bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of phase-field model based on balance laws and thermodynamic discussion
Authors: Muramatsu Mayu , Shizawa Kazuyuki
Published: September 2020

In this work, a phase-field model for recrystallization is developed based on the conservation laws. There has been no attempt to develop a phase-field model of recrystallization based on the conservation laws, even though various phase-field simulation models to reproduce the recrystallization phenomenon have been proposed. However, it is unclear what conservation laws are required for such a model. In the previous paper, toward solving this problem, we developed conservation laws of mass, momentum, angular momentum, and energy and a law of entropy at the lattice scale for the process of recrystallization. In this paper, first, two continuous variables, i.e., the order parameter and crystal orientation, are introduced into the balance equation of mass for a single phase and that of angular momentum for the lattice, respectively. Next, the fluxes of the order parameter and crystal orientation are derived from the law of entropy by the use of rational thermodynamics. Moreover, the diffusion coefficient and mass source are modeled to derive the evolution equations, i.e., phase-field equations of the order parameter and crystal orientation. Finally, for the phase-field equation of the crystal orientation, neglecting the conservative part and integrating the equation with respect to time under the first-order approximation, a phase-field model that is used for stable calculations is developed. This work aims to develop a phase-field theory on the basis of the change in crystal lattice during recrystallization. This paper gives a physical background to the methodological phase-field approach in the case of recrystallization.
Vertical Transmission
Authors: Lyras Dena
Published: September 2020

Climate change is one of the most important issues faced by our planet. As scientists, we seek to understand and find ways to
prevent, and perhaps reverse, its effects. Although the impacts of climate change on the environment and extinction of animal and plant life are well studied, the consequences of climate change on microbes are largely unknown. We now understand that microbes underpin a healthy global ecosystem and that disruptions to microbial diversity often have devastating repercussions on the affected ecological niche. Indeed, changes in microbial diversity induced by climate change may therefore have important downstream consequences on the resilience of such ecosystems and their ability to withstand climate change.
One health probiotics
Authors: Sidjabat Hanna E , Blackall Linda
Published: September 2020

This issue of Microbiology Australia is on the topic of probiotics. The word ‘probiotic’ meaning ‘for life’ is derived from Latin ‘pro’ and Greek ‘bios’. According to the World Health Organization and the Food and Agriculture Organization of the United Nations, probiotics are defined as ‘live microorganisms which when administered in adequate amounts confer a health benefit on the host’1, while a very similar definition, albeit with the term ‘body’ (i.e. not plant), is used by the US National Institutes of Health: ‘. . . live microorganisms that are intended to have health benefits when consumed or applied to the body.’2.
Towards
Authors: Palmer Symon , Ripeka Mercier O. , King-Hunt Alan
Published: September 2020

In Aotearoa New Zealand, the government has set a target for the country to become predator-free by 2050, largely as a response to the threat of extinction that introduced mammals pose to native birds. Current pest management tools lack the scalability required to reach pest eradication; thus, new technologies are being explored through public research funding, including controversial techniques such as gene drive. While the need to listen to Māori perspectives on genetic technologies is broadly recognised, the Treaty relationship between government and Māori demands more, including attention to rangatiratanga (autonomy for Māori) and tikanga (Māori customary protocols). A ‘social licence to operate’ is argued to be key to getting public support for such technologies, but is that consistent with a Māori approach? To address this question, we gathered Māori perspectives on novel biotechnological controls for pest wasps through three distinct studies. Study participants included tertiary students, businesses, and spiritual or religiously affiliated groups. All participants drew from their identities as Māori people to help identify their position on these issues. Their perspectives on issues of consent and Social Licence suggest a preference for processes based upon rangatiratanga. Participant perspectives were also organised according to a take utu ea decision-making framework. This exercise suggests that this framework continues to be a relevant and useful contribution to how biotechnology discussions are framed. Rangatiratanga and tikanga are underlying considerations for Māori in relation to novel biotechnologies, and, if recognised more widely, have potential to transform conservation biology in Aotearoa New Zealand.
Health benefits of on-road transportation pollution control programs in China
Authors: Wang Haikun , He Xiaojing , Liang Xinyu , Choma Ernani F. , Liu Yifan , Shan Li , Zheng Haotian , Zhang Shaojun , Nielsen Chris P. , Wang Shuxiao , Wu Ye , Evans John S.
Published: September 2020

Significance

On-road vehicles have become a significant source of air pollution in Chinese cities. Air pollution in China has substantial impacts on public health and is thought to be responsible for millions of deaths each year. The government has implemented comprehensive control measures to mitigate vehicle emissions since the late 1990s. In this paper, we analyze how the historical control actions during 1998–2015 affected air quality and public health. We constructed an integrated framework combining emission scenarios, air quality modeling, and population health risk assessment. Our results demonstrate that the control measures have led to substantial improvements in public health and provide insight into strategies for further mitigation of negative effects from China’s future motorization on air quality and public health.
Trajectory and uniqueness of mutational signatures in yeast mutators
Authors: Loeillet Sophie , Herzog Mareike , Puddu Fabio , Legoix Patricia , Baulande Sylvain , Jackson Stephen P. , Nicolas Alain G.
Published: September 2020

