bluuebunny/crossref_metadata_2025_split huggingface dataset:
Study on Levodopa Sensor Modified by Ti3C2 MXene
Authors: Zhang Yi , Xia Xiaoyu , Shou Jialong , Liang Bo , Fang Lu 
Published: June 2023

Abstract
The dosage management of levodopa is of great significance in the treatment of Parkinson’s disease. Measuring the patient’s levodopa concentration helps the physician to adjust the dose in time, so as to avoid complications. In this paper, we report an electrochemical sensor based on titanium carbide (Ti3C2) MXene nanomaterials, which can sensitively reflect the change of levodopa concentration. In the study, Ti3C2/GCE electrode was prepared by a simple drop-coating method, and then the Ti3C2/Pt/GCE electrode was prepared by modifying Pt nanoparticles on the electrode surface by impregnation adsorption method. After modification, the response of the GCE electrode to levodopa before and after Ti3C2 decoration was compared by cyclic voltammetry (CV), and the diffusion control process on the electrode surface was also investigated. Then, differential pulse voltammetry (DPV) was used to explore the relationship between the peak current of the oxidation anode and the concentration of levodopa. Finally, the relationship between current and concentration at a certain time point was explored by chronoamperometry (CA), and the time charge curves were plotted to explore the functional relationship between the amount of charge exchanged in the reaction and the concentration of levodopa (Coulomb analysis method). The research shows that the coulomb analysis method has a higher detection sensitivity and linear range, and this technology is expected to be applied to the in vivo monitoring of levodopa in our future research.
Enhanced Energy Density of Polyetherimide Using Low Content Barium Titanate Nanofillers
Authors: Wu Xudong , Tan Daniel Q. 
Published: June 2023

Abstract
Dielectric polymer composites containing low content inorganic fillers exhibit superior dielectric properties and endurance for simultaneous enhancement of dielectric constant and breakdown strength. Polyetherimide (PEI) composites incorporated with nanosized BaTiO3 of 0.1vol.% loading fraction was developed in this work and their breakdown strength reached a maximum value along with the highest dielectric constant. The discharged energy density increased to 8.33 J/cm3, which is 2.33 times that of pure PEI. The composite film also maintained an excellent endurance after 104 cycles of fatigue test. The low-level filling for enhanced composite property may reduce the challenge in film scale-up manufacturing.
Multiclass Support Vector Data Description in Extreme Learning Machine Feature Space
Authors: Lei Yaolin , Ding Wenrui , Wang Yufeng , Zhang Wenbo , Chai Xinghua 
Published: May 2023

Abstract
Support vector data description (SVDD) method aims to address the one-class classification (OCC) problem to find a hypersphere-shaped description of target data set. For extending SVDD to multiclass classification while remaining the ability of detecting outliers, we propose a novel multiclass SVDD scheme which can be used in effective feature mapping and meta-class separation based on the extreme learning machine algorithm (ELM-MSVDD). Accordingly, the imprecise data difficult to distinguish in specific classes is classified to a meta-class,the meta-class is defined by the disjunction of these specific classes, this operation can reduce the error rate effectively. Experimental results of our ELM-MSVDD method show well performance on the datasets from the UCI machine learning library and radar signal source recognition. Meanwhile, our proposed method provide a theoretical and practical support for other relevant pattern recognition field.
Research on Att-NFM Recommendation Algorithm Based on Attention Mechanism
Authors: Huang Zhen , Zhao Suling , Ye Renzhen 
Published: May 2023

Abstract
Attentional mechanisms play an important role in artificial intelligence, and inspired by other work, we apply them in the field of recommendation algorithms. By combining factorization machine and neural network together through parallel structure, the linear model of factorization machine is used for binary feature interaction, and the nonlinear relationship between features is explored in using neural network. We propose the Att-NFM model to learn attention coefficients in neural networks to characterize the importance between features. Through experiments, we use AUC and LogLoss as evaluation metrics, and the results show that the evaluation metrics are improved by 3% and 2%, respectively.
Simple and Effective Complementary Label Learning Based on Mean Square Error Loss
Authors: Wang Chenggang , Xu Xiong , Liu Defu , Niu Xinyu , Han Shijiao 
Published: May 2023

