bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on compound comprehensive energy evaluation and intelligent control optimization
Authors: Li Tiecheng , Zeng Siming , Yang Zhengfu , Liu Zhibin , Liu Yajing
Published: February 2021

Under the background of increasing energy demand, integrated energy system is an important way to promote the development of clean energy, support the efficient use of energy and energy saving and emission reduction. This paper firstly establishes a comprehensive energy system evaluation index model composed of three sub-models of economy, environment and energy. On the basis of this model, a comprehensive energy multi-objective optimization mathematical model with the lowest total cost, excellent environmental benefits and high comprehensive energy utilization efficiency is established, and the energy supply and demand allocation optimization method based on improved genetic algorithm is adopted for solution. The simulation proves the effectiveness of the proposed optimization model.
Analysis of thread schedulability in Huawei LiteOS
Authors: Wang Xiaochun
Published: February 2021

Huawei LiteOS is a real-time operating system. Thread schedulability is an important thing to be considered first when we use the RTOS in an application. There are a lot of methods to value thread schedulability in practical application. Rate monotonic scheduling algorithm is a widely used static priority scheduling algorithm. We discussed the thread schedulability in Huawei LiteOS.
Human action recognition based on mixed gaussian hidden markov model
Authors: Xu Jiawei , Luo Qian
Published: February 2021

Human action recognition is a challenging field in recent years. Many traditional signal processing and machine learning methods are gradually trying to be applied in this field. This paper uses a hidden Markov model based on mixed Gaussian to solve the problem of human action recognition. The model treats the observed human actions as samples which conform to the Gaussian mixture model, and each Gaussian mixture model is determined by a state variable. The training of the model is the process that obtain the model parameters through the expectation maximization algorithm. The simulation results show that the Hidden Markov Model based on the mixed Gaussian distribution can perform well in human action recognition.
Intelligent masked-person detection system for epidemic prevention and control
Authors: Zhou Yizhuo , Sa Jiming , Xiang Yang , Zhang Yihao , Zheng Fenghao , Li Jin
Published: February 2021

In order to control the epidemic and complete the supervision of increasing population, we devised a kind of face detection system. This system detected face with MTCNN and then it detect whether the person wears the mask with MobileNet. Also we added non-standardized samples in the model training so that it can detect pedestrians who are not properly worn. The experimental results showed that the system can effectively identify the wearing of masks.
A 3M K-means algorithm for fast and practicably identifying COVID-19 close contacts
Authors: Liang Xun , Feng Zihuan , Xue Yang , Zhao Xiaolei
Published: February 2021

Given that the risk of the COVID-19 epidemic still exists and the flow of patients is difficult to monitor, identifying the people who have had close contact with the confirmed cases is important in anti-epidemic tasks whether in areas where the epidemic is developing rapidly or in areas where the epidemic has been phase-controlled. This article discusses how to locate people who have been in close contact with confirmed cases quickly and determine the risk of infection. From the perspective of the government, this work proposes a multi-snapshot multi-stage minority K-means (3M K-means) algorithm. This algorithm reduces the amount of data and considerably improves the speed of clustering by quickly ignoring the excluded risk classes and points in the process in the early stages, whereas traditional algorithms involve with O(N2) computational complexity which needs several days, impracticably for the COVID-19 urgent situations. The 3M algorithm greatly cuts down the computational time, thereof making the rapid warning of close contacts practicable. The methods are simple, yet efficient and practicable for the COVID-19 urgent situations The use of this algorithm can help control the COVID-19 epidemic, achieve significant cost savings, and provide the psychological guarantee of people for work resumption.
Research on recognition method of cloud precipitation particle shape based on BP neural network
Authors: Dong Haonan , Jiao Ruili , Huang Minsong
Published: February 2021

In order to solve the problem that the shape of cloud particle images measured by airborne cloud imaging probe (CIP) cannot be automatically recognized, this paper proposes an automatic recognition method of cloud and precipitation particle shape based on BP neural network. This method mainly uses a set of geometric parameters which can better describe the shape characteristics of cloud precipitation particles. Based on the cloud precipitation particle images measured by CIP in the precipitation stratiform clouds in northern China, a particle shape data training set and a testing set were constructed to train and verify the effect of the selected BP neural network model. The selected BP neural network model can classify the cloud particle image into tiny, column, needle, dendrite, aggregate, graupel, sphere, hexagonal and irregular. Utilizing the field campaign data measured by CIP, the habit identified results by the improved Holroyd method and by the selected BP neural network model were compared, which shows that the accuracy of BP neural network method is better than that of improved Holroyd method.
Selection of acoustic modeling unit for Tibetan speech recognition based on deep learning
Authors: Gong Baojia , Cai Rangzhuoma , Cai Zhijie , Ding Yuntao , Peng Maozhaxi
Published: February 2021