Significance

Deficiencies in genome maintenance genes result in increased mutagenesis and genome rearrangements that impact cell viability, species adaptation, and evolvability. The accumulation of somatic mutations is also a landmark of most tumor cells but it remains difficult to retrospectively determine their mechanistic origin(s). Here, we conducted a prospective reciprocal approach to inactivate evolutionary conserved genes involved in various genome maintenance processes and characterize de novo mutations in diploid
S. cerevisiae
mutation accumulation lines. Our results revealed the diversity, trajectory, complexity, and ultimate uniqueness of the clonal mutational landscapes. Some mutational signatures resemble those found in human tumors.
Innovation on screen
Authors: Reichelt Susan
Published: September 2020

This study explores marked affixation as a possible cue for characterization in scripted television dialogue. The data used here is the newly compiled TV Corpus, which encompasses over 265 million words in its North American English context. An initial corpus-based analysis quantifies the innovative use of affixes in word-formation processes across the corpus to allow for comparison with a following character analysis, which investigates how derivational word-formation supports characterization patterns within a specific series,Buffy the Vampire Slayer. For this, a list of productive prefixes (e.g.de-,un-) and suffixes (e.g. -y, -ish) is used to elicit relevant contexts. The study thus combines two approaches to word-formation processes in scripted contexts. On a large scale, it shows how derivational neologisms are spread across TV dialogue and on a much smaller scale, it highlights particular instances where these neologisms are used to aid character construction.
Strategic Alliance or Hegemonic Strategy? Conservatism Among the Dispossessed
Authors: Apple Michael W.
Published: September 2020

This article examines a growing phenomenon—the growth of seemingly conservative sentiments among disenfranchised groups. I take as a prime example of such growth the strategic support of neo-liberal and neo-conservative policies by an African American activist group, the Black
Alliance for Educational Options (BAEO). At the core of my analysis is a concern about what is at stake for all of us if rightist multiculturalism succeeds in redefining what and whose knowledge is of most worth and what our social and educational policies are meant to do. I argue that no
matter what one's position is on the wisdom of BAEO's strategic actions, the entire case provides a crucial example of the politics of disarticulation and re-articulation, on the ways in which social movements and alliances are formed and re-formed out of the material and ideological conditions
of daily life, and of the politics of discursive re-appropriation. Thus, an analysis of such movements is important both in terms of the balance of forces and power involved in specific educational reforms, but also in terms of more general issues concerning the processes of social transformation
and agency. A critical but sympathetic understanding of groups such as BAEO may enable us to avoid the essentialism and reductionism that enters into critical sociological work on the role of struggles over state policies. Further, it can provide a more nuanced sense of social actors and the
possibilities and limits of strategic alliances in a time of conservative modernization.
Education, Globalisation and the Role of Comparative Research
Authors: Green Andy
Published: September 2020

Comparative education has traditionally meant the study of national education systems. But how far is this approach valid today? Doesn't the 'decline' of the nation state make national systems obsolete? Isn't the very idea of a 'system' anachronistic in a world of market triumphalism
and global disorganization? The purpose of this article is to explore how globalisation is changing education and the implication of this for comparative study. Why study education systems and why study national education systems in particular? What else should comparativists study, and how?
What defines the field of comparative education? These questions are approached first historically and secondly methodologically.
Explaining Differences in Key Stage 2 Pupil Attainments: a multilevel analysis
Authors: Marchant Paul , Hall Kathy
Published: September 2020

This paper examines the effects of key characteristics on achievement at the end of Key Stage 2 with reference to the results of all KS2 pupils in one LEA in the north of England. It offers new evidence of the relative impact of various pupil characteristics on performance by using
generalised regression methods, which take account of the structure of the data. The paper explores simultaneously the impact on achievement of school attended and school cluster (based on groups of feeder schools) and four pupil factors; namely, gender, age, social disadvantage (defined in
terms of entitlement to free school meals) and having English as an additional language. It discusses the findings with respect to other recent studies in the field.
The Relationship between Teacher Turnover and School Performance
Authors: Dolton Peter , Newson David
Published: September 2020

This article explores the relationship between teacher turnover and school performance. It uses a database of 316 primary schools in six London Local Education Authorities derived by merging Government data on teachers and schools. This rich data includes school-level variables on teacher
turnover matched to school characteristics. The study shows that high levels of teacher turnover can be shown to have detrimental effects on pupil progress and achievement.
Towards defining reference materials for measuring extracellular vesicle refractive index, epitope abundance, size and concentration
Authors: Welsh Joshua A. , van der Pol Edwin , Bettin Britta A. , Carter David R. F. , Hendrix An , Lenassi Metka , Langlois Marc‐André , Llorente Alicia , van de Nes Arthur S. , Nieuwland Rienk , Tang Vera , Wang Lili , Witwer Kenneth W. , Jones Jennifer C.
Published: September 2020

