bluuebunny/crossref_metadata_2025_split huggingface dataset:
New frontiers in extreme conditions science at synchrotrons and free electron lasers
Authors: Cerantola Valerio , Rosa Angelika Dorothea , Konôpková Zuzana , Torchio Raffaella , Brambrink Erik , Rack Alexander , Zastrau Ulf , Pascarelli Sakura 
Published: April 2021

Abstract
Synchrotrons and free electron lasers are unique facilities to probe the atomic structure and electronic properties of matter at extreme thermodynamical conditions. In this context, ‘matter at extreme pressures and temperatures’ was one of the science drivers for the construction of low emittance 4th generation synchrotron sources such as the Extremely Brilliant Source of the European Synchrotron Radiation Facility and hard x-ray free electron lasers, such as the European x-ray free electron laser. These new user facilities combine static high pressure and dynamic shock compression experiments to outstanding high brilliance and submicron beams. This combination not only increases the data-quality but also enlarges tremendously the accessible pressure, temperature and density space. At the same time, the large spectrum of available complementary x-ray diagnostics for static and shock compression studies opens unprecedented insights into the state of matter at extremes. The article aims at highlighting a new horizon of scientific opportunities based on the synergy between extremely brilliant synchrotrons and hard x-ray free electron lasers.
Analysis of different asymmetries in B → K
*
ℓ
+
ℓ
− in 2D and 1D Scenarios
Authors: Shafaq Saba , Ahmad Ishtiaq , Bano Rawish , Javaid Nida , Nasrullah Aqsa , Barakat Thabit 
Published: April 2021

Abstract
At present experimental study, B meson decays, show some deviations from it’s SM predictions of certain physical observables, the focus of this study is to investigate the causes of these deviations. The effects are thus first studied by introducing the chirality flipped operators (not present in SM) and by giving non zero values to at least two different Wilson Coefficients. Then for

C

9

NP

=
−

C

10

NP

condition effects has been studied using the couplings of LQ and

Z

′

models. A general comprehensive expression for different observables in terms of WC’s is given for both the scenarios, and the simultaneous plots of different observables are also presented which can help us to narrow down the range of NP parameters for future studies.
Application of Natural Language Processing Technology in Educational Resources Retrieval
Authors: Huang Yibao 
Published: April 2021

Abstract
According to the data structure characteristics of educational resources, natural language processing technology is used to process the original educational resources data from the aspects of word segmentation processing, named entity recognition, part of speech tagging, synonym analysis, word vector analysis, etc. For complex image data, OCR preprocessing and coding format conversion are used to reduce the complexity of the original data; according to the difficulty requirements of the user to retrieve data, key words or ontology are selected to further expand and enrich the semantics and improve the accuracy of the retrieval results.
Problems and Countermeasures for the Data Mining of Medical Records of Traditional Chinese Medicine in the Era of Big Data
Authors: Wang Xiaofang , Hu Xiaojie , Liu Cheng 
Published: April 2021

Abstract
The medical records of Traditional Chinese Medicine (TCM) such as Huangdi’s Canon of Medicine and Treatise on Typhoid and Miscellaneous Diseases have always been the hot research objects in the field of TCM. Many papers on the summary and statistical analysis of TCM medical records have appeared one after another, which has played a huge role in promoting the development of TCM. However, with the progress of the times, the research of Traditional Chinese Medicine should keep pace with the new times. The authors in this paper introduced the research status and role of the data mining technology and mining methods, the problems and countermeasures of data mining technology in the research of Traditional Chinese Medicine medical records at this stage.
Application of Science and Technology Project Learning in English Linguistics Teaching with Integration of Machine Learning
Authors: Liu Tianning 
Published: April 2021

