bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Rigid and Flexible Combined Finger Exoskeleton Design with Variable Curvature
Authors: Lan Zixuan 
Published: December 2022

Abstract
Some hand exoskeletons have been used in the rehabilitation treatment of paralyzed patients caused by stroke and cerebral apoplexy as a result of research into hand exoskeletons. However, how to make the patient more comfortable to use and how to make the exoskeleton more precise are still key issues that need to be studied. Aiming at the motion law and skeletal characteristics of the human hand, this paper proposes a soft exoskeleton machine for hand rehabilitation training that combines rigidity and flexibility. Using a simplified two-layer sliding spring mechanism, the device combines the comfort and fit of a flexible device with the accurate kinematics of a rigid device. The mechanism requires only one degree of freedom to drive the normal flexion and expansion of the three finger joints. By changing the section thickness of the spring sheet to change its section moment of inertia, the bending effect of the mechanism is optimized to better fit the bending characteristics of the finger joints. To simplify the simulation of the mechanism, the method of the pseudo-rigid-body model is used to target the spring leaf’s nonlinear deformation, to achieve intuitively understandable kinematics. Finally, the accuracy of the design is demonstrated through simulation and prototype models.
Design of Low-Power High-Speed Double-Tail Dynamic Comparator Based on Charge-Steering Concept
Authors: Zhang Zhe , Lei Qianqian , Zhao Erhu , Yang Yanfei , Feng Song 
Published: December 2022

Abstract
In this thesis, a low-power dissipation high-speed double-tail dynamic comparator is designed. Based on the principles of charge-steering, a clock charge pump is used at the tail of the latch stage to replace the clock current source, which reduce the output swing and kick-back noise of the comparator. Meanwhile, digital logic is added to the pre-amplification stage to eliminate quiescent current and further reduce power consumption. Based on the SMIC 28nm CMOS process, the proposed comparator is simulated and verified. The results show that under the condition of 0.9V supply voltage and 1MHz sampling frequency, the power dissipation is 5.4μW, the delay is 245ps, and the offset voltage is 6.9mV. Compared with a high-speed comparator, under the premise of achieving the same performance, the power consumption is reduced by 69%.
Design of a Monitoring System for Power-consumption Devices Based on Modbus Protocol
Authors: Sun Jinhua , Lin Jiayuan , Meng Zhaorui 
Published: December 2022

Abstract
To better monitor the usage status of electric energy for production and living devices, the paper has put forward an electric energy monitoring system based on Modbus protocol, starting by analyzing the demand for monitoring and management of users’ electric energy. A three-layer structure has been used in the system, which consists of the sensing layer, control layer, and application layer. The lower computer of the sensing layer used the one-master-multi-slave mode to collect the power data of multiple devices in the bus in real time and transmits the voltage, current, power consumption, and other data of power-consumption devices to the host through the RS-485 interface, Modbus protocol, and GPRS network. Meanwhile, the lower computer interacts with the remote user monitoring terminal to realize the monitoring and management of power consumption data of the devices. The test has shown that the system can steadily monitor and manage the power consumption status of the devices in real time, and can be applied to energy saving, monitoring of power-consumption devices, property management, and other fields, which have good application prospects.
Machine Vision Product Appearance Defect Detection Based on Deep Learning
Authors: Wang Chengyan , Shi Weiping , Li Yuling 
Published: December 2022

Abstract
Due to the influence of objective environmental conditions in the image acquisition stage, the image quality of the existing methods is poor, which makes the effect of defect detection not ideal. In this paper, research on product appearance defect detection based on deep learning machine vision is proposed. A product appearance model is constructed by using a Gaussian mixture, and multiple sub-images with different sizes are used as the data support of the multi-scale expression of the Gaussian model to calculate the Gaussian difference features of the image. A Faster R-CNN network algorithm in deep learning is used to recognize defects. The multi-task mechanism is introduced in the setting of the loss function to realize the concurrent function of defect detection. The mean pooling and maximum pooling operations are introduced in the setting of the classification regression network to realize the comprehensive detection of target images. Judge whether that product has a defect according to the output result of the algorithm and the DOG characteristics of the image. The test results show that the accuracy of the designed method for various types of defect detection is more than 90. 00% and it has a high detection efficiency.
Space Manipulator Assembly Operation Technique based on Deep Residual Reinforcement Learning
Authors: Huang Kui , Liang Binyan , Quan Junyu , Luo Zhiwen , Jiang Shan 
Published: December 2022