The selection of the speech recognition modeling unit is the primary problem of acoustic modeling in speech recognition, and different acoustic modeling units will directly affect the overall performance of speech recognition. This paper designs the Tibetan character segmentation and labeling model and algorithm flow for the purpose of solving the problem of selecting the acoustic modeling unit in Tibetan speech recognition by studying and analyzing the deficiencies of the existing acoustic modeling units in Tibetan speech recognition. After experimental verification, the Tibetan character segmentation and labeling model and algorithm achieved good performance of character segmentation and labeling, and the accuracy of Tibetan character segmentation and labeling reached 99.98%, respectively.
Music generation and human voice conversion based on LSTM
Authors: Li Guangwei , Ding Shuxue , Li Yujie , Zhang Kangkang
Published: February 2021

Music is closely related to human life and is an important way for people to express their feelings in life. Deep neural networks have played a significant role in the field of music processing. There are many different neural network models to implement deep learning for audio processing. For general neural networks, there are problems such as complex operation and slow computing speed. In this paper, we introduce Long Short-Term Memory (LSTM), which is a circulating neural network, to realize end-to-end training. The network structure is simple and can generate better audio sequences after the training model. After music generation, human voice conversion is important for music understanding and inserting lyrics to pure music. We propose the audio segmentation technology for segmenting the fixed length of the human voice. Different notes are classified through piano music without considering the scale and are correlated with the different human voices we get. Finally, through the transformation, we can express the generated piano music through the output of the human voice. Experimental results demonstrate that the proposed scheme can successfully obtain a human voice from pure piano Music generated by LSTM.
A language model for Amdo Tibetan speech recognition
Authors: Suan Taiben , Cai Rangzhuoma , Cai Zhijie , Zu Ba , Gong Baojia
Published: February 2021

We built a language model which is based on Transformer network architecture, used attention mechanisms to dispensing with recurrence and convalutions entirely. Through the transliteration of Tibetan to International Phonetic Alphabets, the language model was trained using the syllables and phonemes of the Tibetan word as modeling units to predict corresponding Tibetan sentences according to the context semantics of IPA. And it combined with the acoustic model as the Tibetan speech recognition was compared with end-to-end Tibetan speech recognition.
Transition based neural network dependency parsing of Tibetan
Authors: Duo Jiecairang , Hua Quecairang , Huan Keyou , Cai Rangdangzhi
Published: February 2021

In order to improve the performance of Tibetan natural language processing applications such as machine translation, sentiment analysis and other tasks, this article proposes a neural network-based method for syntactic analysis of Tibetan language dependence. Part of the corpus of Qinghai Normal University’s part-of-speech tag set is marked by the corresponding mapping relationship is transformed into the corpus annotated by the national standard part-of-speech tag set. At the same time, the CoNLL format Tibetan language dependency syntax tree library is constructed, and the method of shift-reduce plus neural network is adopted to systematically study and analyze the Tibetan language dependency syntax. Thereby improving the quality of Tibetan dependency syntactic analysis, and its accuracy rate reaches UAS:94.59%
Algorithm for estimating error of symbolic simplification
Authors: Kolka Zdenek , Biolkova Viera , Dobes Josef , Horak Martin
Published: February 2021

The paper deals with an improved algorithm for estimating errors during approximate symbolic analysis. A linear system can be solved symbolically. However, the size of the resulting formula grows exponentially with the matrix size. The approximate symbolic analysis omits insignificant terms of the exact formula to decrease its size, which, on the other hand, limits the validity of the approximate result. The proposed algorithm estimates, in a computationally feasible way, the approximation error over a region of system parameters. This makes it possible to maintain the validity of the results even if the tolerances of the system parameters are defined. The method is based on the first-order approximation of error functions. The algorithm is demonstrated using the SNAP symbolic analyzer, which has been developed by the authors.
Vegetation variation trend and its influencing factors in Urad Grassland over remote sensing
Authors: Zhang Yueying , Liu Tiantian , Wang Yuxi , Zhang Ming , Zheng Yu
Published: February 2021

In this work, the temporal-spatial dynamic variation of vegetation coverage from 2010 to 2019 in Urad Grassland has been explored by remote sensing technique. The change of precipitation and temperature has obvious effects on the vegetation, which affects the agriculture and the stability of the ecosystem. Pixel dichotomy approach and correlation analysis are introduced to analyse the temporal and spatial evolution trend of vegetation coverage and its response to climate change in the past 10 years. Specifically, the correlation between vegetation coverage and the critical climate impacting factors such as temperature and precipitation are fully investigated. The results show the vegetation coverage in the study area was influenced by effectors including climate, topography, human activities and government policies. The average annual vegetation coverage showed a downward trend in general from 2010 to 2019. Statistical correlation analysis indicates that the correlation between the vegetation coverage and the precipitation is positive in most parts of the area, while the correlation between the vegetation coverage and the precipitation shows negative with difference with various geographic features. As a result, precipitation was the major natural factor that affected dynamics of vegetation coverage in Urad grassland.
Feature selected based on PCA and optimized LMC
Authors: Xi Ke , Cai Cheng
Published: February 2021