Abstract
In this paper, we propose a simple and effective complementary label learning approach to address the label noise problem for deep learning model. Different surrogate losses have been proposed for complementary label learning, however, are often sophisticated designed, as the losses are required to satisfy the classifier consistency property. We propose an effective square loss for complementary label learning under unbiased and biased assumptions. We also show theoretically that our method assurances that the optimal classifier under complementary labels is also the optimal classifier under ordinary labels. Finally, we test our method on three different benchmark datasets with biased and unbiased assumptions to verify the effectiveness of our method.
Att GGO-Net:A Semantic Segmentation Method of Lung CT Images with Self and Cross Attention Mechanism
Authors: Yang Xiao Ling , Jin Zhe Yi 
Published: May 2023

Abstract
Chest computed tomography imaging (CT) has become a diagnostic method for many lung diseases due to its high sensitivity and low missed rate of infection. Convolutional neural network based semantic segmentation model that can assist physicians in auxiliary diagnosis. The existing semantic segmentation models do not work well for lesion segmentation of lung CT images, small lesions are not sensitive, and the contour segmentation is coarse, for which this paper designs a network named Att GGO-Net specifically for lung CT images ground glass opacity(GGO) and Lung consolidation segmentation.The basic encoder-decoder network structure used in this study is the U-Net++ model. After feature mapping is finished at each level of U-Net++, cross attention mechanism is introduced to optimize the semantic information in the decoder layer. Finally, self attention mechanism is added to complete the overall network structure.The extensive attention mechanism will cause the network to have many parameters. To solve the issue of large network parameters, the research substitutes depthwise separable convolution for traditional convolution. The data collection of lung CT scans with ground glass opacity and lung consolidation was used for the experiments. The segmentation performance was compared between Att GGO-Net and three classical medical image segmentation networks, namely, U-Net, U-Net++, and Attention U-Net (Att U-Net). The experimental findings demonstrate that the strategy used in this research has a superior segmentation effect than the comparison method.
Application of Deep Reinforcement Learning in Quantum Control
Authors: Cheng Yiming , Yin Yixi , Lin Jiaying , Wang Yaobin 
Published: May 2023

Abstract
Machine learning technology based on artificial neural network has been successfully applied to solve many scientific problems. One of the most interesting areas of machine learning is reinforcement learning, which has natural applicability to optimization problems in physics. In the quantum control task, it is necessary to find a set of optimal control functions to transfer a quantum system from the initial state to the target state with the highest fidelity possible, which is essentially an optimization task. In this paper, we use Deep Deterministic Policy Gradient algorithm (DDPG) to study the quantum control tasks. We use the algorithm to control the transfer of several quantum systems from one state to another. The results show that DDPG algorithm can find a control strategy to make the fidelity of the final state and the target state of the quantum system be maximum value 1. The results show the potential of DDPG in quantum control.
Relation-enhanced Diffusion Model with LightGCN for Information Diffusion Prediction
Authors: Shang Yingdan , Zhou Bin , Zeng Xiang , Chen Kai , Yu Han 
Published: May 2023

Abstract
Modeling and predicting the information diffusion process on social platforms is a critical task in many real-world applications. Recent studies generally model the diffusion graph using graph neural networks to capture the implicit dependencies among users. However, existing studies construct the diffusion graph in a way which cannot fully describe the global dependencies of users due to their narrow definition of user relationship. Meanwhile, graph neural networks in these methods are not suitable for the social network scenario which has scarce node attributes. Therefore, we propose a novel diffusion graph construction method which can enhance relations among users and adopt a simplified graph convolutional operation which is suitable for diffusion prediction scenario. The learned user embedding in our model can effectively preserve the microscopic structure and the high-order proximities between users lies in both the social graph and diffusion graph. Experimental results on four real-world datasets show that the proposed model is superior to the most advanced information diffusion prediction methods.
Design of a Multi-modal Sensor Fusion Unmanned Vehicle System based on Computer Vision
Authors: Li Yaxin , Chen Yan , Yang Zhen , Tong Chao , Yang Xinxing , Zhong Weiliang 
Published: May 2023

