bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on point cloud registration of industrial parts based on FGR-ICP algorithm
Authors: Zha Yongjian , Chen Ming , Lu Shenglian 
Published: June 2021

Abstract
In the industrial application environment, there are some challenges to point cloud registration caused by disordered and occluded industrial parts. A point cloud registration method combining fast global alignment (FGR) and improved ICP is proposed for the problem that the traditional Iterative Closest Point (ICP) needs to rely on good initialization quality and easily falls into local optimal solutions. The method can optimize the different components of the objective function alternately by FGR to obtain the optimal initial transformation matrix. An improved ICP algorithm was used for exact matching of the initial transformation results. The experimental results showed that the method in this paper had greatly improved the matching accuracy, and the speed had been improved by an order of magnitude compared with the traditional method.
Outlier detection algorithm based on density and distance
Authors: Qu Ying , Guo Fei 
Published: June 2021

Abstract
In order to solve the problem that the existing outlier detection algorithm is difficult to detect the one-dimensional integer data set with uneven frequency distribution and uniform distance distribution and low accuracy, the advantages of density outlier detection and distance outlier detection can be combined. An outlier detection algorithm DAD (Density and Distance) based on density and distance was proposed. In order to improve the possibility of outlier sample distance, the algorithm can define the weight distance; introduce the global density outlier factor combined with the weight distance as the relative distance, and use the cutting edge strategy to quickly cut the outliers based on the minimum spanning tree. Then, an artificial data set was used to test the algorithm. Experimental results showed that the algorithm had a good outlier detection accuracy in dealing with data sets with uneven frequency distribution and uniform distance distribution.
Research on inquiry letter supervision and stock price crash risk based on fixed effects model
Authors: Zeng Meng 
Published: June 2021

Abstract
This paper examined whether different types of inquiry letters had effects on stock price crash risk. To measure the stock price crash risk, a model was built. Additionally, to study the effects, this paper used a fixed effects model based on the panel data from Shenzhen Stock Exchange and CSMAR database from 2014 to 2019. The results showed that during 2014 and 2016, inquiry letters about asset restructuring significantly reduced stock price crash risk; the risk of stock price crash may be lower when the company received more letters about asset restructuring. However, inquiry letters about regular report and others had no significant effects. During 2017 and 2019, inquiry letters about regular report significantly reduced the stock price crash risk. Similarly, the company’s stock price crash risk may be lower when the company received more letters about regular report. However, inquiry letters about asset restructuring and others had no significant impact. The research aimed to explain the role of different types of inquiry letters in the Chinese stock market through data analysis, which may provide suggestions to prevent stock price crash risk, eventually promoting the capital market's stable development.
Excel VBA programming with Excel to compute the load tranceverse distribution coefficient of G-M method
Authors: Ma Lijun , Che Guowen 
Published: June 2021

Abstract
In this paper, by analyzing the calculation principles of load transverse distribution coefficient of G-M method, using office software Excel, the calculation model of a universal worksheet is given, making the computation of load transverse distribution coefficient with G_M method more accurate and swift. The model is very practical, and can offer reference for bridge and highway designers.
Agglomeration and balance: the evolution patterns for regional economic divergence in the pearl river delta area
Authors: Peng Minjing , Xu Zhicheng 
Published: June 2021

Abstract
This study analysed the differences in the process of regional economic development and revealed the dynamic imbalance mechanism of regional economic development in Pearl River Delta (PRD). Based on the statistical yearbook of PRD 1978-2018 GDP and per capita disposable income (PCDI) data, using standard deviation, Gini index, range rate and clustering analysis methods, we analysed regional inequalities to the evolution dating back to nearly 40 years in the PRD. The cross-sectional data showed that the relative difference is stable and the absolute has always been rising in the evolution of the difference in GDP of PRD. However, in terms of PCDI, the relative difference shows a shrinking trend on the whole. Furthermore, the cluster analysis indicated that with the construction of the PRD Metropolitan Areas, the economic development is concentrated in the core cities. However, the PCDI between regions is converge. We suggested that the three major metropolitan areas should further expand and radiate outward to achieve the overall coordinated development of PRD. At the same time, less developed regions should not be afraid of the concentration of production factors in core cities. With the free flow of production factors, the gap between regions has actually narrowed.
Application of ARIMA model on prediction of China’s corn market
Authors: Zhou Liwen 
Published: June 2021

