bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improved Synthesis of the Antitubercular Agent SQ109
Authors: Kolocouris Antonios , Stampolaki Marianna 
Published: September 2021

AbstractWe present here an improved procedure for the preparation of the promising antitubercular drug SQ109 that is currently in phase Ib/III of clinical trials against Mycobacterium tuberculosis. We investigated and tested the literature synthetic procedure that enables the development of structure–activity relationships and report the observed inconsistencies as well as presenting improvements or novelties for the more efficient preparation of SQ109. Most significantly we applied a novel reduction step of the aminoamide precursor using Me3SiCl­/LiAlH4 under mild conditions. These findings are important for research groups investigating the efficacy of this drug and analogues in academia and industry.
Justice beyond repair: Negative Dialectics and the politics of guilt and atonement
Authors: Cucharo Stephen 
Published: September 2021

AbstractThis article draws out a critical, yet under-appreciated political theme in Adorno’s Negative Dialectics, namely his emphasis on guilt and atonement. First, the article assesses how Adorno’s Marxism allows him to think justice and guilt beyond the familiar legalistic frame. Second, the article reconstructs Adorno’s treatment of guilt as a distinctly political capacity to imagine one’s boundedness and indebtedness to others, and the affective engine enabling us to engage in a political ethic distinct from familiar categories of reparation. Third, the article shows how the themes of guilt and atonement give us a more complete picture of Negative Dialectics. This inquiry also intervenes in contemporary debates regarding the political status and emancipatory potential latent within guilt-feelings, and claims Adorno gives us a path forward to imagine the relation between guilt and politics in a novel way.
Understanding West Africa’s informal workers as working class
Authors: McDermott Joshua Lew 
Published: September 2021

ABSTRACTInformal workers in Africa are very often portrayed as primarily self-employed entrepreneurs and unemployed individuals largely excluded from capitalism, and thus insulated from class analysis and class dynamics. Drawing on a case study of informal workers in Sierra Leone, the article challenges this dominant understanding, arguing that informal workers experience the reality of class relations and that their material lives are shaped by, and help to shape, broader dynamics of capital accumulation. The research applies a holistic class analysis rooted in Marxist and feminist thought, arguing for an understanding of informal workers, including even small-scale ‘self-employed’ individuals, as workers exploited by, and opposed to the interests of, capital. In so doing, it challenges the simple understandings of working class as existing only and exclusively through formalised wage work, in favour of a more complex and inductive understanding of the reality of global capitalism, highlighting the relevance of class, value and exploitation to the lived reality of informal workers in Africa.
Revisiting isocurvature bounds in models unifying the axion with the inflaton
Authors: Ballesteros Guillermo , Ringwald Andreas , Tamarit Carlos , Welling Yvette 
Published: September 2021

Abstract
Axion scenarios in which the spontaneous breaking of the
Peccei-Quinn symmetry takes place before or during inflation, and in
which axion dark matter arises from the misalignment mechanism, can
be constrained by Cosmic Microwave Background isocurvature
bounds. Dark matter isocurvature is thought to be suppressed in
models with axion-inflaton interactions, for which axion
perturbations are assumed to freeze at horizon crossing during
inflation. However, this assumption can be an oversimplification
due to the interactions themselves. In particular,
non-perturbative effects during reheating may lead to a dramatic
growth of axion perturbations. We perform lattice calculations in
two models in which the Peccei-Quinn field participates in
inflation. We find that the growth of axion perturbations is such
that the Peccei-Quinn symmetry is restored for an axion decay
constant f
A ≲ 1016–1017 GeV, leading to an
over-abundance of dark matter, unless f
A ≲ 2 × 1011 GeV. For f
A ≳ 1016–1017 GeV we still find
a large growth of axion perturbations at low momentum, such that a
naive extrapolation to CMB scales suggests a violation of the
isocurvature bounds.
Ground shear strain value based on microseismic data in Oebelo, Kupang NTT
Authors: Mala H U , Lalus H F , Nanlohy P , Elake A Y 
Published: September 2021

