bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multi-Source Error Calibration and Observability Analysis of Integrated Inertial Navigation System/Polarization Sensor Navigation System
Authors: Zhang Hengbo , Liu Xin , Dou Qingfeng , Han Qiongyao 
Published: March 2023

Abstract
Accurate polarization information acquisition is the key technique of the integrated inertial navigation system (INS)/polarization sensor (PS) navigation system. To solve the problem of poor performance of polarization sensors that real-time error calibration by using a rotary table, we propose a fast calibration method that polarization sensor combined with inertial navigation systems. Firstly, we construct the calibration model of polarization sensor. A tightly coupled INS/PS calibration model is proposed, specifically, the polarization sensor errors, which are the intensity gain coefficient, the degree of polarization coefficient, and the installation angle error of a polarizer, are augmented into the state of the integrated navigation systems. By introducing the error term of polarization sensor into the state of the integrated navigation systems, the error estimation can be carried out and the speed of error calibration can be improved. Secondly, considering the nonlinearity of error calibration model, we use the Unscented Kalman Filter (UKF) to calibrate the error. On this basis, we make use of the observability analysis to verify its validity of the proposed model in a dynamic scene. Finally, real-world experiments are conducted on the self-developed multi-channel polarization sensor. The experimental results show that the calibration method can be done quickly without the use of a rotary table.
Application of Robot Obstacle Avoidance Algorithm and Attack Strategy on ROS
Authors: Ye Jiancai , Kang Nan , Guan Biaoliang , Cai Shicheng , Zhang Tengyu , Yang Yingze 
Published: March 2023

Abstract
China Robot Competition, which integrates the theory and practice of multi-disciplinary intelligent robot system, is one of the most influential robot competitions in China. In this paper, we apply RRT * and APF to the Middle Size Simulation League of soccer on ROS and propose an attack strategy based on extremum. Our experiments showed that the implementation of RRT* and APF optimizes the multiple iterations existing in original obstacle avoidance algorithm and greatly improves the performance of robots when there are fewer obstacles and the maximum speed of obstacle avoidance is limited. Besides, our attack strategy has proved effective on boosting the chances of goal and helped the Central South University to win the championship of China Robot Competition Middle Size Simulation League in 2021.
Combining Multi-Scale Convolutional Neural Network with Long Short-Term Memory Neural Network for State of Charge Estimation of Lithium-ion Batteries
Authors: Liu Cheng , Wang Bingchuan , Zou Yiqun 
Published: March 2023

Abstract
To develop safe and intelligent battery management systems for electric vehicles, it is necessary to accurately estimate the state of charge (SOC) of lithium-ion batteries. At present, deep learning methods have been broadly applied in the field of SOC estimation of lithium-ion batteries. However, existing deep SOC estimators are difficult to capture global trends due to being too sensitive to the changes of continuous time data points. In addition, a single non-linear neural network tends to ignore the linear features of the data, which makes the robustness of the estimation not good enough. To address these two issues, this paper proposes a SOC estimation method by combining multi-scale convolutional neural network with long short-term memory neural network (called MCNN-LSTM). Specifically, on the one hand, multiple one-dimension convolutions with different dilation rates are used to extract features at different time scales, and LSTM neural networks are used to acquire the long-term dependencies of the data. On the other hand, an additional fully connected layer is used to extract the linear features, which reduces the volatility of the estimation and enhances the robustness of the estimation. The results suggest that MCNN-LSTM has higher estimation accuracies and better robustness than DNN, LSTM, and CNN-LSTM.
Self-learning Time-varying Digital Twin System for Intelligent Monitoring of Automatic Production Line
Authors: Hao Caihua , Wang Zhaoyu , Zou Yi , Zhao Zunyuan 
Published: March 2023