Accurate characterization of extracellular vesicles (EVs) is critical to explore their diagnostic and therapeutic applications. As the EV research field has developed, so too have the techniques used to characterize them. The development of reference materials are required for the standardization of these techniques. This work, initiated from the ISEV 2017 Biomarker Workshop in Birmingham, UK, and with further discussion during the ISEV 2019 Standardization Workshop in Ghent, Belgium, sets out to elucidate which reference materials are required and which are currently available to standardize commonly used analysis platforms for characterizing EV refractive index, epitope abundance, size and concentration. Due to their predominant use among EV researchers, a particular focus is placed on the optical methods nanoparticle tracking analysis and flow cytometry.
Applying pulsed corona discharge in hypersaline droplets
Authors: Almada L F A , Fontes K E S , Vitoriano J O , Melo V R M , Fraga F E N , Jr C Alves
Published: September 2020

Crystallization of salts in droplets of hypersaline solution placed on a glass slide was monitored and evaluated under controlled conditions of temperature and humidity. Pulsed corona discharge (PCD) was applied over sessil droplets on a glass surface, and its effect in the crystallization is demonstrated for the first time. These results were compared with natural evaporation. Under natural evaporation, crystals are firstly formed on surface, near edges of the drop and grow towards the center, eventually acquiring a pyramidal morphology. Under PCD, a Taylor cone is formed between electrode and droplet surface and then, 4 s after the beginning of the discharge, small crystals were formed on the cone besides a dense crystal film around the discharge incidence point in the droplet. These results are discussed considering existing knowledge about plasma–liquid interaction (PLI) and dynamical of crystal growth. This paper explores an interesting aspect of PLIs, which has both fundamental and practical usefulness, for understanding desalination processes, salt crystallization on water surface and biomedical science for advances in electrosurgical methods.
Flexible Sb-graphene-carbon nanofibers as binder-free anodes for potassium-ion batteries with enhanced properties
Authors: Huang Zhao , Ding Shuangshuang , Li Pengchao , Chen Changmiao , Zhang Ming
Published: September 2020

Potassium-ion batteries (KIBs) are emerging as attractive alternatives to lithium-ion batteries for the large scale energy storage and conversion systems, in view of the natural abundance and low cost of potassium resources. However, the lack of applicable anodes for reversible accommodation to the large K+ limits the application of KIBs. Herein, porous Sb-graphene-carbon (Sb-G-C) nanofibers are fabricated via a scalable and facile electrospinning approach. As an attempt, the nanofibers weaving into flexible mats are introduced as binder-free anode materials of KIBs, presenting a great cycle life (204.95 mAh g−1 after 100 cycles at 100 mA g−1), as well as the excellent rate capability (120.83 mAh g−1 at 1 A g−1). The superior performances of the Sb-G-C anodes can be derived from the dispersed graphene, which offers enhanced tolerance to the volume change and promotes the electron transportation, accounting for the outstanding cyclability and rate capability. Furthermore, the extrinsic pseudocapacitance created from the 1D porous nanostructure of the Sb-G-C also boosts the K+ storage capacity. The presented results may pave a new pathway for future high-performance KIBs.
Space weather interference in Earth communications and construction of a small-scale radio telescope for Sun observation in radio waves using Arduino
Authors: Renan Aryel F Silva , Porto Cavalcante Isabela , Hamilton Perez S Corrêa
Published: September 2020

This work describes the construction of a small-scale radio telescope using open source hardware technologies which can be used as a tool in the training of engineers and physicists, as well as in the dissemination of important technologies for space weather monitoring studies, security of radio communication networks and maintenance of essential services for contemporary society.
Surveying Kazakh high school students’ attitudes and beliefs about physics and learning with the Colorado learning attitudes about science survey
Authors: Balta Nuri , Cessna Stephen G , Kaliyeva Assem
Published: September 2020

The Colorado learning attitudes about science survey (CLASS) was given to 308 high school physics students in Almaty, Kazakhstan, where physics is taught every year through middle and high school. Student responses were tested using several different latent factor models: two based on those published in the recent literature and two new models developed through exploratory factor analysis. Four subscales containing 25 of the original CLASS items were found to be internally reliable and cross-culturally valid. These are Conceptual Connections, Personal Interests, Sense-Making/Effort, and Personal Application and Relation to the Real World. Scores on Conceptual Connections correlated significantly with grade level; Sense-Making correlated most with exam scores; and Personal Applications and Relation to Real World scale best differentiated males and females, where males score significantly higher. Our findings are compared to those in previous international reports.
A Sarong rolled around a body demonstrates that the force for separating two sheets joined by folding and rolling is very large
Authors: Akbar Fathan , Abdullah Mikrajuddin
Published: September 2020