Abstract
ARIMA is a common model for non-stationary time series modeling, the model is essentially the combination of ARMA model and differentiate operation. This study indicated that most of the non-stationary time series can be differentially stationary by the difference of the appropriate order, and can be fitted by the ARMA model. As a relatively mature statistical method, ARIMA has been widely used in the price prediction of energy, agricultural products and stocks due to its advantages such as solid theoretical foundation and good short-term forecasting effect. In this paper, ARIMA was used to take the monthly corn price data of 23 months from April 2019 to February 2021 as the sample, processed and analyzed the data, and made a forecast analysis of the corn price in March 2021 in China. By comparing the experimental results with the actual price, it can be seen that the model established has a good fitting degree and can predict the corn price in China more accurately.
Research on short video recommendation strategy based on big data analysis
Authors: Gao Ting 
Published: June 2021

Abstract
In the era of information explosion, "information overload" has become an unavoidable problem. Vital information acquisition is an unavoidable problem. In the era of big data, using big data technology is undoubtedly an important thinking direction to solve data problems. Based on big data analysis and recommendation, this paper takes DIKW as the model support and takes short video personalized recommendation as an example to provide different personalized short video recommendations for different scenes. Aimed at the current information explosion era, looking for a good recommendation information system.
Evaluation model construction of automobile appearance design based on random forest algorithm
Authors: Zheng Xundong , Yi Shanshan , Deng Xiaotong 
Published: June 2021

Abstract
Random forest algorithm is a nonlinear supervised classification algorithm, which is based on the idea of ensemble learning and consists of multiple decision tree classifiers. The algorithm has strong generalization ability, fault tolerance ability and nonlinear fitting ability, and can learn the deep-seated laws in the sample system according to a small number of samples using information entropy as the criterion. In the form of questionnaire survey, this paper collects the main evaluation factors of automobile appearance modeling design for 100 consumers or potential consumers. After selecting the evaluation factors, 50 consumers or potential consumers score a car according to these evaluation factors, to collect the original data. Experimental results show that the accuracy of the algorithm is as high as 84%, which shows that the model has such a high accuracy that it can be applied to the evaluation of automobile appearance modeling design. At the same time, this method can also be transferred to the appearance modeling design of other models.
Face value big data multiple linear regression model analysis
Authors: Cui Faqiang 
Published: June 2021

Abstract
In order to explore the impact of the live video host’s face value on the consumption of viewers watching the live video, the author formed a 28-person research team from 20:00 pm to 22:00 pm every evening to conduct a 336-hour follow-up survey. A total of 488 valid online questionnaires and 56 personal in-depth interview records were collected. The author classifies the big data obtained in the survey, and then establishes a multiple linear regression analysis model, using a stepwise regression analysis method. At the same time, the Eviews10.0 is used to objectively analyze and accurately verify the classified big data. The conclusion is that the beautification degree of the live video host’s face, eyes and ears is positively correlated with the live video sales revenue, while the live video host’s mouth shape is negatively correlated with the live video sales revenue.
Construction of legal incentive evaluation model based on BP neural network with multiple hidden layers
Authors: Li Jing 
Published: June 2021

