bluuebunny/crossref_metadata_2025 huggingface dataset:
Prediction of age and brachial-ankle pulse-wave velocity using ultra-wide-field pseudo-color images by deep learning
Authors: Nagasato Daisuke , Tabuchi Hitoshi , Masumoto Hiroki , Kusuyama Takanori , Kawai Yu , Ishitobi Naofumi , Furukawa Hiroki , Adachi Shouto , Murao Fumiko , Mitamura Yoshinori
Published: November 2020

This study examined whether age and brachial-ankle pulse-wave velocity (baPWV) can be predicted with ultra-wide-field pseudo-color (UWPC) images using deep learning (DL). We examined 170 UWPC images of both eyes of 85 participants (40 men and 45 women, mean age: 57.5 ± 20.9 years). Three types of images were included (total, central, and peripheral) and analyzed by k-fold cross-validation (k = 5) using Visual Geometry Group-16. After bias was eliminated using the generalized linear mixed model, the standard regression coefficients (SRCs) between actual age and baPWV and predicted age and baPWV from the UWPC images by the neural network were calculated, and the prediction accuracies of the DL model for age and baPWV were examined. The SRC between actual age and predicted age by the neural network was 0.833 for all images, 0.818 for central images, and 0.649 for peripheral images (all P < 0.001) and between the actual baPWV and the predicted baPWV was 0.390 for total images, 0.419 for central images, and 0.312 for peripheral images (all P < 0.001). These results show the potential prediction capability of DL for age and vascular aging and could be useful for disease prevention and early treatment.
In situ formation of photoactive B-ring reduced chlorophyll isomer in photosynthetic protein LH2
Authors: Saga Yoshitaka , Otsuka Yuji , Funakoshi Daichi , Masaoka Yuto , Kihara Yu , Hidaka Tsubasa , Hatano Hiroka , Asakawa Hitoshi , Nagasawa Yutaka , Tamiaki Hitoshi
Published: November 2020

Natural chlorophylls have a D-ring reduced chlorin π-system; however, no naturally occurring photosynthetically active B-ring reduced chlorins have been reported. Here we report a B-ring reduced chlorin, 17,18-didehydro-bacteriochlorophyll (BChl) a, produced by in situ oxidation of B800 bacteriochlorophyll (BChl) a in a light-harvesting protein LH2 from a purple photosynthetic bacterium Phaeospirillum molischianum. The regioselective oxidation of the B-ring of B800 BChl a is rationalized by its molecular orientation in the protein matrix. The formation of 17,18-didehydro-BChl a produced no change in the local structures and circular arrangement of the LH2 protein. The B-ring reduced 17,18-didehydro-BChl a functions as an energy donor in the LH2 protein. The photoactive B-ring reduced Chl isomer in LH2 will be helpful for understanding the photofunction and evolution of photosynthetic cyclic tetrapyrrole pigments.
Structural and proteomic analyses of vitelline membrane proteins of blackbird (Turdus merula) and song thrush (Turdus philomelos)
Authors: Damaziak Krzysztof , Kieliszek Marek , Gozdowski Dariusz
Published: November 2020

In this study, we aimed to perform structural and proteomic analysis of the vitelline membrane (VM) of two species birds belonging to the family Turdidae: blackbird (Turdus merula) and song thrush (Turdus philomelos). We performed structural analyses using scanning electron microscopy. The VM proteins were identified and compared to the best-known chicken VM proteins. According to our results, VM of both species has a typical three-layered structure: the outer layer, inner layer, and the continuous membrane between them. An unusual observation was the finding of “convexity” formed by the inner layer in blackbird. The role of these convex structures is not known, but they can be typical for the species and can be used in their identification. In addition, we identified two proteins in the VM of both species of birds, of which U3KEZ1 FICAL was not previously identified in any other bird species, and the U3JXV8 FICAL protein was confirmed only once in cockatiel parrot VM. The function of these proteins is not exactly known, but their structure shows similarities to the SERPIN proteins that are involved in microbiological defense, i.e., they are immune proteins. This study contributes to the current knowledge about the structure and composition of proteins of VM, especially because similar analyses have never been performed for Turdidae family. Knowledge of the structure and specific proteins of blackbird and song thrush VM can be beneficial in research on ecology and bird biology and also helpful in developing noninvasive and nongenetic identification methods.
Generating high-fidelity synthetic patient data for assessing machine learning healthcare software
Authors: Tucker Allan , Wang Zhenchen , Rotalinti Ylenia , Myles Puja
Published: November 2020