Abstract
Information the ground shear strain in an area are very important. Quantitatively, this characteristic is reflected in the value of shear strain. This value provides an illustration of the ability of the soil layer to vibrate, stretch or shift even broken during an earthquake. The potential of vibrating and stretching of the soil is not only influenced by the magnitude and hypocenter but also the geological conditions of the local area. Geological characteristics in the form of dominant frequency values and amplification factors were obtained through the HVSR method using microseismic waves. The study was conducted in Oebelo, Kupang with a distribution of 52 points of microseismic measuring. Its parameters are used to calculate the seismic vulnerability index value and PGA value. These two values are used to determine the shear strain value at each measuring point. The results showed the magnitude of shear strain values was range from 1.5 × 10−2 to 1.6 × 10−1. The highest is located at coordinates 10.111280 S and 123.74180 E, while the area with the lowest at the coordinates 10.115780 S and 123.73960 E. Areas with shear strain values in the range 10−2 to 10−1 above, have dynamic properties are collapse, repeat-effects and speed-effects of loading. Phenomena that will occur are landslides, soil compaction and liquefaction during an earthquake. In the context of risk reduction, it is necessary to avoid areas with high shear strain values for human activities and infrastructure development.
Design of VR application in detection line based on industrial robot for optical routers
Authors: Zhang Bei 
Published: September 2021

Abstract
In this study, an automated detection line for optical routers is designed. The output of the production line is evaluated and optimized on the basis of the product lifecycle management (PLM) data. The VR technology is used to build a 3D virtual reality scene to realize the layout design and planning of industry, while providing functions such as employee training, technological process roaming and interactive experience. Thus, a set of representative transformation schemes for flexible manufacturing cells can be obtained.
Trip-GhostNet for Hyperspectral Image Classification
Authors: Zhang Zitong , Hu Xiaoqing , Wang Siqing , Kang Lei , Ma Qiaoyu 
Published: September 2021

Abstract
The classification of hyperspectral remote sensing images (HSI) is an important task in the processing and application of hyperspectral images. Convolutional neural network (CNN) has significant advantages in the extraction and fusion of hyperspectral image spectrum and spatial features, so it has become a common method in the field of HSI classification. However, the spectral domain feature redundancy of hyperspectral images is very high, and the spatial domain feature structure is complex, which makes CNN more time-consuming and higher memory requirements in the feature extraction process. Based on this problem, lightweight networks with fewer parameters have gradually become the main application method in the field of HSI classification. In order to reduce the computational complexity of deep feature extraction and take into account the shallow features, a high-order nonlinear Ghost module is proposed on the basis of the original Ghost linear transformation module. Furthermore, in view of the independent characteristics of each dimension of HSI, a Trip-GhostNet is proposed, which simultaneously extracts and fuses features from three dimensions in a lightweight manner. According to the distribution characteristics of HSI, the influence of attention embedding methods in the high-order Ghost module of each branch on the feature extraction is compared and analyzed. The results show that the proposed model can reduce model calculations and improve classification accuracy, and is suitable for HSI classification problems.
Research on Algorithm of fighter landing gear in bad video image
Authors: Tang Miao , Fang Wei , Yan Wenjun , Ling Qing 
Published: September 2021

Abstract
The retracted and retracted state of landing gear is a problem that needs to be paid close attention to in the landing process of fighter. The traditional visual observation is easy to be affected by the climate environment. Aiming at the problems of small target, low definition and difficult detection of fighter landing gear in bad video images, a YOLOv4 target detection algorithm based on image enhancement is proposed to detect fighter landing gear. In order to improve the accuracy of landing gear detection, ACE algorithm is first applied to the video image to enhance the video image, then the main feature extraction network of YOLOv4 is used to extract the feature information, and K-means clustering algorithm is used to cluster and analyze the number and aspect ratio dimension of target candidate frames. Experiments on the dataset show that the detection accuracy after image enhancement reaches 88.12%, which is 1.19% higher than the original image; The recall rate reached 73.44%, an increase of 4.17%; The value of mAP reached 82.65%, an increase of 1.08%.
Infrared small target detection method based on multi-scale feature fusion
Authors: Wang Fang , Li Chuanqiang , Wu Bo , Yu Kun , Jin Chan 
Published: September 2021