Abstract
At present, the automation production line has problems such as insufficient intelligence level. The intelligent monitoring, control and improvement of product quality and efficiency are the key common technologies faced by advanced manufacturing industry. Self-learning time varying digital twin (DT) system for intelligent monitoring is proposed in the paper. In the process of automatic production line processing and workpiece detection, an DT consisting of physical production line layer, edge monitoring layer and cloud evolution layer is built. The DT system realizes self-learning time-varying through active excitation of processing parameter optimization. The workpiece quality is a real-time representation of the tool condition, and the tool wear sensitive features extracted by the deep learning algorithm. Through the two-way drive of time-varying physical and virtual data, the tool wear characterization model can be evaluated, self-learning, updated and verified timely in the light of the actual condition to achieve tool condition monitoring and processing parameter optimization. The prediction model is self-iterative and simplified in the cloud, and the edge side is quickly matched and adaptive. Self-learning time-varying DT system based on self-driving of manufacturing process can adaptively improve the ability of intelligent monitoring.
SATVSR: Scenario Adaptive Transformer for Cross Scenarios Video Super-Resolution
Authors: Chen Yongjie , Wu Tieru 
Published: March 2023

Abstract
Video Super-Resolution (VSR) aims to recover sequences of high-resolution (HR) frames from low-resolution (LR) frames. Previous methods mainly utilize temporally adjacent frames to assist the reconstruction of target frames. However, in the real world, there is a lot of irrelevant information in adjacent frames of videos with fast scene switching, these VSR methods cannot adaptively distinguish and select useful information. In contrast, with a transformer structure suitable for temporal tasks, we devise a novel adaptive scenario video super-resolution method. Specifically, we use optical flow to label the patches in each video frame, only calculate the attention of patches with the same label. Then select the most relevant label among them to supplement the spatial-temporal information of the target frame. This design can directly make the supplementary information come from the same scene as much as possible. We further propose a cross-scale feature aggregation module to better handle the scale variation problem. Compared with other video super-resolution methods, our method not only achieves significant performance gains on single-scene videos but also has better robustness on cross-scene datasets.
A Hierarchical Accident Recognition Method for Highway Traffic Systems
Authors: Chen Menghuan , Liang Jiarong , Shu Jingyao , Zhu Ren 
Published: March 2023

Abstract
With the rapid development of highway traffic systems, the accident rate of highway remains high. Once an accident occurs, it will cause serious casualties. In this paper, we proposed a highway accident identification method for real-time accident detection through high-speed monitoring. The proposed method focuses on high detecting speed and high recall for car accident. The designed system consists of four parts: first, yolov3 is used to detect the car. Second, identified car frames are put the identified car frame into the Mosse Tracker for tracking. Third, speed and future center point are estimated according to its size and pixel movement. If the speed exceeds a certain threshold or the future center points among other trackers are too close, the object frame will be put into a trained SVM model. Finally, the flow vector estimation calculation will be used to determine whether there is an accident. The system has fast detection speed, high recall and strong real-time performance. Through this system, we can send warning information to upstream car drivers and traffic management personnel to improve the efficiency of accident handling and reduce the rate of highway traffic accidents and casualties.
Image Classification of Car Paint Defect Detection Based on Convolutional Neural Networks
Authors: Jiang Zhaolin , Hu Xueyuan , Wang Sunxin 
Published: March 2023

Abstract
In the study of using images to display car paint defects, the current need is to use deep Convolutional Neural Networks (CNN) to identify and classify different types of car paint defects, so as to give full play to the application of image processing in the field of automatic car paint defect detection. Using the collected car paint defect images, the car paint defects dataset is established. The preprocessing process of original data and the application of three image classification models based on CNN are visually displayed. First, the dataset of 7 types of car body defects including bubble, dust, fouling, pinhole, sagging, scratch, and shrink has been established, with a total of 2468 images. The model of MobileNet-V2, Vgg16, and ResNet34 are selected for training. As a result, after 30 training iterations, the MobileNetV2 algorithm achieved 94.3% accuracy, the accuracy of the Vgg16 algorithm is as high as 99.9%, and the accuracy of the ResNet34 algorithm is maintained at 99.2%. To sum up, for car paint defect detection, deep learning has great potential and deserves further development.
The Status and Challenges of Image Data Augmentation Algorithms
Authors: Wei Jian , Wang Qinzhao , Song Xiaoqing , Zhao Zixu 
Published: March 2023