Abstract
Linguistics is a compulsory course for college English majors. The purpose of setting up linguistics is to cultivate students’ English language awareness, improve their comprehension of English language, and jump out of the Chinese-English language circle. Project learning came into being under this trend. It is student-centered and focuses on improving students’ desire for learning. It also differs from the past in teaching mode. Project learning allows students to pass through a relatively free space. Questions to stimulate students’ desire to explore, so as to trace the source of the entire problem, such links are discovered by students’ cooperation is essential. They are not teachers and professors. This can improve students’ learning efficiency, increase their understanding of English language, and increase their confidence in learning English linguistics. Therefore, this paper discusses the application of science and technology project learning in English linguistics teaching with integration of machine learning. In our designed model, the neural networks and the PCA are integrated to present the proper combination. The experimental performance is validated through the public databases and the proposed model outperforms compared with the other state-of-the-art methodologies.
Self-supervised Dense Depth Prediction in Monocular Endoscope Video for 3D Liver Surface Reconstruction
Authors: Cao Zhengtao , Huang Wenfeng , Liao Xiangyun , Deng Xuesong , Wang Qiong 
Published: April 2021

Abstract
In this paper, we propose a self-supervised monocular depth prediction method which combines traditional multi-view stereo method and fully convolutional network to predict the depth map in monocular endoscopic video and achieve 3D dense reconstruction of liver surface. We adopt the sparse data generated by COLMAP supervision signal as the training data, and integrate the attention model in the fully convolutional network to effectively extract the channel features to improve the accuracy of depth prediction. Taking into account the problem of insufficient supervision ability of sparse data, the projection transformation of two images within a certain range is carried out to make up for the missing supervision points. Experimental results show that this method has achieved good results in the depth prediction of monocular endoscopic video and has good applicability to the whole liver.
Traffic data prediction of mobile communication base station based on wavelet neural network
Authors: Lei Ming , Qin Rui , Mao Wentao , Lu Hongxia 
Published: April 2021

Abstract
With the wide application of new media, users require more and more mobile communication. In order to satisfy users’ high-quality experience and save resources, it is necessary to predict the traffic data of mobile communication base station, so that mobile communication base station can adjust the frequency load quantity according to the traffic fluctuation. From March 1 to April 9, 2018, this paper collects traffic data, selects 40, 000 sets of data, uses python to mine data, and predicts the traffic data of mobile communication base station by establishing wavelet neural network short-time traffic prediction model. The results show that the average accuracy of the short-term prediction model is 43. 15 and the root mean square error is 0. 0076.
The Reconstruction of Accounting Information Disclosure System Based on Blockchain Technology
Authors: Chen Yuxue , Chen Zihong , Hu Die 
Published: April 2021

Abstract
In recent years, the trust relationship of financial reports in the capital market has been severely challenged. The reliability, relevance and timeliness of the accounting information provided by the company’s financial reports are obviously insufficient, and the efficiency of capital market accounting supervision has been greatly reduced. In the digital economy era, traditional double-entry accounting and financial accounting reports are no longer sufficient to serve the new economy, and it is imperative to reconstruct the accounting information disclosure system. This article attempts to implement the nine-character policy of capital market reform. Firstly, to reconstruct the accounting information disclosure system based on blockchain technology; and under the background of the system with less government intervention, various entities Automatically realize the maintenance of distributed ledger and the disclosure of financial accounting information, based on the principle of self-interest. Finally, in addition to legal restrictions, it is more difficult for untrustworthy persons to participate in the maintenance of distributed ledger, in order to increase their economic costs and achieve zero tolerance for them.
An Efficient OpenCL-Based FPGA Accelerator for MobileNet
Authors: Wei Liu , Peng Lv 
Published: April 2021