In this article, we propose an optimization algorithm for the original LMC [1] (Large Margin Classifier). We use PCA [2] (Principal Component Analysis) to reduce the dimensionality of the images, and then put the data after dimensionality reduction into the optimized LMC for the feature selection [3]. We will get several features with the greatest distinction. We use these features to classify images. Finally, the experiment shows that the accuracy of the optimized LMC under the same dimensions is higher than that of the original LMC, and in many cases, the accuracy of the optimized LMC after taking 6 feature vectors has exceeded the highest accuracy of the original LMC.
Research on an intelligent traffic congestion control model
Authors: Xu Bo , Chen Jianbing , Tang Wei
Published: February 2021

This paper summarizes the status quo of intelligent traffic congestion control and vehicle following on traffic road, puts forward the key technology model and its content of intelligent traffic control, elaborates the model and content in detail, and summarizes the research done, hoping to provide reference for the related research on intelligent traffic congestion control.
Computer vision based obstacle detection and target tracking for autonomous vehicles
Authors: Fang Ruoyu , Cai Cheng
Published: February 2021

Obstacle detection and target tracking are two major issues for intelligent autonomous vehicles. This paper proposes a new scheme to achieve target tracking and real-time obstacle detection of obstacles based on computer vision. ResNet-18 deep learning neural network is utilized for obstacle detection and Yolo-v3 deep learning neural network is employed for real-time target tracking. These two trained models can be deployed on an autonomous vehicle equipped with an NVIDIA Jetson Nano motherboard. The autonomous vehicle moves to avoid obstacles and follow tracked targets by camera. Adjusting the steering and movement of the autonomous vehicle according to the PID algorithm during the movement, therefore, will help the proposed vehicle achieve stable and precise tracking.
Research on path planning of multi-rotor UAV based on improved artificial potential field method
Authors: Liu Zhengqing , Wang Xinhua , Li Kangyi
Published: February 2021

UAV needs sensor to fly in an environment with obstacles. However, UAV may not be able to move forward when it encounters a large obstacle, or UAV will be in a dangerous state when the sensor fails briefly which disturbed by the environment factors. In order to solve these problems, the following methods are proposed in this paper. Aiming at the first problem, this paper proposes an improved APF method for path planning, and verified by simulation experiments that this method can find the optimal path. Aiming at the second problem, this paper proposes a solution to expand the range of obstacles and dynamically change the distance in the APF repulsion function. It is verified that the UAV can fly safely within the short time of the sensor problem by simulation experiments. In conclusion, this paper has an important reference value for the application of UAV online dynamic path planning in engineering.
An evaluation method of urban epidemic emergency response capability based on fuzzy logic
Authors: Cheng Xu , Huang Yanyan , Wu Peiting
Published: February 2021

In 2020, the outbreak of COVID-19 seriously threatened many people's life and it had brought unexpected impacts upon the global economy. In order to understand the emergency response ability of cities with similar backgrounds, an evaluation method based on fuzzy comprehensive evaluation was proposed. First, we established the evaluation index system of urban epidemic response capability. Meantime, a combination way to calculate the index weights of the evaluation system was developed based on analytic hierarchy process. Finally, we have an empirical analysis on the evaluation activity of epidemic emergency management in area H, J province. The evaluation results can be used as the basis for selecting advanced cities. At the same time, it is of great importance for area H to improve urban resource allocation and emergency response capability.
Research on the prediction model of elderly fall
Authors: Guo Shaohua , Xie Yinggang , Li Yuxin
Published: February 2021

As the world’s aging process accelerates, the issue of elderly safety is about to become a serious social problem. The elderly are prone to falls due to physiological reasons such as decreased physical function, weakened balance and coordination ability, and poor vision. The study of fall prediction models can predict the impending fall behavior in time before the fall, and have enough time to remind the elderly to adjust or take corresponding protective measures. Reduce the damage caused by falls to the human body, reduce the medical expenses caused by falls, and enhance the confidence of the elderly to live independently. This article gives a detailed overview of the research on the wearable device-based fall prediction system, and introduces the entire process of falling. According to the work flow of the wearable device fall detection system, it includes data collection, data preprocessing, feature extraction, and discrimination algorithms. Several aspects of the current research work are introduced, and the existing research results are classified, compared and statistically analyzed to provide meaningful reference and reference for subsequent research work. Finally, a fall prediction model based on an improved ConvLSTM is proposed.
Research on load balancing technology for microservice architecture
Authors: Wang Hao , Wang Yong , Liang Guanying , Gao Yunfan , Gao Weijian , Zhang Wenping
Published: February 2021