Abstract
Image data-augmentation algorithms effectively trump the problem of insufficient training samples for deep learning in some application fields, and it is typically for scholars to choose some of them for various computer vision tasks. But as the algorithms develop rapidly, the early proposed classification that the data-augmentation algorithms are sorted into classical ways and generating methods is no more suitable, because such classification misses some other meaningful strategies. Besides, it is frustrating for someone to decide which is the exact method to undertake, though there are too many optional algorithms to choose. Towards the goal of making some suggestions, the paper categorizes image data-augmentation algorithms into three kinds from the perspective of algorithm strategy, and they are matrix transformation algorithm, feature expansion algorithm and model generation based on neural network algorithm. The paper analyzes the typical algorithm principle, performance, application scenarios, research status and future challenges, and forecasts the development trend of data augmentation algorithms. The paper can provide academic reference for data augmentation algorithm in the fields of medicine and military.
Optimized Operation of Park-level Integrated Energy System Based on Particle Swarm Optimization with Curve Increasing
Authors: Cheng Lei , Sun Xianbo , Zhang Yi 
Published: March 2023

Abstract
In the traditional park, the electric energy is completely provided by the power grid, in addition, the thermal energy is provided by the gas boiler. The single energy structure leads to higher operating cost and lower energy utilization rate of the park. Therefore, firstly, a park-level integrated energy system composed of gas turbine, heat recovery boiler, gas boiler, storage battery, and the load of heat and power has been proposed in this composition. Secondly, considering time-sharing tariff and the randomness of load, the objective function is established with the minimum daily operation cost as the optimization aim. Then, a particle swarm optimization with curve increasing strategy is proposed to solve the objective function. Finally, the load data of a typical day in winter is analyzed as an example. The simulation results show that the proposed method can optimize the energy structure of the park, improve the economy of the system operation, and reduce the daily operation cost of the system by 12.32%.
National Identity, Social Preferences, and Foreign Policy Attitudes: Experimental Evidence from Japan
Authors: Chung Eunbin 
Published: March 2023

AbstractAre strong national identities in the public an impediment to cooperative foreign policy attitudes? Researchers have proposed the promotion of universalism—and, accordingly, the submergence of existing national identities—as a viable path toward cooperative international relations. Conversely, I suggest that strong national identities can actually encourage public support for cooperative foreign policy. Evidence from field experiments in Japan that integrates psychological affirmation theory and a game measuring social preferences indicates that Japanese who were affirmed of the positive aspects of their national identity held more prosocial attitudes toward their Chinese counterpart in the game, which in turn led to cooperative foreign policy preferences. In contrast, participants who were not affirmed of their national identity exhibited more proself tendencies in the game, which predicted support for militaristic foreign policy. Finally, in contrast to national identity affirmation, national chauvinism, or perceived superiority over other countries, was associated with militant internationalist attitudes.
Clinical evaluation of BioFire® multiplex-PCR panel for acute undifferentiated febrile illnesses in travellers: a prospective multicentre study
Authors: Camprubí-Ferrer Daniel , Cobuccio Ludovico , Van Den Broucke Steven , Balerdi-Sarasola Leire , Genton Blaise , Bottieau Emmanuel , Navero-Castillejos Jessica , Martinez Miguel J , Jay Corinne , Grange Anne , Borland Stéphanie , Vaughn Mike , Rodriguez-Valero Natalia , Almuedo-Riera Alex , D’Acremont Valérie , Subirà Carme , de Alba Tessa , Cruz Angeline , Van Esbroeck Marjan , Smith Crystal , Hillman Ashley , Hanberg Brandon , Trauscht Rob , Spampanato Nerissa , Muñoz Jose 
Published: March 2023

Abstract

Background
Identifying the causes of Acute Undifferentiated Febrile Illness (AUFI) is key to improve the management of returning travellers with fever. We evaluated a BioFire®FilmArray® prototype panel of multiplex nucleic acid amplification tests (NAAT) targeting different relevant pathogens in travellers returning with fever.

Methods
Prospective, multicentre study to evaluate a prototype panel in whole blood samples of adult international travellers presenting with AUFI in three European travel Clinics/Hospitals (November 2017–November 2019). We evaluated 15 target analytes: Plasmodium spp., Plasmodium falciparum, Plasmodium knowlesi, Plasmodium malariae, Plasmodium ovale, Plasmodium vivax, chikungunya virus, dengue virus, Zika virus, Anaplasma phagocytophilum, Borrelia spp., Leptospira spp., Orientia tsutsugamushi, Rickettsia spp. and Salmonella spp. Results were compared with composite reference standards (CRSs) for each target infection, including direct methods [smear microscopy, rapid diagnostic test (RDT), reference NAAT and blood cultures] and indirect methods (paired serology).