Abstract
BP neural network, the basic algorithm of deep learning, is a multilayer feedforward algorithm trained according to the error back propagation algorithm. BP neural network has the function of realizing any complex nonlinear mapping, which makes it especially suitable for solving problems with complex internal mechanism and has strong fitting ability. In this paper, the number, type, propagation rate and usage rate of the objects from 2013 to 2019 are collected as input and output neurons to establish a 6-layer BP neural network model. After experimental verification, the average relative error between the statistical value and the predicted value is 0.23%, which proves that the model has high prediction accuracy and can be applied in the evaluation of professional development.
Research progress and trend analysis of computer vision based on cite space
Authors: Liu Yuzhi 
Published: June 2021

Abstract
In recent years, with the rapid development of artificial intelligence and the proposal of efficient algorithms such as deep learning, the ability of computer vision to deal with complex problems has made a qualitative leap. Therefore, the study of computer vision is deepenly expanded from academia to industry, and the application of industry continues to be enriched. It involves many disciplines in natural science and social science. The main applications and technologies include image classification, image recognition, deep learning, convolutional neural network, etc. In this paper, the bibliometrics software CiteSpace was used to conduct visualization processing based on the literatures in the core database of Web of Science with the time span from 2010 to 2020 and the subject words as computer vision. The current situation of scientific research cooperation is analyzed from the perspectives of countries, institutions, individuals and cited literatures themselves. The involvement of computer vision discipline is analyzed from the perspectives of natural sciences and social sciences in front, and analyzed from the funding institutions. Funded institutions are analyzed from the perspective of computer vision discipline. The current research hotspots and development frontiers are analyzed by using maps. It is found that the future computer vision will focus on deep learning, transfer learning and semantic segmentation. Finally, it points out that there are some problems in the current research, such as the lack of close scientific research cooperation, the lack of comprehensive discipline involved, and the difficulty of technology implementation. Three suggestions are put forward to solve these problems: (1) Holding trans-regional academic exchange meetings to promote the sharing of academic achievements. (2) Raising the attention to the partial humanities and promote the depth of disciplines. (3) Considering the cost and efficiency in practical application to accelerate the realization of scenario application.
CO2 adsorption on graphene supported Ni catalysts
Authors: Muttaqien F , Namari N A P , Wella S A , Wungu T D K , Suprijadi  
Published: June 2021

Abstract
We perform density functional theory (DFT) study of CO 2 adsorption on graphene supported Ni catalyst. We implement ordinary PBE functional as well as van der Waals density functional (vdW-DF) to accommodate weakly interaction between CO 2 and graphene. Based on vdW-DF calculations, we obtained that CO 2 is physisorbed on pristine graphene, in contradiction with PBE results. The adsorption energy further increases when we introduce single Ni adatom and small Ni cluster on graphene. The adsorbed CO 2 bond angle and bond length on graphene with single Ni adatom and cluster are deformed from its gas phase condition, indicating that CO 2 is chemically adsorbed on the decorated graphene area. Our results provide useful insight into appropriate design of graphene supported metal catalysts.
Post-disaster effect on radiation safety in multihospital radiological unit: a one-year post-earthquake observation report in Lombok
Authors: Suwandi G R F , Haekal M , Bayuadi I , Craig L A , Fahmi A , Khotimah S N , Prawiro S A , Haryanto F 
Published: June 2021

Abstract
A series of earthquakes that hit Lombok, Nusa Tenggara Barat, in the span of August - September 2018 had caused a massive destruction and major damage especially in the structure of buildings including the radiological unit in the hospitals around the Lombok island. The damage in radiological unit building structures could cause a radiation leak in the surrounding areas and endanger the radiation safety of the hospital personnel. Many efforts were taken from the hospital itself and the government to immediately repair the damages in the structure. This study aimed to reports analysis the damage caused by the earthquake to the building structure and the efforts that were taken to repair the damages one year after the events. The measurements of exposure around the buildings were also performed to assess the results of the building renovation. The results were then compared with the radiation exposure measurements report prior to the events. A series of recommendations was then offered based on the results analysis regarding the enhancement of the shielding.
The Matrix Elements of Area Operator in (2+1) Euclidean Loop Quantum Gravity
Authors: Fahmi K , Zen F P 
Published: June 2021