With the emergence and development of new software architectures such as microservices, how to effectively handle the service load and ensure the service capability of the system has become an urgent problem to be solved. Load balancing technology needs to achieve high availability of microservices without affecting the delayed response of requests. According to different principles of adoption, mainstream load balancing technologies have emerged, such as polling methods, hash algorithms, and artificial intelligence technologies. This article categorizes and summarizes load balancing technologies for microservice architecture, and elaborates the methods and characteristics of current mainstream load balancing technologies. Based on the comparative analysis of existing technologies, this paper summarizes and points out the future development direction of load balancing technology.
A feature selection algorithm combining information gain and multi-objective genetic search for intrusion detection system
Authors: Xie Tao
Published: February 2021

In order to improve the detection rate and speed of intrusion detection system, this paper proposes a feature selection algorithm. The algorithm uses information gain to rank the features in descending order, and then uses a multi-objective genetic algorithm to gradually search the ranking features to find the optimal feature combination. We classified the Kddcup98 dataset into five classes, DOS, PROBE, R2L, and U2R, and conducted numerous experiments on each class. Experimental results show that for each class of attack, the proposed algorithm can not only speed up the feature selection, but also significantly improve the detection rate of the algorithm.
New application of blockchain: digital currency model design in the context of Winter Olympics
Authors: Feng Zihuan , Liang Xun
Published: February 2021

With the further expansion of blockchain application ecology, various applications and infrastructure around digital currency are constantly enriched and improved, and the whole market is booming along the healthy track. The Chinese government also attaches great importance to the development and application of block chain technology. President Xi Jinping stressed the importance of the block chain as an important breakthrough in the core technology independent innovation in the eighteenth collective learning of the Central Political Bureau. In October 2020, the pilot project of digital RMB will be launched in Shenzhen. Under the blockchain empowerment, the design, issuance and landing preparation of China's digital RMB have been in the forefront of the central bank's digital currency. Although the technical feature of blockchain is decentralization, the central bank can effectively integrate the distributed operation by using blockchain, so as to better realize the centralized management and control of digital currency. Taking the Beijing Olympic Games as an example, this paper analyzes the problems that may occur in the use of digital RMB in the Winter Olympics, puts forward an assumption of using digital currency based on partial decentralization, and puts forward the overall architecture design, and analyzes its feasibility and practicability.
Research on software trustworthiness measurement evaluation model based on data driven
Authors: Xu Zhaosheng
Published: February 2021

in this paper, we construct a data-driven trust evaluation model based on perceptual source. The model takes the monitoring module as the evaluation unit, and the relay node completes the trust evaluation of the sensing node in its monitoring module. The direct trust calculation is realized by the relationship between the sensing node's own data, and the recommended trust is calculated by using the relationship between the neighbor nodes in the monitoring module. Combined with the historical trust, the comprehensive trust of the sensing node is output. In this paper, the credibility of existing database driven cognitive software is summarized, and then the key technologies of reliability evaluation and classification of database driven cognitive software are analyzed.
Multiobjective fractional programming problems and the sufficient condition involving Hb – (p, r)-η- invex function
Authors: Gao Xiaoyan , Niu Huan
Published: February 2021

On the basis of arcwise connected convex functions and (p, r) −η - invex functions, we established Hb –(p, r) –η- invex functions. Based on the generalized invex assumption of new functions, the solutions of a class of multiobjective fractional programming problems are studied, and the sufficient optimality condition for the feasible solutions of multiobjective fractional programming problems to be efficient solutions are established and proved.
The Signed (|G|–1) subdomination number of Product Graphs
Authors: Shi Wei , Wu Suichao
Published: February 2021

In this paper, the singed (|G | −1) - subdomination number of product graphs G, such as P2 × Pn, P2 ×Cn, P3 × Pn, are determined by classified discussion and exhaustived method.
Event-triggered H∞ state estimation for time-varying neural networks with variance-constraint and fading measurements
Authors: Gao Yan , Hu Jun , Chen Dongyan , Du Junhua
Published: February 2021

This paper addresses the event-triggered H∞ state estimation problem for a class of discrete recurrent neural networks subject to variance-constraint and fading measurements. The phenomena of fading measurements are described by introducing a set of mutually independent random variables, which reflect that each sensor has individual missing probability. In addition, for the purpose of energy saving, an event-triggered H∞ state estimation scheme is used for time-varying neural networks to determine whether the measurement output is transmitted to the estimator or not. Some sufficient conditions are obtained to guarantee that the estimation error system satisfies both estimation error variance constraint and prescribed H∞ performance requirement. Finally, the feasibility of the proposed event-triggered H∞ state estimation method is verified by a numerical example.
A blockchain-based rice supply chain system
Authors: Xue Yang , Liang Xun , Zhao Dongyan
Published: February 2021