A lot of new science has been inspired by common phenomena and even by old traditions practiced in our daily lives. Eventually, after deep exploration, this may engender unexpected new technologies. In this paper, inspired by the wearing of a traditional cloth called a sarong, by the community in South East Asian countries and others, we investigate the behavior of sheets folded in the same way as the rolling of the sarong around the stomach. Simple equipment was designed to qualitatively collect the data which was combined with simple modeling. The rolling of the sarong around the stomach generates a joining force between two sheets, increasing proportionally to the square of the number of rolls. This finding can potentially be applied for developing a method for strongly joining sheets by simply rolling them and releasing the join by unrolling. This work can also be simply duplicated elsewhere, so it is worthy of teaching materials at undergraduate level. Both the scientific and the teaching contents can be extracted simultaneously.
Monte Carlo simulations of energy deposition and DNA damage using TOPAS-nBio
Authors: Wu Jianan , Xie Yaoqin , Wang Luhua , Wang Yuenan
Published: September 2020

Purpose. Monte Carlo (MC) track structure codes are commonly used for predicting energy deposition and radiation-induced DNA damage at the nanometer scale. Various simulation parameters such as physics model, DNA model, and direct damage threshold have been developed. The differences in adopted parameters lead to disparity in calculation results, which requires quantitative evaluation. Methods. Three simulation configurations were implemented in TOPAS-nBio MC toolkit to investigate the impact of physics models, DNA model, and direct damage threshold on the prediction of energy deposition and DNA damage. Dose point kernels (DPKs) of electrons and nanometer-sized volumes irradiated with electrons, protons, and alpha particles were utilized to evaluate the impact of physics models on energy deposition. Proton irradiation of plasmid DNA was used to investigate the disparity in single-strand break and double-strand break (DSB) yields caused by differences in physics models, DNA models, and direct damage thresholds. Results. Electron DPKs obtained with different physics models show similar trends but different diffusiveness and maximums. Energy deposition distributions in nanometer-sized volumes irradiated with electrons, protons, and alpha particles calculated using different physics models have the same trend although discrepancies can be observed at the lowest and highest energy deposits. Strand breaks from incident protons in DNA plasmids vary with adopted parameters. For the configurations in this study, changing physics model, DNA model, and direct damage threshold can cause differences of up to 57%, 69%, and 15% in DSB yields, respectively. All these simulation results are essentially in agreement with previously published simulation or experimental studies. Conclusion. All the physics models, DNA models, and direct damage thresholds investigated in this study are applicable to predict energy deposition and DNA damage. Although the choice of parameters can lead to disparity in simulation results, which serves as a reference for future studies.
2D simulation of solar/lamp two-chamber photoelectric converter with different sodium–noble gas mixtures
Authors: Mandour M M , Astashkevich S A , Kudryavtsev A A
Published: September 2020

This work is devoted to the numerical analysis of the opportunity to obtain a steady generation of electromotive force (EMF) in a two-chamber gas cell exposed to concentrated solar/lamp radiation. For this purpose, we have carried out 2D simulations of the low-pressure photoplasma in the mixtures of sodium atom vapor and different noble gases and their pressures at different values of photoexcitation rate of resonance levels of Na. Herewith plasma chemistry and transfer of charges and radiation in the volume have been taken into account. It has been established that argon is a better candidate to be used as a buffer gas for photoplasma electric converter based on the obtained plasma parameters and commercial aspects. A parametric study has been carried out for the Na–Ar mixture to investigate the effect of increasing homogeneous photoexcitation rate in the small chamber on plasma parameters and obtained EMF. Another parametric study has been conducted to calculate the output current and electric power at a loaded chain. The results show that EMF could be obtained using the photovoltaic effect in photoplasma for the two-chamber cell. The present paper gives a key to the understanding of the effect of different processes (radiation, chemical, and electric) in the cell with these gas mixtures. The obtained results can be used in projecting a solar photoelectric converter based on a two-chamber device with a mixture of sodium and noble gases.
Dual-frequency on–off modulation and slow light analysis based on dual plasmon-induced transparency in terahertz patterned graphene metamaterial
Authors: Li Min , Li Hongjian , Xu Hui , Xiong Cuixiu , Zhao Mingzhuo , Liu Chao , Ruan Banxian , Zhang Baihui , Wu Kuan
Published: September 2020