Abstract
Convolutional neural network is widely used in image processing and image recognition. In order to obtain higher accuracy, the computational complexity and the scale of model and parameters are increasing. FPGA has become a good choice because of its low power consumption and high flexibility. MobileNet replaces the standard convolution by depthwise convolution and pointwise convolution, which greatly reduces the computational complexity and parameters of the model in the case of less precision loss, so that it can be applied to the equipment with limited computing resources. In this paper, we propose an efficient OpenCL-based FPGA CNN accelerator to realize inferencing acceleration of MobileNet. We designed the convolution layer with modularization. We used pipeline to design a depth separable convolution parallel acceleration scheme, and made full use of the DSP resources of FPGA. This design finally achieved a good balance of hardware resources, processing speed and power consumption. The experiments show that the accelerator can reach the inferencing speed of 32.56ms, and the energy consumption is 20W. The speedup is 4x compared with CPU and energy efficiency is 3x compared with GPU.
Research on Network Security Situation Assessment Technology Based on Fuzzy Evaluation Method
Authors: Kong Dehua , li Huyuan , dong Hongyan 
Published: April 2021

Abstract
With the development of Internet technology and the continuous improvement of social informatization, the network has gradually become an indispensable part of people’s production and life. The importance of network security has received more and more attention. A variety of security products have been applied to the network to strengthen and maintain the safe operation of the network. However, these security means can only play a specific role in a certain range, and lack of effective data fusion and collaborative management mechanism. In the face of many scattered information, network security managers cannot respond to security incidents in time. For the purpose of grasping the current situation and changes of network security, network security situation awareness technology came into being, it has become a new hotspot in network security research. This paper proposes a fuzzy comprehensive evaluation model which combines AHP and fuzzy evaluation method to evaluate network security situation.
An improved dynamic window approach for local trajectory planning in the environment with dense objects
Authors: Mai Xiquan , Li Di , Ouyang Jian , Luo Yongchao 
Published: April 2021

Abstract
The Dynamic Window Approach (DWA) has been one of the most popular solutions in local trajectory planning due to the advantages of movement fluency. However, the traditional DWA faces the challenge of low-efficiency in the case of local trajectory planning since the robot cannot perceive the density of the obstacle. In this paper, we propose an improved DWA to solve this problem. First, we use multi-sensor technology and new evaluation algorithms to enable the capability of density perception for the disorderly environment. Second, the evaluation factor related to the density change is introduced into the path evaluation function, which will facilitate the robot to perceive the distribution of dense objects in advance. Computer simulation and field experiments show that the efficiency of the improved DWA is increased by 25%. The improved DWA not only considers the original factors such as heading angle, but also introduces a density factor to evaluate the next path, so as to avoid entering dense areas in advance. It can be seen that the improved DWA has relatively stable speed and acceleration, and can avoid dense areas in advance, which can be widely used in robots running in dense environments.
Research on a lift car collision avoidance strategy for a circulatory multipath elevator based on digital twins
Authors: Chen Jiwen , Wang Lei , Li Xin , Yang Hongjuan , Yang Rui 
Published: April 2021

Abstract
To reduce the lateral movement time of elevator passengers in large-span high-rise buildings, a circulatory multipath elevator design scheme is proposed. To reduce the complexity of the circulatory multipath elevator control process due to the increase in the number of lift cars, a circulatory multipath elevator control system framework is established based on digital twin technology. To ensure the safe operation of the elevator, a lift car following model is established, and a circulatory multipath elevator collision avoidance strategy is designed. The lift car collision avoidance strategy can effectively reduce collision accidents in the process of multi lift car operation and ensure the safety of elevator passengers.
Simulations of the COVID-19 Epidemic in Nigeria Using SIR Model
Authors: Duan Yimeng 
Published: April 2021