Findings
Among 455 travellers with AUFI, 229 target infections were diagnosed; the prototype panel detected 143 (overall sensitivity and specificity of 62.5 and 99.8%, respectively). The panel identified all Plasmodium infections (n = 82). Sensitivity for dengue (n = 71) was 92.9, 80.8 and 68.5% compared with RDT, NAAT and CRS, respectively. Compared with direct methods and CRS, respectively, the prototype panel detected 4/4 and 4/6 chikungunya, 2/2 and 4/29 Leptospira spp., 1/1 and 1/6 O. tsutsugamushi and 2/2 and 2/55 Rickettsia spp., but 0/2 and 0/10 Zika, 0/1 and 0/11 A. phagocytophylum and 0/3 Borrelia spp. diagnosed by serology and only 1/7 Salmonella spp. diagnosed by blood cultures. 77/86 (89.5%) infections not detected by the panel were diagnosed by serology.

Interpretation
The prototype panel allowed rapid and reliable diagnosis for malaria, dengue and chikungunya. Further improvements are needed to improve its sensitivity for Zika and important travel-related bacterial infections.
Deep Learning-Assisted Multivariate Analysis for Nanoscale Characterization of Heterogeneous Beam-Sensitive Materials
Authors: Kosasih Felix Utama , Su Fanzhi , Du Tian , Ratnasingham Sinclair Ryley , Briscoe Joe , Ducati Caterina 
Published: March 2023

Abstract
Nanoscale materials characterization often uses highly energetic probes which can rapidly damage beam-sensitive materials, such as hybrid organic–inorganic compounds. Reducing the probe dose minimizes the damage, but often at the cost of lower signal-to-noise ratio (SNR) in the acquired data. This work reports the optimization and validation of principal component analysis (PCA) and nonnegative matrix factorization for the postprocessing of low-dose nanoscale characterization data. PCA is found to be the best approach for data denoising. However, the popular scree plot-based method for separation of principal and noise components results in inaccurate or excessively noisy models of the heterogeneous original data, even after Poissonian noise weighting. Manual separation of principal and noise components produces a denoised model which more accurately reproduces physical features present in the raw data while improving SNR by an order of magnitude. However, manual selection is time-consuming and potentially subjective. To suppress these disadvantages, a deep learning-based component classification method is proposed. The neural network model can examine PCA components and automatically classify them with an accuracy of &gt;99% and a rate of ∼2 component/s. Together, multivariate analysis and deep learning enable a deeper analysis of nanoscale materials’ characterization, allowing as much information as possible to be extracted.
Symbiosis between Dendrobium catenatum protocorms and Serendipita indica involves the plant hypoxia response pathway
Authors: Xu Zhi-Xiong , Zhu Xin-Meng , Yin Huachun , Li Bo , Chen Xiao-Jie , Fan Xu-Li , Li Neng-Qi , Selosse Marc-André , Gao Jiang-Yun , Han Jia-Jia 
Published: March 2023

Abstract
Mycorrhizae are ubiquitous symbioses established between fungi and plant roots. Orchids, in particular, require compatible mycorrhizal fungi for seed germination and protocorm development. Unlike arbuscular mycorrhizal fungi, which have wide host ranges, orchid mycorrhizal fungi are often highly specific to their host orchids. However, the molecular mechanism of orchid mycorrhizal symbiosis is largely unknown compared to that of arbuscular mycorrhizal and rhizobial symbiosis. Here, we report that an endophytic Sebacinales fungus, Serendipita indica, promotes seed germination and the development of protocorms into plantlets in several epiphytic Epidendroideae orchid species (6 species in 2 genera), including Dendrobium catenatum, a critically endangered orchid with high medicinal value. Although plant–pathogen interaction and high meristematic activity can induce the hypoxic response in plants, it has been unclear whether interactions with beneficial fungi, especially mycorrhizal ones, also involve the hypoxic response. By studying the symbiotic relationship between D. catenatum and S. indica, we determined that hypoxia-responsive genes, such as those encoding alcohol dehydrogenase (ADH), are highly induced in symbiotic D. catenatum protocorms. In situ hybridization assay indicated that the ADH gene is predominantly expressed in the basal mycorrhizal region of symbiotic protocorms. Additionally, the ADH inhibitors puerarin and 4-methylpyrazole both decreased S. indica colonization in D. catenatum protocorms. Thus, our study reveals that S. indica is widely compatible with orchids and that ADH and its related hypoxia-responsive pathway are involved in establishing successful symbiotic relationships in germinating orchids.
Psychological distress in survivors of childhood cancer and their parents before and after a follow-up appointment: the need for screening and validation of the emotion thermometer
Authors: Harju Erika , Roser Katharina , Eisenreich Bernhard , Scheinemann Katrin , Michel Gisela 
Published: March 2023

