of lack of fault-tolerant processing, high bandwidth requirements and low efficiency in traditional ECU software update flashing are also solved. In this article, a remote update system is built, including the server-side and the vehicle, and the interface and communication protocols are defined based on HTTPS. The above methods have been verified on NXP IMX 6 platform. Associated testing of the simulation of the ECU by the STM32 and ROCKCHIP demo board has been made based on the CAN and Ethernet simulating the vehicle network architecture. By the real data catching from Ethernet and the CAN, the details of the log file of the programs’ event tracking information are analyzed to obtain the experimental results. The results indicated that the incremental remote data updating system built in this article can remotely update the CAN software accurately and efficiently in all simulation tests, which proved the practicability, accuracy, and efficiency of this method.
A flexible under-driven bionic climbing robot design
Authors: Guo Ai , Zeng Chao , Li Guo , Fang Ranran , Liu Yang , Li Rui
Published: April 2022

The study of a reliable bionic climbing robot to replace humans in dangerous work at height has been a hot and difficult research topic in the academic and engineering communities. Researchers have developed climbing robots such as magnetic adsorption, vacuum adsorption, claw-stick adhesion, bionic adhesion and wet adhesion, depending on the mode of adhesion. However, due to their single adhesion method and limited means of regulation, most of them can only climb under specific experimental conditions and cannot meet complex engineering environments such as variable diameter, roughness and curvature. In order to solve the above technical problems to a certain extent, this paper draws on the excellent grasping ability of primates and the excellent movement form of the inchworm to design a flexible under-driven bionic climbing robot, which includes under-driven fingers made of coupled rigid and soft materials, and a lightweight torsional and telescopic system with good climbing adaptability and variable diameter wall transition capability. The flexible under-driven fingers are made of coupled bones, joints, muscles and skin of different modulus. The telescopic system consists of an open round thin shell rod, a two-axis motor and a flexible torso. Based on the mechanical design, the flexible under-driven finger is fabricated and subjected to finite element simulations and experiments, which show that the flexible gripper has good gripping adaptability for different shapes of objects.
Longitudinal Driving Force Distribution of Four In-wheel Motors Drive Electric Vehicle Based on Fuzzy Logic
Authors: Zhao Yongqiang , Li Jun , Li Chang
Published: April 2022

In order to improve the vehicle economy, this paper establishes the drive system efficiency optimization objective function and vehicle longitudinal driving constraints, design the drive system efficiency optimization control algorithm based on tolerance. Then establish the objective function of longitudinal adhesion coefficient utilization optimization algorithm, complete the development of longitudinal adhesion coefficient utilization optimization control algorithm to improve the vehicle stability. In order to give consideration to the economy and stability of vehicle longitudinal driving, the switching logic of longitudinal driving force distribution algorithm is designed. Fuzzy control is used in the switching logic to realize the smooth switching of longitudinal driving force in the transition process, and realize the optimal distribution of longitudinal driving force. Finally, compare the simulation to the typical control algorithm under multiple working conditions to comfirm the actively effect of the control algorithm.
Path planning of mobile robot based on genetic-ant colony algorithm
Authors: Hou Chenfei , Li Chenxu , Yang Jing , Zhu Jianhui , Wang Wenxuan
Published: April 2022

Mobile robots are widely used in daily life, and ant colony algorithm is often adopted for its path planning. However, the classic ant colony algorithm has poor search ability and search speed due to the monotony of individuals within the population. In order to solve the problems of local optimal solution, slow convergence speed and low search efficiency of classic ant colony algorithm, a genetic-ant colony algorithm is proposed in this paper. The algorithm parameters are optimized by simulation experiment to determine the best parameter combination. The method is to give a certain range of random pheromone values to the primary ant individuals to enrich the population diversity. In each cycle, m ants are sent for foraging. After each foraging cycle, aiming at the growth of individual pheromone value, the fitness function of genetic algorithm is established to evaluate individual fitness value, and the parental individuals carry out genetic operations such as selection, crossover and variation to produce offspring individuals with the same number and higher pheromone level, the offspring individuals carry out the next cycle of foraging and keep cycling. The population diversity is maintained at a high level, while the increase in the level of individual pheromone speeds up the convergence and operation of the algorithm. The simulation results show that the running time of the improved algorithm is significantly shorter than that of the traditional ant colony algorithm in the 40 * 40 grid map, and the running time of the algorithm is reduced by 21.34%; in the complex grid environment, the frequency of obtaining the optimal path of the algorithm is significantly higher than that of ordinary ant colonies, and the frequency is increased; and the number of iterations when the optimal path is reached for the first time is significantly reduced,–which has verified the effectiveness, stability and superiority of genetic-ant colony algorithm.
Programmable Current Source Design for Multi-channel Magnetic Field Generator* Programmable current source
Authors: Ding Ning , Zhang Qi , Xu Han , Feng Junfu , Li Xiao
Published: April 2022