With the continuous development of blockchain technology, “blockchain+” has gradually evolved into a new form of social and economic development. Blockchain technology can be applied to the rice supply chain management field. Consider integrating the encryption algorithm, timestamp technology, consensus algorithm, and sidechain technology of blockchain with the rice supply chain, analliance chainbetween companies upstream and downstream of the supply chain, including producers, suppliers, and vendors can be built to create a safe and efficient supply chain information management. This information system uses the unique encryption technology of the blockchain and its unforgeable features of irreversibility and decentralization to help the supply chain break the current bottleneck, solve the problem of information security in the rice supply chain and realize the practical traceability of rice. At the same time, the involvement of the blockchain will also open up new opportunities for supply chain finance and bring about fundamental changes in supply chain management.
The effect of characteristics differences in vertical pair of executives and equity incentives on enterprise performance
Authors: Lu Yu , Xu Ruijie
Published: February 2021

Taken the leading enterprises in the Shanghai and Shenzhen A-share listed companies from 2013 to 2017 as the research samples, the effect of characteristics differences in vertical pair of executives and equity incentives on enterprise performance was empirically analyzed. Constructing multiple linear regression model tested hypothesis. The results show that the interaction between equity incentives and the difference in age of vertical pair of senior executives has a significant negative impact on enterprise performance; the interaction between equity incentives and the difference in education of vertical pair of senior executives has a significant positive impact on enterprise performance.
Credit of small and medium sized scientific and technological enterprises based on BP neural network Evaluation research
Authors: Zhang Liulu , Zuo Xiao
Published: February 2021

Aiming at the problem of credit evaluation of science and technology-based small and medium-sized enterprises in China, a credit evaluation system based on machine learning is proposed. A total of 17 indicators are selected from five aspects of solvency, profitability, operation ability, growth ability and R & D ability. Finally, 11 representative indicators are selected. Then through BP neural network algorithm to build a credit evaluation model, training and Simulation of the credit rating of science and technology-based SMEs. The results show that the evaluation model has good generalization ability, and can effectively evaluate the credit of science and technology-based SMEs.
Analysis of spatial variation of credit risk of China listed companies based on spatially varying coefficient logistic models
Authors: Du Ying , Wang Chanchan
Published: February 2021

Nowadays, since the booming economy of china, the development of financing behaviors, represented by the financial took it such as stocks and bonds, is increasing continuously in our country, meanwhile, it is more and more prominent that the credit risks problems brought by the frequent defaults in credit transactions. Analysis of credit risk characteristics become a very important study topic. Whether A-share listed companies in some provinces in the central and eastern regions of my country being special treatment (ST) are used as a sample to study credit risk. Based on the spatially varying coefficient logistic models, this paper analyzes the spatial variation characteristics of industry type and ownership nature affecting the probability of listed companies being ST. The results show that there is a large spatial variation in the intensity of the influence of these factors on the risk of listed companies being ST.
Performance evaluation of business administration training room in application-oriented universities
Authors: Xu Zaijuan
Published: February 2021

The training room is an important platform for building theory and practice, a link and bridge between universities and society, and an important place for students to improve their practical and practical ability and strengthen the application of knowledge. This paper selected the performance of business administration training rooms of 10 representative application-oriented universities in the Pearl River Delta as a case study, selected the evaluation indexes of input layer and output layer, and used DEA model to analyze the comprehensive efficiency, technical efficiency and scale efficiency of decision-making units. Furthermore, it was proposed to increase the opening and sharing of training rooms, broaden social service channels, optimize teachers, deepen training curriculum reform to improve performance.
Analysis of the regional differences between fixed assets investment and China's economic growth from the perspective of functional data
Authors: Cheng Tianqi , Chen Guici , Zhang Zuo , Jiang Feng
Published: February 2021

Investment, consumption and export are the troika which promotes China's economic development. Investment is the most important factor and plays a vital role to drive economic growth. However, due to the differences in the geographical location, education level and infrastructure construction of various regions in China, the structure of economic development and the imbalance of growth still exist. This paper takes the fixed asset investment of 31 provinces, municipalities and autonomous regions in my country from 1999 to 2017 as the research objects in our country and uses functional data analysis methods to functionalize discrete data, uses Fourier basis functions to fit the smooth curve, and then uses FPCA analysis of the characteristics of the principal component function shows that the contribution rates of the first two principal components to the regional economy are 96.4% and 2.8% respectively, and the cumulative contribution rate reaches 99.2%, which can represent most of the sample information to a certain extent. Therefore, this paper uses the first two main components to analyse the overall change for law of China's regional economy, and roughly divides my country's 31 provinces and municipalities into 4 categories and adjust my country's economic development structure and promote coordinated regional development.
Research on financial supervision in Northeast Asia Free Trade Zone — Based on the "Regulatory Sandbox" model
Authors: Li Qian , Xi Juying , Zhang Chuang , Zheng Yue
Published: February 2021