Abstract
With the development of artificial intelligence and Internet of Things technology, more and more intelligent products are appearing around us, such as sweeping robots, library service robots and drug delivery robots, etc. In the practical application of these intelligent robots, the core technology of spatial positioning is inseparable, and considering the cost, signal interference and positioning accuracy, it is necessary to study the positioning technology with low cost, small size and strong anti-interference factors. To solve the problem of poor performance of positioning accuracy when using low-cost sensors in the physical environment, we use a four-wheel drive vehicle model as a carrier to build an unmanned vehicle system based on multi-modal sensor fusion, binocular vision localization and other technologies. The core of the system is the MM32F3277G9P chip from MindMotion and the Raspberry Pi embedded development board. The proposed vision information is based on the Intel Realsense T265 camera, which is fused with the data from the nine-axis inertial measurement unit (IMU) and the dual-frequency global positioning system (GPS), so that the positioning algorithm can continuously provide robust and accurate state estimation results in the physical environment through the complementary advantages.
A New Data Dimension Reduction Method Based On Convolution In The Application Of Authenticity Identification Of Traditional Chinese Medicine LongGu
Authors: Wu Qiang , Zhu YuTing , Shi Wei , Wang TianYan , Huang YaWei , Jiang DongJing , Liu Xun 
Published: May 2023

Abstract
As a commonly used Chinese herbal medicine in clinic, LongGu is often used as a powder, which is difficult to identify manually. In this study, unsupervised learning method was used to model and identify LongGu, calcining LongGu and LongGu counterfeits by infrared spectroscopy. After preprocessing the original data, the current commonly used principal component analysis method, which was used to reduce the data dimension, and then the KMeans algorithm was used to realize the classification and identification of the samples. The classification results were better in the theoretical samples, but not in the actual samples. Based on this problem, this paper proposes a convolution-based machine learning feature dimension reduction method for spectral data. Comparing with the data method of principal component analysis, the partial classification effect is obviously better on the theoretical data. The effect of identifying calcining LongGu and LongGu counterfeits in actual data has been significantly improved, and it also provides a reference for the application of machine learning technology in the field of spectral identification of traditional Chinese medicine.
A CAPTCHA Recognition Algorithm Based on Character Segmentation and Random Forest
Authors: Kong Suke , Chang Yu-Chung 
Published: May 2023

Abstract
In this paper, the CAPTCHA code is recognized based on character segmentation and random forest algorithm. In the recognition process, the gray conversion method, binarization method, and Gaussian filtering method are used to process the image, and then the projection segmentation method is used to segment the characters. Using the random forest algorithm to recognize a single character, the recognition accuracy reaches 94%. In character recognition, it is found that 5 and S, 8 and B, 4 and A, and T and F have the lowest resolution accuracy ratio.
Multi-factor network modeling and simulation analysis of time-series dynamic and static climate
Authors: Luo Guilan , Hu Anshun , Wang Caikui , Ma Xin , Fang Lianbiao , Liu Xuan 
Published: May 2023

Abstract
With the development of complex networks and big data disciplines, complex networks and big data are widely used in various fields to reveal the changing characteristics of things. This paper applies the method of complex network to climatology, explores the complex interaction of various climatic factors in China’s regional climate system with the method of complex network, selects the meteorological big data of 288 meteorological stations in China from 1984 to 2016, defines the meteorological stations and connecting network under various climatic factors as a single factor climate network, and adds the connecting edge between meteorological stations with different climatic factors on this basis, Then the multi-factor climate network is constructed by the network characteristic parameters. The analysis of correlation and stability shows that the correlation of climate network from strong to weak is spring, autumn, winter and summer; In the process of 33 years of time series change, the correlation of climate network shows a weak downward trend, with strong stability and good viability.
Curriculum Learning for Debiased Recommendation with Explicit and Implicit Feedback
Authors: Fu Tianren , Chen Jiawei 
Published: May 2023