There is a growing demand for the uptake of modern artificial intelligence technologies within healthcare systems. Many of these technologies exploit historical patient health data to build powerful predictive models that can be used to improve diagnosis and understanding of disease. However, there are many issues concerning patient privacy that need to be accounted for in order to enable this data to be better harnessed by all sectors. One approach that could offer a method of circumventing privacy issues is the creation of realistic synthetic data sets that capture as many of the complexities of the original data set (distributions, non-linear relationships, and noise) but that does not actually include any real patient data. While previous research has explored models for generating synthetic data sets, here we explore the integration of resampling, probabilistic graphical modelling, latent variable identification, and outlier analysis for producing realistic synthetic data based on UK primary care patient data. In particular, we focus on handling missingness, complex interactions between variables, and the resulting sensitivity analysis statistics from machine learning classifiers, while quantifying the risks of patient re-identification from synthetic datapoints. We show that, through our approach of integrating outlier analysis with graphical modelling and resampling, we can achieve synthetic data sets that are not significantly different from original ground truth data in terms of feature distributions, feature dependencies, and sensitivity analysis statistics when inferring machine learning classifiers. What is more, the risk of generating synthetic data that is identical or very similar to real patients is shown to be low.
In vitroandin vivoosteogenesis up-regulated by two-dimensional nanosheets through a macrophage-mediated pathway
Authors: Liu Haoming , Yang Gaojie , Yin Hao , Wang Zhenxing , Chen Chunyuan , Liu Zhengzhao , Xie Hui
Published: November 2020

Two-dimensional calcium phosphate nanomaterials are able to stimulatein vitroosteogenic differentiation of stem cells andin vivobone regeneration by inducing M2 polarization of macrophages, rather than manipulating stem cells’ fate directly.
Adsorption of arsenite to polystyrene microplastics in the presence of humus
Authors: Dong Youming , Gao Minling , Qiu Weiwen , Song Zhengguo
Published: November 2020

This work confirms HA interacts with the PSMP to form a π complex, providing more sites on the microplastics for As(iii) adsorption while reducing the time required to reach equilibrium in waters.
Behaviour of calcium phosphate ester salts in a simulated body fluid modified with alkaline phosphatase: a new concept of ceramic biomaterials
Authors: Yokoi Taishi , Ujiyama Tatsuki , Nakamura Jin , Kawashita Masakazu , Ohtsuki Chikara
Published: November 2020

We have demonstrated that calcium phenylphosphate, which is a type of salt of calcium ion and phosphate esters, can act as bioresponsive ceramic biomaterials.
Enhanced synthesis method of graphene oxide
Authors: Benzait Zineb , Chen Pengwan , Trabzon Levent
Published: November 2020

A simple and environmentally friendly pre-treatment step allows the production of graphene oxide with few defects and high mechanical and electrical properties.
The unimolecular dissociation of magnesium chloride squarate (ClMgC4O4−) and reductive cyclooligomerisation of CO on magnesium
Authors: Jestilä Joakim S. , Iker Zsuzsanna , Ryding Mauritz J. O. , Uggerud Einar
Published: November 2020

Cyclooligomerisation to squarate is initiated by the coupling of two CO units in MgCl−.
The effect of graphitized carbon on the adsorption and photocatalytic degradation of methylene blue over TiO2/C composites
Authors: Cai Jinlu , Hu Shenghua , Xiang Junhuai , Zhang Honghua , Men Dandan
Published: November 2020

The TiO2/C composites with approximately 40 wt% of carbon were prepared by calcination of precursors, formed from a one-pot liquid phase reaction between Ti(SO4)2 and flour.
Polyurea-crosslinked biopolymer aerogel beads
Authors: Paraskevopoulou Patrina , Smirnova Irina , Athamneh Tamara , Papastergiou Maria , Chriti Despoina , Mali Gregor , Čendak Tomaž , Raptopoulos Grigorios , Gurikov Pavel
Published: November 2020

Polyurea-crosslinked calcium alginate and chitosan aerogel beads: novel fibrous biopolymer-based aerogels.
Assessing potential inhibitors of SARS-CoV-2 main protease from available drugs using free energy perturbation simulations
Authors: Ngo Son Tung , Nguyen Hung Minh , Thuy Huong Le Thi , Quan Pham Minh , Truong Vi Khanh , Tung Nguyen Thanh , Vu Van V.
Published: November 2020

Free Energy Pertubation (FEP) can be used to accurately predict the binding affinity of a ligand to the main protease (Mpro) of the novel coronavirus SARS-CoV-2.
Refractive index of delignified wood for transparent biocomposites
Authors: Chen Hui , Montanari Céline , Yan Max , Popov Sergei , Li Yuanyuan , Sychugov Ilya , Berglund Lars A
Published: November 2020

Developed light transmission model for determining refractive index of wood, complex porous or layered solid materials and composites.
Effect of the heating process on the physicochemical characteristics and nutritional properties of whole cotyledon soymilk and tofu
Authors: Yang Yuexi , Ji Zhoujieyu , Wu Cheng , Ding Yin-Yi , Gu Zhenyu
Published: November 2020

This study focused on the effect of the heating process on the whole cotyledon soymilk and tofu.
Probing the high performance of photoinduced birefringence in V-shaped azo/PMMA guest–host films
Authors: Silva Lidiana M. , Silva Daniel L. , Boas Mariana V. , Bretonniere Yann , Andraud Chantal , Vivas Marcelo G.
Published: November 2020