Abstract
Infrared small target detection is widely used in aerial target detection and tracking system because of its long detection distance and strong anti-jamming ability. Aiming at the shortcomings of the current infrared small target detection algorithm, such as low precision rate and high false alarm rate when dealing with complex background. In this paper, we propose an end-to-end infrared small target detection model (called MFSSD) based on multi-scale feature fusion. Considering the trait of the targets, we propose a feature fusion module by using a refinement and fusion feature map method, and improve the correlation of different channels through the SP Module. The experimental results of three different sequences of infrared image detection show that the average detection accuracy of the MFSSD algorithm in infrared small target detection is as high as 87.8%. Compared with the traditional multi-scale feature fusion detection algorithm, both the precision rate and the recall rate have been significantly improved.
Optical-imaging detection of apple sugar content based on OpenCv and Pytorch
Authors: Hu Shuhe , Yang Yuping 
Published: September 2021

Abstract
Based on the Rayleigh scattering and Mie scattering, a scattering model in the apple pulp tissue is theoretically established, and the relationship between the sugar content of the apple pulp and the light intensity distribution of the diffuse reflection image is derived.Firstly, a homemade reflection-collection system has been applied to collect the scattering light intensity distribution of 133 apples with different varieties, skin colors, and sugar contents. According to acquired optical images 133 original images are processed based on OpenCv, and the appropriate region is extracted to recreate the image set, and the image features of the image set are derived with the aid of the denoising self-encoding neural network for data dimensionality reduction. The image feature and the recorded fructose content table are made into a data set, which is divided into a training set and a test set at a ratio of 3:1. Finally, through the training of the neural network, the non-destructive prediction of the apple sugar content is successfully achieved within a certain range.
Academic literature recommendation technology based on two-layer attention network
Authors: Deng Hanyang , Tang Shiping 
Published: September 2021

Abstract
Recently, academic literature recommendation for learners has become an important topic. Recently deep learning based models are used in literature recommendations, which follow a similar Embedding&MLP paradigm. However, in the base model and its follow-up model Deep Interest Network (DIN), users only have to click or not click on items, limiting the expression of users' specific interests. Therefore, we propose a two-layer attention model (BIH) based on DIN. BIH adds specific behaviors to the user behavior sequence and adds a behavior attention layer, which can learn the expression of user interests more accurately. Experiments on a public dataset and users' behavior logs of academic literature demonstrate the effectiveness of proposed approaches, which achieve superior performance on both Macro-F1 and Micro-F1 compared with the base model and DIN.
Visual comparison and analysis of the speed and strength of different versions of Beethoven piano sonata no. 8 (op. 13no.1), 1st moveme nt based on Vmus.net
Authors: Liu Shuqing 
Published: September 2021

Abstract
This paper is based on Vmus.net platform, using music visualization analysis technology.Through three experiments, combined with the relevant music background information of Beethoven piano sonata no. 8 (op. 13no.1), 1st movement the audio of four pianists were selected as the research content, and the elastic expansion and strength fluctuation of music speed were compared and analyzed from different perspectives of micro and macro.In this way, we can understand the characteristics of personalized processing and performance of Beethoven piano sonata no. 8 (op. 13no.1), 1st movement by different players in different periods.
A Metal Artifact Reduction Method with Bone Segmentation for CBCT Images
Authors: Zhang Songze , Jiang Benxiang , Shi Hongjian 
Published: September 2021