Abstract
In this article we discuss the matrix elements of area operator in (2+1) Euclidean Loop Quantum Gravity. The Euclidean signature is chosen because it has the same group as (3+1) Lorentzian Loop Quantum Gravity that is SU (2). The techniques used follows what has been done on the volume operator in (3+1) Lorentzian Loop Quantum Gravity. Although both are referred to as volume operator, they both have significantly different forms, so the initial steps of the discussion will be different. The result show that we can express the matrix elements in terms of standard recoupling scheme basis.
Hypocenter Relocation, Determination of Velocity Model and Correction Station in Gede Volcano for December 2017 Data
Authors: Reny  , Srigutomo W. , Faizi H.N. 
Published: June 2021

Abstract
The distribution of the volcanoes in indonesia gives positive impacts in the form of fertile land where many residents live to grow crops. Besides having positive impacts, there are also negative impacts in the form of natural disasters. Improvement in mitigation of natural disasters is needed to avoid any form of harm and undesired losses. Information on the accuracy of earthquake parameters is an important part of natural disaster mitigation efforts. Hypocenter relocation of earthquakes is a step to improve the accuracy of earthquake parameter information. The relocation of hypocenters earthquake is performed using the Coupled Velocity-Hypocenter method. The research area is Gede volcano which is included as type A active volcanoes. The data were processed using GAD and VELEST 3.3 software resulted in epicenters that were spread around Gede and Pangrango volcanoes. The hypocentre were distributed between -2.5 – 5.2 km depths based on GAD calculated. After relocation using VELEST 3.3, they were distributed between 1.0 – 10.5 km depth. The calculated. The calculated magnitudes of volcanic earthquakes occurred in December 2017 vary between 0.6 to 2.7. the difference between the initial and final velocity models is small. After station correction step, the velocity profiles beneath the most of station is indicated as sedimentary layers, and beneath of the peak station, it is indicated as instrusive rocks.
The electrical complex impedance measurement of rock samples using simple method on Halmahera rock samples
Authors: Handayani G , Usman M H K 
Published: June 2021

Abstract
The electrical complex impedance is very important physical parameter to determine the fluid and matrix condition of the rock sample. One mechanism in the samples that influences this complex, frequency – dependent behaviour of resistivity is the disseminated metal ores which can block the pores and subsequently trigger the mechanism for storage/delay. Pore water ions build up on either side of the grain, results in the effect of a capacitor. This paper presents a simple experimental set up to measure the complex resistivity of rock samples. The main mechanism is generation of high voltage sinusoidal signal. This is implemented as collector voltage of a transistor. The high voltage sinusoidal signal then is applied on either side of the rock sample. At two distances of the sample we measure the resulting voltage using the oscilloscope. The observed delay can be considered as the measured phase, whereas the amplitude of the observed voltage is considered as the voltage. We determine the absolute impedance as the voltage divided by the current. Using this simple method we measured the complex impedances of 14 rock samples obtained from Halmahera Island. From the results of measurement, we tried to infer and to model the disseminated metal ores of the samples.
Laboratory study of the performances of two individual Condensation Particle Counters
Authors: Bau Sébastien , Payet Raphaël 
Published: June 2021

Abstract
In the general framework of investigating the performances of instruments that can be deployed in occupational hygiene to assess exposure to airborne nanoparticles, this study aims at providing new data relative to two new specimens of individual Condensation Particle Counters (CPC). Though a variety of instruments are available on the market, CPC constitute the devices that are used the most. Designed by the American company Enmont LLC, the first individual CPC allow personal measurements to be carried out. Here, the reliability of the PUFP C110 and C200 has been characterized in the laboratory. Our experimental results highlight that both individual CPC under study are impacted by both particle size (underestimation of the number concentration of ~20% when the modal diameter is lower than 40 nm, n = 21) and hydrophobicity (-60% to -90% compared to the reference concentration when aerosols are hydrophobic, n = 15). Except from these specific cases (n = 44), both individual CPC have a comparable and satisfying response (+30% with regards to the reference concentration).
Assessing a national nanotechnology infrastructure for enforcing nanosafety in consumer food - Funding the infrastructure
Authors: McCarron E , Chambers G 
Published: June 2021