The underlying mechanisms that rule the high performance of optical birefringence in V-shaped azo/PMMA films were elucidated through the ground-state transient absorption technique and quantum chemical calculations.
The oxazolomycin family: a review of current knowledge
Authors: Oleksak Patrik , Gonda Jozef , Nepovimova Eugenie , Kuca Kamil , Musilek Kamil
Published: November 2020

Oxazolomycin A and neooxazolomycin were firstly isolated in 1985 by the group of Uemura et al. from the Streptomyces sp. bacteria.
Catalytic oxidative desulfurization of model fuel utilizing functionalized HMS catalysts: characterization, catalytic activity and mechanistic studies
Authors: Wang Fang , Xiao Ke , Shi Lin , Bing Liancheng , Han Dezhi , Wang Guangjian
Published: November 2020

In this study, a novel Fe(ii) porphyrin conjugated amino modified HMS catalyst was synthesized and characterized by BET, XRD, TEM, SEM, and UV-vis techniques.
Nb-incorporated Fe (oxy)hydroxide derived from structural transformation for efficient oxygen evolution electrocatalysis
Authors: Wang Chizhong , Wang Rong , Peng Yue , Chen Jianjun , Chen Zhen , Yin Haibo , Li Junhua
Published: November 2020

FeNb (oxy)hydroxide derived from structural transformation exhibited an overpotential of 340 mV at 10 mA cm−2 for oxygen evolution electrocatalysis.
Porous evaporators with special wettability for low-grade heat-driven water desalination
Authors: Zhu Zhigao , Xu Ying , Luo Yifei , Wang Wei , Chen Xiaodong
Published: November 2020

Design of novel special wettable evaporators with robust stability for high-performances porous interface distillation.
Low-cost valence-rich copper–iron–sulfur–oxygen porous nanocluster that drives an exceptional energy-saving carbohydrazide oxidization reaction in alkali and near-neutral electrolytes
Authors: Wang Yanqing , Li Yuemeng , Ding Liping , Tang Yanfeng , Ding Jun
Published: November 2020

Significantly reducing the anodic oxidization reaction potential is very important for realizing highly efficient, energy-saving hydrogen production.
An opportunity for utilizing earth-abundant metals through the mesostructural design of metal phosphate-based materials
Authors: Kimura Tatsuo
Published: November 2020

This review focused on the surfactant-assisted synthesis of metal phosphate-based materials and summarized their potential based on the design of a mesoporous structure as well as the composition and crystal structure of the inorganic frameworks.
Emerging flexible sensors based on nanomaterials: recent status and applications
Authors: Wen Nan , Zhang Lu , Jiang Dawei , Wu Zijian , Li Bin , Sun Caiying , Guo Zhanhu
Published: November 2020

The flexible materials, nanomaterials, and fabrication strategy of flexible sensors with stretchable and self-healing properties were reviewed.
Ultra elastic, stretchable, self-healing conductive hydrogels with tunable optical properties for highly sensitive soft electronic sensors
Authors: Wu Meng , Chen Jingsi , Ma Yuhao , Yan Bin , Pan Mingfei , Peng Qiongyao , Wang Wenda , Han Linbo , Liu Jifang , Zeng Hongbo
Published: November 2020

A self-healing hydrogel ionic conductor has been developed by combining dynamic covalent chemistry with nanofiller reinforcement and micelle crosslinking, and used for sensing of diverse human activities.
Excellent charge and spin transport in insulating hexagonal boron nitride with one-dimensional electron channel
Authors: Zeng Jing , Chen Ke-Qiu
Published: November 2020

Hexagonal boron nitride with a one-dimensional electron channel has profound potential for developing overcurrent protection devices, and spin logic and memory devices.
Defending pollution attacks in network coding enabled wireless ad hoc networks: a game‐theoretic framework
Authors: Liu Xiang , Huang Jie , Yao Yiyang , Qi Chunyang , Zong Guowen
Published: November 2020

Network coding is a promising technique to improve the throughput and robustness of wireless ad hoc networks. However, the packet‐mixing nature of network coding also renders it more prone to pollution attacks. Most existing schemes to combat pollution attacks did not consider the defender's resource limit, nor the trade‐off between defensive performance and other metrics such as delay and resource consumption. The authors investigate how to achieve such a trade‐off optimally by proposing a two‐player strategic game model between the attack and the defender. In this model, the utilities of both players are well defined, and thus the defender can obtain its best strategy by maximising its utility. To do so, a graph‐based simulated annealing algorithm is proposed to derive the utility‐maximising strategy. Finally, they conduct extensive experiments to evaluate their scheme from different aspects. The results show that their scheme can achieve better utility than existing schemes, and is more computationally efficient in the meanwhile. Moreover, their scheme can obtain a sub‐optimal solution within a small number of iterations, which implies that it can be implemented in the short‐session communication scenario where it is required to find a sufficiently good solution within a short time.
Structure‐based joint estimation algorithm for distributed coherent aperture radar
Authors: Liu Yin
Published: November 2020