Abstract
Nowadays, CBCT images become popular and are widely used in the dental diagnosis. The image quality is related to the accuracy of diagnosis and treatment. When metal objects such as the dental implants are present in CBCT scanning, the metal artifacts will appear and decrease the image quality. In order to reduce the metal artifacts, this paper proposes a metal artifact reduction method for the dental CBCT images. This method is based on inpainting the sinogram and mainly has three innovations. First, this method forward-projects only the region around the metal implants. Second, the bones in the forward projection region are segmented and their intensity values are set to zero before the forward projection. Finally, this method computes an image to directly correct the original CBCT image, this correction image is the back projection of the difference between the interpolated sinogram and the original sinogram. The proposed method reduces the metal artifacts and improves the image quality, it may potentially increase the diagnosis value of the CBCT images corrupted by the metal artifacts.
Comparative analysis of 2D-PCA based dimensionality reduction and feature extraction
Authors: Zhang Kai , Hu Xiaoqing , Kang Lei , Ma Qiaoyu , Zhang Xin 
Published: September 2021

Abstract
The complex spectral and spatial characteristics of hyperspectral remote sensing images (HSI) lead to higher time-consuming in classification task. To address this question, we introduced the 2D-PCA dimensionality reduction method of linear mapping in the two-dimensional spatial domain on the basis of linear dimensionality reduction in the spectral domain, thereby compressing the complex spatial structure information of HSI into a limited low-dimensional space, and realizing space-spectrum dimensionality reduction and information fusion. The experimental results on three classic data sets of Salinas, Tea Farm, and Indian Pines show that 2D-PCA has a strong ability to condense and compress spatial structure characteristics. Compared with popular deep learning frameworks such as CNN and Mixer-MLP, conventional machine learning models based on 2D-PCA have significant advantages in terms of computing time under the premise of controllable accuracy loss, which makes 2D-PCA a promising method for dimensionality reduction and feature expression in hyperspectral pixel-wise classification.
Prediction of Aircraft Aerodynamic Coefficient Based on Data-Driven Method
Authors: Liu Chang , Wang Xingbo , Liu Xinghua 
Published: September 2021

Abstract
To identify the aerodynamic coefficient of aircraft that cannot be directly measured in actual flight, in this paper, the problem of aerodynamic coefficient identification has been solved with the rigid body three-degree-of-freedom model using aircraft dynamics model based approaches. And based on the data-driven method to predict the aerodynamic coefficients, based on radial basis function neural network (RBFNN) with particle swarm optimization (PSO) has been used to achieve the task. Taking the external disturbance and the noise of flight data into account, a section of flight data of the XXX aircraft is preprocessed. The aerodynamic coefficients are identified based on the pre-processed flight data, and the prediction results of the aerodynamic coefficients of the above methods are analyzed, which proves the effectiveness of using the data-driven method to predict the aerodynamic coefficients.
Feature investigation on the ROP machine learning model using realtime drilling data
Authors: Cao Jie , Gao Jiaxuan , Jiao Tong , Xiang Ruiyi , Pang Yanyan , Li Tiantai 
Published: September 2021

Abstract
To meet the demand for reducing drilling costs in petroleum engineering, improving drilling efficiency is one of the main objectives in field operations. Highly accurate prediction of ROP is an essential basis for improving drilling efficiency and reducing development cycle time. However, realtime prediction of the Rate of Penetration (ROP) is not straightforward, affected by a number of operational and mechanical parameters. The interactions between these parameters also complicated the analysis and modeling ROP. In the presented study, we apply the Feature Engineering approach to analyze the features affecting ROP according to their relevance and relative importance. In addition, the input features are reduced from 14, manually selected based on physical relevance, to optimized 8. Then the model is retrained again for comparing the accuracy of the two prediction models. As a result, it is concluded that by reducing the input of low impact features, the model is substantially simplified, while there are only insignificant accuracy changes.
Prediction model of needle valve body extrusion grinding process based on PSO-SVR
Authors: Xie Ruibin , Yang Shuzhen , Sun Chenzhe , Yu Tao 
Published: September 2021