Abstract
Crucial to assessing any national risk assessment infrastructure is the development of keen insights into the funding landscape, the base skill set and expertise levels, risk prioritisation, and stakeholder determination. This paper presents an overview of the first of these criteria with respect to the Irish funding landscape for nanotechnology and nano-agri-food. It examines difficulties with policy enforcement due to a lack of clarity and varying interpretations of the EU definition of nanomaterials and how the funding landscape could potentially facilitate the necessary infrastructure, to underpin regulatory enforcement and risk assessment for nanotechnology in food. In 2008 an assessment of the potential risks associated with nanotechnologies was carried out in Ireland which resulted in a number of recommendations centred around the provision of funding. This study examines Exchequer/public investment over the last decade to develop research infrastructure and it identifies if such investment has helped enhance the enforcement of nano-food safety legislation in Ireland.
Thiolate chitosan nanoparticles capable to delivery nitric oxide: synthesis, characterization and antibacterial potential
Authors: Pelegrino M T , Natividade T R , Gonçalves M C , Nakazato G , Seabra A B 
Published: June 2021

Abstract
Nitric oxide (NO) is a small but powerful molecule. It is involved in several biological pathways such as vasodilation, wound healing and toxicity towards pathogens. At high concentrations, NO has a toxicity towards bacteria, virus and fungi which has a great biomedical potential. However, NO has a small half-life of just a few seconds and this hazards its clinical application. In this scenario, the combination of nanotechnology with NO donors can create new strategies to load and deliver NO. Polymeric nanomaterial intrinsically have advantages such as low toxicity, biodegradability and low-cost. In this study, we used chemically modified chitosan (CS) to prepare nanoparticles capable of loading and releasing NO with antibacterial activity. CS was chemically modified to add a thiol group (-SH) to its structure. This modification was performed by the reaction with thioglycolic acid (TGA) in the presence of a carbodiimide (EDC). The thiol groups in CS structure serve a double function: create an anchorage site for NO and increase polymer mucoadesivity. The synthesis of thiolated chitosan nanoparticles (TCS NP) occurred by ionotropic gelation method using sodium tripolyphosphate (TPP) as counter ion. The NO donor precursor molecule, mercaptosuccinic acid (MSA), was encapsulated into TCS NP to increase loading capacity of NO. To identify the best paraments of the synthesis we used the ratios 1:3, 1:4, 1:5, 1:6 and 1:7 for TCS:TPP. TCS NPs were characterized by dynamic light scattering (DLS), microscopy electron transmission (MET) and nanoparticle tracking analysis (NTA). The release of NO was characterized by a kinetic using Uv-vis spectroscopy. Finally, the antibacterial potential was evaluated by minimum inhibitory concentration (MIC) assay against Staphylococcus aureus, Streptococcus mutans and Escherichia coli strains. The ratio 1:5 showed the most adequate size parameters and the other analysis were performed using it. The hydrodynamic size was found to be 113.0 ± 1.6 nm, PDI of 0.292 ± 0.035 and zeta potential of 27.1 ± 0.9. The MET images indicated small and spherical nanoparticles. The kinetic profiles showed a linear release of NO reaching the 100% after 150 min. The antibacterial effect was tested for E. coli, S. aureus and S. mutans. The MIC values was 50 µg mL for NO-CS NP, this result was 50% lower compared to TCS NPs for S. mutans and E. coli. The TCS NP has suitable properties for the biomedical field with potential for antibacterial application.