Abstract
In recent years, there are more and more space complex operational tasks such as the maintenance and assembly of on-orbit aircraft. Traditional robot planning and control methods require precise dynamic models, which are difficult to accommodate to on-orbit assembly operations in extreme space environments. Typical space operation tasks, such as plug and pull operation, whose control strategy can be artificially designed. Being artificially designed by combining the output control strategy and deep reinforcement learning algorithm, which can simplify the training difficulty of deep reinforcement learning, making the learning process more efficient and training results better. In this paper, a deep Residual reinforcement learning algorithm combined with a heuristic control strategy is constructed to complete the space mechanical arm assembly operation training in a highly realistic simulation environment. Based on the experimental data, the Residual deep reinforcement-learning algorithm designed in this paper shows the performance of rapid convergence and can complete the on-orbit assembly operation task with a high probability.
Research on Static Test Verification Technology of the Connection Between APU and Fuselage of Civil Aircraft
Authors: Zhu Zhengyi 
Published: December 2022

Abstract
This paper studies the static strength verification of the connection between APU and fuselage of large civil aircraft, which provides a verification method for the airworthiness verification of the connection between APU and fuselage. Based on the internal force analysis results of APU finite element, this paper selects the static test load, completes the design from the theoretical load to the test load, designs a set of APU dummy loading device to introduce the load to complete the static test. The finite element analysis model is verified by the test results. The verified finite element analysis, limit load and limit load static strength test are used to show the airworthiness compliance of the static strength of the connection between APU and fuselage.
Study of COSPAS-SARSAT Beacon Device Used in Chinese Crewed Spaceflight
Authors: Ji Xue , He Ze Hua , Song Xing Xue , Ye Jian She , Zhang Bei Ming 
Published: December 2022

Abstract
Positioning and carrying out search and rescue as soon as possible after a spacecraft is forced to land or crash is a key issue in the field of the Global Human Spaceflight Program. To promptly obtain an emergency location in the event of an unplanned landing, the Search and Rescue Satellite System (COSPAS-SARSAT) has been widely used in Global Human Spaceflight Program. Due to the currently widely used distress beacon landing crash activation, the spacecraft often lost the chance to survive at that time. Therefore, the Search and Rescue Satellite System regards the Emergency Locator Transmitter for In-Flight Distress Tracking (ELT-DT) alike device as the main research direction of technology. This paper introduces the application of COSPAS-SARSAT in global human spaceflight programs, the operation performance of the ELT-DT, the operation of the custom ELT alike device on the re-entry module and the prospects of the application.
Forecasting and analysis of PM2.5 concentration in Hainan Province based on LSTM Neural Networks
Authors: Hu Shuning , Liu Bing , Wang Jizhen , Zhang Youchao 
Published: December 2022

Abstract
The change of PM2.5 concentration not only reflects the change of air quality level, but also affects human life and health. However, because the PM2.5 change is caused by wide-ranging factors and the occurrence mechanism is complex, it is difficult to build a stable forecasting model with general physical methods. This study builds three different LSTM models to forecast the PM2.5 concentration of Hainan Province from 2019 to 2021. Model 1 is a fully connected neural network, which is also the base model. Model 2 is a single unit LSTM model. Model 3 is a multi-LSTM layer model. The conclusion indicate that the multi-layer LSTM model has the best forecasting precison for PM2.5 concentration, and the single unit LSTM model quite the opposite. The length of the time series has certain influence on the precision of PM2.5 forecasted by LSTM models, suggesting that a suitable time series length should be selected when building LSTM models.
Intrusion Detection System for Internet of Vehicles Based on Ensemble Learning and CNN
Authors: Luo Anlun 
Published: December 2022