The three-dimensional motion of magnetic micro-robots requires the control of multiple magnetic field generators working simultaneously. The current program-controlled current sources in the market are generally expensive and it is difficult to control multiple program-controlled current sources by a single controller to change the output current at the same time. In order to compress the experimental cost, reduce the experimental threshold, realize the simultaneous control of multiple magnetic field generators and make them work stably, this paper proposes a special program-controlled current source design for multiple magnetic field generators, and verifies the feasibility of the design through experimental tests. The results show that the dedicated current source is able to output 0.3∼6.5A under ±12V switching power supply test when each current source is connected to a 1.5 ohm solenoid coil with internal resistance. The system meets the requirements of 3% or less output current error and fast response time while greatly reducing costs and achieving good interactivity.
Local to global Tracker: A Siamese Network for Long-term Tracking
Authors: Yang Ya , Zhou Xinyu , Jiang KaiXun , Hou XiaoNan , Wang ChunLei , Zhang WenQiang
Published: April 2022

Visual object tracking is a basic task in computer vision, which can be applied to different applications. Former methods of single object tracking problem demands to perform target state estimation according to object position of previous frame, but these methods are poorly capable of handling the fast movement of the target. In this paper, we introduce a Siamese network for single object tracking task. The network consists of two branches, one of which is the classification branch used to predict positive or negative samples, and the other is the regression branch used to predict the specific location of the object in sequence. In addition, we propose a local to global algorithm for long-term tracking to expand the search region when the target is out-of-view or full occlusion. Experiments show that our tracker achieves very promising results on VOT2018LT datasets while running at over 89 FPS.
Single-channel speech separation method based on attention mechanism
Authors: Xiao Ting , Mo Jialing , Hu Weiping , Chen Dandan , Wu Qing
Published: April 2022

In order to improve the efficiency of speech interaction so that both humans and machines can hear it clearly, to solve the problem of single channel speech separation, a singlechannel speech separation method combined with attention mechanism is proposed. Based on the time-domain speech separation network, the method uses Encoder-separator-decoder framework and improves the Separator structure of the framework. First, the encoder transforms the input mixed speech signal into the latent space and extracts the feature of the speech signal to obtain the deep representation of the speech. Then, the deep attention features of speech were extracted by the splitter combined with the attention mechanism, and the mask was estimated for each independent sound source. Finally, the decoder inversely transforms the separated source signals back to the time domain. Experiments on WSJ0 speech separation data set show that the proposed method can effectively improve the SI-SNR of mixed speech separation, which is 0.31dB higher than the original time-domain network, and 0.94dB higher for the mixed case of boys and girls.
Torque Distribution Algorithm for Four In-wheel Motors Drive Electric Vehicle Based on Torque Vector Control
Authors: Zhao Yongqiang , Li Jun , Zhao Yang
Published: April 2022

Torque distribution algorithm, including the calculation of additional yaw moment and the distribution of four in-wheel motors torque, is studied. An additional yaw moment controller based on the MPC is designed. The predictive model is constructed based on 2-DOF vehicle model. The optimal additional yaw moment is obtained by solving the equivalent quadratic programming problem, which considers the constraints of target yaw rate, CG sideslip angle and minimum variation of additional yaw moment.Wheel torque distribution based on the optimal tire adhesion coefficient is established to realize additional yaw moment. Combined with the constraints of front and rear axle driving force, additional yaw moment and tire friction circle, the optimal wheel force distribution problem is transformed into a quadratic programming problem. In the special conditions such as lateral force saturation and driving anti-skid function trigger, the constraints are relaxed to avoid the equation without feasible solution.The simulation results of transient steering test, steady state steering test and extreme steering test showed that the application of the algorithm can shorten the response time of yaw rate, improve the cornering behavior of the vehicle under large lateral acceleration, and reduce the maximum passing speed under in slalom test to improve the handling and stability of the vehicle.
Kinematics modeling and trajectory planning of KUKA manipulator based on MATLAB
Authors: Deng Haofeng , Zhu Liucun , Wang Jiyue , Chen Mingyou , Wu Hongwei , He Congwei
Published: April 2022

For the purpose of engraving, KUKA KR60 mechanical arm was used in the laboratory for research and analysis.Firstly, the improved D-H parameter method was used to establish the D-H coordinate system of the manipulator, and the kinematic equation of the manipulator was obtained by calculating the homogeneous transformation matrix.Secondly, mathematical modeling was carried out with MATLAB Robbtics Toolbox, and the forward and inverse kinematics problems were analyzed and demonstrated to verify the correctness of the kinematic model of the mechanical arm. Thirdly, Monte Carlo method was used to calculate the movement Angle of the mechanical arm joint to reach the working area. Finally, trajectory planning was adopted.The variation curves of Angle, angular velocity and angular acceleration were obtained, and the rationality of its trajectory planning was verified by simulation analysis, which provided a research basis for the next step of carving trajectory path optimization.
WAVE communication based on improved NLMS adaptive filtering delay compensation method*
Authors: Wu Xingchen , Lee Guodong
Published: April 2022