This paper analyzes the status quo of financial supervision in the Northeast Asia Free Trade Zone and finds that the coordination and cooperation of financial supervision in the Northeast Asia Free Trade Zone is in the exploratory stage. There is room for cooperation in financial supervision, which creates conditions for the implementation of the "supervisory sandbox" in the region. It demonstrates the significance of introducing the “regulatory sandbox” model in the Northeast Asia Free Trade Zone from three aspects: balancing financial innovation and supervision, promoting the realization of inclusive finance, and improving the global shared supervision database. Build a relatively complete "supervision sandbox" operation process design. Finally, the countermeasures and suggestions for the introduction of the "supervisory sandbox" model in the Northeast Asia Free Trade Zone are proposed.
The causal relationship k
Authors: Barukčić Ilija
Published: February 2021

Aim: A detailed and sophisticated analysis of causal relationships and chains of causation in medicine, life and other sciences by logically consistent statistical meth¬ods in the light of empirical data is still not a matter of daily routine for us.

Methods: In this publication, the relationship between cause and effect is characterized while using the tools of classical logic and probability theory.

Results: Methods how to determine conditions are developed in detail. The causal rela-tionship k has been derived mathematically from the axiom +1 = +1.

Conclusion: Non-experimental and experimental data can be analysed by the methods presented for causal relationships.
Intimate consumptions
Authors: Rüdiger Sofia
Published: February 2021

In this paper, I investigate how the structure of and discursive performance on North American YouTube eating
shows contribute to the creation of intimacy and informality. In a typical eating show, the performer eats copious amounts of food
while talking to their non-copresent audience, making use of interactional registers to ‘break’ from the solitary setting and to
build rapport with their viewers. The material for this study is based on two case studies drawn from a corpus of YouTube eating
shows. At the heart of the eating show performances lies the persona of a ‘friend’ of the viewer, diverting from their highly
structured and technologically-mediated communicative nature. While the language used on eating shows may thus seem spontaneous
and unprompted, the videos are not only planned but also edited. The simultaneous presence of selected unedited moments (i.e.,
slips of the tongue and other mishaps), however, is evidence of a conscious blending of front- and backstage performance (Goffman 1959; Shulman 2017).
Negotiating patients’ therapy proposals in paternalistic and humanistic clinics
Authors: Odebunmi Akin
Published: February 2021

The negotiation of patients’ therapy proposals often makes a strong statement about doctors’ consultative styles
in Nigerian clinical encounters. This invites a search into the relationship between patients’ preferred treatment options and
doctors’ and patients’ approaches to negotiating them. Analysis reveals the sequential and face orientation mechanisms deployed in
negotiating patients’ proposals in predominantly doctor-centred clinics, the interactional moves made by them in negotiating the
proposals in predominantly patient-centred clinics, and the pragmatic implications of the proposals negotiated in both
clinics. The negotiations in the clinics are anchored to strategic rapport building, the colonisation of patients’ lifeworld and
constrained joint decisions. Rapport is poorly built in the doctor-centred clinic with power-imbued strategies which stifle
patients’ voice and lead to completely-constrained joint decisions on therapy proposals by patients. Participatory consultation
enhances negotiation in the patient-centred clinic, but the physician’s misleading strategic sequences and exaggerated emotions
somewhat weaken the ultimate consultative outcome.
Children’s sociolinguistic preferences
Authors: Kaiser Irmtraud , Kasberger Gudrun
Published: February 2021

The Bavarian-speaking part of Austria is often
referred to as an instantiation of a dialect-standard continuum with
a range of speech forms between (Austrian) Standard German and the
respective base dialects. The socio-indexical meaning (i.e. the
speaker characteristics associated with certain varieties) of
different speech forms has been the object of several studies with
adults in Austria. Our aim was to gain an understanding of the
acquisition process regarding these socio-indexical values of
L1-varieties. In order to do so, we studied the sociolinguistic
preferences of Austrian children aged between 3 and 10 years, using
adapted ‘matched-guise’ experiments in which the child had to choose
between two doctors speaking different varieties (dialect, standard
German). Whereas the younger children do not show consistent
preferences, children from grade 2 (age 7/8) onwards prefer the
standard-speaking doctor.
Adult learners’ (non-) acquisition of speaker-specific
variation
Authors: Hudson Kam Carla L.
Published: February 2021

This study is concerned with understanding how
learners acquire sociolinguistic variation. It examines the
possibility that learners gain entry into socially-conditioned
variation by first associating patterns with particular speakers.
Adult participants were exposed to a miniature artificial language
spoken by two different speakers, each exhibiting a different
variable pattern of determiner usage. After exposure, participants
were tested to see if they had acquired the speaker-specific
patterns using production and judgment measures. The data show no
evidence that participants had learned the speaker-specific
patterns. How then do learners acquire sociolinguistic variation? I
suggest that learners need a more socially relevant variable to
index variation to, that is, that sociolinguistic variation really
is social at its core.
Hamiltonian estimation based on adaptive weak value amplification
Authors: Fang Chen , Xia Binke , Huang Jing-Zheng , Xiao Tailong , Yu Yang , Li Hongjing , Zeng Guihua
Published: February 2021