Abstract
With the combination of network and automotive technology, more and more modern cars are controlled based on electronic control units (ECUs). And multiple ECUs in modern cars are interconnected and cooperate through the in-vehicle network. But such in-vehicle networks do not consider potential security issues, which might lead to property damage and life threat. An intrusion Detection System (IDS) is a method to detect anomalies and warn about network intrusions. In this study, an IDS model based on Convolutional Neural Networks (CNNs) and ensemble learning is proposed. Meanwhile, to further improve the capability of the proposed IDS model, the structure of some CNN basic models is modified. Finally, the proposed model is validated on a representative standard Internet of Vehicles (IoV) dataset Car-Hacking Dataset. In the experiment, the proposed model achieves 100% accuracy and fl-score, and the detection time is from 1.0ms to 2.8ms, which demonstrates that the proposed IDS model detects the effectiveness of network intrusions.
An effective algorithm for offshore air temperature prediction with LSTM neural network and wavelet decomposition and reconstruction
Authors: Wang Longfei , Song Miaomiao , Liu Shixuan , Wang Bo , Chen Shizhe , Hu Tong , Hu Wei 
Published: December 2022

Abstract
Offshore air temperature is an important parameter in marine scientific research. The change of offshore air as an indicator of the marine ecological environment is not only related to the growth of offshore organisms but also affects the development of the marine economy. Effective prediction of offshore air temperature is significant. The prediction model of offshore air temperature data is established by using wavelet decomposition and reconstruction algorithm combined with long short-term memory neural network (LSTM). We use the wavelet decomposition to decompose the offshore air temperature data of the ocean station into the overview signal and the detail signal, and the decomposed signal is reconstructed by a single branch to obtain the reconstructed signal. Then, input the reconstructed signals into LSTM model to predict the future offshore temperature. Finally, through the experiments, the proposed model is verified that has more advantages than the prediction effect of the LSTM offshore air temperature prediction model based on seasonal-trend decomposition procedure based on seasonal trend loss (STL) decomposition and the traditional LSTM prediction model. The proposed model has better prediction accuracy for offshore air temperature, and the prediction model can achieve effective prediction of the offshore air temperature.
On the formation of lines in quantum phase space
Authors: Steuernagel Ole , Yang Popo , Lee Ray-Kuang 
Published: December 2022

Abstract
We theoretically study the formation of lines in phase space using Wigner’s distribution

W
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In trapped quantum systems such lines form generically, crisscrossing phase space and they can have astonishing extent, reaching across the entire state. In classical systems this does not happen. We show that the formation of such straight line patterns is due to the formation of ‘randomized comb-states’. We establish their stability to perturbations, and that they are tied to coherences in configuration space. We additionally identify generic higher-order ‘eye’ patterns in phase space which occur less often since they arise from more specific symmetric comb-states; we show that the perturbation of eye patterns through their randomization tends to deform them into lines. Lines in phase space should give rise to large probability peaks in measurements.
Gene amplification mutations originate prior to selective stress in Acinetobacter baylyi
Authors: Herrmann Jennifer A , Koprowska Agata , Winters Tesa J , Villanueva Nancy , Nikityuk Victoria D , Pek Feini , Reis Elizabeth M , Dominguez Constancia Z , Davis Daniel , McPherson Eric , Rocco Staci R , Recendez Cynthia , Difuntorum Shyla M , Faeth Kelly , Lopez Mario D , Awwad Habeeba M , Ghobashy Rola A , Cappiello Lauren , Neidle Ellen L , Quiñones-Soto Semarhy , Reams Andrew B 
Published: December 2022