In a distributed coherent aperture radar, the time delay differences and the phase differences among received echoes from each pair of transmitter–receiver should be estimated and then can be compensated to realise coherent processing. Most of the existing algorithms improve the outputs of the matched filtered signals of each transmitter–receiver pair separately. In this study, a structure of equal time delay differences existing in the signal model can be exploited to jointly perform coherence parameter estimation. With this idea, a structure‐based joint estimation algorithm is presented to enhance the performance of the coherence parameter estimation. Simulation results verify that this algorithm can have better estimation performance compared with some existing algorithms in the case of low signal‐to‐noise ratios. Especially, its performance advantage is more significant in the multiple nearfield target scenarios.
Vortex flow properties in simulations of solar plage region: Evidence for their role in chromospheric heating
Authors: Yadav N. , Cameron R. H. , Solanki S. K.
Published: November 2020

Context. Vortex flows exist across a broad range of spatial and temporal scales in the solar atmosphere. Small-scale vortices are thought to play an important role in energy transport in the solar atmosphere. However, their physical properties remain poorly understood due to the limited spatial resolution of the observations.

Aims. We explore and analyze the physical properties of small-scale vortices inside magnetic flux tubes using numerical simulations, and investigate whether they contribute to heating the chromosphere in a plage region.

Methods. Using the three-dimensional radiative magnetohydrodynamic simulation code MURaM, we perform numerical simulations of a unipolar solar plage region. To detect and isolate vortices we use the swirling strength criterion and select the locations where the fluid is rotating with an angular velocity greater than a certain threshold. We concentrate on small-scale vortices as they are the strongest and carry most of the energy. We explore the spatial profiles of physical quantities such as density and horizontal velocity inside these vortices. Moreover, to learn their general characteristics, a statistical investigation is performed.

Results. Magnetic flux tubes have a complex filamentary substructure harboring an abundance of small-scale vortices. At the interfaces between vortices strong current sheets are formed that may dissipate and heat the solar chromosphere. Statistically, vortices have higher densities and higher temperatures than the average values at the same geometrical height in the chromosphere.

Conclusions. We conclude that small-scale vortices are ubiquitous in solar plage regions; they are denser and hotter structures that contribute to chromospheric heating, possibly by dissipation of the current sheets formed at their interfaces.
HBIM : une analyse de la littérature scientifique
Authors: Stoleru Alexandra , Jacquot Kévin
Published: November 2020

Dans le sillage de la lente diffusion du BIM dans les milieu professionnels de la construction, un nombre croissant d’expérimentations sont menées dans le domaine de l’existant à forte valeur patrimoniale, principalement dans le monde de la recherche. Cette communication étudie l’évolution de la production scientifique pour dégager des tendances et identifier les futurs axes de développement de cette approche relativement nouvelle et encore mal définie.
Une catégorisation des maquettes BIM au sein des modèles scientifiques
Authors: Catros Aurélien
Published: November 2020

À la faveur d’un regard rétrospectif sur l’histoire des modèles d’architecture et des modèles scientifiques depuis le Quattrocento, le présent article se propose de situer les modèles BIM utilisés en architecture au sein des catégorisations générales des modèles proposées par M. Weisberg et M. Echenique. Par là même, il contribue à préciser leur définition en les situant dans un cadre épistémologique plus large.
Annotations numériques en phase d’exécution
Authors: Rahhal Anabelle , Berthelot Charline , Safin Stéphane , Leclercq Pierre
Published: November 2020

Suite à la mise en place de trois expériences et observations en laboratoire, nous tentons de déterminer le type, la fonction et la valeur ajoutée des annotations numériques, dans la résolution d’un problème sur chantier. Dans cet article, nous explorons la problématique des annotations numériques en phase d’éxecution. Nous décrivons la méthodologie de mise en place des expériences d’annotations (condition papier, condition numérique 2D et condition numérique 3D). Enfin, nous présentons des résultats et conclusions intéressantes pour la spécification d’un outil de revue de projet, dédicacé au chantier.
Altruistic punishment in modern intentional communities
Authors: Qirko Hector
Published: November 2020

Evolutionists studying human cooperation disagree about how to best explain it. One view is that humans are predisposed to engage in costly cooperation and punishment of free-riders as a result of culture/gene coevolution via group selection. Alternatively, some researchers argue that context-specific cognitive mechanisms associated with traditional neo-Darwinian self- and kin-maximization models sufficiently explain all aspects of human cooperation and punishment. There has been a great deal of research testing predictions derived from both positions; still, researchers generally agree that more naturalistic data are needed to complement mathematical modeling and laboratory and field experiments. Most of these data have been obtained from small-scale forager and other societies, but modern intentional communities offer another productive source of information. This exploratory study describes context-specific patterns of punishment in 46 American intentional communities that cast doubt on the prediction that people are predisposed to punish free-riders in naturalistic interactions.
Effect of local dissociations in bidirectional transport of driven particles
Authors: Jindal Akriti , Kolomeisky Anatoly B , Gupta Arvind Kumar
Published: November 2020