In order to improve the signal quality of the Wireless Access Vehicle Environment (WAVE), in this paper, the dynamic step factor is introduced into the Normalized Least Mean Square (NLMS) algorithm based on multi-step step descent, so that the algorithm has a wide dynamic adaptive range. And an improved NLMS adaptive filtering method is proposed to reduce the noise of the communication signal of the WAVE system. At the same time, by introducing the unit delay operator z
−Δ of the adaptive FIR filter and the transmission delay t
p of the WAVE system, a delay compensation function is constructed to solve the delay problem in the signal transmission and processing process of the WAVE system and meet the real-time requirement of the system. In this paper, the above method is simulated and tested under typical working conditions of the Cooperative Adaptive Cruise Control (CACC) system. Experimental results show that the NLMS adaptive filtering method can effectively reduce the error between the acquired signal and the expected signal and make the signal waveform smoother by introducing the dynamic step factor. By introducing the filtering delay compensation function into the signal filtering results, it can effectively reduce the adverse effects of the delay problem caused by signal transmission and signal processing on the desired target signal.
Particle swarm optimization gray wolf fusion algorithm for solving the optimal solution of function
Authors: Jiang Lei , Liu kunkun , Wu LiJuan
Published: April 2022

In order to improve the local difference problem of wolf swarm algorithm, which is easy to fall into local optimization in solving function optimization, a particle swarm grey wolf fusion algorithm is proposed based on the characteristics of particle swarm algorithm, such as fast search speed, strong ability and easy implementation. Firstly, this paper compares particle swarm optimization and wolf swarm algorithm, and introduces particle swarm optimization gray wolf fusion algorithm by analyzing the root of the problem. The experimental results show that the particle swarm wolf swarm algorithm is feasible, and the search accuracy, stability and the optimal value of the objective function are significantly improved in the solution process.
A Novel NLOS Suppression Algorithm for Indoor Location based on FCM and REKF
Authors: Jin JiaWei , Cheng Long , Zhou JiaBao
Published: April 2022

Abstarct. The indoor positioning based on wireless sensor networks (WSN) has become one of the research hotpots. However, the NLOS propagation of the distance signals greatly challenges the accuracy and robustness of the algorithm. In this paper, we take the suppression of NLOS as the core goal and proposed the FCM-REKF-based positioning method. We firstly identify the signal states through the fuzzy c-means clustering (FCM), for the measurement distance judged to be NLOS, a refactoring method based on FCM is used. Then the corrected distance is smoothed by Kalman filter, and the Robust Extended Kalman Filter is used to calculate the final position. The simulation results show that our method has higher accuracy than EKF, REKF and IMM-EKF under NLOS environment.
Improved Image Super-Resolution Using Frequency Channel Attention and Residual Dense Networks
Authors: Sun Zhikuan , Li Zheng , Sun Mengchuan , Hu Ziwei
Published: April 2022

In our real life, due to the various influences, low-resolution images exist widely. The resolution of the image represents the amount of information carried by the image and the quality of the image. Image super-resolution means reconstructing low-resolution images, and it can help us improve the image quality to get more information. This paper attempts to improve the existing super-resolution reconstruction model based on deep learning. We use nested residual dense connection to prompt the model to focus on the recovery of detailed textures and accelerate convergence. Meanwhile, we use frequency channel attention mechanism to weight channels. By comparing the experimental results with other methods including FSRCNN, VDSR and MemNet, our proposed method has achieved better results and visual improvements. Especially on the Urban100 test dataset, the increases of PSNR and SSIM reach higher.
Error matching elimination based on a local affine algorithm
Authors: Hu Zhifeng , Xu Gang , Chen Ling , Fu Nana , Zhen Shuzhan
Published: April 2022

Aiming at the shortcomings of orb algorithm using the traditional RANSAC (random sampling consistency) algorithm to eliminate mismatches, such as the high number of iterations when the sample size is large, and the proportion of error points also affects the fitting accuracy of the traditional algorithm, a rough and fine method to eliminate mismatches is proposed; The threshold screening method is used to select the optimal threshold and preliminarily eliminate a large number of wrong matching pairs to achieve the effect of rough elimination; Then the sub-window method is used to calculate the local affine matching under all windows, reduce the number of error points, and increase the correctness of the fitting model of RANSAC algorithm. The experimental results are compared with the traditional algorithm, and the matching accuracy is improved by 6% and combined with orb-slam2.
Research on Situational Awareness Technology of Industrial Control Network Based on Big Data
Authors: Wu Yi , Guo Naiwang , Wang Binbin , Zhang Lei
Published: April 2022

