bluuebunny/crossref_metadata_2025_split huggingface dataset:
Newspapers’ coverage of the COVID-19 pandemic in Eswatini: from distanciated re/presentations to socio-health panics
Authors: Evans Henri-Count 
Published: December 2021

AbstractThis paper examines the coverage and re/presentation of the coronavirus pandemic by two mainstream newspapers in the Kingdom of Eswatini, namely, the Times of Eswatini and the Eswatini Observer between January and June 2020. Framing and discourse analyses are used in the examination of news stories. The key to this study is how the coverage and re/presentation evolved as ‘new facts’ about the virus emerged. From being re/presented in a distanciated form to becoming a localised scare, the travelling of the virus in space and time and its profile in the newspapers are examined. When the virus began to enjoy widespread coverage, news stories focused on virus incidence and later started paying attention to the internal evolution of the virus and how the government was responding to it. The analysis shows that political indexing sustained the coverage of the COVID-19 pandemic. Due to political and official indexing, media coverage largely reproduced the views of those in power, especially the construction of lockdown regulations as rational and legitimate. The government and security officials characterised the coronavirus as an invading enemy that could only be defeated through ‘war’. The news media reproduced the war language of the government and security officials, and thus legitimised the lockdowns and security surveillance. In addition to regulatory interventions, the results reveal that the government and civil society initiated prayer and fasting sessions as part of response interventions. This paper concludes that health journalism pays less attention to health scares that are seen to be happening ‘elsewhere’. However, once the problems become local, the news value of proximity enables journalists to provide extensive coverage. In addition, the coverage of pandemics begins with increased coverage and panic, followed by constant attention and after some time, the stories leave front pages as journalism fatigue kicks in.
Systems biology analysis of human genomes points to key pathways conferring spina bifida risk
Authors: Aguiar-Pulido Vanessa , Wolujewicz Paul , Martinez-Fundichely Alexander , Elhaik Eran , Thareja Gaurav , Abdel Aleem Alice , Chalhoub Nader , Cuykendall Tawny , Al-Zamer Jamel , Lei Yunping , El-Bashir Haitham , Musser James M. , Al-Kaabi Abdulla , Shaw Gary M. , Khurana Ekta , Suhre Karsten , Mason Christopher E. , Elemento Olivier , Finnell Richard H. , Ross M. Elizabeth 
Published: December 2021

SignificanceGenetic investigations of most structural birth defects, including spina bifida (SB), congenital heart disease, and craniofacial anomalies, have been underpowered for genome-wide association studies because of their rarity, genetic heterogeneity, incomplete penetrance, and environmental influences. Our systems biology strategy to investigate SB predisposition controls for population stratification and avoids much of the bias inherent in candidate gene searches that are pervasive in the field. We examine both protein coding and noncoding regions of whole genomes to analyze sequence variants, collapsed by gene or regulatory region, and apply machine learning, gene enrichment, and pathway analyses to elucidate molecular pathways and genes contributing to human SB.
Lattice defects induced by microtubule-stabilizing agents exert a long-range effect on microtubule growth by promoting catastrophes
Authors: Rai Ankit , Liu Tianyang , Katrukha Eugene A. , Estévez-Gallego Juan , Manka Szymon W. , Paterson Ian , Díaz J. Fernando , Kapitein Lukas C. , Moores Carolyn A. , Akhmanova Anna 
Published: December 2021

Significance
Microtubules are major cytoskeletal filaments important for cell division, growth, and differentiation. Microtubules can rapidly switch between phases of growth and shortening, and this dynamic behavior is essential for shaping microtubule arrays. To obtain insights into mechanisms controlling microtubule dynamics, here we used microtubule-stabilizing agents such as Taxol and their fluorescent analogs to manipulate microtubule protofilament number and generate stable defects in microtubule lattices that can be visualized using fluorescence microscopy. We show that microtubule polymerization rate increases with protofilament number and that drug-induced microtubule lattice discontinuities can promote plus-end catastrophes at a distance of several micrometers. Our data indicate that structural defects in the microtubule wall can have long-range propagating effects on microtubule tip dynamics.
Adult mouse and human organoids derived from thyroid follicular cells and modeling of Graves’ hyperthyroidism
Authors: van der Vaart Jelte , Bosmans Lynn , Sijbesma Stijn F. , Knoops Kèvin , van de Wetering Willine J. , Otten Henny G. , Begthel Harry , Borel Rinkes Inne H. M. , Korving Jeroen , Lentjes Eef G. W. M. , Lopez-Iglesias Carmen , Peters Peter J. , van Santen Hanneke M. , Vriens Menno R. , Clevers Hans 
Published: December 2021