A dual-frequency on–off modulator with considerable modulation depth (MD) and relatively low insertion loss (IL) is performed with patterned monolayer graphene metamaterial. Destructive interference in this structure gives rise to the dual plasmon-induced transparency (DPIT) phenomenon. The coupled mode theory, confirmed by simulated values, is comprehensively introduced to expound the physical mechanism of the DPIT effect. In addition, the influences of the Fermi level on the DPIT transmission spectrum and the carrier mobility of graphene on the on–off modulation are researched. It is found that the dual-frequency on–off modulator exhibits remarkable modulation performance on both switches and is easier to fabricate in operation than other multi-layer graphene-based modulators. In the ‘on1/off1’ state, the MD and IL are 93%, 0.32 dB, respectively. In the ‘on2/off2’ state, the MD and IL are 85%, 0.25 dB, separately. Moreover, the property of slow light reflected by the group index is analyzed. It exhibits that the group index of the proposed structure with multi-channel can reach 358. Thus, the proposed structure stretches the versatile applications in multi-function modulators and multi-channel slow light devices at the terahertz band.
Solving the all-FLR ICRH integro-differential wave equation as a high-order differential equation for studying combined ICRH-NBI heating
Authors: Van Eester Dirk , Lerche E A
Published: September 2020

A fast and intuitive method is proposed to solve the integro-differential wave equation relevant for modelling ion cyclotron resonance heating (ICRH) at any cyclotron harmonic, for arbitrary ion distributions and retaining all finite Larmor radius effects. The method is inspired on recent work of Budé who proposed to fit the dielectric tensor in k-space by a higher order polynomial, allowing to derive a suitable differential operator that conveniently mimics the integro-differential operator in a sufficiently large

k
⃗

-range to describe all physically relevant wave modes in

k
⃗

-space. Eliminating a drawback of the Budé method by going back to first principles yields a wave equation dielectric response operator that guarantees a positive definite power absorption for populations in thermal equilibrium—as physically expected—and that can directly be used in the Fokker–Planck equation. The method is exploited for 1D application but retaining the toroidal curvature. Analytical expressions of the components of the dielectric tensor are available for Maxwellian distributions as well as bi-Maxwellians with a finite parallel drift. Numerical integration of the velocity space integrals allows to account for arbitrary distributions. A number of examples relevant for high performance tokamak plasma operation and exploiting simultaneous ICRH and neutral beam injection heating are given to illustrate the method’s potential.
Multi-category Classification Problem Oriented Subsampling-Based Active Learning Method
Authors: Shi Wei , Feng Yanghe , Cheng Guangquan , Liu Shixuan , Liu Zhong
Published: September 2020

Traditional active learning methods have achieved gratifying results in the classification tasks of less categories such as binary classification, the application research of active learning in the field of big data problems still faces enormous challenges. Since many active learning query strategies need to perform matrix inversion, the amount of calculation increases exponentially with the increase of the scale of the problem, it is difficult to apply these active learning methods in large scale multi-category data classification task. In order to solve this problem, this paper designed a subsampling-based active learning model, and integrate unsupervised clustering algorithm with traditional active learning method, then conducted experiments on Binary Alphadigits and OMNIGLOT data sets. This paper compares the performance of five traditional active learning algorithms using this subsampling method, namely random sampling, uncertainty sampling, query-by-committee, density weighting and learning-based active learning. Through comparative experiments, the feasibility of active learning based on subsampling for solving the multi-category classification problem is verified, and it is found that the subsampling-based method can break the limitations of traditional active learning methods that cannot deal with large-scale data classification.
Real-Time Multi-object Grasp Based on Convolutional Neural Network
Authors: Yu Hui , Xu Yulin
Published: September 2020

The grasping of the robotic arm is the basic ability of the robot to perform a variety of complex tasks. For the robotic arm, visual-based grasping tasks are still a challenging problem. This paper presents a system to implement dynamic grasping of the robot arm. The neural network is used to detect objects in multiple scenes, and then the robot arm is controlled to achieve closed-loop grasp of the target object. In the process of grasping the target object, if the position of the target object moves, the robot arm will immediately stop and go to the new target position for grasping. The extensive experiments demonstrate that the proposed method enables the robotic arm to achieve dynamic grasping in real world.
3D Pose Registration of Antenna Board Based on Genetic Algorithm for AR Application
Authors: Zhu Teng , Su Yunzhong , He Junyong , Zhang Po
Published: September 2020

In view of the requirement of high geometric consistency and robustness of the Augmented Reality technology in the field of industrial auxiliary assembly applications, a theoretical model is proposed which can detection and tracking target pose and position in short period time by using the point features on the surface of base station antenna. Based on the Genetic Algorithm, the PnP matching problem can be transformed into gene evolution in the feature space. Comparing with the Hough transform method, the experimental result shows that the antenna pose detection method proposed in this paper can effectively adapt to the actual working scenario.
Remote Sensing Image Building Extraction Method Based on Deep Learning
Authors: Wang Minshui , Wang Mingchang , Yang Guodong , Liu Ziwei
Published: September 2020