With the rapid advancement of national strategies such as "Internet+" and "Made in China 2025", industrial control systems have been widely used in various industries such as energy, municipalities, transportation, water conservancy and aerospace, etc. The security of industrial control networks is always affecting the lifeline of the national economy. Therefore, the security of industrial control networks has not been given enough attention, resulting in frequent industrial control network security incidents, which makes us realize the importance of creating an industrial control network situational awareness system that integrates assessment and prediction. In this paper, we study the situational awareness technology of industrial control network based on big data, and integrate situational extraction as the premise, situational assessment as the core, and situational prediction as the goal to comprehensively sense the situational awareness of industrial control network system. Firstly, we adopt two ways of industrial control network data collection in the situational extraction, one is the use of traffic mirroring bypass access to sensor-aware terminals, without affecting the original production services on the premise of network traffic data collection. The second is the use of WireShark tools to achieve industrial control network traffic packet collection and statistics of traffic packets per second, analysis of the current network state, build Hadoop big data platform to achieve offline data pre-processing and feature extraction, and build Flink and TensorFlow model for graph neural network model training and complete prediction. Secondly, we use a combination of hierarchical analysis and correlation analysis to evaluate the situation, and use the evaluation graph to show the current network security state. Thirdly, the situation prediction is done by training the graph neural network model offline. We use the Flink real-time computation engine to read the data of the industrial control network into the graph neural network model in real time, which is used to enhance the feature representation of each node and to predict the probability of anomaly occurrence of the industrial control network in the future period. Finally, the graph neural network model of this paper is compared with traditional neural networks and machine learning models, and the accuracy and false alarm rate indexes are used to demonstrate the high accuracy and robustness of this model.
Research on Image Defogging Algorithms Based on Color Attenuation Prior
Authors: Tang Jialin , Zhang Zhongda , Niu Lihong , Feng Yi , Su Binghua , Lin Shounan
Published: April 2022

As for the defogging algorithm of color attenuation prior, an optimization algorithm based on color attenuation prior is proposed to solve the problem of image blurring in high frequency area of close-up view. Firstly, by improving the scene depth estimation information, the nonlinear model about scene depth is re-established to find the appropriate scene depth. Second, binary tree search algorithm is used to find the optimal atmospheric light value, and finally, a certain range of parameters are adjusted to achieve model optimization. The experimental results show that the algorithm solves the problem of blurring the close-up area and improves the speed of the algorithm.
Research and Implementation of Embedded Real-time Target Detection Algorithm Based on Deep Learning
Authors: Yang Dan , Yang Lichun , Liu Yiming
Published: April 2022

In view of the poor detection effect and low robustness of traditional target detection algorithms, this paper studies target detection algorithms based on deep learning, and designs an embedded real-time target detection evaluation board based on AI chip. It is realized that most of the common deep convolutional neural network models represented by YOLOv3 can be transplanted to the board. Although there is a certain accuracy loss within the acceptable range, this implementation achieves a faster speed to meet the needs of real-time detection. Meanwhile, a complete set of video component calling and network transmission schemes are proposed. By designing a unified standard interface accessed to the program framework, the implementation board can be flexibly extended to meet the needs of various artificial intelligence applications.
Unifying Classification and Bounding Box Regression Head For Object Detection
Authors: Gao Cunzhang , Gu Haitao , Yu Siquan , Li Xingzhen
Published: April 2022

Object detection usually includes two parts: objection classification and location. At present, the popular object detectors usually use two detection heads: one head is used to predict classification score, and the other one is used to predict the bounding box (bbox), respectively. In this paper, we first stack classification head after feature extract convolutional neural networks of bbox regression head. Then, we establish the classification networks by using a bounding box feature. The bounding box feature is very useful when the classification head uses soft Intersection over Union (IoU) labels. In experiment parts, only using PASCAL VOC 2007 datasets, soft Centerness labels, and soft IoU labels get 50.06 mAP and 52.08 mAP on VOC 2007 test. Compared with FCOS, they have 1.08% and 1.12% improvements. Using PASCAL VOC 2007 and 2012 datasets, our Union A*B head gets 78.71mAP after 12 epochs training with ResNet-101 as backbone and FPN as the neck. Extensive experimental results show that the proposed algorithm is superior to other detection methods.
A Preliminary Frame of Improved TCP/IP Model for Metaverse Networks
Authors: Zhu Weijun , Liang Xinyue , Chen Mengyao , Wang Yiran
Published: April 2022

It is well known that internet will play a key role to the infrastructure of metaverse, while different immerging virtual worlds communicate with each other. However, the existing TCP/IP model is not suitable enough for metaverse networks. To this end, we explore a preliminary frame of revised TCP/IP model. At first, a new layer called metaverse layer is defined. And then, this layer is placed as the top layer, i.e., the upper of the application layer in TCP/IP model. As a result, a novel network model having the six layers is obtained. Our simulated experiments indicate the feasibility of the new model.
Incline-dependent adjustments of toes in geckos inspire functional strategies for biomimetic manipulators
Authors: Song Yi , Weng Zhiyuan , Yuan Jiwei , Zhang Linghao , Wang Zhouyi , Dai Zhendong , Full Robert J
Published: April 2022

