Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on cyclic voltammetric electrochemical fingerprint method for origin traceability of rice

The origin traceability of geographical indication rice has always been highly concerned, this paper focused on the perspective of non-equilibrium samples, and the feasibility of using the cyclic voltammetric electrochemical fingerprint of rice to identify the origin of rice was studied. Jiansanjiang, Fangzheng and Xiangshui rice, whose production places were close, were selected, and the fingerprint collection method and data preprocessing method were explored respectively. Fingerprints with rich information, significant features were obtained by fast detection. Principal component analysis combined with support vector machine analysis were used to establish the discrimination model, the recognition accuracy of the prediction set was over 90.0%, showing a good application prospect. The electrochemical fingerprint of the non-equilibrium sample measured by real-time tracking of the reaction process could not only reflect the differences in the types and contents of the sample components, but also show the differences in the chemical properties of the components, providing a new idea for easy and accurate identification of food.
Research on multi-sensor data fusion algorithm for unmanned vehicles under extreme conditions

Aiming at the problem of insufficient accuracy in Simultaneous Localization and Mapping of vehicle robot using a single sensor in extreme environment scenes, according to the characteristics of the sensors, a method of fusing the data of lidar and inertial sensors is proposed, the vehicle robot system is designed, and the positioning principle of lidar and inertial sensor is elaborated. Two data fusion algorithms, weighted fusion and Kalman filter, are mainly studied, and the experiments prove that the Kalman filter algorithm has higher positioning accuracy.
Symmetry analyses of high-order harmonic generation in monolayer hexagonal boron nitride

The high-order harmonic generation (HHG) of the monolayer hexagonal boron nitride driven by near-infrared laser pulses with different polarization is investigated with the real-time time-dependent density functional theory. The harmonic yield can be manipulated by tuning the laser intensity, ellipticity, waveform, and polarization. The symmetry of the interacting system is used to analyze various characteristics of the harmonic spectra. We theoretically verify the generation of circular high-order harmonics from solid by a single circularly polarized and a counter-rotating bichromatic circularly polarized field, which is consistent with the predictions of the selection rules. The selection rules based on dynamical symmetry analyses are derived in detail. We find that for the same energy the incident laser pulse, the bichromatic co-rotating and counter-rotating circularly polarized laser fields can produce harmonics more efficiently than a linearly polarized laser. Extension of HHG in 2D materials to the XUV regime and the possible realization of circularly polarized harmonics pave a way to the development of novel nanoscale attosecond light sources.
A study on optimal dispatch of meical resources under the situation of COVID-19

In order to improve the efficiency of production, transportation, storage and use of medical resources under COVID-19 pandemic, we propose a simplified model for medical resources supply chain management by mathematical programming. Based on the reserve and demand of medical resources in a given area, the proposed model provides daily real-time transportation decisions and expansion decisions of hospitals, factories and warehouses under epidemic situation by analysing factors such as different regions, duration, factory capacity, warehouse reserves, hospital demand, transportation and expansion costs of factories, warehouses and hospitals. The promo package of Python language is used to formulate a mixed integer linear programming model. In this model, under the conditions of meeting the demand of medical resources in the case of slow development and sudden outbreak of epidemic situation, a commercial solver, CPLEX, is able to provide the solution for each situation with the lowest supply cost within one second CPU time. This model provides a feasible scheme for further solving the complicated forecasting and planning of medical resources dispatch under the situation of pneumonia in COVID-19.
Experimental Study of the Acoustic Pressure Distribution in a Sonochemical Reactor

The work is devoted to an experimental study of the main parameters of the acoustic flow that occurs in a liquid under the influence of an ultrasound source (US) with a frequency of 1.7 MHz. To study the type of emerging currents, the method of tracer and fluorescence imaging was used; the distribution of relative acoustic pressure was found using a vibration sensor; to measure the intensity of cavitation events, thermocouple measurements were used. Experiments have shown that an increase in the concentration of the NaCl salt in the water solution reduces the intensity of vibrations when the sensor is removed from the ultrasonic source. The maximum intensity of cavitation events also changes its position, moving to the area near the ultrasonic source. Thus, the effect of an increase in the salt concentration on the type of flow in a sonochemical reactor was noted, which was also experimentally recorded in the work using light-reflecting particles.
What determines the effectiveness of national protected area networks?