Using random patches and deeplabV3+ network can effectively improve the building extraction accuracy and ensure the integrity of building. First, acquisiting the image of a 5000×5000 pixel one, and using the random Patch Extraction Datastore function to create a number of random patches with the size of 224×224 pixels as network input images. Second, creating a convolutional neural network based on resnet50 by using the deeplabv3plusLayers function, and then projecting the learned discrimination features with lower resolution to the pixel space with higher resolution, to realise the automatic extraction of the building. Third, two images were input to verify the extraction accuracy of the trained network. The results showed that the Pixel accuracy of image 1 and image 2 reached 97.98% and 92.59%. Compared with other building extraction algorithms, this method has higher extraction accuracy. This method has strong expansibility and It can be used for automatic extraction of other feature types.
A Ship Ranging and Positioning System Based on Binocular Vision Technology
Authors: Zheng Yabo , Wang Lixia , Zhou Ming , Wang Yu
Published: September 2020

Relative ranging and positioning is the basis to ensure the navigation safety of ships, and it has a broad application prospect in various maritime activities, especially in the aspects of docking and undocking, sailing in crowded areas, towing, convoy sailing, and warship replenishment, etc. In order to improve the autonomy and automation of ship ranging and positioning and reduce the cost, a novel type of ship ranging and positioning system based on binocular vision technology is proposed. The proposed system includes seven modules, i.e. video information acquisition module, ship attitude acquisition module, camera parameter calibration module, target recognition and segmentation module, image matching module, ranging and positioning module, and information display module. After acquiring the target image sequences by using two cameras, the proposed system identifies and segments maritime targets quickly by using Canny algorithm in the artificial interactive mode, realizes reduced-space search by using epipolar constraint method, achieve fast target features matching to obtain the vision disparity of the feature points by using the SURF algorithm, and calculates the distance of the target according to the binocular vision ranging principle, so as to realize interactive visual ranging and positioning. The proposed system has the advantages of low cost, simple structure and convenient operation. It can realize relative ranging and positioning with only one computer and two cameras. Experiments demonstrate that the proposed system, with fast running speed and high accuracy, can satisfy the relative ranging and positioning needs of ships.
Named Entity Recognition for the Horticultural Domain
Authors: Liu Ziyu , Luo Meng , Yang Hehe , Liu Xingwang
Published: September 2020

Named Entity Recognition (NER) is one of the fundamental tasks in natural language processing and knowledge engineering, as well as a prerequisite step of many downstream applications. Horticulture, a major branch of agricultural science, means the cultivation, processing, and sale of fruit, nuts, vegetables, and ornamental plants as well as numerous additional services. NER for the horticulture domain means to find key biological traits and state-of-art experimental methods for the horticulturists, new cultivation methods and useful tools for farmers, as well as other important information for planners and policy makers to trigger decision-making procedures. In this paper we designed an NER tagging-set of 7 fine-grained types, and since there is no publicly shared annotated corpus available in horticulture domain, we constructed training and testing corpora manually. Thus, we realized Horticulture NER in scientific literature abstracts with CRF method. Results showed that our system’s accuracy and precision were satisfactory but still have room for improvement.
The Enlightenment of the Theory Model of Audio-Visual Integration to Human-Computer Interaction Technology
Authors: Lu Hong , Pan Chaochao
Published: September 2020

The visual and auditory systems are the most important information processing systems in the brain. We usually sense external stimuli in the way of audio-visual integration. In this process, “visual” and “auditory” nervous form two functional systems which are relatively independent and interactive. It is of great significance for the fusion of multi-sensor data and human-computer interaction technology to understand the theoretical principles of integrating visual and auditory cues and the neural mechanism of audio-visual integration. Here, we try to explore new methods of audio-visual integration and develop new potential of human-computer interface. Previous studies have shown that the principles of integrating visual and auditory cues mainly include spatio-temporal rule, inverse-effectiveness rule and reliability rule. On this basis, researchers have gradually developed a theoretical model of audio-visual integration—causal inference model. By clarifying the mechanism of audio-visual integration, it provides a theoretical basis for the multimodal interaction technology of human-computer interaction, so as to improve the naturalness and efficiency of human-computer interaction and overcome the shortcomings of traditional human-computer interaction.
Implementation of License Plate Recognition System of Electric Bicycles in Nantong Area
Authors: Lv Xingqin , Wang Yan
Published: September 2020

Electric bicycle is a very important means of transportation for Chinese people. The regularization management of electric bicycles is an important means to ensure the orderly operation of traffic. This article will adopt LabVIEW software to identify the number of the new national standard electric bicycle license plate in Nantong area through image pre-processing, image correction, threshold processing, character recognition and other methods. Then the system will determine its place of ownership automatically and save the recognition results to the designated access database of the license plate number, which is convenient for the traffic management department to manage the corresponding violations, and also can effectively inhibit the occurrence of theft or hit-and-run of electric bicycles.
Effect of State Loneliness on Robot Anthropomorphism: Potential Edge of Social Robots Compared to Common Nonhumans
Authors: Li Sijia , Yu Feng , Peng Kaiping
Published: September 2020