Significance
The thyroid is essential for maintaining systemic homeostasis by regulating thyroid hormone concentrations in the bloodstream. This study describes an organoid-based model system to study mouse and human thyroid biology. Moreover, the study explores the potential of human organoids for modeling autoimmune disease, the anti-TSH receptor (TSHR) antibody-driven Graves’ hyperthyroidism.
Exploring task-based cognitive processes
Authors: Révész Andrea 
Published: December 2021

AbstractThis paper argues that TBLT researchers should dedicate more effort to investigating the cognitive processes in which L2 learners engage during task work to facilitate theory-construction and to inform pedagogical practices. To help achieve this, a review follows of various subjective (questionnaires, interviews, think-aloud/stimulated recall protocols) and objective (dual-task methodology, keystroke-logging, eye-tracking) methods that are available to TBLT researchers to examine cognitive processes underlying task-based performance. The paper concludes that, to obtain a more valid understanding of task-generated cognitive processes, it is best to combine various methods to overcome the limitations of each. Finally, some methodological recommendations are provided for future cognitively-oriented TBLT research.
A multi-scale E-jet 3D printing regulated by structured multi-physics field
Authors: Li Kai , Zhao Yihui , Liu Maiqi , Wang Xiaoying , Zhang Fangyuan , Wang Dazhi 
Published: December 2021

Abstract
Micro/nano scale structure as important functional part have been widely used in wearable flexible sensors, gas sensors, biological tissue engineering, microfluidic chips super capacitors and so on. Here a multi-scale electrohydrodynamic jet (E-jet) 3D printing approach regulated by structured multi-physics fields was demonstrated to generate 800 nm scale 2D geometries and high aspect ratio 3D structures. The simulation model of jetting process under resultant effect of top fluid field, middle electric field and bottom thermal field was established. And the physical mechanism and scale law of jet formation were studied. The effects of thermal field temperature, applied voltage and flow rate on the jet behaviors were studied; and the range of process parameters of stable jet was obtained. The regulation of printing parameters was used to manufacture the high resolution gradient graphics and the high aspect ratio structure with tight interlayer bonding. The structural features could be flexibly adjusted by reasonably matching the process parameters. Finally, polycaprolactone/polyvinylpyrrolidone (PCL/PVP) composite scaffolds with cell-scale fiber and ordered fiber spacing were printed. The proposed E-jet printing method provides an alternative approach for the application of biopolymer materials in tissue engineering.
Simplifying the hardware requirements for fast neural EIT of peripheral nerves
Authors: Ravagli Enrico , Mastitskaya Svetlana , Holder David , Aristovich Kirill 
Published: December 2021

Abstract

Objective. The main objective of this study was to assess the feasibility of lowering the hardware requirements for fast neural electrical impedance tomography (EIT) in order to support the distribution of this technique. Specifically, the feasibility of replacing the commercial modules present in the existing high-end setup with compact and cheap customized circuitry was assessed. Approach. Nerve EIT imaging was performed on rat sciatic nerves with both our standard ScouseTom setup and a customized version in which commercial benchtop current sources were replaced by custom circuitry. Electrophysiological data and images collected in the same experimental conditions with the two setups were compared. Data from the customized setup was subject to a down-sampling analysis to simulate the use of a recording module with lower specifications. Main results. Compound action potentials (573 ± 287 μV and 487 ± 279 μV, p=0.28) and impedance changes (36 ± 14 μV and 31 ± 16 μV, p=0.49) did not differ significantly when measured using commercial high-end current sources or our custom circuitry, respectively. Images reconstructed from both setups showed neglibile (<1voxel, i.e. 40 μm) difference in peak location and a high degree of correlation (R
2 = 0.97). When down-sampling from 24 to 16 bits ADC resolution and from 100 to 50 KHz sampling frequency, signal-to-noise ratio showed acceptable decrease (<−20%), and no meaningful image quality loss was detected (peak location difference <1voxel, pixel-by-pixel correlation R
2 = 0.99). Significance: The technology developed for this study greatly reduces the cost and size of a fast neural EIT setup without impacting quality and thus promotes the adoption of this technique by the neuroscience research community.
Investigation of 6S
1/2–8S
1/2 two-photon transition of cesium atoms by a single 822 nm laser
Authors: Liu Ning , Wang Sandan , Yuan Jinpeng , Wang Lirong , Xiao Liantuan , Jia Suotang 
Published: December 2021

Abstract
We experimentally investigate the 6S
1/2–8S
1/2 two-photon transition in cesium vapor by a single laser. A blue (455.5 and 459.3 nm) fluorescence signal is observed as a result of 822.5 nm laser beams illuminating the Cs vapor with a counter-propagating configuration. The dependences of the fluorescence intensity on the polarization combinations of the laser beams, laser power and vapor temperature are studied to obtain optimal experimental parameters. The frequency difference between the two hyperfine components of 4158 (7) MHz is measured with a Fabry–Perot interferometer as a frequency reference. Such a large spectral isolation and the insensitivity to the Earth’s magnetic field enable the 6S
1/2–8S
1/2 transition to be a stable frequency standard candidate for a frequency-doubled 1644 nm laser in the U-band window for quantum telecommunication.
Impact of skull sutures, spongiform bone distribution, and aging skull conductivities on the EEG forward and inverse problems
Authors: McCann Hannah , Beltrachini Leandro 
Published: December 2021