Abstract
The needle valve body is one of the core components of the diesel engine injection system, there is a complicated non-linear relationship between process parameters and process effects in its extrusion and grinding process, and it is difficult to establish specific mathematical expressions to describe the process rules. Therefore, this paper adopts support vector regression (SVR) establish a needle valve body extrusion grinding process model, and uses flow coefficient and grinding efficiency as dual output prediction targets. The particle swarm optimization algorithm (PSO) with better global search ability is introduced to optimize and improve the model. The results show that the optimized model has a smaller error between the predicted value and the actual value of the data, and the prediction accuracy is significantly improved. The model can reflect the process law of the needle valve body extrusion and grinding process to a certain extent, and it can provide certain guidance for selecting process parameters in the extrusion and grinding process.
Emotion Prediction of EEG Signals based on 1D Convolutional Neural Network
Authors: Gao Jiaman , Yang Chengyi , Liu Feng , Qi Jiayin 
Published: September 2021

Abstract
Artificial intelligence has been widely used in the field of biological signal recognition. However, most researches use deep learning to classify emotions, which has limitations in its application in the medical field. To this end, this paper proposes a one-dimensional convolutional neural network (1D-CNN) model for regression tasks. After we standardize, transform and slice the data, we divide the training set, validation set, and test set at a ratio of 8:1:1, and feed the data into the neural network for training to achieve emotion prediction. Experiments on the DEAP dataset show that the model we built has good performance for emotion prediction, which provides new insights for the medical field. The source codes are available at https://github.com/gjm-web/1D-CNN.
A Novel Text Ensemble Clustering Based on Weighted Entropy Filtering Model
Authors: Shen Qiaoyun , Qiu Yican 
Published: September 2021

Abstract
Text clustering is one of the important technical bases of natural language processing, and ensemble clustering improves the robustness of text clustering. According to the existing research of scholars and experts, the quality and diversity of basic clustering have a great influence on consensus clustering, and it has a particularly significant effect on text clustering. However, there are a few pieces of research aiming at reducing the number of low-quality clustering in ensembles. This paper proposes a novel clustering filtering model based on entropy criteria. The entropy criterion is used to evaluate the uncertainty of each cluster w.r.t. the ensemble. Two indexes are proposed on the basis of the uncertainty of cluster, namely, Clustering Trend Index (CTI) which indicates the contribution of each cluster w.r.t. basic clustering, and Cluster Consistency Index(CCI) which indicates the degree of cluster dispersion in the basic clustering. The proposed clustering filtering model is built on the basis of new weight using two proposed indexes. Thereby, by dropping the low-quality clustering, the percentage of high-quality clustering will increase. A large number of experiments on various real text data sets using optimal thresholds show that the proposed method has greatly improved accuracy and robustness, and is superior to existing ensemble clustering algorithms.
Multi-objective discrete salp swarm optimizer for solving multiple-product U-shaped disassembly line balancing problem
Authors: Li Jiawei , Guo Xiwang , Peng Fangjie , Qi Liang , Qin Shujin 
Published: September 2021

Abstract
The disassembly line balancing problem is one of the most significant methods in handling large quantities of waste electrical and electronic equipment efficiently problems. In this paper, we propose a U-shaped layout disassembly line and set up a mathematical model to maximize the disassembly profit and minimize the disassembly time of hazardous parts. Therefore, an improved Multi-objective Salp swarm algorithm based on Pareto which combines with a stochastic simulation method is proposed to solve such a U-shaped disassembly line balancing problem. Some cases are used to verify the effectiveness of the proposed method. The results show the proposed method can solve the U-shaped disassembly line balancing problem and outdo the other two optimization algorithms: Nondominated Sorting Genetic Algorithm-II (NSGA-II) and Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) in the quality of solutions.