Motivated by the complex processes of cellular transport when different types of biological molecular motors can move in opposite directions along protein filaments while also detaching from them, we developed a theoretical model of the bidirectional motion of driven particles. It utilizes a totally asymmetric simple exclusion process framework to analyze the dynamics of particles moving in opposite directions along the lattice of discrete sites while the particles might also dissociate from the filament in the bulk of the system. Mean-field theoretical arguments supported by extensive Monte Carlo simulations are presented in order to understand how the localized particle dissociations affect the bidirectional dynamics and spontaneous symmetry-breaking phenomena. It is found that changes in the amplitudes and in the symmetry of dissociation rates lead to significant modifications in the dynamic properties and in the stationary phase diagrams. These changes are explained using simple physical arguments. Our theoretical method clarifies some aspects of microscopic mechanisms of complex transport phenomena in biological systems.
A prediction model of buses passenger flow based on neural networks
Authors: Zhang Shuyu , Liu Zikang , Shen Fengting , Wang Shumeng , Yang Xuelin
Published: November 2020

With the popularization of smart buses, the ways to obtain buses operation information are becoming increasingly diversified, and passenger flow prediction based on big data information has also emerged. In this work, Principal Component Analysis (PCA) and error Back Propagation (BP) neural networks were combined to propose a prediction model of short-term buses passenger flow on the basis of PCA-BP neural networks. Firstly, the PCA method was adopted to reduce the dimensionality of the indices of buses passenger flow and improve the input nodes of the BP networks. Secondly, the fully trained network was employed to predict the buses passenger flow. More specifically, the passenger flow data set pair of the 17th bus in Guiyang on November 29, 2019 was used to carry out the PCA-BP neural networks model test. The test results indicated that the predicted passenger flow of each time period was very close, and the passenger flow prediction error in most time periods was very small. Besides, the relative error could achieve a satisfactory fitting effect, which was able to provide a reliable basis for bus dispatch. We found that the proposed PCA-BP neural networks model had high prediction accuracy and prediction performance. Therefore, it is expected to be used as a model for final short-term passenger flow prediction.
Research on the selection method of FY-3D/MWHTS clear sky observation data based on neural network
Authors: He Qiurui , Guo Xiao , Li Deguang , Jin Yanling , Zhang Lanjie , Zhang Ruanyu
Published: November 2020

The selection of clear sky data in space-borne remote sensing data is very important for its data application. For FY-3D satellite microwave humidity and temperature sounder (MWHTS), an inversion system of atmospheric cloud water content by MWHTS is established based on neural network. The cloud water content inversion value is used to select clear sky data from MWHTS observation data. The experimental results show that FY-3D/MWHTS clear sky data selection method based on neural network can effectively select MWHTS observation data, thus improving the simulation brightness temperatures accuracy of MWHTS by radiative transfer model. This method can be used to select clear sky data by using space-borne observation data itself. It is easy to operate and has important practical value for climate change research, numerical weather forecast, etc., based on space-borne observation data.
Deep heading estimation for pedestrian dead reckoning
Authors: Lin Jiayi , Zou Chengming , Lan Long , Gu Shanzhi , An Xinshang
Published: November 2020

Currently, the heading estimation could be easily achieved by many built-in direction sensors, such as the smartphone. However, the obtained heading angle is only suitable for situations where an equipped pedestrian’s movement and orientation keep the same, such as normal forward walking and turning. When the pedestrian faces a different direction from his movement, the heading angle remains the pedestrian orientation but not the movement and thus causes a heading estimation error. In this paper, we introduce several related deep learning techniques to explore their respective abilities in heading estimation of the waist-mounted Miniature Inertial Measurement Unit (MIMU). Specifically, this paper adopts two kinds of methods to analyze the data collected from MIMU, include acceleration, angular velocity, or their combination, to predict the heading angle. Firstly, considering the heading estimation is a time series prediction problem actually, this paper introduces the powerful Long Short-Time Memory (LSTM) model. On the other hand, this paper uses the Graph Convolutional Network (GCN) model to consider the relationship between the orientation and the direction of motion at different times. In experiments, we show that the accuracy of the proposed LSTM model in the test set achieved a promising 99.12%. To test our method in the real scenes, this paper designs simulation experiments and mobile terminal tests based on Tensorflow Lite. The experimental results show that the movement heading can be effectively judged based on the waist-mounted sensor data, and has a very significant accuracy.
Multi-structure non-linear semi-parametric optimal weighting parameters estimation algorithm based on adaptive sparse representation deconstruction model and integrative COD comprehensive model
Authors: Zhao Deyong , Xie Zhiying , Liu Jie , Gu Ping
Published: November 2020

For the combined orbit determination (COD) satellite-network system which is made of GEO-IGSO-MEO satellite-constellation and LEO satellites, the satellites-trajectory are not precise enough, because the meticulous satellite dynamics model, credible observation data model and optimal parameters estimation method are not taking into consideration. The high-level adaptive sparse-deconstruction dynamics model, non-linear semi-parametric combined orbit determination comprehensive model is put forward. By using the above models, adaptive weighting iterative wavelets decomposition algorithm of sparse-representation deconstruction model and multi-structure non-linear semi-parametric optimization weighted estimation algorithm of integrative combined orbit determination comprehensive models are designed. Simulated calculation results show that results showed that the high-precision sparse representation method and the optimization model estimation method can improve COD precision, and when comprehensive model systematic & random error parameters and adaptive sparse-representation deconstruction model parameters are estimated by integrative nonlinear semi-parametric comprehensive model synchronously, COD ultimate trajectory calculation estimation precision can get significant improvement.
Disruption management for solving FJSP with improved genetic algorithm
Authors: Sun Jinghua , Xu Li
Published: November 2020