Geckos show versatility by rapidly maneuvering on diverse complex terrain because they benefit from their distributed, setae-covered toes and thus have the ability to generate reliable and adaptive attachment. Significant attention has been paid to their adhesive microstructures (setae), but the effectiveness of the gecko’s adaptive attachment at the level of toes and feet remains unclear. In order to better understand the geckos’ attachment, we first focused on the deployment of toes while challenging geckos to locomote on varying inclines. When the slope angle was less than 30°, their feet mainly interacted with the substrate using the bases of the toes and generated anisotropic frictional forces. As the slope angle increased to 90°, the participation of the toe bases was reduced. Instead, the setae contribution increased for the middle three toes of the front feet and for the first three toes of the hind feet. As the incline changed from vertical to inverted, the adhesive contribution of the toes of the front feet became more equal, whereas the effective adhesion contact of the hind feet gradually shifted to the toes oriented rearwards. Second, a mathematical model was established and then suggested the potential advantages of distributed control among the toes to regulate foot force. Finally, a physical foot model containing five compliant, adjustable toes was constructed and validated the discoveries with regard to the animals. Using the gecko toes’ control strategies, the artificial foot demonstrated diverse behavior regulating attachment forces. The success of the foot prototype not only tested our understanding of the mechanism of biological attachment, but also provided a demonstration for the design and control of gecko-inspired attachment devices, grippers and other manipulators.
Polyester microplastic fibers in soil increase nitrogen loss via leaching and decrease plant biomass production and N uptake
Authors: Ingraffia Rosolino , Amato Gaetano , Iovino Massimo , Rillig Matthias C , Giambalvo Dario , Frenda Alfonso S
Published: April 2022

Microplastic contamination, like other global change factors, can induce effects on ecosystem functions and processes, affecting various soil biophysical properties. However, effects of such contaminants on nutrient cycles in agroecosystems are still poorly understood. We here performed two pot experiments to investigate the effect of polyester microplastic fibers (PMFs) on soil physical properties, nitrogen cycle, and plant performance in a maize-based agroecosystem. Moreover, we followed the N loss via leaching in soil contaminated or not with PMFs by simulating heavy rainfall events that mimic a future scenario of climate change. Our results show that soil contaminated with PMFs (at a concentration of 0.5% w/w) can jeopardize agroecosystem sustainability by affecting soil physical properties and in particular soil macro- and microporosity, the nitrogen cycle, and plant performance. In particular, we found that soil PMF contamination limited crop growth and N uptake by circa 30%, and consequently increased N loss via leaching. Overall, our findings show that soil contamination with PMFs may pose problems to future agricultural challenges like food security and environmental protection.
The Effect of liquid organic fertilizer (LOF) goat biourin with various decomposers on some chemical properties of ultisol
Authors: Sulistyani Dwi Probowati , Napoleon Adipati , Hermawan Agus , Lola Chintya Vika
Published: April 2022

This study aims to improve soil quality, so that the soil used can be maintained against theavailability of soil nutrients. Efforts are made to apply fertilizers that are in accordance withthe needs of plants and in a balanced manner. Organic fertilizer is one of the efforts toimprove the physical and chemical properties of the soil, so that it is expected to increase thegrowth and production of plants to be applied. Liquid organic fertilizer has many advantages, namely the ease of application to soil and plants, all you need to do is water it. The rawmaterial forliquid organic fertilizer from goat biourin is goat urine, while the decomposersare banana hump, feces and EM4. This researchwas conducted in a greenhouse, was carried out for 4 months, Departmet of Soil and Laboratory of Chemistry, Biology and Soil Fertility, Department of Soil, Faculty of Agriculture, Sriwijaya University. The study using acompletely randomized design with 4 treatments and 3 replications. The treatment consistedof the ratio of biourin to water, namely: A (control), B (l:l), C (1:2), and D (1:3). Parametersobserved and measured in this study were C/N Ratio, Total N, and soil pH. Based on theresults of the study, it was concluded that the type and dosage of goat Biourin liquid fertilizer could increase soil pH, N-total and C/Nratio.
Effects of Organic and Inorganic Farming Systems in Different Environment on the Availability and Uptake Of Zn and Cd in Rice Plants
Authors: Iskyati Wuri , Hidayah Utami Sri Nuryani , Hanudin Eko
Published: April 2022

The effectiveness of the cultivation process is dependent on the planting medium. Zinc (Zn) and Cadmium (Cd) are metal elements whose presence in the soil is influenced by the same factors. However, their effects on plants might be different. Therefore, this study was conducted to determine the effects of sites with different environmental conditions and farming systems on the uptake of Zn and Cd in rice plants. The research was arranged in a two-stage nested randomized block design, consisting of two sites (Fluvaquentic Epiaquepts and Typic Epiaquepst) and two treatments (organic and inorganic farming systems). The effect of soil type and the farming system resulted in the highest yield of Zn availability in inorganic Fluvaquentic Epiaquepts treatment with a value of 5,42mg.kg-1 at two days after planting (DAP), and the highest uptake was in the organic Fluvaquentic Epiaquepts treatment with a value of 1,04 mg.plant-1. The inorganic Typic Epiaquepst treatment had the maximum Cd availability and uptake, with 0.45 mg.kg-1 and 1.15 mg.plant-1, respectively, at 63 DAP. Zn uptake was significantly affected by different sites with different soil types, while Cd was significantly affected by both sites with different soil types and farming systems. Zn and Cd levels in soil and plants were within the normal threshold.
The impact of seasonal changes on tidal water quality in acid sulfate soils for rice cultivation and water management strategies in South Kalimantan, Indonesia
Authors: Multazam Z , Utami S N H , Maas A , Anwar K
Published: April 2022