Abstract
The recommender system (RS) has played an increasingly important role in Internet applications. Recent literature on RS mainly focused on better fitting the user behavior data. However, user behavior data is observational, not experimental. This makes for a wide range of biases in the data. In this paper, we introduce a novel framework to combine the advantages of both multi-task and curriculum learning for the debiased recommendation. Unlike existing methods that are limited to specific feedback, our method follows multi-task learning to unify both explicit and implicit feedback. And these two feedbacks are learned in a curriculum learning manner by shifting from explicit to implicit. In this way, our method not only makes better use of the available information in user behavior data but also overcomes the task-balancing problem in multi-task learning. Extensive experiments have been conducted on two real-world datasets and prove that our method delivers state-of-the-art performance and significantly improves the debiasing ability of the recommendation model.
Research on Military Logistics Data Warehouse
Authors: Zhou Zhaoguo , Yang Yang , Yan Bowen , Wen Hongwu , Gao Dongdong , Guo Hongbiao 
Published: May 2023

Abstract
According to military logistics data warehouse dealing with large-scale data effect is poor. This paper puts forward the construction scheme of dual computing engine military logistics data warehouse based on hive in order to improve the military logistics support capability. The scheme adopts Hadoop and Spark dual search architecture to support efficient storage of heterogeneous data. In addition, a data block placement strategy based on correlation analysis was used. This article use the intersection relationship matrix and the concurrency relationship matrix to calculate the correlation between the data block placed on the node and the data block to be placed. The access frequency of the target data block was integrated to select the appropriate node storage. The scheme can effectively improve hive’s query efficiency and provide more effective information aided decision support for commanders.
Intelligent Table Lamp Based on Single Chip Microcomputer
Authors: Long Tao 
Published: May 2023

Abstract
At present, modern smart home is increasingly favored by people, and the functions of intelligent desk lamps become diversified with the demand updating. In the process of using desk lamps, poor sitting posture or overuse of eyes may cause vision loss and physical pain. Therefore, it is of great theoretical significance and practical value to actively explore and develop an intelligent desk lamp with low cost and functions of automatic dimming, sitting posture correction, reminder of rest, energy conservation and environmental protection. In the intelligent desk lamp designed in this paper, STC89C51 microcontroller is used as the core controller, BH1750 light sensor module is used to detect the current ambient light intensity, and the pulse width modulation (PWM) technology is used to control the brightness of the desk lamp for TB6612 motor drive. The infrared probe module is used for personnel detection. When the infrared probe module detects that someone is approaching, the desk lamp will be automatically turned on through the relay module. When no one is detected, the microcontroller controls the relay to automatically turn off the desk lamp and power off. The intelligent desk lamp is very convenient to use. It can not only save electric energy effectively, but also solve the problem of touching the lamp in the dark and the potential safety hazard for people by automatically switching on and off the lamp. It has good application value and market prospects.
Prediction of Liaoning Maritime Traffic Accidents Based on Improved SCGM(1,1)c Model
Authors: Ma Xushi , Zhou Zhaoxin , Wang Shipeng , Qin Qi , Yang Siyuan , Zhu Junjie , Zhu Zhaoxin , Li Xin 
Published: May 2023