Previous studies have widely demonstrated that loneliness will increase people’s anthropomorphic tendency on nonhuman agents. This research extends the effect of loneliness to social robots and differentiates them and common nonhuman agents (e.g., gadgets, animals). Remarkably divergent effects have been verified: experimentally induced loneliness has a positive effect on anthropomorphism of a social robot but not a gadget clocky or an animal. Practical implications are: (1) could prime consumers’ state loneliness when marketing domestic robot products; (2) emphasize humanlike warmth when designing social robots for companion. The findings have found social robots’ edge in providing companionship compared with common nonhuman agents.
A Deep Learning Approach to Reduce False Alarms for Optical Smoke Detectors
Authors: Liu Ming , Zhou Hongbin , Ren Yupeng , Lu Wei
Published: September 2020

Optical smoke detectors (OSDs) are fire-fighting equipment used to detect fire by detecting smoke with scattering phenomenon. From the principle of OSDs we can see that they are vulnerable to false alarms caused by dust or water steam. To reduce false alarms and to make OSDs more reliable, we present a deep learning approach to train a classifier to distinguish fire event from non-fire ones based on time series data. The classifier is modelled with a 1-Dimension convolutional neural network, and generative adversarial network is used to augment and balance training data. Experiment shows that our classifier can reduce more than 50% false alarms caused by water steam while maintaining sensitivity for fire events.
Explore Deep Learning for Chinese Essay Automated Scoring
Authors: Li Haojin , Dai Tao
Published: September 2020

Automated Essay Scoring (AES) has gained increasing attention in recent years. In this paper, we propose a novel automated Chinese essay scoring model, called BLA (BERT and Bi-LSTM with Attention), by using neural network. The model uses a BERT network to obtain the sentence vectors for an essay, and then uses a Bi-LSTM network with two layers to extract the essay vector. We also consider topic instruction as a long sentence and obtain its vector representation by BERT. The obtained topic instruction vector is then used as attention information to further help obtaining more effective essay vector. Moreover, we also present a new dataset that contains topic instruction and related essays collected from Chinese high school. The detailed experimental results show that our proposed model outperforms other baseline methods.
Ferroelectric Memristors Based Hardware of Brain Functions for Future Artificial Intelligence
Authors: Shi Mingmin , Zhang Miaocheng , Yao Suhao , Qin Qi , Wang Min , Wang Yu , He Nan , Zhu Jiale , Liu Xiaoyan , Hu Ertao , Xu Jianguang , Tong Yi
Published: September 2020

Brain-inspired neuromorphic computing systems have long attracted significant interests to replace the conventional Von-Neumann systems because the development of big data analysis and artificial intelligence has put forward higher requirements for computing speed and energy consumptions. Memristive devices are known as one of the most significant candidates to implement brain-inspired neuromorphic computing systems due to their special properties to emulate biological synapses of human brains. Ferroelectric material is a breakthrough for the resistive-switching layer of memristors due to their continuously tunable resistive switching behaviors. However, the application of ferroelectric memristors is limited by unstable performance and the low switching ratio. In this work, ferroelectric memristors have been fabricated and two-dimensional (2D) material MXene (Ti3C2) has been firstly introduced to the Cu/PZT/Pt devices. The insertion of MXene can enhance the performance of the devices by boosting the formation of conductive filaments of oxygen vacancies. The Cu/MXene/PZT/Pt devices exhibit stable resistive switching phenomena and have a high switching ratio about 106. The resistance of Cu/MXene/PZT/Pt memristors can be regulated under continuous voltage pulses. More interestingly, some synaptic functions have been successfully mimicked using Cu/MXene/PZT/Pt memristors. This work will pave the way for the development of the implementation of brain-inspired computing systems.
An Innovative Method for Locating the Welded Circular Seam on the Inner Surface of Cylinder Pipeline to Inspector Robot
Authors: Dong Xiang , Feng Qiaosheng , Xia Junfei , Zhang Yaping
Published: September 2020

Locating the welded seam on the inner surface of a pipeline from the frame image captured by the monocular optical camera is a key and challenging problem in intelligentizing the defect inspection robot. Due to the lack of feature points and feature straight lines on the image of the welded seam, the existing visual odometry method cannot be used to locate the welded seam relative to the inspector robot. In this paper, an innovative method is proposed for locating the welded circular seam on the inner surface cylinder pipeline to inspector robot. In this method, the whole shape of the welded seam is retrieved from the frame captured by the monocular camera of the robot and is used as the feature for the location. Through combining the retried shape features and the moving velocity of the robot, the distance from the robot to the welded seam can be calculated quickly based on the optical imaging principle. Experiments show that our method can effectively locate the welded seam. Our method can used in intelligent pipeline inspector robot to locate the welded seam to defect test.
Auxiliary Decoder and Classifier for Imbalanced Skin Disease Diagnosis
Authors: Xu Zili , Zhuang Jiaxin , Zhang Rong , Wang Ruixuan , Guo Xuemei , Zheng Wei-Shi
Published: September 2020