Abstract
The controversial theory of adaptive amplification states gene amplification mutations are induced by selective environments where they are enriched due to the stress caused by growth restriction on unadapted cells. We tested this theory with three independent assays using an Acinetobacter baylyi model system that exclusively selects for cat gene amplification mutants. Our results demonstrate all cat gene amplification mutant colonies arise through a multistep process. While the late steps occur during selection exposure, these mutants derive from low-level amplification mutant cells that form before growth-inhibiting selection is imposed. During selection, these partial mutants undergo multiple secondary steps generating higher amplification over several days to multiple weeks to eventually form visible high-copy amplification colonies. Based on these findings, amplification in this Acinetobacter system can be explained by a natural selection process that does not require a stress response. These findings have fundamental implications to understanding the role of growth-limiting selective environments on cancer development. We suggest duplication mutations encompassing growth factor genes may serve as new genomic biomarkers to facilitate early cancer detection and treatment, before high-copy amplification is attained.
Environmental Interaction of Resolved Human Cytomegalovirus Infection With Crohn’s Disease Location
Authors: Shih Terri , Yusung Susy , Gonsky Rivkah , Dutra-Clarke Rhiannon , Ziring David , Rabizadeh Shervin , Kugathasan Subra , Denson Lee A , Li Dalin , Braun Jonathan 
Published: December 2022

Lay Summary
Active cytomegalovirus (CMV) infection complicates management of inflammatory bowel disease, but the relationship of resolved CMV infection to Crohn’s disease (CD) behavior or localization is unknown. This article reports a striking risk (9-fold) of Crohn’s disease localization to the colon with prior CMV infection. It also reports imputed mucosal cellular composition, HLA class 1, and KIR gene variants that delimit prior observations regarding HLA and KIR associations with Crohn’s disease risk and behavior.
The Power of Keratinous Matrices (Head Hair, Body Hair and Nail Clippings) Analysis in a Case of Death Involving Anabolic Agents
Authors: Gheddar Laurie , Blanchot Adeline , Ameline Alice , Arbouche Nadia , Raul Jean-Sébastien , Kintz Pascal 
Published: December 2022

Abstract
A 29-year-old man with no previous medical history was found dead at home. Anabolic products (tablets and oily solutions) and syringes were found at the scene. The man was known to train regularly at a fitness club and to use anabolic drugs. Following an unremarkable autopsy with normal histology, toxicological analyses were requested by the local prosecutor to provide further information. Blood, head hair (5 cm, black), body hair (axillary and leg) and toe and finger nail clippings were submitted to liquid and gas chromatography coupled to tandem mass spectrometry (LC and GC–MS-MS) methods to test for anabolic steroids. Blood tested positive for testosterone (4 ng/mL), boldenone (26 ng/mL), stanozolol (3 ng/mL) and trenbolone (&lt;1 ng/mL). Segmental head hair tests (2 × 2.5 cm) revealed a repeated consumption of testosterone (65–72 pg/mg), testosterone propionate (930–691 pg/mg), testosterone isocaproate (79 pg/mg to &lt;5 pg/mg), nandrolone decanoate (202–64 pg/mg), boldenone (16 pg/mg), stanozolol (575–670 pg/mg), trenbolone (4 pg/mg–not detected), drostanolone (112–30 pg/mg), drostanolone enanthate (26–5 pg/mg) and drostanolone propionate (15–4 pg/mg). In addition to the substances identified in head hair, testosterone decanoate, testosterone cypionate and nandrolone were identified in both body hair and nails. The experts concluded that the manner of death can be listed as toxic due to massive repetitive use of anabolic steroids during the previous months. For anabolic agents, blood does not seem to be the best matrix to document a fatal intoxication. Indeed, these products are toxics when abused long term and are known to cause cardiac, hepatic and renal diseases. When compared to blood, hair and nails have a much larger window of detection. Therefore, keratinous matrices seem to be the best approach to test for anabolic steroids when a sudden death is observed in the context of possible abuse of steroids.
Network Topology Can Explain Differences in Pleiotropy Between Cis- and Trans-regulatory Mutations
Authors: Vande Zande Pétra , Wittkopp Patricia J 
Published: December 2022