AbstractThis study aims to review some applications of SIR model in COVID-19 epidemic situations, and tries to simulate the predictions of COVID-19 epidemic in a specific country using SIR model and R coding. This article picks Nigeria as the example, applying data from 28 February to 31 May and simulating the infected cases with Coronavirus. Based on literature review about usage of SIR model in this epidemic, the current trend is focusing on the optimization of parameters and algorithm. As a consequence, this study specifically initializes the SIR model with certain parameters and optimizes it through Maximum Likelihood Estimation using the Nelder-Mead algorithm. As the results show, the epidemic peak of Coronavirus cases in Nigeria is predicted to be reached around 20 May 2020, yet the peak has not been reached in reality and the growing rate is also quite different from the predicted curve. This phenomenon is considered as a consequence of a combined action and multiple factors that might attribute to it are discussed.
More than a nuisance: measuring how sea level rise delays commuters in Miami, FL
Authors: Hauer Mathew , Mueller Valerie , Sheriff Glenn , Zhong Qing 
Published: April 2021

Abstract
Sea level rise increases coastal cities’ exposure to tidal flooding and elevates the risk of transportation routes being compromised at high tide. Using Miami, Florida as a case study, we combine tide gauge, elevation, road network, and worker location data with a route optimization algorithm to model how tidal flooding affected commute times between 2002–2004 and 2015–2017. Results suggest tidal flooding increases annual commutes by 15 min on average and 274 min among the most heavily impacted areas. Additionally, approximately 14 000 commuters may be unable to reach their workplace due to tidal flooding at least once per year. Accommodation via dynamic adjustments in residential and work locations may reduce tidal commuting delays by as much as 70%, particularly among the highest earners. Many of the most affected areas do not experience flooding directly, expanding the purview of vulnerability beyond simple residential risk. Using 2060 extreme sea-level rise scenarios without accommodating behavior, mean annual commute delays are expected to reach 220 min with over 55 000 commuters potentially unable to reach their destinations.
O8: DIRECT MANIPULATION OF THE INTESTINAL MICROBIOME TO INFLUENCE POST-OPERATIVE OUTCOMES
Authors: Dilke SM , Durant LR , Stentz R , Wilson A , Tozer PJ , Vaizey CJ , Hoyles L , Carding S , Knight SC , Noble A 
Published: April 2021

Abstract

Introduction
Distal feeding (DF) involves intubation of the distal limb of a loop ileostomy to feed with a prebiotic mix. Studies suggest that distally feeding patients following anterior resection prior to ileostomy closure may be beneficial as a form of bowel pre-habilitation. These pilot data examine the adaptive immune response to DF.

Method
Ten healthy controls with an intact GI tract were compared with 10 patients following rectal cancer resection and chemo/radiotherapy, prior to closure of ileostomy over 8 weeks of DF. We examined B and T cell memory responses from peripheral blood using cell proliferation assays. Cells were cultured with dead antigen to reflect the microbiota in the small and large bowel. Two negative and two positive controls were used to assess minimal and maximum cell proliferation.

Result
B cell responses prior to DF were increased in the defunctioned group compared to the normal controls to significance (p=0.0014). After 8 weeks of DF the groups were comparable. T cell responses to bacteria had significant differences in proliferation rate following DF commencement, CD4 week 0 vs 3 p=0.0001, week 3 vs 8 p=0.000034, CD8 week 0 vs 3 p=0.0001, week 3-8 p=0.00024). In individual patients, CD4 responses were shown to shift from responses to facultative aerobic species, to strict anaerobes.

Conclusion
These data suggest that distal feeding fundamentally resets peripheral circulating memory and it may be of use in pre-habilitating the bowel prior to restoration of continuity. Early clinical data suggests that distal feeding improves post-operative outcomes.

Take-home message
distal feeding is an easy pre-operative intervention that has a significant effect on cell proliferation and antigen response, which may contribute to improved post operative outcomes.
Adults Hospitalized With Coronavirus Disease 2019 (COVID-19)—United States, March–June and October–December 2020: Implications for the Potential Effects of COVID-19 Tier-1 Vaccination on Future Hospitalizations and Outcomes
Authors: Sami Samira , Tenforde Mark W , Talbot H Keipp , Lindsell Christopher J , Steingrub Jay S , Shapiro Nathan I , Ginde Adit A , Douin David J , Prekker Matthew E , Erickson Heidi L , Brown Samuel M , Peltan Ithan D , Gong Michelle N , Khan Akram , Exline Matthew C , Files D Clark , Gibbs Kevin W , Rice Todd W , Casey Jonathan D , Grijalva Carlos G , Stubblefield William B , Womack Kelsey N , Hager David N , Qadir Nida , Chang Steven Y , Henning Daniel J , Wilson Jennifer G , Self Wesley H , Patel Manish M 
Published: April 2021