Water management is the leading key to success in tidal swamp rice farming of acid sulfate soils toward seasonal and climate changes to increase rice productivity for overcoming the threat of food crisis. This study aimed to assess and know the dynamics of tidal water quality from the dry peak season to the rainy season’s peak and the best practices on tidal water management strategies for rice cultivation in acid sulfate soils. Measurement started from the peak of the dry season to that of the rainy season, carried out every month at the spring and neap tides. The first measurement was conducted in the Ray 5 Channel located 3 km from the Barito River, hourly for 24 hours. The second one was performed every kilometer from the Barito’s Riverbanks up to 8 km during high and low tides. The main parameters measured were pH and electrical conductivity (EC). The results explained that water quality in tidal lands dynamically fluctuated, spatially, and temporally, hourly, daily, and monthly. Spatially, good water quality is only found at locations 0-4km on the water channel from the Barito River. The water quality decreases farther from the estuary channel and towards the rain peak. The best water quality in the channel occurs at the in-situ tide peak, usually for 2-3hours closest to or from the highest tide. However, the water quality is very acidic, with the pH∼3 value often overflowing the land at the height of the rainy season. This research can be used as a guide in water management in acid sulfate soils and other similar locations. Proper management and location-specific and compatible water systems are proposed to ensure good quality water can optimally improve agricultural success in tidal swamps of acid sulfate soils, namely by holding backwater during the dry season, water drain from the swampland in the rainy season, with leaching and flushing at the beginning rain, and closing the floodgates from the poor water quality.
Biophysical and Socioeconomic Characterization of the Timari River Micro-basin in the Central Tropics of Peru
Authors: Erazo-Toscano E , Alvarez-Montalván C , Caballero-Salas R , Baltazar-Ruíz A , Rojas-Castillo Y , Alomia-Lucero M
Published: April 2022

The study was carried out in the Timari river micro-basin belonging to the Río Negro district of the Satipo province, with the objective of evaluating the influence of the vulnerability to contamination of surface water resources in the Timari-Río Negro river micro-basin. The results in the physical aspect were: area 1061.16 ha with length of channel 7.36 km and 44 tributaries, slope of the channel 12%, mean elevation of 1176.47 meters above sea level; the biophysical aspect presents: temperature from 19.87°C to 24.16°C, precipitation from 1693 mm to 2063 mm, evapotranspiration from 1083.31 mm to 1519.81 mm and slopes ranging from flat to steep. The population in the socioeconomic aspect presents 40% illiteracy and 60% some degree of education, productive activities are based 83% on agriculture and 17% trade, monoculture predominates with 60% and diversified crops at 40%. The flow rate was 0.248 m3 /s; while the raw surface water quality index presents 81.28 characterized as good quality and ends with 41.87 as poor quality.
Maintaining microstructure ultra-refinement in the austenitic stainless steel by inducing χ phase precipitating through severe cold rolling and annealing
Authors: Fu Bin , Ge Yao , Guan Wei , Guo Yanhui , Wang Zemin , Ding Yi
Published: April 2022

In the present study, an austenitic stainless steel (2Cr17Ni13Mo5) was processed by severe cold rolling (SCR) and subsequent annealing. The grain size was severely refined to ∼100 nm after SCR. The yield strength (YS) and ultimate tensile strength (UTS) increased to 1510.5 MPa and 1660.4 MPa, respectively. Nevertheless, the ductility decreased significantly. Full recrystallization was completed after annealing at 800 °C for 1h. However, the ultrafine-grained structure was maintained with the grain size of 780 nm, which is mainly attributed to the pinning effect of χ phase precipitated at the grain boundary during annealing. Good comprehensive mechanical properties with high strength (YS = 977.6 MPa, UTS = 1197.7 MPa) and good ductility (elongation = 17%) were obtained. The high strength is mainly attributed to the ultrafine-grained structure maintaining, while the annihilation of dislocation contributes to the ductility improvement.
P-CRITICAL: a reservoir autoregulation plasticity rule for neuromorphic hardware
Authors: Balafrej Ismael , Alibart Fabien , Rouat Jean
Published: April 2022