Hamiltonian estimation is a cardinal task in applications of quantum metrology, quantum computation, quantum simulation, etc. When the evolution time is short in order to suppress noise and enhance sensitivity, the measurement and postprocessing may incur high cost. In this paper, we propose an adaptive weak measurement scheme for Hamiltonian estimation, which adjusts the pre- and post-selection according to the difference of the mean shift between two orthogonal post-selected states. According to the angle of the post-selected state, the proposed scheme can change from weak value amplification (WVA) to joint weak measurement (JWM). A study of the quantum Fisher information shows that the JWM situation is always optimal. Meanwhile, the WVA approach loses a little information but it can inherit the WVA’s technical advantages, which are suitable for short evolution times. The widely applicable scope of the proposed scheme makes us believe that it offers a new path for the understanding of unknown Hamiltonians.
Laser interferometry based on atomic coherence
Authors: Bhattarai Mangesh , Khan Sumanta , Natarajan Vasant , Pandey Kanhaiya
Published: February 2021

We demonstrate laser interferometry based on phase difference between the two arms of an interferometer. The experiments are done with a Cs atomic vapor cell at room temperature and use atomic coherence. The interference can be tuned from constructive to destructive by tuning the relative phase between the two arms. It is similar to the Michelson interferometer, but differs in the important aspect of allowing interference when the polarizations in the two arms are orthogonal. This would be a novel method for interfering two independent lasers, which can even allow interfering two independent lasers of completely different wavelengths.
Quantitative detection of optical anisotropy of single microtubules by polarization-sensitive interferometric scattering microscopy
Authors: Vala Milan , Palounek David , Robert Hadrien M L , Piliarik Marek
Published: February 2021

Microtubules (MTs) are ubiquitous cytoskeletal biopolymers essential for diverse cellular processes. MTs consist of strictly ordered tubulin dimers arranged into hollow cylindrical filaments and are known to be optically anisotropic, which enables their direct observation in microscopes based on polarization contrast. However, there are no experimental data to quantify the relation between the momentary optical anisotropy of the MT and the immediate arrangement of proteins in the MT structure. In this work, we introduce polarization-sensitive microscopy based on interferometric detection of scattering to quantify the scattering anisotropy of single unlabeled MTs with high precision. Our data explain the structural origin of MT anisotropy with a marginal contribution of the intrinsic asymmetry of a single tubulin molecule. We monitor changes in the MT scattering resulting from the binding of tau proteins to single MTs with a resolution of several proteins per diffraction-limited spot. We associate the changes in the contrast as well as in the scattering anisotropy with the formation of a shell around the MT formed by densely packed tau proteins. Our experimental results match closely with the theoretical model of the MT and include quantitative details about the polarization-dependent interaction of light with biological matter.
Enhanced

α

α-particle production from fusion evaporation reactions leading to 46Ti
Authors: Cicerchia M , Gramegna F , Fabris D , Cinausero M , Marchi T , Andreetta G , Barlini S , Bini M , Bolzonella R , Bruno M , Buccola A , Caciolli A , Camaiani A , Casini G , Cieplicka-Orynczak N , Collazuol G , D’Agostino M , Degerlier M , Dell’Aquila D , Domenichetti L , Fornal B , Frosin C , Gelli N , Gruyer D , Kravchuk V L , Leoni S , Lombardo I , Mabiala J , Managlia M V , Mantovani G , Mengoni D , Morelli L , Olmi A , Ottanelli P , Pasquali G , Pastore G , Piantelli S , Scomparin L , Valdré S , Verde G
Published: February 2021

In this paper, we report a detailed study on the de-excitation of the 46Ti compound nucleus populated by means of four different reactions: 16O + 30Si at 7 and 8 AMeV, 18O + 28Si at 7 AMeV and 19F + 27Al at 7 AMeV. The GARFIELD detection array was used to measure the light charged particles and the heavy fragments emitted. This setup covers a large fraction of the solid angle with high-granularity. Complete events are chosen through total charge (Z
TOT = 22) detection. Fusion-evaporation events are selected based on the condition that only one heavy fragment is detected in coincidence with the light charged particles. The analysis of global observables, such as charge distribution and light charged particle multiplicities, is compatible with the formation of a thermalized compound nucleus for all the four reactions. Nevertheless, an enhanced emission of α-particles with respect to a pure statistical picture is observed at very forward angles (<20°). This enhancement is mainly visible in the even-Z residue exit channels populated through the emission of only α-particles.
Quantum spin Hall and quantum anomalous Hall states in magnetic Ti2Te2O single layer
Authors: Ma Hai-Yang , Guan Dandan , Wang Shiyong , Li Yaoyi , Liu Canhua , Zheng Hao , Jia Jin-Feng
Published: February 2021