In order to solve the Multi-objective flexible job shop scheduling, an improved cloud adaptive genetic annealing algorithm is proposed. Firstly, the cross-variation, improved fitness calculations, and selection operations are built. Moreover, a novel multi-objective optimization model for flexible job-shop scheduling (FJSP) is established. At the same time, the idea of gradual optimization for the target is introduced to obtain the optimal scheme of FJSP. According to the similarity test of Hamming ones, the cross operation is selected. It can improve the efficiency and convergence of the proposed algorithm. The experiment result verifies the effectiveness of the proposed improved cloud adaptive genetic annealing algorithm (CAGA), and which is compared with other algorithms experiments the existing classical algorithms.
Research on the north polar tourism resources evaluation model based on BP neural network algorithm
Authors: Jin Lili
Published: November 2020

Polar tourism resources have unique properties. The Polar resource evaluation is comprehensive and multi-angle based on communication value, cultural value, economic value, educational value and so on. A scientific and reasonable evaluation model can provide the basis for the exploitation and utilization of polar tourism resources. The purpose of this paper is to apply BP neural network algorithm to self-learning intelligent evaluation, especially those not fully explored polar tourism resources. The subjective random factors are avoided to objectively evaluate polar tourism resources.
The study on the index system of the smart city based on knowledge map
Authors: Liu Jialu , Ning Huicong , Bai Yuanhui , Yan Tianzi
Published: November 2020

Urbanization is the inevitable trend of urban development. In order to conducive to the supervision and guidance of smart city construction facts and the assessment and feedback of smart city operations, we propose a method to build the index system of the smart city based on the knowledge map. This method can effectively improve the query, visualization and reasoning ability of the index system, and provide the basis for using big data to build the index system automatically or semi-automatically, and finally give the knowledge map about the index system of the smart city.
Automated construction technology of the government agencies knowledge graph based on the topical crawler
Authors: Xiong Ziqi , Kong Dezhi , Xia Zhichao , Xue Yankai , Song Ziyu , Wang Peng
Published: November 2020

Government agencies are the skeleton of the knowledge graph in the field of government affairs; it is the starting and ending point of various government affairs. This paper proposes an automated construction technology of the government agencies knowledge graph based on the topical crawler, which collects government and department information from relevant government websites and constructs the knowledge graph automatically. Sampling verification shows that the knowledge precision of this government agencies knowledge graph exceeds 96%, and it covers nearly 99% of municipal government information and more than 92% of government department information across the country. Furthermore, through the tracking of government websites, the knowledge graph of government agencies can be automatically updated. As far as our understanding is concerned, this is the first time to explore the technology of automated construction of knowledge graph in the related fields of government affairs combined with the collection system of the web crawler.
Research on the construction and visualization of agricultural products circulation big data model from the perspective of regional economic integration
Authors: Zhao Hua
Published: November 2020

The degree of regional economic integration is one of the main factors affecting the efficiency of regional agricultural product circulation, and the two are generally positively correlated. My country’s agricultural product logistics have been developed for a long time, but it is still low in level, high in cost and large in the loss. We analyzed the current situation and existing problems of agricultural product logistics, combined with the background of big data, summarize the role of big data in agricultural product logistics, and the measures for agricultural product logistics construction in the era of big data. To effectively promote the improvement of regional agricultural product circulation efficiency, after analyzing the influencing factors, this paper proposes that the government should build a full marketing system to improve circulation efficiency. It builds a multi-channel platform to improve circulation efficiency and create an era of big data. Data integration and smooth information have further improved the circulation efficiency of China’s agricultural products.
Disease model identification methods based on maximum test and performance analysis
Authors: Yin Yaling
Published: November 2020

Combinatorial explosion and computational burden are always the challenges for genome-wide association study. In order to reduce the computation cost, many multi-stage methods were put forward to identify the disease models. However, one-way and two-way disease models always can be detected to leave out some SNPs for non-significance. And these SNPs are combined with other SNPs to get higher disease models. In this paper, three test statistics, Max Gtest, Max Entropy Difference and Max Relative Entropy, had been presented for the first stage to detection disease models with main effect and without main effect. Five testing methods were used for examining multiply simulation datasets and real dataset. Results were revealed that Max Entropy Difference test is the best method of recognition in five filtering methods with main-effect and max-statistic test is just right method to identify model without main-effect. Results also were showed that five statistics can get interest power for two-ways on simulation datasets and real dataset. We believe that these statistics can find strong and weak SNPs for next step in computationally and statistically.
Design of monitoring system for intelligent windows based on cloud platform
Authors: Liu Changyuan , Jia Yunxiang , Guo Penggang , Zhang Yifan
Published: November 2020