Abstract
In order to ensure the safety of maritime traffic in Liaoning sea area and improve the prediction accuracy of maritime traffic accidents. In this paper, the number of ship collision accidents in Liaoning sea area from 2012 to 2021 have been statistically analyzed, and the number of accidents have predicted by using the modified SCGM(1,1)C model modified by Markov chain, and have solved by Matlab programming. The results show that Markov theory can effectively overcome the shortage of volatility prediction of cloud grey system. Compared with SCGM(1,1)C model only, the average relative error of the improved model is reduced by 6.349 %, which provides a strong reference for the establishment of ship collision traffic warning mechanism in Liaoning sea area.
Characterization of the TruSense S310 laser range system for contact-less measurement of liquid levels in large-volume neutrino detectors
Authors: Steiger H.Th.J. , Theisen E. , Oberauer L. , Pilarczyk O. , Wurm M. 
Published: June 2023

Abstract
Neutrino experiments often use large volumes of water, organic scintillators or noble
liquids as active detection material. Due to the large hydrostatic and buoyancy forces involved,
precise knowledge of the liquid levels inside the detector tank are mandatory. Here we present the
main characteristics of the TruSense S310 Laser Range System. Level measurements can be performed
without direct contact to the liquid and through a gas-proof acrylic window, thus preserving the
strict radiopurity and chemical requirements of the target liquid. We report the results of a suit
of laboratory experiments for short-term precision tests (±5 mm) and long-term stability
studies. Moreover, we demonstrate that the infrared laser can be used while standard bi-alkali
PMTs are operational. We discuss the mechanical layout and integration of the system in the OSIRIS
pre-detector that will monitor the radiopurity of the liquid scintillator for the large-volume
neutrino experiment JUNO.
Divergent response of crops and natural vegetation to the record-breaking extreme precipitation event in 2020 modulated by topography
Authors: Chen Jiuyi , Qiu Bo , Guo Weidong , Li Lingfeng , Miao Xin 
Published: June 2023

Abstract
Extreme precipitation events have posed a threat to global terrestrial ecosystems in recent decades. However, the response of terrestrial ecosystems to extreme precipitation in areas with various vegetation types and complex topography remains unclear. Here, we used satellite-based solar-induced chlorophyll fluorescence measurements, a direct proxy of photosynthetic activity, to assess the response of vegetation to the record-breaking extreme precipitation event during the East Asia monsoon season in eastern China in 2020. Our results demonstrate that vegetation was adaptable to moderate increases in precipitation, but photosynthetic activity was significantly inhibited by exposure to extreme precipitation because of insufficient photosynthetically active radiation and waterlogging. The responses of vegetation photosynthesis to extreme precipitation were regulated by both vegetation type and topography. Crops in the lowland areas in eastern China were severely damaged due to their higher vulnerability and exposure to extreme precipitation. The topography-induced redistribution of precipitation accounts for the modulation of vegetation response to extreme precipitation. Our research highlights the urgent need for effective management and adaptive measures of croplands under the elevated risk of extreme precipitation in the future.
Equipment for Testing the Tensile Strength of Natural Fibers: Design and Implementation
Authors: Arifin Y F , Siswanto R , Arsyad M , Shintia Novi , Ridha M H , Ramadhani M R 
Published: June 2023

Abstract
The use of fibers, especially natural fibers, as construction materials has begun to develop. Natural fibers are also used for soil stabilization, where there are two mechanisms for increasing soil strength: tensile strength and fiber-soil friction. The purpose of this research is to design and build simple, inexpensive tools that can be used to test the tensile strength of a natural fiber. The tool consists of three main parts, namely the pull tool, loadcell, and display, and a supporting component, namely the computer. Prior to use, two calibrations were performed with respect to load cell accuracy and the vertical deformation of the tool. The equipment was used directly to measure the tensile strength of one of the natural fibers from oil palm empty fruit bunches. The relationship between stress and strain, fiber diameter, and fiber strength is discussed and compared with behaviors reported from the existing literature. The tensile strength of oil palm empty fruit bunches was found to be 107 MPa on average, with a strain at break of 6%. Fiber behavior, independent of diameter, was discovered to include elastic and plastic regions in the stress-strain relationship.