Abstract
A mutation's degree of pleiotropy (i.e., the number of traits it alters) is predicted to impact the probability of the mutation being detrimental to fitness. For mutations that impact gene expression, mutations acting in cis have been hypothesized to generally be less pleiotropic than mutations affecting the same gene's expression in trans, suggesting that cis-regulatory mutations should be less deleterious and more likely to fix over evolutionary time. Here, we use expression and fitness data from Saccharomyces cerevisiae gene deletion strains to test these hypotheses. By treating deletion of each gene as a cis-regulatory mutation affecting its own expression and deletions of other genes affecting expression of this focal gene as trans-regulatory mutations, we find that cis-acting mutations do indeed tend to be less pleiotropic than trans-acting mutations affecting expression of the same gene. This pattern was observed for the vast majority of genes in the data set and could be explained by the topology of the regulatory network controlling gene expression. Comparing the fitness of cis- and trans-acting mutations affecting expression of the same gene also confirmed that trans-acting deletions tend to be more deleterious. These findings provide strong support for pleiotropy playing a role in the preferential fixation of cis-regulatory alleles over evolutionary time.
Unscheduled epigenetic modifications cause genome instability and sterility through aberrant R-loops following starvation
Authors: Sun Bing , Sherrin McLean , Roy Richard 
Published: December 2022

Abstract
During starvation, organisms modify both gene expression and metabolism to adjust to the energy stress. We previously reported that Caenorhabditis elegans lacing AMP-activated protein kinase (AMPK) exhibit transgenerational reproductive defects associated with abnormally elevated trimethylated histone H3 at lysine 4 (H3K4me3) levels in the germ line following recovery from acute starvation. Here, we show that these H3K4me3 marks are significantly increased at promoters, driving aberrant transcription elongation resulting in the accumulation of R-loops in starved AMPK mutants. DNA-RNA immunoprecipitation followed by high-throughput sequencing (DRIP-seq) analysis demonstrated that a significant proportion of the genome was affected by R-loop formation. This was most pronounced in the promoter–transcription start site regions of genes, in which the chromatin was modified by H3K4me3. Like H3K4me3, the R-loops were also found to be heritable, likely contributing to the transgenerational reproductive defects typical of these mutants following starvation. Strikingly, AMPK mutant germ lines show considerably more RAD-51 (the RecA recombinase) foci at sites of R-loop formation, potentially sequestering them from their roles at meiotic breaks or at sites of induced DNA damage. Our study reveals a previously unforeseen role of AMPK in maintaining genome stability following starvation. The downstream effects of R-loops on DNA damage sensitivity and germline stem cell integrity may account for inappropriate epigenetic modification that occurs in numerous human disorders, including various cancers.
Disclosing the molecular basis of salinity priming in olive trees using proteogenomic model discovery
Authors: Skodra Christina , Michailidis Michail , Moysiadis Theodoros , Stamatakis George , Ganopoulou Maria , Adamakis Ioannis-Dimosthenis S , Angelis Lefteris , Ganopoulos Ioannis , Tanou Georgia , Samiotaki Martina , Bazakos Christos , Molassiotis Athanassios 
Published: December 2022

AbstractPlant responses to salinity are becoming increasingly understood, however, salt priming mechanisms remain unclear, especially in perennial fruit trees. Herein, we showed that low-salt pre-exposure primes olive (Olea europaea) plants against high salinity stress. We then performed a proteogenomic study to characterize priming responses in olive roots and leaves. Integration of transcriptomic and proteomic data along with metabolic data revealed robust salinity changes that exhibit distinct or overlapping patterns in olive tissues, among which we focused on sugar regulation. Using the multi-crossed -omics data set, we showed that major differences between primed and nonprimed tissues are mainly associated with hormone signaling and defense-related interactions. We identified multiple genes and proteins, including known and putative regulators, that reported significant proteomic and transcriptomic changes between primed and nonprimed plants. Evidence also supported the notion that protein post-translational modifications, notably phosphorylations, carbonylations and S-nitrosylations, promote salt priming. The proteome and transcriptome abundance atlas uncovered alterations between mRNA and protein quantities within tissues and salinity conditions. Proteogenomic-driven causal model discovery also unveiled key interaction networks involved in salt priming. Data generated in this study are important resources for understanding salt priming in olive tree and facilitating proteogenomic research in plant physiology.