More than 15% of global terrestrial area is under some form of protection and there is a growing impetus to increase this coverage to 30% by 2030. But not all protection is effective and the reasons some countries’ protected areas (PAs) are more effective than others’ are poorly understood. We evaluate the effectiveness of national PA networks established between 2000 and 2012 globally in avoiding forest loss, taking into account underlying deforestation threats using a combination of matching methods and cross-sectional regressions. We then assess which demographic, agricultural, economic, and governance factors are most strongly associated with national PA effectiveness using machine learning methods. We estimate that national PAs established between 2000 and 2012 reduced deforestation in those areas by 72%, avoiding 86 062 km2 of forest loss. The effectiveness of national PAs varied by strictness of protection based on International Union for Conservation of Nature category. Strictly PAs reduced forest loss by 81% compared to what would have occurred without protection, while less strictly PAs reduced forest loss by 67%. Thus, the 26% of new PAs that were strictly protected contributed 39% of the total forest loss avoided within PAs between 2000 and 2012. If every country’s PAs were as effective as the country with the most effective PAs within the same region, they would have increased the area of deforestation avoided by 38%, saving a further 119 082 km2 of forest. Part of the variation in PA effectiveness across countries is explained by the placement of PA in areas facing higher deforestation threat. Countries with lower agricultural activity, higher economic growth and better governance are most strongly associated with greater country-level PA effectiveness.
A Qualitative Comparative Analysis of the Academic Media Using Frequency on Networking Platforms

This paper takes the determinants of the academic social media using frequency of Chinese young scholars as the research topic, aiming to get the causal relationship between the factors and the academic social media using frequency of young scholars. Based on the Expectation-Conformation Model, the Technology Acceptance Model and existed research results, this paper constructs an analysis framework according to two elements of which are users’ characteristics and perception effects. By using Linear Regression and Qualitative Comparative Analysis, this paper makes a comparative analysis of the following factors; they are literature acquisition, recommendation function, effect of use, purpose of use and discipline. The conclusion of Linear Regression is contrary to the empirical theory and practical knowledge, which means the weak applicability of this method to this research topic. While Qualitative Comparative Analysis can be more reasonable to analyze the configuration that led young researchers to use academic social media more frequently.
An Improved Fully Convolutional Network for Semantic Segmentation

A novel loss function based on category distribution for semantic segmentation is proposed in this paper. The new loss function is composed of two parts: (1) Pixel-wise cross-entropy. (2) Category distribution loss proposed. Most of existing semantic segmentation networks adopt a single pixel-wise cross-entropy loss function, which guides the network to independently predict the class each pixel belongs to. The downside is that ignoring global information of the image -- the distribution of all kinds of objects in the image, resulting in an unsatisfactory segmentation result. Category distribution loss proposed in this paper obtins category distribution information of the whole image by calculating the percentage of pixels to each class of objets. Acquired category distribution information can be represented as a feature vector, the distance of two vectors between prediction and ground truth forms category distribution loss. The new loss function measures the difference between pixels as well as the difference between global information. We apply the new loss to several classical networks, its pixel accuracy and mIoU accuracy on two benchmark datasets CamVid and Pascal VOC2012 are improved compared to using only pixel-wise cross-entropy. In addition, we also introduced the attention mechanism, which proved to be able to improve segmentation accuracy of the networks as well.
Research on Highway Traffic Event Detection Method Based on Image Processing

Aiming at the problem of the image processing technology of the expressway event detection system, this paper uses the background subtraction method and Camshift algorithm to detect and track the moving vehicle. Through sorting out the traffic event detection and processing process of Lian-Xu Expressway, the traffic event detection system is constructed, and the data processing module of the system is mainly studied to realize the detection and alarm of traffic events. The results show that the highway traffic incident detection system studied in this paper can complete the detection of traffic events in the video, improve the detection rate, and provide technical support for the highway safety management.
Determination of multi-chemical constitutes for Ginkgo biloba leaves by gas chromatography with pyrolytic methylation