Backpropagation algorithms on recurrent artificial neural networks require an unfolding of accumulated states over time. These states must be kept in memory for an undefined period of time which is task-dependent and costly for edge devices. This paper uses the reservoir computing paradigm where an untrained recurrent pool of neurons is used as a preprocessor for temporally structured inputs and with a limited number of training data samples. These so-called reservoirs usually require either extensive fine-tuning or neuroplasticity. We propose a new local and unsupervised plasticity rule named P-CRITICAL designed for automatic reservoir tuning that translates well to physical and digital neuromorphic processors. The spiking neuronal architecture implementation is simulated on the Loihi research chip from Intel and on a conventional CPU. Comparisons on state-of-the-art machine learning datasets are given. Improved performance on visual and auditory tasks are observed. There is no need to a priori tune the reservoir when switching between tasks, making this approach suitable for physical implementations. Furthermore, such plastic behaviour of the reservoir is a key to end-to-end energy-efficient neuromorphic-based machine learning on edge devices.
Illustration of 2 Fusion Designs and Estimators
Authors: Cole Stephen R , Edwards Jessie K , Breskin Alexander , Rosin Samuel , Zivich Paul N , Shook-Sa Bonnie E , Hudgens Michael G
Published: April 2022

“Fusion” study designs combine data from different sources to answer questions that could not be answered (as well) by subsets of the data. Studies that augment main study data with validation data, as in measurement-error correction studies or generalizability studies, are examples of fusion designs. Fusion estimators, here solutions to stacked estimating functions, produce consistent answers to identified research questions using data from fusion designs. In this paper, we describe a pair of examples of fusion designs and estimators, one where we generalize a proportion to a target population and one where we correct measurement error in a proportion. For each case, we present an example motivated by human immunodeficiency virus research and summarize results from simulation studies. Simulations demonstrate that the fusion estimators provide approximately unbiased results with appropriate 95% confidence interval coverage. Fusion estimators can be used to appropriately combine data in answering important questions that benefit from multiple sources of information.
Fire and summer temperatures interact to shape seed dormancy thresholds
Authors: Zomer Maya , Moreira Bruno , Pausas Juli G
Published: April 2022

Background and Aims

In Mediterranean ecosystems, the heat shock of wildfire disrupts physical seed dormancy in many plant species. This triggers germination in the post-fire environment where seedling establishment is optimal due to decreased competition and increased resource availability. However, to maintain the soil seed bank until a fire occurs, the minimum heat capable of breaking seed dormancy (i.e. the lower heat threshold) must be above the maximum temperatures typically observed in the soil during the summer. We therefore hypothesized that summer temperatures have shaped heat requirements for physical dormancy release. Specifically, we predicted that seeds from populations growing under warmer summers will have higher values of the lower heat threshold.

Methods

To evaluate this prediction, we collected seeds from two Cistus species in 31 populations (20 Cistus albidus and 11 Cistus salviifolius) along a climate gradient of summer temperatures on the eastern coast of Spain. For each population, seeds were treated to 10 min heat shocks, from 30 to 120 °C in 5 °C increments (19 treatments), to simulate increasing heat doses from summer to fire-related temperatures. Seeds were then germinated in the lab.

Key Results

For all populations, maximum germination was observed when applying temperatures associated with fire. Lower heat thresholds varied among populations, with a positive relationship between summer temperatures at seed population origin and the heat dose required to break dormancy.

Conclusions

Our results suggest that fire drives maximum dormancy release for successful post-fire germination, while summer temperatures determine lower heat thresholds for ensuring inter-fire seed bank persistence. Significant among-population variation of thresholds also suggests that post-fire seeder species have some potential to modify their dormancy release requirements in response to changing climate.
Hybridization and introgression are prevalent in Southern European Erysimum (Brassicaceae) species
Authors: Osuna-Mascaró Carolina , Rubio de Casas Rafael , Gómez José M , Loureiro João , Castro Silvia , Landis Jacob B , Hopkins Robin , Perfectti Francisco
Published: April 2022

Background and Aims

Hybridization is a common and important force in plant evolution. One of its outcomes is introgression – the transfer of small genomic regions from one taxon to another by hybridization and repeated backcrossing. This process is believed to be common in glacial refugia, where range expansions and contractions can lead to cycles of sympatry and isolation, creating conditions for extensive hybridization and introgression. Polyploidization is another genome-wide process with a major influence on plant evolution. Both hybridization and polyploidization can have complex effects on plant evolution. However, these effects are often difficult to understand in recently evolved species complexes.

Methods

We combined flow cytometry, analyses of transcriptomic sequences and pollen tube growth assays to investigate the consequences of polyploidization, hybridization and introgression on the recent evolution of several Erysimum (Brassicaceae) species from the South of the Iberian Peninsula, a well-known glacial refugium. This species complex differentiated in the last 2 million years, and its evolution has been hypothesized to be determined mainly by polyploidization, interspecific hybridization and introgression.

Key Results

Our results support a scenario of widespread hybridization involving both extant and ‘ghost’ taxa. Several taxa studied here, most notably those with purple corollas, are polyploids, probably of allopolyploid origin. Moreover, hybridization in this group might be an ongoing phenomenon, as pre-zygotic barriers appeared weak in many cases.