To date, deep learning has been widely adopted in medical diagnosis systems and made great achievements in real-world applications. However, in medical image-based intelligent diagnosis, the phenomenon of class imbalance often appears due to the substantially smaller available training data for rare diseases compared to common diseases, which usually degrade the performance of classification dramatically. In this paper, we propose a novel learning framework to effectively alleviate the impact of class imbalance, adding an auxiliary decoder to reconstruct original images and reusing the original CNN classifier to help the classifier more likely extract disease-relevant features for both rare and common diseases. Throughout experiments on two skin disease datasets support that the proposed framework outperforms strong baselines with a very explicit margin. The proposed method is also independent of network architecture so that it can be flexibly combined with different model structures. Besides, applying our proposed method with existing training strategies designed for class imbalance can still improve the classification performance.
3D Densely Connected Convolution Neural Networks for Pulmonary Parenchyma Segmentation from CT Images
Authors: Zhao Liang
Published: September 2020

Lung cancer is one of the deadliest diseases in the world today. It kills many people every year. For the diagnosis and treatment of lung cancer, accurate segmentation of lung tissue from CT images is an important process. It is necessary to design a fast and accurate segmentation method to accomplish this task. In the traditional computer-aided diagnosis system, the segmentation of Lung parenchyma is very complex, and the segmentation result depends on the performance of the parameters set in the previous stage. In order to solve these problems, we propose a 3D densely connected convolution neural network which based on deep learning. It has three densely connected blocks and three deconvolution layers. The experimental data set was taken from the public LIDC-IDRI database. A total of 888 samples with slice thickness less than 2.5 mm were selected in the experiments. And the number of samples of training set, test set and validation set is 708, 90 and 90 respectively. In addition, the experimental results show that our method is more accurate than 3D-Unet, but it requires less training parameters.
Research on pH Value Control System of Haematococcus Pluvialis Algae Based on Fuzzy PID Algorithm
Authors: Cui Shigang , Cai Xingzong , Zhang Yongli , Wu Xingli , He Lin
Published: September 2020

In recent years, inducing the production of astaxanthin by Haematococcus pluvialis, the application of extracting astaxanthin from it has become more and more widespread. The pH of the algae fluid has an important influence on the rate of accumulation of astaxanthin by Haematococcus pluvialis. However, the pH control system of Haematococcus pluvialis algae fluid has many shortcomings, such as non-linearity, time-varying, severe lag, large inertia and uncertain mathematical model. Therefore, to address these shortcomings, a method of pH control based on fuzzy PID is proposed. In the Matlab simulation software, using traditional PID control system adjust and control the pH value of Haematococcus pluvialis. And then using the fuzzy PID control system adjust and control the Haematococcus pluvialis. Finally, comparing the simulation results of the two systems. According to the Simulink results, we finally get the following conclusion: the fuzzy PID control system has smaller overshoot, no vibration, and the control effect is better.
Multi-point Mooring PID Control Based on Kalman Filtering-Genetic Algorithm-Slow Disturbance Observer Compensation
Authors: Zhang Guichen , Lu Run , Chen Mengwei
Published: September 2020

An adaptive PID control technique based on the Kalman Filtering (KF) Genetic Algorithm (GA) and Slow Disturbance Observer Compensation (SDOC) is proposed for multi-point mooring system in this paper. The multi-point mooring model, which is simplified to two-point mooring, is established, its self-tuning PID mooring controller based on KF-GA-SDOC is designed. The multi-point mooring model is updated on-line with the KF and SDOC to filter and observe time-varying signal; the PID mooring control gains are adjustable parameters by selection, crossover and mutation operator of GA; the mooring control system based on KF-GA-SDOC PID is optimized. The simulation results show that the designed KF-GA-SDOC PID mooring controller can maintain good control performance under different conditions.
Multi-threshold Image Segmentation by Improved Lion Swarm Optimization Algorithm
Authors: Li Xiaogang , Jiang Mingyan
Published: September 2020

In this paper we propose an improved Lion Swarm Optimization (ILSO) algorithm for multi-threshold image segmentation. We introduce the global search method of Artificial Bee Colony optimization (ABC) algorithm and revise the updating function of LSO algorithm to improve the global and local search performance of LSO algorithm. We introduce a sign to record the number of times an individual falls into the local optimum, and attenuate it so that the algorithm can jump out of the local optimum more quickly in the early stage and accelerate the convergence speed in the later stage. The maximum inter-class variance criterion is selected as the fitness function to solve the Multi-threshold image segmentation problem by ILSO. Experiment results show this algorithm can obtain ideal image segmentation result. And when the dimension of the problem is higher, the advantage of the improved Lion Swarm Optimization algorithm proposed in this paper is more obvious.