With the development of Internet of Things technology, the intelligent window is entering people’s lives. There are many intelligent window products in the market, but most of them are costly or functions-limited with insufficient consideration of intelligence and humanization. Therefore, an intelligent window monitoring system based on Alibaba Cloud is introduced in this paper. This product can be automatically controlled according to environmental changes or manually controlled using the APP/infrared remote control. The indoor situation is displayed on the mobile phone and OLED screen in real-time, so that people can always grasp the situation and make correct judgment and operation in time. The product has a wide scope of applications. It is suitable for most families, rich in functions, more humanized, and has certain practicality.
Research on QoS routing method based on NSGAII in SDN
Authors: Li Daoquan , Wang Xue , Jin Yingnan , Liu Haoxin
Published: November 2020

Software Defined Network (SDN) makes routing decisions according to the shortest path. This routing scheme does not consider link information such as link residual bandwidth, forwarding delay, packet loss rate and so on. In the case of large data flow, it is easy to cause network congestion, thus reducing the network quality of service assurance. For this reason, using the ability of SDN network controller to monitor the underlying network information and control the route forwarding, we try to set multiple objective functions according to different QoS parameters, and use the non-dominated sorting multi-objective optimization genetic algorithm (NSGA II) based on the elite strategy to make routing decisions. Compared with other routing algorithms, the proposed multi-objective optimization genetic algorithm reduces forwarding delay and packet loss rate, so can effectively prevent network congestion.
Research on motor imagery EEG signal processing algorithm
Authors: Ma Ye , Jiang Guangping , Chang Tanqing
Published: November 2020

To address the difficulty of extracting features from motor imagery EEG signals and the low classification accuracy, a method for feature extraction and classification of multiple types of motor imaging EEG signals based on wavelet and support vector machine (SVM) is proposed. This work first calculated the power of motor imagery EEG data and selected the scale of wavelet packet by theoretical analysis. Then, wavelet packet decomposition on power was discussed, wavelet packet entropy (WPE) of power was calculated, and wavelet packet entropy interpolation of leads C3, C4 was extracted, which composed the feature vector. Finally, this work fed the feature vector as the classifier input into a support vector machine to achieve classification. From Graz’s EEG data from the international BCI competition in 2003, the highest accuracy rate of classification was 97.56%. The feature vectors of this algorithm are low in dimension, are small in data size, and have high classification accuracy, which provides a reference method for the task of EEG feature extraction and classification.
A method for detecting weak signals using chaotic systems based on multiple correlation and phase-locked loop
Authors: He Qiurui , Li Deguang , Jin Yanling , Zhang Yang , Zhang Lanjie
Published: November 2020

In order to improve the detection ability of chaotic system to detect weak periodic signal frequency, phase and amplitude, a chaotic system based on multiple correlation and phase-locked loop is established. Firstly, the signal to be measured is input into the detection frequency of chaotic system. Also, the reference signal of multiple correlation technology is constructed, and the signal to be tested is processed by multiple cross-correlation. Then, the signal processed by multiple correlation is input into the phase-locked loop to obtain the phase of the signal to be measured. Finally, the signal to be measured is input into the chaotic system again, and the amplitude of the signal to be measured is detected according to the change of the chaotic oscillator in critical periodic state and large-scale periodic state. The experimental results show that the detection system can detect the frequency, phase and amplitude of weak periodic signal, which is easy to operate and has high detection ability.
Study on photometric data visualization based on IESNA LM-63 standard
Authors: Zheng G P , Guo H Y
Published: November 2020

The luminous intensity distribution (LID) data file is a written description of the spatial distribution characteristics of the light intensity emitted by light sources or luminaires, which is the prerequisite for the accurate lighting analysis e.g. point-by-point method. Firstly, this paper introduces the format and composition of LID file based on the widely used IESNA LM-63 standard at present. Then, three visualization methods are established to understand the luminaire in an intuitive way. The first one is the photometric curve, which is encoded and integrated into the self-developed lighting analysis software; the second one is the contour map obtained by the rectangular coordinate system in Surfer software through the data exchange file; the last one is the contour map in Origin software represented in the polar coordinate system. It is expected to provide a reference for lighting designers to develop their point-by-point calculation program.
Text line segmentation based on local baselines and connected component centroids for Tibetan historical documents
Authors: Hu Pengfei , Chen Yang , Hao Yusheng , Wang Yiqun , Wang Weilan
Published: November 2020

Text line segmentation is one of the key contents in document image analysis and recognition. Due to the complex situation of text images in Tibetan historical document, such as the coexistence of slanting and distortion of text lines, especially the adhesion between lines, text line segmentation has become a challenging task. In this paper, a new method of text segmentation for Tibetan historical document is proposed. This method first obtains the local baseline information of the text line, and then performs the detection and segmentation of the adhesion area. Finally, according to the barycentre coordinates of the connected component and the local baseline information, each connected component is assigned to the corresponding text line. The method avoids the effect of text line distortion on the segmentation of the stuck text line and greatly reduces the error rate of the adhesion text line segmentation and can effectively deal with the segmentation of slanted and distorted Tibetan sticky text lines.
Categorization of precipitation changes in China under 1.5 °C and 3 °C global warming using the bivariate joint distribution from a multi-model perspective
Authors: Qiu Liying , Im Eun-Soon , Kwon Hyun-Han
Published: November 2020