Abstract

Background:
Childhood cancer affects patients and their parents. Childhood cancer survivors are at risk of mental health problems. Regular psychological screening is recommended. The emotion thermometer (ET) is a short, validated tool to detect psychological distress in adult patients with cancer. However, its suitability and acceptability in young cancer populations have not yet been reported. We (1) describe mental health in survivors and their parents before and after a follow-up appointment and (2) investigate the ET's usefulness for screening in young childhood cancer survivors.

Methods:
Survivors (15 years or older at study, ≥1 year after treatment ended) and their parents completed two electronic surveys to assess mental health (including somatization, depression, anxiety, a Global Severity Index [GSI], fear of cancer recurrence/relapse and post-traumatic stress). The first questionnaire was completed in the weeks leading up to the follow-up appointment and the second one after. We assessed survivors' opinion on acceptability of the ET.

Results:
Forty-five survivors (age at study: 22 years, 54% female) and 46 parents (age: 54 years, 63% female) participated. Scores for somatization, depression, and the GSI were lower after the appointment (p<.05 for all). For parents, there was no change. A subset of survivors reported clinically relevant post-traumatic stress (Nbefore = 3 [8%], Nafter = 4 [10%]). Fear of cancer recurrence/relapse remained stable. The ET identified moderate-to-severe distress in 13 survivors (29%), showing good specificity (0.91) and sensitivity (0.91) in relation to the gold standard Symptom Checklist-90-R. Survivors found the ET to be easy to use and understand.

Conclusions:
Psychological distress in survivors attending follow-up care is common, and screening is imperative. The ET may be helpful for initial psychological screening.
Prospective Assessment of the Prevalence of Enter Hyperoxalosis in Kidney Transplant Candidates
Authors: Mejia Christina , Tariq Anam , Alotaibi Manal , Lakhani Laila , Greenspan Wendy , Naqvi Fizza , Alasfar Sami , Brennan Daniel C. 
Published: March 2023

Background.
Enteric hyperoxalosis (EH) is an emerging cause of kidney transplantation (KT) dysfunction. We sought to determine the prevalence of EH and factors that affect plasma oxalate (POx) among at-risk KT candidates.

Methods.
We prospectively measured POx among KT candidates evaluated at our center from 2017 to 2020 with risk factors for EH namely bariatric surgery, inflammatory bowel disease, or cystic fibrosis. EH was defined by a POx ≥10 μmol/L. Period-prevalence of EH was calculated. We compared mean POx across 5 factors: underlying condition, chronic kidney disease (CKD) stage, dialysis modality, phosphate binder type, and body mass index.

Results.
Of 40 KT candidates screened, 23 had EH for a 4-y period prevalence of 58%. Mean POx was 21.6 ± 23.5 μmol/L ranging from 0 to 109.6 μmol/L. 40% of screened had POx >20 μmol/L. Sleeve gastrectomy was the most common underlying condition associated with EH. Mean POx did not differ by underlying condition (P = 0.27), CKD stage (P = 0.17), dialysis modality (P = 0.68), phosphate binder (P = 0.58), and body mass index (P = 0.56).

Conclusions.
Bariatric surgery and inflammatory bowel disease were associated with a high prevalence of EH among KT candidates. Contrary to prior studies, sleeve gastrectomy was also associated with hyperoxalosis in advanced CKD. POx concentrations observed in EH reached levels associated with tissue and potentially allograft deposition. Concentrations can be as high as that seen in primary hyperoxaluria. More studies are needed to assess if POx is indeed a modifiable factor affecting allograft function in patients with EH.