Abstract

Objective. Source imaging is a principal objective for electroencephalography (EEG), the solutions of which require forward problem (FP) computations characterising the electric potential distribution on the scalp due to known sources. Additionally, the EEG-FP is dependent upon realistic, anatomically correct volume conductors and accurate tissue conductivities, where the skull is particularly important. Skull conductivity, however, deviates according to bone composition and the presence of adult sutures. The presented study therefore analyses the effect the presence of adult sutures and differing bone composition have on the EEG-FP and inverse problem (IP) solutions. Approach. Utilising a well-established head atlas, detailed head models were generated including compact and spongiform bone and adult sutures. The true skull conductivity was considered as inhomogeneous according to spongiform bone proportion and sutures. The EEG-FP and EEG-IP were solved and compared to results employing homogeneous skull models, with varying conductivities and omitting sutures, as well as using a hypothesised aging skull conductivity model. Main results. Significant localised FP errors, with relative error up to 85%, were revealed, particularly evident along suture lines and directly related to the proportion of spongiform bone. This remained evident at various ages. Similar EEG-IP inaccuracies were found, with the largest (maximum 4.14 cm) across suture lines. Significance. It is concluded that modelling the skull as an inhomogeneous layer that varies according to spongiform bone proportion and includes differing suture conductivity is imperative for accurate EEG-FP and source localisation calculations. Their omission can result in significant errors, relevant for EEG research and clinical diagnosis.
The Importance Evaluation of Weighted Supply Chain Network Nodes Based on Big Data Considering the Degree of Coupling and Coordination
Authors: Tan Kangye , Xu Fang 
Published: December 2021

Abstract
Through big data and computer intelligent software, it is possible to quickly and accurately calculate the supply chain coordination relationship between global enterprises, and due to comprehensively, quickly and objectively assess the importance and stability of each node in the supply chain network where the company is located in the wave of supply chain globalization, it is convenient for decision makers to effectively and accurately identify the key nodes in the supply chain network system, thereby improving the supply chain of the stability and sustainability of the network. The paper proposes an improved comprehensive evaluation method of weighted node entropy weight based on coupling coordination, supply chain and complex network theories, taking into account the coupling and coordination of nodes in the supply chain network system, as well as the importance and connection of nodes. The importance of edges. With the mining and support of big data technology, this improved method can comprehensively and objectively evaluate the importance of nodes in the supply chain network. Finally, taking 2827 companies as examples, the experimental results verify the rationality and effectiveness of the method.
Research on Mnist Handwritten Numbers Recognition based on CNN
Authors: Gong Yang , Zhang Pan 
Published: December 2021

Abstract
In view of the increasing demand for handwritten digit recognition, a handwritten digit recognition model based on convolutional neural network is proposed. The model includes 1 input layer and 2 convolutional layers (5*5 convolution Core), 2 pooling layers (2*2 pooling core), 1 fully connected layer, 1 output layer, and use the mnist data set for model training and prediction. After a lot of training and participation, the accuracy rate of the training set was finally reached to 100%, and the accuracy rate of 99.25% was also achieved on the test set, which can meet the requirements of recognizing handwritten digits.
Aggregation Method of Distributed Load Resources Based on Non-intrusive Load Identification
Authors: Liu Wei , Wang Chaoliang , Li Yilong 
Published: December 2021

Abstract
Because the power system contains a large number of user-side adjustable load resources, it can effectively enhance the operational flexibility of the power system and realize the safe, economical and efficient operation of the power grid by aggregating and modeling all kinds of resources and participating in the interactive response of the system as a whole. In this paper, a user-side adjustable load resource aggregation method based on non-intrusive load identification is proposed, which aims to obtain the load response potential of various users without intruding into the users, thus providing important support for power grid dispatching. Specifically, starting from the basic attributes of electrical equipment, considering the influence of numerical features such as current, harmonics, power, and V-I trajectory image features on load identification, the deep learning algorithm is used to deeply fuse the numerical features and image features in high-dimensional space, and then the fused advanced features are supervised by the Softmax classification algorithm, so as to effectively identify different types of electrical equipment. Finally, a bottom-up aggregation strategy is adopted to aggregate and model all kinds of load resources under the same station, so as to realize the accurate evaluation of the response ability of station resources. The simulation results of a numerical example verify the correctness and effectiveness of the proposed method.
Mining the Coopetition Relationship of Urban Public Traffic Lines Based on Time Series Correlation
Authors: Li Haibo , Wang Cheng , Wei Gengqian , Xu Sina 
Published: December 2021