Abstract

Background
Because of the increased risk for severe coronavirus disease 2019 (COVID-19), the Advisory Committee on Immunization Practices (ACIP) initially prioritized COVID-19 vaccination for persons in long-term care facilities (LTCF), persons aged ≥65 years, and persons aged 16–64 years with high-risk medical conditions when there is limited vaccine supply. We compared characteristics and severe outcomes of hospitalized patients with COVID-19 in the United States between early and later in the pandemic categorized by groups at higher risk of severe COVID-19.

Methods
Observational study of sampled patients aged ≥18 years who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and admitted to one of 14 academic hospitals in the United States during March–June and October–December 2020. Demographic and clinical information were gathered from electronic health record data.

Results
Among 647 patients, 91% met ≥1 of the following risk factors for severe COVID-19 [91% March-June (n = 434); 90% October–December (n = 213)]; 19% were LTCF residents, 45% were aged ≥65-years, and 84% had ≥1 high-risk condition. The proportion of patients who resided in a LTCF declined significantly (25% vs 6%) from early to later pandemic periods. Compared with patients at lower risk for severe COVID-19, in-hospital mortality was higher among patients at high risk for severe COVID-19 (20% vs 7%); these differences were consistently observed between March–June and October–December.

Conclusions
Most adults hospitalized with COVID-19 were those recommended to be prioritized for vaccination based on risk for developing severe COVID-19. These findings highlight the continued urgency to vaccinate patients at high risk for severe COVID-19 and monitor vaccination impact on hospitalizations and outcomes.
Retrospective Detection of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Symptomatic Patients Prior to Widespread Diagnostic Testing in Southern California
Authors: Hilt Evann E , Boocock James , Trejo Marisol , Le Catherine Q , Guo Longhua , Zhang Yi , Sathe Laila , Arboleda Valerie A , Yin Yi , Bloom Joshua S , Wang Pin-Chieh , Elmore Joann G , Kruglyak Leonid , Shrestha Lasata , Bakhash Shah A Mohamed , Lin Michelle , Xie Hong , Huang Meei-Li , Roychoudhury Pavitra , Greninger Alexander , Chandrasekaran Sukantha , Yang Shangxin , Garner Omai B 
Published: April 2021

Abstract

Background
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused one of the worst pandemics in recent history. Few reports have revealed that SARS-CoV-2 was spreading in the United States as early as the end of January. In this study, we aimed to determine if SARS-CoV-2 had been circulating in the Los Angeles (LA) area at a time when access to diagnostic testing for coronavirus disease 2019 (COVID-19) was severely limited.

Methods
We used a pooling strategy to look for SARS-CoV-2 in remnant respiratory samples submitted for regular respiratory pathogen testing from symptomatic patients from November 2019 to early March 2020. We then performed sequencing on the positive samples.

Results
We detected SARS-CoV-2 in 7 specimens from 6 patients, dating back to mid-January. The earliest positive patient, with a sample collected on January 13, 2020 had no relevant travel history but did have a sibling with similar symptoms. Sequencing of these SARS-CoV-2 genomes revealed that the virus was introduced into the LA area from both domestic and international sources as early as January.

Conclusions
We present strong evidence of community spread of SARS-CoV-2 in the LA area well before widespread diagnostic testing was being performed in early 2020. These genomic data demonstrate that SARS-CoV-2 was being introduced into Los Angeles County from both international and domestic sources in January 2020.