Pyrolytic methylation-gas chromatography using a vertical microfurnace pyrolyzer was applied to analysis of multi-chemical constitutes for G.biloba leaves simultaneously and directly.Pyrolytic methylation of G.biloba leaves in the injector at 300 ℃ in the presence of tetramethylammonium hydroxide(TMAH),allowed quantitative analysis of 5 kinds of phenols and carboxylic acid,6 kinds of fatty acid and 9 kinds of alkylphenol as their corresponding methyl derivatives,with the relative standard deviations(RSDs) less than 5.9%(n=3). The developed method allows the analysis small samples without laborious pre-treatment and would be a simple,accurate and highly sensitive way to detect multi-constitutions in Ginkgo biloba leaves.
Foreign Object Detection of Electric Transmission Line with Dynamic Federated Learning

The bird’s nests and other foreign objects on the transmission lines bring about huge threats to the electric power security. The aggregation of foreign objects data of different regions can boost the generalization of foreign objects with deep learning. However, due to policy constraints and other data security reasons, it is impossible to collect the data of different power companies to train a joint foreign object detection model. Therefore a dynamic federated learning based foreign object detection method for transmission lines is proposed. For the clients distributed in power companies, each model is trained and uploaded to the central server in an asynchronous way for dynamic fusion. For the model trained with pre-trained model, the pre-trained part is frozen and will not be trained. Thus the feature extraction backbone is not uploaded to central sever, which contributes to the decrease of the communication consumption by reducing the amount of uploaded data. The experimental results demonstrate that the dynamic federated learning based foreign object detection algorithm can maintain the same accuracy level compared with centralized training. It can train the joint model without uploading the original data of the edge nodes, which guarantees the security and privacy of local data. Compared with training individually, it has great improvement in accuracy. And it is also applicable with different network sizes. Therefore the dynamic federated learning based foreign object detection has strong privacy, accuracy, efficiency, flexibility and scalability.
An elevator passenger behavior recognition method based on two-stream convolution neural network

In order to reduce elevator accidents caused by passengers’ incorrect use, a method for elevator passenger dangerous behaviour recognition based on two-stream convolutional neural network is proposed after analysing existing elevator passenger behavior recognition methods. A two-stream convolution neural network model framework and its parameters for elevator passenger behavior recognition are also given. Herein, the appearance features of human behavior are extracted from space domain, and the motion features of human behavior are extracted from time domain. Finally, the softmax outputs of the two-stream are merged with the liner weighting to realize human behavior recognition. The model is trained and evaluated on the video dataset of elevator passenger behavior. The results show that this method can be used to identify the unsafe behavior of elevator passengers, and the average recognition rate is 96.87%.
Community Search in Spatial Uncertain Network

Community search is to explore valuable target community structure from a large social network. In real community, every point has a geographic location information, and many edges are uncertain. Such a network is called spatial uncertain network. In order to meet the existing needs, this paper studies the community search in the spatial uncertain network so that the searched communities can be relatively close in the geographical location and the relationship between each other is also very close. Based on the spatial uncertain graph, this paper proposes K-R-τ community, and proposes a linear algorithm and a two-dimensional algorithm for community search in the spatial uncertain network. A large number of experiments are carried out on several real datasets, and the results show that the algorithm proposed in this paper is effective and accurate.
Boosting the Transferability of Adversarial Examples with Translation Transformation

Although the adversarial examples have achieved an incredible white-box attack rate, they tend to show poor transferability in black-box attacks. Date augmentation is considered to be an effective means of enhancing the adversarial examples transferability. To this end, based on translation transformation we propose a new method to generate more mobile adversarial examples to attack the advanced defense model. By optimizing the original image, the input diversity is improved and the transferability of the adversarial examples generated by further training is enhanced. This method can also be combined with the attack method based on gradient, and the new method can make the attack success rate higher. Experiments on ImageNet data sets show that the proposed method is superior to gradient based methods such as MI-FGSM in black box attack, while maintaining a high success rate of white box attack. We hope that our proposed method of attack can serve as a benchmark for assessing the robustness of networks to opponents and the effectiveness of the different methods of defence.
A Study on Comprehensive Industry Data System Based on Computer Exploring Big Data and Resource Integration