Abstract
Along with the evolution of passenger flows within cities, the coordination between public traffic lines should be sustainably optimized with respect to the spatial distribution of the flow, though the lines were planned well at the beginning of the construction. It is critical to determine the coopetition between bus lines to optimize a transit network continuously. A method of mining coopetition relationship (MCBTC, Mining Coopetition relationship between Bus lines based on a Time series Correlation) based on passenger flow is proposed in this study. First, noisy, inconsistent or missing data are eliminated to obtain a passenger flow time series, and the proposed merging algorithm is used to extract the line passenger flow time series (LPFTS, Line Passenger Flow Time Series) by merging the passenger flow of adjacent buses from the same line. Then, to calculate the positive and negative correlation sequence sets, a clustering algorithm is proposed. The two sequence sets represent the competition and cooperation relationships, respectively. The MCBTC method has been tested with a practical data set, and the results show that it is very promising.
Double Genes Improved Genetic Algorithm for Solving Two-Dimensional Rectangular Layout Problem
Authors: Cui Min , Yang Kun , Deng Xiangming , Lyu Shuqing , Feng Miaomiao , Lu Zesen 
Published: December 2021

Abstract
Two-dimensional rectangular layout is according to the number of rectangular pieces and the size of the area of the rectangular pieces into the plate. Depending on the iteration of population in genetic algorithm, better utilization rate of plate is obtained. However, due to the characteristics of vertical and horizontal rows of rectangular pieces, relying on the sequence of rectangular pieces alone as the gene cannot guarantee the genetic diversity of the population, and leads to premature algorithm. In view of the special characters of rectangular layout, Double Genes improved genetic algorithm is proposed according to the order of rectangular layout and its own placement characteristics. In order to improve population diversity, Angle genes were added on the basis of rectangular sequencing genes. In view of the particularity of double genes, double random crossover operators and double mutation operators are proposed to improve the population diversity and randomness of genetic algorithm. Experimental results show the effectiveness of the improved algorithm.
Prediction of the COVID-19 Spread in China Based on Long Short-Term Memory Network
Authors: Zhang Haifei , Xu Jian , Qian Lanmei , Qiu Jianlin 
Published: December 2021

AbstractThe sudden outbreak of COVID-19 has caused great losses to the economy and the life of the masses. Long short-term memory (LSTM) network is a time recursive neural network, which is suitable for processing and predicting important events with relatively long interval and delay in time series. Using LSTM network to predict and analyze the development trend of epidemic situation, it is imperative to prevent epidemic situation from causing secondary harm to China’s development. In this paper, we first obtained the COVID-19 data published by China Health Net using crawler technology, which is the accurate value of infection trend after the outbreak of COVID-19 in China. Then, based on these data, the LSTM model is used to predict the development trend of the epidemic in one year, and the mean square error is used to calculate the error between the prediction and the real data. The experimental model is used to predict and analyze the development trend of COVID-19. The results show that the error between predicted data and real data is small and the effect is very good, which provides a reasonable basis and forecast for scientific prevention and control of epidemic situation.
A Design of Information Teaching Platform Based on Linux Operating System
Authors: Zhao Huimin 
Published: December 2021

Abstract
Linux operating system provides a good platform for the research on computer science due to its characteristics of open source, stability and efficiency. The teaching platform based on Linux has many advantages over that of Windows operating system. To promote the application of Linux and develop domestic software with independent intellectual property rights, the author studies and analyzes the feasibility of Linux teaching platform which can adapt to the current cross platform teaching of information technology in Colleges and universities and meet the requirements of computer grade examination. The author proposes to build an information technology teaching platform based on Linux operating system, and realize the cross platform of mainstream application software through Wine, and meanwhile use virtual machine to support Windows operating system environment. The author puts forward suggestions and measures in the application process, and verifies the feasibility of platform implementation, and proposes the preliminary scheme of platform construction.
Selection Technology of Deep Leak Proof Drilling Fluid in Changsheng Block Based on BP Grey Wolf Neural Network
Authors: Yang Jingna , Sun Weixu , Xie Shuai , Yu Yang , Zhang Xinfa , Wu Xiaoyu 
Published: December 2021

Abstract
In this paper, a BP neural network model with 5-9-6 structure is constructed according to various fault gases and different fault types of drilling fluid. On the basis of BP neural network model, genetic algorithm and gray wolf algorithm are used to optimize the threshold and weight of neural network. By applying the system to Horizontal Well Changshen #5 on site used Grey Wolf algorithm, lost circulation problem in the horizontal section of the reservoir was effectively controlled, ensuring rapid and safe drilling operation.