Conclusions

The evolution of Erysimum spp. has been determined by hybridization to a large extent. Species with purple (polyploids) and yellow flowers (mostly diploid) exhibit a strong signature of introgression in their genomes, indicating that hybridization occurred regardless of colour and across ploidy levels. Although the adaptive value of such genomic exchanges remains unclear, our results demonstrate the significance of hybridization for plant diversification, which should be taken into account when studying plant evolution.
Fake News, Defamation, Online Reviews, and Their Potential Devastating Consequences for Aesthetic Plastic Surgeons
Authors: Wokes James E T , McLean Neil R , Boyd Magnus
Published: April 2022

Background

Anecdotal evidence of the exposure and vulnerability of aesthetic plastic surgeons to fake news and online defamation by a minority of vociferous patients has been accruing over the past 10 to 20 years and lurks, hidden like an iceberg, beneath our specialty. Because of acute embarrassment, it is rarely, if ever, discussed in public, and the true extent of the underlying problem remains unknown.

Objectives

The authors sought to accurately document the true magnitude of defamation in British aesthetic plastic surgery.

Methods

An anonymous online survey was distributed to all full members of the British Association of Aesthetic Plastic Surgeons in the summer of 2020.

Results

Forty-six percent of full British Association of Aesthetic Plastic Surgeons members responded. Over one-half had experienced denigration of their professional reputation; the most common medium reported was digital defamation. Over three-quarters of the respondents had been the subject of patient blackmail in an attempt to refund professional fees, and, most distressingly, almost one-third stated that the incident had significantly impacted their mental well-being. The majority had found help from their professional bodies to be significantly lacking.

Conclusions

These findings reveal a torment among aesthetic plastic surgeons that has not, to the authors’ knowledge, been previously recognized and requires urgent attention by professional organizations.
A new lanostane triterpenoid from the mushroom Hypholoma fasciculare
Authors: Wang Junhong , Wu Jing , Tsutsumi Kenta , Choi Jae-Hoon , Hirai Hirofumi , Kobori Hajime , Takikawa Yuichi , Kawagishi Hirokazu
Published: April 2022

A novel compound (1) and 3 known compounds (2−4) were isolated from the fruiting bodies of Hypholoma fasciculare. The structure of 1 was determined by the interpretation of spectroscopic data. Compounds 2−4 were identified by comparing the spectra data of known compounds. In the bioassay examining growth inhibitory activity against phytopathogenic bacteria Clavibacter michiganensis, Burkholderia glumae, and Peptobacterium carotovorum, compounds 1, 2, and 4 showed inhibition effects on C. michiganensis only.
tRNAstudio: facilitating the study of human mature tRNAs from deep sequencing datasets
Authors: Murillo-Recio Marina , Martínez de Lejarza Samper Ignacio Miguel , Tuñí i Domínguez Cristina , Ribas de Pouplana Lluís , Torres Adrian Gabriel
Published: April 2022

High-throughput sequencing of transfer RNAs (tRNA-Seq) is a powerful approach to characterize the cellular tRNA pool. Currently, however, analyzing tRNA-Seq datasets requires strong bioinformatics and programming skills. tRNAstudio facilitates the analysis of tRNA-Seq datasets and extracts information on tRNA gene expression, post-transcriptional tRNA modification levels, and tRNA processing steps. Users need only running a few simple bash commands to activate a graphical user interface that allows the easy processing of tRNA-Seq datasets in local mode. Output files include extensive graphical representations and associated numerical tables, and an interactive html summary report to help interpret the data. We have validated tRNAstudio using datasets generated by different experimental methods and derived from human cell lines and tissues that present distinct patterns of tRNA expression, modification and processing.

Availability and implementation

Freely available at https://github.com/GeneTranslationLab-IRB/tRNAstudio under an open-source GNU GPL v3.0 license.

Supplementary information

Supplementary data are available at Bioinformatics online.
TPpred-ATMV: therapeutic peptide prediction by adaptive multi-view tensor learning model
Authors: Yan Ke , Lv Hongwu , Guo Yichen , Chen Yongyong , Wu Hao , Liu Bin
Published: April 2022

Motivation

Therapeutic peptide prediction is important for the discovery of efficient therapeutic peptides and drug development. Researchers have developed several computational methods to identify different therapeutic peptide types. However, these computational methods focus on identifying some specific types of therapeutic peptides, failing to predict the comprehensive types of therapeutic peptides. Moreover, it is still challenging to utilize different properties to predict the therapeutic peptides.

Results

In this study, an adaptive multi-view based on the tensor learning framework TPpred-ATMV is proposed for predicting different types of therapeutic peptides. TPpred-ATMV constructs the class and probability information based on various sequence features. We constructed the latent subspace among the multi-view features and constructed an auto-weighted multi-view tensor learning model to utilize the high correlation based on the multi-view features. Experimental results showed that the TPpred-ATMV is better than or highly comparable with the other state-of-the-art methods for predicting eight types of therapeutic peptides.

Availability and implementation

The code of TPpred-ATMV is accessed at: https://github.com/cokeyk/TPpred-ATMV.

Supplementary information

Supplementary data are available at Bioinformatics online.