This study examines the changes in the intensity and frequency of precipitation in China from a multi‐model perspective on 20 statistically downscaled fine-scale climate projections and categorizes them into four distinct patterns in response to globally targeted warming (1.5 °C and 3 °C). In a multivariate setting, the asymmetric responses of frequency and intensity to different levels of warming can be considered jointly. This study focuses on relatively moderate precipitation to determine if the ensemble of a subset of climate models, which are selected based on the categorization, can provide a better interpretation of the changing patterns compared to that from the conventional unweighted ensemble mean. The results show that the spatial distribution of the predominant category and inter-model agreement are dependent mainly on the degree of warming. As warming becomes more extensive, the projected change in precipitation tends to converge to the category that indicates an increase in both the intensity and frequency of precipitation, from the mixed-mode and even decreasing pattern. The use of subsampling to produce an ensemble of joint probability (or return period) has potential benefits in detecting asymmetric changes in the intensity and frequency of precipitation that is seen in the majority of models but hidden by the unweighted ensemble average particularly for regions where different models show mixed signals. A substantial portion of the region in China is likely to experience a transition of changes in precipitation frequency and (or) intensity under continuous warming, which would not be revealed clearly by univariate analysis.
Quantum and semi-classical aspects of confined systems with variable mass
Authors: Gazeau Jean-Pierre , Hussin Véronique , Moran James , Zelaya Kevin
Published: November 2020

We explore the quantization of classical models with position-dependent mass terms constrained to a bounded interval in the canonical position. This is achieved through the Weyl–Heisenberg covariant integral quantization by properly choosing a regularizing function Π(q, p) on the phase space that smooths the discontinuities present in the classical model. We thus obtain well-defined operators without requiring the construction of self-adjoint extensions. Simultaneously, the quantization mechanism leads naturally to a semi-classical system, that is, a classical-like model with a well-defined Hamiltonian structure in which the effects of the Planck’s constant are not negligible. Interestingly, for a non-separable function Π(q, p), a purely quantum minimal coupling term arises in the form of a vector potential for both the quantum and semi-classical models.
Wear assessment of 3–D printed parts of PLA (polylactic acid) using Taguchi design and Artificial Neural Network (ANN) technique
Authors: Pant Meena , Singari Ranganath M , Arora Pawan Kumar , Moona Girija , Kumar Harish
Published: November 2020

Additive manufacturing (AM) is a rapidly growing technology with promising results and challenges. The aim of this study is to optimize the process parameters of fused deposition modeling (FDM) by exploring the wear performance of Polylactic acid (PLA). In this work, variation of process parameters like layer thickness, orientation and extruder temperature has been investigated. Based on these parameters wear specimen (accordance to ASTM G99) was printed by using FDM. The wear behavior of polymer pin under low sliding speed was investigated. Taguchi Design of experiments by using L9 orthogonal array is applied to optimize the process parameters at which minimum wear rate is obtained and the same has also been investigated by using analysis of variance (ANOVA) and artificial neural network (ANN) technique for rigorous validation / optimization. Results shows that build orientation have major influence on the wear performance of polymer pin. The paper is presented with the display of results, discussion, and conclusions drawn.
Development of a finite element biomechanical whole spine model for analyzing lumbar spine loads under caudocephalad acceleration
Authors: Goertz Alan R , Yang King H , Viano David C
Published: November 2020

Background: Spine injury risk due to military conflict is an ongoing concern among defense organizations throughout the world. A better understanding of spine biomechanics could assist in developing protection devices to reduce injuries caused by caudocephalad acceleration (+Gz) in under-body blasts (UBB). Although some finite element (FE) human models have demonstrated reasonable lumbar spine biofidelity, they were either partial spine models or not validated for UBB-type loading modes at the lumbar functional spinal unit (FSU) level, thus limiting their ability to analyze UBB-associated occupant kinematics. Methods: An FE functional representation of the human spine with simplified geometry was developed to study the lumbar spine responses under +Gz loading. Fifty-seven load curves obtained from post mortem human subject experiments were used to optimize the model. Results: The model was cumulatively validated for compression, flexion, extension, and anterior-, posterior-, and lateral-shears of the lumbar spine and flexion and extension of the cervical spine. The thoracic spine was optimized for flexion and compression. The cumulative CORrelation and Analysis (CORA) rating for the lumbar spine was 0.766 and the cervical spine was 0.818; both surpassed the 0.7 objective goal. The model’s element size was confirmed as converged. Conclusions: An FE functional representation of the human spine was developed for +Gz lumbar load analysis. The lumbar and cervical spines were demonstrated to be quantitatively biofidelic to the FSU level for multi-directional loading and bending typically experienced in +Gz loading, filling the capability gap in current models.