Sports tourism resources are divided into two types: sports tourism natural resources and sports tourism human resources. The natural resources of sports tourism are divided into landform resources and water resources. The human resources of sports tourism are divided into folk customs and events, venues and buildings. The thesis explores the advantages and disadvantages of Chinese existing sports tourism information system through the combination of big data and sports tourism. At the same time, we analyzed and established the hierarchical structure of the sports tourism information system big data platform and the logical structure of the sports tourism data warehouse, and constructed a sports tourism information data model system based on big data analysis, laying a foundation for further research on the sports tourism information system based on big data.
Improving the Transferability of Adversarial Examples with Image Affine Transformation

Deep learning is widely regarded as a black-box technology. We all know its performance is very good, but we have limited understanding of why it is so good. At present, there are many researches on the interpretability of deep neural network. By studying adversarial example, we can understand the internal semantics of neural network and find the decision boundary with problems, which in turn helps to improve the robustness and performance of neural network and its interpretability. With the development of adversarial example research, more and more adversarial example generation methods are proposed. Although the attack from adversarial example poses a great security threat to the deep learning system, it can also be used as an effective tool to measure the robustness and reliability of the model, and the attack and defense are two mutually promoting processes. Therefore, how to generate adversarial example with stronger attack ability is worth further study. And this study proposes a method named Affine-Invariant Method, aimed to improve the transferability of adversarial examples in black-box environment.
Research on energy densities of secondary batteries

Improving the energy densities of secondary batteries has always been the core goal of scientific research and engineering development. In this paper, by testing the energy densities of the test samples, the gravimetric energy density (GED) and volumetric energy density (VED) of Li-ion and Li-metal batteries are analyzed. By comparing with the development trend of batteries at home and abroad, we can see that Li-metal batteries are the development direction of high-energy-density batteries in the future. By comparing the test results of this paper with the energy densities data published at home and abroad, we can see that the GED and VED of Li-ion and Li-metal batteries in China have reached the international advanced level. The research results of this paper can basically reflect the development status and provide guidance for the development of high-energy-density batteries in China.
Asian Giant Hornets Recognition Using Deep Convolutional Neural Network

We trained 2 deep neural networks to identify whether the images with high resolution in the dataset provided by COMAP contain any Asian giant hornet. We divide the classification problem into two subproblems: feature extraction, and image classification. In order to reduce the impact of sample imbalance, we apply image flipping and Borderline-SMOTE methods for data augmentation first, and then divide the data into the training set and the validation set (testing set). Next, we utilize auto-encoder to collect key features of images and the testing loss dips to 0.0274 after training. Next, we establish the Classification-Net to solve the identification problem based on the features just extracted, and the accuracy in testing set reaches 0.8030. Finally, we summarize the main features of having negative labels from three aspects: species characteristics, subject definition and background softness.
An Empirical Analysis of the Bidirectional Dynamic Effect between Financial Competition and Industrial Agglomeration

This paper uses the data of 30 provincial-level panel in China to establish a dynamic panel simultaneous equations model. The system generalized moment estimation (sysgmm) and three-stage least squares (3SLS) are used to estimate and test the robustness of the model, respectively, to study the relationship between financial competition and industrial agglomeration. The results show that: (1) there is a bidirectional causal relationship between financial competition and industrial agglomeration. Financial competition through preferential policies to encourage technology research and development, resulting in technological innovation spillover effect to attract industrial agglomeration, but the effect is not good, and industrial agglomeration will also encourage finance to increase the competitiveness; (2) the bidirectional effect has time lag, the early financial competition and industrial agglomeration In order to have an effect in this period. All of conclusions above have important implications for changing the mode of financial competition and improving the quality of industrial agglomeration.
Institutional Investors’ shareholding, CEO Tenure and the Speed of Enterprise Internationalization—Based on Regression Method in Statistics

Based on the sample of Chinese A-share listed companies in Shanghai and Shenzhen from 2012 to 2019, this paper focuses on the effect of institutional shareholding on the speed of enterprise internationalization. The empirical results show that the overall institutional investor ownership has a significant negative impact on the rapid international expansion, and the classified independent institutional investors and non-independent institutional investors have a significant inhibitory effect on the speed of corporate internationalization, and with the extension of the term of CEO, the negative relationship between institutional investor shareholding and the speed of enterprise internationalization has been alleviated. The research of this paper can be used as a reference for optimizing the investor structure of the capital market and improving the strategic decision-making efficiency of enterprise management.