Magnetic topological insulators, such as MnBi2Te4 have attracted great attention recently due to their application to the quantum anomalous Hall (QAH) effect. However, the magnetic quantum spin Hall (QSH) effect in two-dimensional (2D) materials has not yet been reported. Here based on first-principle calculations we find that Ti2Te2O, a van der Waals layered compound, can cherish both the QAH and QSH states, depending on the magnetic order in its single layer. If the single layer was in a chessboard antiferromagnetic (FM) state, it is a QSH insulator which carries two counterpropagating helical edge states. The spin–orbit-couplings induced bulk band gap can approach as large as 0.31 eV. On the other hand, if the monolayer becomes FM, exchange interactions would push one pair of bands away from the Fermi energy and leave only one chiral edge state remaining, which turns the compound into a Chern insulator (precisely, it is semimetallic with a topologically direct band gap). Both magnetic orders explicitly break the time reversal symmetry and split the energy bands of different spin orientations. To our knowledge, Ti2Te2O is the first compound that predicted to possess both intrinsic QSH and QAH effects. Our works provide new possibilities to reach a controllable phase transition between two topological nontrivial phases through magnetism tailoring.
Determination of the magnetic structures in orthoferrite CeFeO3 by neutron powder diffraction: first order spin reorientation and appearance of an ordered Ce-moment
Authors: Ritter Clemens , Ceretti Monica , Paulus Werner
Published: February 2021

High resolution and high intensity neutron powder diffraction are used to determine the temperature dependence of the crystallographic and magnetic structure of the orthoferrite CeFeO3. The high temperature G

x
-type magnetic coupling of the Fe-sublattice described by the Γ4 (G

x

A

y

F

z
) irreducible representation changes at the spin reorientation temperature T
SR = 228 K to a G

y
-type coupling of Γ1 (A

x

G

y

C

z
). The spin reorientation is of first order and sees a hysteresis of about 2.5 K at T
SR. Below 35 K faint magnetic peaks reflecting C

z
type magnetic coupling appear and are argued to be related to the Ce-sublattice. Magnetic moments at 2 K amount to μ
Fe = 4.15 μ
B and μ
Ce = 0.11 μ
B. CeFeO3 is only the second RFeO3 compound after DyFeO3 showing this ground state magnetic structure of the Fe-sublattice. The orthorhombic structure Pbnm is kept over the whole temperature range.
Quantum capacitance of coupled two-dimensional electron gases
Authors: Balasubramanian Krishna
Published: February 2021

Quantum capacitance effect is observed in nanostructured material stacks with quantum limited density of states. In contrast to conventional structures where two-dimensional electron gases (2DEG) with reduced density of states interact with a metal plate, here we explore the quantum capacitance effect in a unique structure formed by two 2DEG in a graphene sheet and AlGaN/GaN quantum well. The total capacitance of the structure depends non-linearly on the applied potential and the linear density of states in graphene leads to enhanced electric field leakage into the substrate causing a dramatic 50% drop in the overall capacitance at low bias potentials. We show theoretical projections of the quantum capacitance effect in the proposed device stack, fabricate the structure and provide experimental verification of the calculated values at various temperatures and applied potentials. The wide swing in the total capacitance is sensitive to the chemical potential of the graphene sheet and has multiple applications in molecular sensing, electro-optics, and fundamental investigations.
Valence tautomeric transition of bis(o-dioxolene) cobalt complex in solid state and solution
Authors: Guda A A , Chegerev M , Starikov A G , Vlasenko V G , Zolotukhin A A , Bubnov M P , Cherkasov V K , Shapovalov V V , Rusalev Yu V , Tereshchenko A A , Trigub A L , Chernyshev A V , Soldatov A V
Published: February 2021

Valence tautomer transition occurs mainly in 3d metalorganic complexes with redox-active ligands and makes them potential candidates for single-molecular switches. The transition occurs under temperature, pressure, or light-induced stimuli and is strongly affected by the intermolecular interactions. However single-crystal x-ray diffraction is not always applicable to such systems when crystal structure is destroyed upon transition or system is studied in the solution. Such an example is bis(o-semiquinonato) cobalt complex with TEMPO-functionalized iminopyridine ancillary ligand. In this work we apply two complementary techniques—ligand-sensitive Fourier transform infrared spectroscopy (FTIR) and metal sensitive Co K-edge x-ray absorption spectroscopy (XAS). In a solid state, a temperature hysteresis of magnetization larger than 40 K was observed upon cyclic cooling-heating. So, the temperature of phase transition upon cooling is about 40 K lower than that upon heating. In solution, the x-ray absorption spectra for high-temperature and low-temperature states were similar to that in the solid form, but the hysteresis was absent. Two methods are can probe valence tautomer transition, but XAS has an advantage for the liquid phase analysis and FTIR has larger sensitivity to the ligand related interactions in solid.